commit 220afe33101a3874cf40e9cc221d27f8a8b1fae1 Author: comfyanonymous Date: Tue Jan 3 01:53:32 2023 -0500 Initial commit. diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..a72d994a --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +__pycache__/ +*.py[cod] +output/ +models/checkpoints +models/vae diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..f288702d --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 00000000..1f21c0fc --- /dev/null +++ b/README.md @@ -0,0 +1,72 @@ +ComfyUI +======= +A powerful and modular stable diffusion GUI. +----------- +![ComfyUI Screenshot](comfyui_screenshot.png) + +This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. + + +# Installing + +Git clone this repo. + +Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints + +Put your VAE in: models/vae + +At the time of writing this pytorch has issues with python versions higher than 3.10 so make sure your python/pip versions are 3.10. + +### AMD +AMD users can install rocm and pytorch with pip if you don't have it already installed: + +```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2"``` + +### NVIDIA + +Nvidia users should install Xformers. + +### Dependencies + +Install the dependencies: + +```pip install -r requirements.txt``` + + + +# Running + +```python main.py``` + + +# Notes + +Only parts of the graph that have an output with all the correct inputs will be executed. + +Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed. + +Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it. + +You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\). + +### Fedora + +To get python 3.10 on fedora: +```dnf install python3.10``` + +Then you can: + +```python3.10 -m ensurepip``` + +This will let you use: pip3.10 to install all the dependencies. + + +# QA + +### Why did you make this? + +I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions. + +### Who is this for? + +This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs. diff --git a/comfy/k_diffusion/augmentation.py b/comfy/k_diffusion/augmentation.py new file mode 100644 index 00000000..7dd17c68 --- /dev/null +++ b/comfy/k_diffusion/augmentation.py @@ -0,0 +1,105 @@ +from functools import reduce +import math +import operator + +import numpy as np +from skimage import transform +import torch +from torch import nn + + +def translate2d(tx, ty): + mat = [[1, 0, tx], + [0, 1, ty], + [0, 0, 1]] + return torch.tensor(mat, dtype=torch.float32) + + +def scale2d(sx, sy): + mat = [[sx, 0, 0], + [ 0, sy, 0], + [ 0, 0, 1]] + return torch.tensor(mat, dtype=torch.float32) + + +def rotate2d(theta): + mat = [[torch.cos(theta), torch.sin(-theta), 0], + [torch.sin(theta), torch.cos(theta), 0], + [ 0, 0, 1]] + return torch.tensor(mat, dtype=torch.float32) + + +class KarrasAugmentationPipeline: + def __init__(self, a_prob=0.12, a_scale=2**0.2, a_aniso=2**0.2, a_trans=1/8): + self.a_prob = a_prob + self.a_scale = a_scale + self.a_aniso = a_aniso + self.a_trans = a_trans + + def __call__(self, image): + h, w = image.size + mats = [translate2d(h / 2 - 0.5, w / 2 - 0.5)] + + # x-flip + a0 = torch.randint(2, []).float() + mats.append(scale2d(1 - 2 * a0, 1)) + # y-flip + do = (torch.rand([]) < self.a_prob).float() + a1 = torch.randint(2, []).float() * do + mats.append(scale2d(1, 1 - 2 * a1)) + # scaling + do = (torch.rand([]) < self.a_prob).float() + a2 = torch.randn([]) * do + mats.append(scale2d(self.a_scale ** a2, self.a_scale ** a2)) + # rotation + do = (torch.rand([]) < self.a_prob).float() + a3 = (torch.rand([]) * 2 * math.pi - math.pi) * do + mats.append(rotate2d(-a3)) + # anisotropy + do = (torch.rand([]) < self.a_prob).float() + a4 = (torch.rand([]) * 2 * math.pi - math.pi) * do + a5 = torch.randn([]) * do + mats.append(rotate2d(a4)) + mats.append(scale2d(self.a_aniso ** a5, self.a_aniso ** -a5)) + mats.append(rotate2d(-a4)) + # translation + do = (torch.rand([]) < self.a_prob).float() + a6 = torch.randn([]) * do + a7 = torch.randn([]) * do + mats.append(translate2d(self.a_trans * w * a6, self.a_trans * h * a7)) + + # form the transformation matrix and conditioning vector + mats.append(translate2d(-h / 2 + 0.5, -w / 2 + 0.5)) + mat = reduce(operator.matmul, mats) + cond = torch.stack([a0, a1, a2, a3.cos() - 1, a3.sin(), a5 * a4.cos(), a5 * a4.sin(), a6, a7]) + + # apply the transformation + image_orig = np.array(image, dtype=np.float32) / 255 + if image_orig.ndim == 2: + image_orig = image_orig[..., None] + tf = transform.AffineTransform(mat.numpy()) + image = transform.warp(image_orig, tf.inverse, order=3, mode='reflect', cval=0.5, clip=False, preserve_range=True) + image_orig = torch.as_tensor(image_orig).movedim(2, 0) * 2 - 1 + image = torch.as_tensor(image).movedim(2, 0) * 2 - 1 + return image, image_orig, cond + + +class KarrasAugmentWrapper(nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + + def forward(self, input, sigma, aug_cond=None, mapping_cond=None, **kwargs): + if aug_cond is None: + aug_cond = input.new_zeros([input.shape[0], 9]) + if mapping_cond is None: + mapping_cond = aug_cond + else: + mapping_cond = torch.cat([aug_cond, mapping_cond], dim=1) + return self.inner_model(input, sigma, mapping_cond=mapping_cond, **kwargs) + + def set_skip_stages(self, skip_stages): + return self.inner_model.set_skip_stages(skip_stages) + + def set_patch_size(self, patch_size): + return self.inner_model.set_patch_size(patch_size) diff --git a/comfy/k_diffusion/config.py b/comfy/k_diffusion/config.py new file mode 100644 index 00000000..4b504d6d --- /dev/null +++ b/comfy/k_diffusion/config.py @@ -0,0 +1,110 @@ +from functools import partial +import json +import math +import warnings + +from jsonmerge import merge + +from . import augmentation, layers, models, utils + + +def load_config(file): + defaults = { + 'model': { + 'sigma_data': 1., + 'patch_size': 1, + 'dropout_rate': 0., + 'augment_wrapper': True, + 'augment_prob': 0., + 'mapping_cond_dim': 0, + 'unet_cond_dim': 0, + 'cross_cond_dim': 0, + 'cross_attn_depths': None, + 'skip_stages': 0, + 'has_variance': False, + }, + 'dataset': { + 'type': 'imagefolder', + }, + 'optimizer': { + 'type': 'adamw', + 'lr': 1e-4, + 'betas': [0.95, 0.999], + 'eps': 1e-6, + 'weight_decay': 1e-3, + }, + 'lr_sched': { + 'type': 'inverse', + 'inv_gamma': 20000., + 'power': 1., + 'warmup': 0.99, + }, + 'ema_sched': { + 'type': 'inverse', + 'power': 0.6667, + 'max_value': 0.9999 + }, + } + config = json.load(file) + return merge(defaults, config) + + +def make_model(config): + config = config['model'] + assert config['type'] == 'image_v1' + model = models.ImageDenoiserModelV1( + config['input_channels'], + config['mapping_out'], + config['depths'], + config['channels'], + config['self_attn_depths'], + config['cross_attn_depths'], + patch_size=config['patch_size'], + dropout_rate=config['dropout_rate'], + mapping_cond_dim=config['mapping_cond_dim'] + (9 if config['augment_wrapper'] else 0), + unet_cond_dim=config['unet_cond_dim'], + cross_cond_dim=config['cross_cond_dim'], + skip_stages=config['skip_stages'], + has_variance=config['has_variance'], + ) + if config['augment_wrapper']: + model = augmentation.KarrasAugmentWrapper(model) + return model + + +def make_denoiser_wrapper(config): + config = config['model'] + sigma_data = config.get('sigma_data', 1.) + has_variance = config.get('has_variance', False) + if not has_variance: + return partial(layers.Denoiser, sigma_data=sigma_data) + return partial(layers.DenoiserWithVariance, sigma_data=sigma_data) + + +def make_sample_density(config): + sd_config = config['sigma_sample_density'] + sigma_data = config['sigma_data'] + if sd_config['type'] == 'lognormal': + loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc'] + scale = sd_config['std'] if 'std' in sd_config else sd_config['scale'] + return partial(utils.rand_log_normal, loc=loc, scale=scale) + if sd_config['type'] == 'loglogistic': + loc = sd_config['loc'] if 'loc' in sd_config else math.log(sigma_data) + scale = sd_config['scale'] if 'scale' in sd_config else 0.5 + min_value = sd_config['min_value'] if 'min_value' in sd_config else 0. + max_value = sd_config['max_value'] if 'max_value' in sd_config else float('inf') + return partial(utils.rand_log_logistic, loc=loc, scale=scale, min_value=min_value, max_value=max_value) + if sd_config['type'] == 'loguniform': + min_value = sd_config['min_value'] if 'min_value' in sd_config else config['sigma_min'] + max_value = sd_config['max_value'] if 'max_value' in sd_config else config['sigma_max'] + return partial(utils.rand_log_uniform, min_value=min_value, max_value=max_value) + if sd_config['type'] == 'v-diffusion': + min_value = sd_config['min_value'] if 'min_value' in sd_config else 0. + max_value = sd_config['max_value'] if 'max_value' in sd_config else float('inf') + return partial(utils.rand_v_diffusion, sigma_data=sigma_data, min_value=min_value, max_value=max_value) + if sd_config['type'] == 'split-lognormal': + loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc'] + scale_1 = sd_config['std_1'] if 'std_1' in sd_config else sd_config['scale_1'] + scale_2 = sd_config['std_2'] if 'std_2' in sd_config else sd_config['scale_2'] + return partial(utils.rand_split_log_normal, loc=loc, scale_1=scale_1, scale_2=scale_2) + raise ValueError('Unknown sample density type') diff --git a/comfy/k_diffusion/evaluation.py b/comfy/k_diffusion/evaluation.py new file mode 100644 index 00000000..2c34bbf1 --- /dev/null +++ b/comfy/k_diffusion/evaluation.py @@ -0,0 +1,134 @@ +import math +import os +from pathlib import Path + +from cleanfid.inception_torchscript import InceptionV3W +import clip +from resize_right import resize +import torch +from torch import nn +from torch.nn import functional as F +from torchvision import transforms +from tqdm.auto import trange + +from . import utils + + +class InceptionV3FeatureExtractor(nn.Module): + def __init__(self, device='cpu'): + super().__init__() + path = Path(os.environ.get('XDG_CACHE_HOME', Path.home() / '.cache')) / 'k-diffusion' + url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt' + digest = 'f58cb9b6ec323ed63459aa4fb441fe750cfe39fafad6da5cb504a16f19e958f4' + utils.download_file(path / 'inception-2015-12-05.pt', url, digest) + self.model = InceptionV3W(str(path), resize_inside=False).to(device) + self.size = (299, 299) + + def forward(self, x): + if x.shape[2:4] != self.size: + x = resize(x, out_shape=self.size, pad_mode='reflect') + if x.shape[1] == 1: + x = torch.cat([x] * 3, dim=1) + x = (x * 127.5 + 127.5).clamp(0, 255) + return self.model(x) + + +class CLIPFeatureExtractor(nn.Module): + def __init__(self, name='ViT-L/14@336px', device='cpu'): + super().__init__() + self.model = clip.load(name, device=device)[0].eval().requires_grad_(False) + self.normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), + std=(0.26862954, 0.26130258, 0.27577711)) + self.size = (self.model.visual.input_resolution, self.model.visual.input_resolution) + + def forward(self, x): + if x.shape[2:4] != self.size: + x = resize(x.add(1).div(2), out_shape=self.size, pad_mode='reflect').clamp(0, 1) + x = self.normalize(x) + x = self.model.encode_image(x).float() + x = F.normalize(x) * x.shape[1] ** 0.5 + return x + + +def compute_features(accelerator, sample_fn, extractor_fn, n, batch_size): + n_per_proc = math.ceil(n / accelerator.num_processes) + feats_all = [] + try: + for i in trange(0, n_per_proc, batch_size, disable=not accelerator.is_main_process): + cur_batch_size = min(n - i, batch_size) + samples = sample_fn(cur_batch_size)[:cur_batch_size] + feats_all.append(accelerator.gather(extractor_fn(samples))) + except StopIteration: + pass + return torch.cat(feats_all)[:n] + + +def polynomial_kernel(x, y): + d = x.shape[-1] + dot = x @ y.transpose(-2, -1) + return (dot / d + 1) ** 3 + + +def squared_mmd(x, y, kernel=polynomial_kernel): + m = x.shape[-2] + n = y.shape[-2] + kxx = kernel(x, x) + kyy = kernel(y, y) + kxy = kernel(x, y) + kxx_sum = kxx.sum([-1, -2]) - kxx.diagonal(dim1=-1, dim2=-2).sum(-1) + kyy_sum = kyy.sum([-1, -2]) - kyy.diagonal(dim1=-1, dim2=-2).sum(-1) + kxy_sum = kxy.sum([-1, -2]) + term_1 = kxx_sum / m / (m - 1) + term_2 = kyy_sum / n / (n - 1) + term_3 = kxy_sum * 2 / m / n + return term_1 + term_2 - term_3 + + +@utils.tf32_mode(matmul=False) +def kid(x, y, max_size=5000): + x_size, y_size = x.shape[0], y.shape[0] + n_partitions = math.ceil(max(x_size / max_size, y_size / max_size)) + total_mmd = x.new_zeros([]) + for i in range(n_partitions): + cur_x = x[round(i * x_size / n_partitions):round((i + 1) * x_size / n_partitions)] + cur_y = y[round(i * y_size / n_partitions):round((i + 1) * y_size / n_partitions)] + total_mmd = total_mmd + squared_mmd(cur_x, cur_y) + return total_mmd / n_partitions + + +class _MatrixSquareRootEig(torch.autograd.Function): + @staticmethod + def forward(ctx, a): + vals, vecs = torch.linalg.eigh(a) + ctx.save_for_backward(vals, vecs) + return vecs @ vals.abs().sqrt().diag_embed() @ vecs.transpose(-2, -1) + + @staticmethod + def backward(ctx, grad_output): + vals, vecs = ctx.saved_tensors + d = vals.abs().sqrt().unsqueeze(-1).repeat_interleave(vals.shape[-1], -1) + vecs_t = vecs.transpose(-2, -1) + return vecs @ (vecs_t @ grad_output @ vecs / (d + d.transpose(-2, -1))) @ vecs_t + + +def sqrtm_eig(a): + if a.ndim < 2: + raise RuntimeError('tensor of matrices must have at least 2 dimensions') + if a.shape[-2] != a.shape[-1]: + raise RuntimeError('tensor must be batches of square matrices') + return _MatrixSquareRootEig.apply(a) + + +@utils.tf32_mode(matmul=False) +def fid(x, y, eps=1e-8): + x_mean = x.mean(dim=0) + y_mean = y.mean(dim=0) + mean_term = (x_mean - y_mean).pow(2).sum() + x_cov = torch.cov(x.T) + y_cov = torch.cov(y.T) + eps_eye = torch.eye(x_cov.shape[0], device=x_cov.device, dtype=x_cov.dtype) * eps + x_cov = x_cov + eps_eye + y_cov = y_cov + eps_eye + x_cov_sqrt = sqrtm_eig(x_cov) + cov_term = torch.trace(x_cov + y_cov - 2 * sqrtm_eig(x_cov_sqrt @ y_cov @ x_cov_sqrt)) + return mean_term + cov_term diff --git a/comfy/k_diffusion/external.py b/comfy/k_diffusion/external.py new file mode 100644 index 00000000..e8563a35 --- /dev/null +++ b/comfy/k_diffusion/external.py @@ -0,0 +1,179 @@ +import math + +import torch +from torch import nn + +from . import sampling, utils + + +class VDenoiser(nn.Module): + """A v-diffusion-pytorch model wrapper for k-diffusion.""" + + def __init__(self, inner_model): + super().__init__() + self.inner_model = inner_model + self.sigma_data = 1. + + def get_scalings(self, sigma): + c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_skip, c_out, c_in + + def sigma_to_t(self, sigma): + return sigma.atan() / math.pi * 2 + + def t_to_sigma(self, t): + return (t * math.pi / 2).tan() + + def loss(self, input, noise, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) + target = (input - c_skip * noised_input) / c_out + return (model_output - target).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip + + +class DiscreteSchedule(nn.Module): + """A mapping between continuous noise levels (sigmas) and a list of discrete noise + levels.""" + + def __init__(self, sigmas, quantize): + super().__init__() + self.register_buffer('sigmas', sigmas) + self.register_buffer('log_sigmas', sigmas.log()) + self.quantize = quantize + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def get_sigmas(self, n=None): + if n is None: + return sampling.append_zero(self.sigmas.flip(0)) + t_max = len(self.sigmas) - 1 + t = torch.linspace(t_max, 0, n, device=self.sigmas.device) + return sampling.append_zero(self.t_to_sigma(t)) + + def sigma_to_t(self, sigma, quantize=None): + quantize = self.quantize if quantize is None else quantize + log_sigma = sigma.log() + dists = log_sigma - self.log_sigmas[:, None] + if quantize: + return dists.abs().argmin(dim=0).view(sigma.shape) + low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2) + high_idx = low_idx + 1 + low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx] + w = (low - log_sigma) / (low - high) + w = w.clamp(0, 1) + t = (1 - w) * low_idx + w * high_idx + return t.view(sigma.shape) + + def t_to_sigma(self, t): + t = t.float() + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t-low_idx if t.device.type == 'mps' else t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp() + + +class DiscreteEpsDDPMDenoiser(DiscreteSchedule): + """A wrapper for discrete schedule DDPM models that output eps (the predicted + noise).""" + + def __init__(self, model, alphas_cumprod, quantize): + super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) + self.inner_model = model + self.sigma_data = 1. + + def get_scalings(self, sigma): + c_out = -sigma + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_out, c_in + + def get_eps(self, *args, **kwargs): + return self.inner_model(*args, **kwargs) + + def loss(self, input, noise, sigma, **kwargs): + c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) + return (eps - noise).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs) + return input + eps * c_out + + +class OpenAIDenoiser(DiscreteEpsDDPMDenoiser): + """A wrapper for OpenAI diffusion models.""" + + def __init__(self, model, diffusion, quantize=False, has_learned_sigmas=True, device='cpu'): + alphas_cumprod = torch.tensor(diffusion.alphas_cumprod, device=device, dtype=torch.float32) + super().__init__(model, alphas_cumprod, quantize=quantize) + self.has_learned_sigmas = has_learned_sigmas + + def get_eps(self, *args, **kwargs): + model_output = self.inner_model(*args, **kwargs) + if self.has_learned_sigmas: + return model_output.chunk(2, dim=1)[0] + return model_output + + +class CompVisDenoiser(DiscreteEpsDDPMDenoiser): + """A wrapper for CompVis diffusion models.""" + + def __init__(self, model, quantize=False, device='cpu'): + super().__init__(model, model.alphas_cumprod, quantize=quantize) + + def get_eps(self, *args, **kwargs): + return self.inner_model.apply_model(*args, **kwargs) + + +class DiscreteVDDPMDenoiser(DiscreteSchedule): + """A wrapper for discrete schedule DDPM models that output v.""" + + def __init__(self, model, alphas_cumprod, quantize): + super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) + self.inner_model = model + self.sigma_data = 1. + + def get_scalings(self, sigma): + c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_skip, c_out, c_in + + def get_v(self, *args, **kwargs): + return self.inner_model(*args, **kwargs) + + def loss(self, input, noise, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) + target = (input - c_skip * noised_input) / c_out + return (model_output - target).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip + + +class CompVisVDenoiser(DiscreteVDDPMDenoiser): + """A wrapper for CompVis diffusion models that output v.""" + + def __init__(self, model, quantize=False, device='cpu'): + super().__init__(model, model.alphas_cumprod, quantize=quantize) + + def get_v(self, x, t, cond, **kwargs): + return self.inner_model.apply_model(x, t, cond) diff --git a/comfy/k_diffusion/gns.py b/comfy/k_diffusion/gns.py new file mode 100644 index 00000000..dcb7b8d8 --- /dev/null +++ b/comfy/k_diffusion/gns.py @@ -0,0 +1,99 @@ +import torch +from torch import nn + + +class DDPGradientStatsHook: + def __init__(self, ddp_module): + try: + ddp_module.register_comm_hook(self, self._hook_fn) + except AttributeError: + raise ValueError('DDPGradientStatsHook does not support non-DDP wrapped modules') + self._clear_state() + + def _clear_state(self): + self.bucket_sq_norms_small_batch = [] + self.bucket_sq_norms_large_batch = [] + + @staticmethod + def _hook_fn(self, bucket): + buf = bucket.buffer() + self.bucket_sq_norms_small_batch.append(buf.pow(2).sum()) + fut = torch.distributed.all_reduce(buf, op=torch.distributed.ReduceOp.AVG, async_op=True).get_future() + def callback(fut): + buf = fut.value()[0] + self.bucket_sq_norms_large_batch.append(buf.pow(2).sum()) + return buf + return fut.then(callback) + + def get_stats(self): + sq_norm_small_batch = sum(self.bucket_sq_norms_small_batch) + sq_norm_large_batch = sum(self.bucket_sq_norms_large_batch) + self._clear_state() + stats = torch.stack([sq_norm_small_batch, sq_norm_large_batch]) + torch.distributed.all_reduce(stats, op=torch.distributed.ReduceOp.AVG) + return stats[0].item(), stats[1].item() + + +class GradientNoiseScale: + """Calculates the gradient noise scale (1 / SNR), or critical batch size, + from _An Empirical Model of Large-Batch Training_, + https://arxiv.org/abs/1812.06162). + + Args: + beta (float): The decay factor for the exponential moving averages used to + calculate the gradient noise scale. + Default: 0.9998 + eps (float): Added for numerical stability. + Default: 1e-8 + """ + + def __init__(self, beta=0.9998, eps=1e-8): + self.beta = beta + self.eps = eps + self.ema_sq_norm = 0. + self.ema_var = 0. + self.beta_cumprod = 1. + self.gradient_noise_scale = float('nan') + + def state_dict(self): + """Returns the state of the object as a :class:`dict`.""" + return dict(self.__dict__.items()) + + def load_state_dict(self, state_dict): + """Loads the object's state. + Args: + state_dict (dict): object state. Should be an object returned + from a call to :meth:`state_dict`. + """ + self.__dict__.update(state_dict) + + def update(self, sq_norm_small_batch, sq_norm_large_batch, n_small_batch, n_large_batch): + """Updates the state with a new batch's gradient statistics, and returns the + current gradient noise scale. + + Args: + sq_norm_small_batch (float): The mean of the squared 2-norms of microbatch or + per sample gradients. + sq_norm_large_batch (float): The squared 2-norm of the mean of the microbatch or + per sample gradients. + n_small_batch (int): The batch size of the individual microbatch or per sample + gradients (1 if per sample). + n_large_batch (int): The total batch size of the mean of the microbatch or + per sample gradients. + """ + est_sq_norm = (n_large_batch * sq_norm_large_batch - n_small_batch * sq_norm_small_batch) / (n_large_batch - n_small_batch) + est_var = (sq_norm_small_batch - sq_norm_large_batch) / (1 / n_small_batch - 1 / n_large_batch) + self.ema_sq_norm = self.beta * self.ema_sq_norm + (1 - self.beta) * est_sq_norm + self.ema_var = self.beta * self.ema_var + (1 - self.beta) * est_var + self.beta_cumprod *= self.beta + self.gradient_noise_scale = max(self.ema_var, self.eps) / max(self.ema_sq_norm, self.eps) + return self.gradient_noise_scale + + def get_gns(self): + """Returns the current gradient noise scale.""" + return self.gradient_noise_scale + + def get_stats(self): + """Returns the current (debiased) estimates of the squared mean gradient + and gradient variance.""" + return self.ema_sq_norm / (1 - self.beta_cumprod), self.ema_var / (1 - self.beta_cumprod) diff --git a/comfy/k_diffusion/layers.py b/comfy/k_diffusion/layers.py new file mode 100644 index 00000000..cdeba0ad --- /dev/null +++ b/comfy/k_diffusion/layers.py @@ -0,0 +1,246 @@ +import math + +from einops import rearrange, repeat +import torch +from torch import nn +from torch.nn import functional as F + +from . import utils + +# Karras et al. preconditioned denoiser + +class Denoiser(nn.Module): + """A Karras et al. preconditioner for denoising diffusion models.""" + + def __init__(self, inner_model, sigma_data=1.): + super().__init__() + self.inner_model = inner_model + self.sigma_data = sigma_data + + def get_scalings(self, sigma): + c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_skip, c_out, c_in + + def loss(self, input, noise, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + model_output = self.inner_model(noised_input * c_in, sigma, **kwargs) + target = (input - c_skip * noised_input) / c_out + return (model_output - target).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + return self.inner_model(input * c_in, sigma, **kwargs) * c_out + input * c_skip + + +class DenoiserWithVariance(Denoiser): + def loss(self, input, noise, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + model_output, logvar = self.inner_model(noised_input * c_in, sigma, return_variance=True, **kwargs) + logvar = utils.append_dims(logvar, model_output.ndim) + target = (input - c_skip * noised_input) / c_out + losses = ((model_output - target) ** 2 / logvar.exp() + logvar) / 2 + return losses.flatten(1).mean(1) + + +# Residual blocks + +class ResidualBlock(nn.Module): + def __init__(self, *main, skip=None): + super().__init__() + self.main = nn.Sequential(*main) + self.skip = skip if skip else nn.Identity() + + def forward(self, input): + return self.main(input) + self.skip(input) + + +# Noise level (and other) conditioning + +class ConditionedModule(nn.Module): + pass + + +class UnconditionedModule(ConditionedModule): + def __init__(self, module): + super().__init__() + self.module = module + + def forward(self, input, cond=None): + return self.module(input) + + +class ConditionedSequential(nn.Sequential, ConditionedModule): + def forward(self, input, cond): + for module in self: + if isinstance(module, ConditionedModule): + input = module(input, cond) + else: + input = module(input) + return input + + +class ConditionedResidualBlock(ConditionedModule): + def __init__(self, *main, skip=None): + super().__init__() + self.main = ConditionedSequential(*main) + self.skip = skip if skip else nn.Identity() + + def forward(self, input, cond): + skip = self.skip(input, cond) if isinstance(self.skip, ConditionedModule) else self.skip(input) + return self.main(input, cond) + skip + + +class AdaGN(ConditionedModule): + def __init__(self, feats_in, c_out, num_groups, eps=1e-5, cond_key='cond'): + super().__init__() + self.num_groups = num_groups + self.eps = eps + self.cond_key = cond_key + self.mapper = nn.Linear(feats_in, c_out * 2) + + def forward(self, input, cond): + weight, bias = self.mapper(cond[self.cond_key]).chunk(2, dim=-1) + input = F.group_norm(input, self.num_groups, eps=self.eps) + return torch.addcmul(utils.append_dims(bias, input.ndim), input, utils.append_dims(weight, input.ndim) + 1) + + +# Attention + +class SelfAttention2d(ConditionedModule): + def __init__(self, c_in, n_head, norm, dropout_rate=0.): + super().__init__() + assert c_in % n_head == 0 + self.norm_in = norm(c_in) + self.n_head = n_head + self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1) + self.out_proj = nn.Conv2d(c_in, c_in, 1) + self.dropout = nn.Dropout(dropout_rate) + + def forward(self, input, cond): + n, c, h, w = input.shape + qkv = self.qkv_proj(self.norm_in(input, cond)) + qkv = qkv.view([n, self.n_head * 3, c // self.n_head, h * w]).transpose(2, 3) + q, k, v = qkv.chunk(3, dim=1) + scale = k.shape[3] ** -0.25 + att = ((q * scale) @ (k.transpose(2, 3) * scale)).softmax(3) + att = self.dropout(att) + y = (att @ v).transpose(2, 3).contiguous().view([n, c, h, w]) + return input + self.out_proj(y) + + +class CrossAttention2d(ConditionedModule): + def __init__(self, c_dec, c_enc, n_head, norm_dec, dropout_rate=0., + cond_key='cross', cond_key_padding='cross_padding'): + super().__init__() + assert c_dec % n_head == 0 + self.cond_key = cond_key + self.cond_key_padding = cond_key_padding + self.norm_enc = nn.LayerNorm(c_enc) + self.norm_dec = norm_dec(c_dec) + self.n_head = n_head + self.q_proj = nn.Conv2d(c_dec, c_dec, 1) + self.kv_proj = nn.Linear(c_enc, c_dec * 2) + self.out_proj = nn.Conv2d(c_dec, c_dec, 1) + self.dropout = nn.Dropout(dropout_rate) + + def forward(self, input, cond): + n, c, h, w = input.shape + q = self.q_proj(self.norm_dec(input, cond)) + q = q.view([n, self.n_head, c // self.n_head, h * w]).transpose(2, 3) + kv = self.kv_proj(self.norm_enc(cond[self.cond_key])) + kv = kv.view([n, -1, self.n_head * 2, c // self.n_head]).transpose(1, 2) + k, v = kv.chunk(2, dim=1) + scale = k.shape[3] ** -0.25 + att = ((q * scale) @ (k.transpose(2, 3) * scale)) + att = att - (cond[self.cond_key_padding][:, None, None, :]) * 10000 + att = att.softmax(3) + att = self.dropout(att) + y = (att @ v).transpose(2, 3) + y = y.contiguous().view([n, c, h, w]) + return input + self.out_proj(y) + + +# Downsampling/upsampling + +_kernels = { + 'linear': + [1 / 8, 3 / 8, 3 / 8, 1 / 8], + 'cubic': + [-0.01171875, -0.03515625, 0.11328125, 0.43359375, + 0.43359375, 0.11328125, -0.03515625, -0.01171875], + 'lanczos3': + [0.003689131001010537, 0.015056144446134567, -0.03399861603975296, + -0.066637322306633, 0.13550527393817902, 0.44638532400131226, + 0.44638532400131226, 0.13550527393817902, -0.066637322306633, + -0.03399861603975296, 0.015056144446134567, 0.003689131001010537] +} +_kernels['bilinear'] = _kernels['linear'] +_kernels['bicubic'] = _kernels['cubic'] + + +class Downsample2d(nn.Module): + def __init__(self, kernel='linear', pad_mode='reflect'): + super().__init__() + self.pad_mode = pad_mode + kernel_1d = torch.tensor([_kernels[kernel]]) + self.pad = kernel_1d.shape[1] // 2 - 1 + self.register_buffer('kernel', kernel_1d.T @ kernel_1d) + + def forward(self, x): + x = F.pad(x, (self.pad,) * 4, self.pad_mode) + weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]]) + indices = torch.arange(x.shape[1], device=x.device) + weight[indices, indices] = self.kernel.to(weight) + return F.conv2d(x, weight, stride=2) + + +class Upsample2d(nn.Module): + def __init__(self, kernel='linear', pad_mode='reflect'): + super().__init__() + self.pad_mode = pad_mode + kernel_1d = torch.tensor([_kernels[kernel]]) * 2 + self.pad = kernel_1d.shape[1] // 2 - 1 + self.register_buffer('kernel', kernel_1d.T @ kernel_1d) + + def forward(self, x): + x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode) + weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]]) + indices = torch.arange(x.shape[1], device=x.device) + weight[indices, indices] = self.kernel.to(weight) + return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1) + + +# Embeddings + +class FourierFeatures(nn.Module): + def __init__(self, in_features, out_features, std=1.): + super().__init__() + assert out_features % 2 == 0 + self.register_buffer('weight', torch.randn([out_features // 2, in_features]) * std) + + def forward(self, input): + f = 2 * math.pi * input @ self.weight.T + return torch.cat([f.cos(), f.sin()], dim=-1) + + +# U-Nets + +class UNet(ConditionedModule): + def __init__(self, d_blocks, u_blocks, skip_stages=0): + super().__init__() + self.d_blocks = nn.ModuleList(d_blocks) + self.u_blocks = nn.ModuleList(u_blocks) + self.skip_stages = skip_stages + + def forward(self, input, cond): + skips = [] + for block in self.d_blocks[self.skip_stages:]: + input = block(input, cond) + skips.append(input) + for i, (block, skip) in enumerate(zip(self.u_blocks, reversed(skips))): + input = block(input, cond, skip if i > 0 else None) + return input diff --git a/comfy/k_diffusion/models/__init__.py b/comfy/k_diffusion/models/__init__.py new file mode 100644 index 00000000..82608ff1 --- /dev/null +++ b/comfy/k_diffusion/models/__init__.py @@ -0,0 +1 @@ +from .image_v1 import ImageDenoiserModelV1 diff --git a/comfy/k_diffusion/models/image_v1.py b/comfy/k_diffusion/models/image_v1.py new file mode 100644 index 00000000..9ffd5f2c --- /dev/null +++ b/comfy/k_diffusion/models/image_v1.py @@ -0,0 +1,156 @@ +import math + +import torch +from torch import nn +from torch.nn import functional as F + +from .. import layers, utils + + +def orthogonal_(module): + nn.init.orthogonal_(module.weight) + return module + + +class ResConvBlock(layers.ConditionedResidualBlock): + def __init__(self, feats_in, c_in, c_mid, c_out, group_size=32, dropout_rate=0.): + skip = None if c_in == c_out else orthogonal_(nn.Conv2d(c_in, c_out, 1, bias=False)) + super().__init__( + layers.AdaGN(feats_in, c_in, max(1, c_in // group_size)), + nn.GELU(), + nn.Conv2d(c_in, c_mid, 3, padding=1), + nn.Dropout2d(dropout_rate, inplace=True), + layers.AdaGN(feats_in, c_mid, max(1, c_mid // group_size)), + nn.GELU(), + nn.Conv2d(c_mid, c_out, 3, padding=1), + nn.Dropout2d(dropout_rate, inplace=True), + skip=skip) + + +class DBlock(layers.ConditionedSequential): + def __init__(self, n_layers, feats_in, c_in, c_mid, c_out, group_size=32, head_size=64, dropout_rate=0., downsample=False, self_attn=False, cross_attn=False, c_enc=0): + modules = [nn.Identity()] + for i in range(n_layers): + my_c_in = c_in if i == 0 else c_mid + my_c_out = c_mid if i < n_layers - 1 else c_out + modules.append(ResConvBlock(feats_in, my_c_in, c_mid, my_c_out, group_size, dropout_rate)) + if self_attn: + norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) + modules.append(layers.SelfAttention2d(my_c_out, max(1, my_c_out // head_size), norm, dropout_rate)) + if cross_attn: + norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) + modules.append(layers.CrossAttention2d(my_c_out, c_enc, max(1, my_c_out // head_size), norm, dropout_rate)) + super().__init__(*modules) + self.set_downsample(downsample) + + def set_downsample(self, downsample): + self[0] = layers.Downsample2d() if downsample else nn.Identity() + return self + + +class UBlock(layers.ConditionedSequential): + def __init__(self, n_layers, feats_in, c_in, c_mid, c_out, group_size=32, head_size=64, dropout_rate=0., upsample=False, self_attn=False, cross_attn=False, c_enc=0): + modules = [] + for i in range(n_layers): + my_c_in = c_in if i == 0 else c_mid + my_c_out = c_mid if i < n_layers - 1 else c_out + modules.append(ResConvBlock(feats_in, my_c_in, c_mid, my_c_out, group_size, dropout_rate)) + if self_attn: + norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) + modules.append(layers.SelfAttention2d(my_c_out, max(1, my_c_out // head_size), norm, dropout_rate)) + if cross_attn: + norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) + modules.append(layers.CrossAttention2d(my_c_out, c_enc, max(1, my_c_out // head_size), norm, dropout_rate)) + modules.append(nn.Identity()) + super().__init__(*modules) + self.set_upsample(upsample) + + def forward(self, input, cond, skip=None): + if skip is not None: + input = torch.cat([input, skip], dim=1) + return super().forward(input, cond) + + def set_upsample(self, upsample): + self[-1] = layers.Upsample2d() if upsample else nn.Identity() + return self + + +class MappingNet(nn.Sequential): + def __init__(self, feats_in, feats_out, n_layers=2): + layers = [] + for i in range(n_layers): + layers.append(orthogonal_(nn.Linear(feats_in if i == 0 else feats_out, feats_out))) + layers.append(nn.GELU()) + super().__init__(*layers) + + +class ImageDenoiserModelV1(nn.Module): + def __init__(self, c_in, feats_in, depths, channels, self_attn_depths, cross_attn_depths=None, mapping_cond_dim=0, unet_cond_dim=0, cross_cond_dim=0, dropout_rate=0., patch_size=1, skip_stages=0, has_variance=False): + super().__init__() + self.c_in = c_in + self.channels = channels + self.unet_cond_dim = unet_cond_dim + self.patch_size = patch_size + self.has_variance = has_variance + self.timestep_embed = layers.FourierFeatures(1, feats_in) + if mapping_cond_dim > 0: + self.mapping_cond = nn.Linear(mapping_cond_dim, feats_in, bias=False) + self.mapping = MappingNet(feats_in, feats_in) + self.proj_in = nn.Conv2d((c_in + unet_cond_dim) * self.patch_size ** 2, channels[max(0, skip_stages - 1)], 1) + self.proj_out = nn.Conv2d(channels[max(0, skip_stages - 1)], c_in * self.patch_size ** 2 + (1 if self.has_variance else 0), 1) + nn.init.zeros_(self.proj_out.weight) + nn.init.zeros_(self.proj_out.bias) + if cross_cond_dim == 0: + cross_attn_depths = [False] * len(self_attn_depths) + d_blocks, u_blocks = [], [] + for i in range(len(depths)): + my_c_in = channels[max(0, i - 1)] + d_blocks.append(DBlock(depths[i], feats_in, my_c_in, channels[i], channels[i], downsample=i > skip_stages, self_attn=self_attn_depths[i], cross_attn=cross_attn_depths[i], c_enc=cross_cond_dim, dropout_rate=dropout_rate)) + for i in range(len(depths)): + my_c_in = channels[i] * 2 if i < len(depths) - 1 else channels[i] + my_c_out = channels[max(0, i - 1)] + u_blocks.append(UBlock(depths[i], feats_in, my_c_in, channels[i], my_c_out, upsample=i > skip_stages, self_attn=self_attn_depths[i], cross_attn=cross_attn_depths[i], c_enc=cross_cond_dim, dropout_rate=dropout_rate)) + self.u_net = layers.UNet(d_blocks, reversed(u_blocks), skip_stages=skip_stages) + + def forward(self, input, sigma, mapping_cond=None, unet_cond=None, cross_cond=None, cross_cond_padding=None, return_variance=False): + c_noise = sigma.log() / 4 + timestep_embed = self.timestep_embed(utils.append_dims(c_noise, 2)) + mapping_cond_embed = torch.zeros_like(timestep_embed) if mapping_cond is None else self.mapping_cond(mapping_cond) + mapping_out = self.mapping(timestep_embed + mapping_cond_embed) + cond = {'cond': mapping_out} + if unet_cond is not None: + input = torch.cat([input, unet_cond], dim=1) + if cross_cond is not None: + cond['cross'] = cross_cond + cond['cross_padding'] = cross_cond_padding + if self.patch_size > 1: + input = F.pixel_unshuffle(input, self.patch_size) + input = self.proj_in(input) + input = self.u_net(input, cond) + input = self.proj_out(input) + if self.has_variance: + input, logvar = input[:, :-1], input[:, -1].flatten(1).mean(1) + if self.patch_size > 1: + input = F.pixel_shuffle(input, self.patch_size) + if self.has_variance and return_variance: + return input, logvar + return input + + def set_skip_stages(self, skip_stages): + self.proj_in = nn.Conv2d(self.proj_in.in_channels, self.channels[max(0, skip_stages - 1)], 1) + self.proj_out = nn.Conv2d(self.channels[max(0, skip_stages - 1)], self.proj_out.out_channels, 1) + nn.init.zeros_(self.proj_out.weight) + nn.init.zeros_(self.proj_out.bias) + self.u_net.skip_stages = skip_stages + for i, block in enumerate(self.u_net.d_blocks): + block.set_downsample(i > skip_stages) + for i, block in enumerate(reversed(self.u_net.u_blocks)): + block.set_upsample(i > skip_stages) + return self + + def set_patch_size(self, patch_size): + self.patch_size = patch_size + self.proj_in = nn.Conv2d((self.c_in + self.unet_cond_dim) * self.patch_size ** 2, self.channels[max(0, self.u_net.skip_stages - 1)], 1) + self.proj_out = nn.Conv2d(self.channels[max(0, self.u_net.skip_stages - 1)], self.c_in * self.patch_size ** 2 + (1 if self.has_variance else 0), 1) + nn.init.zeros_(self.proj_out.weight) + nn.init.zeros_(self.proj_out.bias) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py new file mode 100644 index 00000000..c809d39f --- /dev/null +++ b/comfy/k_diffusion/sampling.py @@ -0,0 +1,607 @@ +import math + +from scipy import integrate +import torch +from torch import nn +from torchdiffeq import odeint +import torchsde +from tqdm.auto import trange, tqdm + +from . import utils + + +def append_zero(x): + return torch.cat([x, x.new_zeros([1])]) + + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): + """Constructs the noise schedule of Karras et al. (2022).""" + ramp = torch.linspace(0, 1, n, device=device) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + +def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): + """Constructs an exponential noise schedule.""" + sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() + return append_zero(sigmas) + + +def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): + """Constructs an polynomial in log sigma noise schedule.""" + ramp = torch.linspace(1, 0, n, device=device) ** rho + sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) + return append_zero(sigmas) + + +def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): + """Constructs a continuous VP noise schedule.""" + t = torch.linspace(1, eps_s, n, device=device) + sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) + return append_zero(sigmas) + + +def to_d(x, sigma, denoised): + """Converts a denoiser output to a Karras ODE derivative.""" + return (x - denoised) / utils.append_dims(sigma, x.ndim) + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.): + """Calculates the noise level (sigma_down) to step down to and the amount + of noise to add (sigma_up) when doing an ancestral sampling step.""" + if not eta: + return sigma_to, 0. + sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) + sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 + return sigma_down, sigma_up + + +def default_noise_sampler(x): + return lambda sigma, sigma_next: torch.randn_like(x) + + +class BatchedBrownianTree: + """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" + + def __init__(self, x, t0, t1, seed=None, **kwargs): + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get('w0', torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2 ** 63 - 1, []).item() + self.batched = True + try: + assert len(seed) == x.shape[0] + w0 = w0[0] + except TypeError: + seed = [seed] + self.batched = False + self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] + + @staticmethod + def sort(a, b): + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + t0, t1, sign = self.sort(t0, t1) + w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """A noise sampler backed by a torchsde.BrownianTree. + + Args: + x (Tensor): The tensor whose shape, device and dtype to use to generate + random samples. + sigma_min (float): The low end of the valid interval. + sigma_max (float): The high end of the valid interval. + seed (int or List[int]): The random seed. If a list of seeds is + supplied instead of a single integer, then the noise sampler will + use one BrownianTree per batch item, each with its own seed. + transform (callable): A function that maps sigma to the sampler's + internal timestep. + """ + + def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x): + self.transform = transform + t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) + self.tree = BatchedBrownianTree(x, t0, t1, seed) + + def __call__(self, sigma, sigma_next): + t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() + + +@torch.no_grad() +def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + + +@torch.no_grad() +def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + return x + + +@torch.no_grad() +def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() + dt_1 = sigma_mid - sigma_hat + dt_2 = sigmas[i + 1] - sigma_hat + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + return x + + +@torch.no_grad() +def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + if sigma_down == 0: + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() + dt_1 = sigma_mid - sigmas[i] + dt_2 = sigma_down - sigmas[i] + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +def linear_multistep_coeff(order, t, i, j): + if order - 1 > i: + raise ValueError(f'Order {order} too high for step {i}') + def fn(tau): + prod = 1. + for k in range(order): + if j == k: + continue + prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) + return prod + return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] + + +@torch.no_grad() +def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigmas_cpu = sigmas.detach().cpu().numpy() + ds = [] + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + d = to_d(x, sigmas[i], denoised) + ds.append(d) + if len(ds) > order: + ds.pop(0) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + cur_order = min(i + 1, order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + return x + + +@torch.no_grad() +def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + v = torch.randint_like(x, 2) * 2 - 1 + fevals = 0 + def ode_fn(sigma, x): + nonlocal fevals + with torch.enable_grad(): + x = x[0].detach().requires_grad_() + denoised = model(x, sigma * s_in, **extra_args) + d = to_d(x, sigma, denoised) + fevals += 1 + grad = torch.autograd.grad((d * v).sum(), x)[0] + d_ll = (v * grad).flatten(1).sum(1) + return d.detach(), d_ll + x_min = x, x.new_zeros([x.shape[0]]) + t = x.new_tensor([sigma_min, sigma_max]) + sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5') + latent, delta_ll = sol[0][-1], sol[1][-1] + ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1) + return ll_prior + delta_ll, {'fevals': fevals} + + +class PIDStepSizeController: + """A PID controller for ODE adaptive step size control.""" + def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): + self.h = h + self.b1 = (pcoeff + icoeff + dcoeff) / order + self.b2 = -(pcoeff + 2 * dcoeff) / order + self.b3 = dcoeff / order + self.accept_safety = accept_safety + self.eps = eps + self.errs = [] + + def limiter(self, x): + return 1 + math.atan(x - 1) + + def propose_step(self, error): + inv_error = 1 / (float(error) + self.eps) + if not self.errs: + self.errs = [inv_error, inv_error, inv_error] + self.errs[0] = inv_error + factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 + factor = self.limiter(factor) + accept = factor >= self.accept_safety + if accept: + self.errs[2] = self.errs[1] + self.errs[1] = self.errs[0] + self.h *= factor + return accept + + +class DPMSolver(nn.Module): + """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" + + def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): + super().__init__() + self.model = model + self.extra_args = {} if extra_args is None else extra_args + self.eps_callback = eps_callback + self.info_callback = info_callback + + def t(self, sigma): + return -sigma.log() + + def sigma(self, t): + return t.neg().exp() + + def eps(self, eps_cache, key, x, t, *args, **kwargs): + if key in eps_cache: + return eps_cache[key], eps_cache + sigma = self.sigma(t) * x.new_ones([x.shape[0]]) + eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) + if self.eps_callback is not None: + self.eps_callback() + return eps, {key: eps, **eps_cache} + + def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + x_1 = x - self.sigma(t_next) * h.expm1() * eps + return x_1, eps_cache + + def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) + return x_2, eps_cache + + def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + s2 = t + r2 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) + eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) + x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) + return x_3, eps_cache + + def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if not t_end > t_start and eta: + raise ValueError('eta must be 0 for reverse sampling') + + m = math.floor(nfe / 3) + 1 + ts = torch.linspace(t_start, t_end, m + 1, device=x.device) + + if nfe % 3 == 0: + orders = [3] * (m - 2) + [2, 1] + else: + orders = [3] * (m - 1) + [nfe % 3] + + for i in range(len(orders)): + eps_cache = {} + t, t_next = ts[i], ts[i + 1] + if eta: + sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) + t_next_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 + else: + t_next_, su = t_next, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + denoised = x - self.sigma(t) * eps + if self.info_callback is not None: + self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) + + if orders[i] == 1: + x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) + elif orders[i] == 2: + x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) + else: + x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) + + x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) + + return x + + def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if order not in {2, 3}: + raise ValueError('order should be 2 or 3') + forward = t_end > t_start + if not forward and eta: + raise ValueError('eta must be 0 for reverse sampling') + h_init = abs(h_init) * (1 if forward else -1) + atol = torch.tensor(atol) + rtol = torch.tensor(rtol) + s = t_start + x_prev = x + accept = True + pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) + info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} + + while s < t_end - 1e-5 if forward else s > t_end + 1e-5: + eps_cache = {} + t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) + if eta: + sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) + t_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 + else: + t_, su = t, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, s) + denoised = x - self.sigma(s) * eps + + if order == 2: + x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) + else: + x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) + delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) + error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 + accept = pid.propose_step(error) + if accept: + x_prev = x_low + x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) + s = t + info['n_accept'] += 1 + else: + info['n_reject'] += 1 + info['nfe'] += order + info['steps'] += 1 + + if self.info_callback is not None: + self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) + + return x, info + + +@torch.no_grad() +def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): + """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(total=n, disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) + + +@torch.no_grad() +def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): + """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) + if return_info: + return x, info + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + """DPM-Solver++ (stochastic).""" + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigmas[i + 1] - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++ + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + s = t + h * r + fac = 1 / (2 * r) + + # Step 1 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) + s_ = t_fn(sd) + x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised + x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + + # Step 2 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) + t_next_ = t_fn(sd) + denoised_d = (1 - fac) * denoised + fac * denoised_2 + x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d + x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + return x + + +@torch.no_grad() +def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + old_denoised = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_denoised is None or sigmas[i + 1] == 0: + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d + old_denoised = denoised + return x diff --git a/comfy/k_diffusion/utils.py b/comfy/k_diffusion/utils.py new file mode 100644 index 00000000..ce6014be --- /dev/null +++ b/comfy/k_diffusion/utils.py @@ -0,0 +1,332 @@ +from contextlib import contextmanager +import hashlib +import math +from pathlib import Path +import shutil +import urllib +import warnings + +from PIL import Image +import torch +from torch import nn, optim +from torch.utils import data +from torchvision.transforms import functional as TF + + +def from_pil_image(x): + """Converts from a PIL image to a tensor.""" + x = TF.to_tensor(x) + if x.ndim == 2: + x = x[..., None] + return x * 2 - 1 + + +def to_pil_image(x): + """Converts from a tensor to a PIL image.""" + if x.ndim == 4: + assert x.shape[0] == 1 + x = x[0] + if x.shape[0] == 1: + x = x[0] + return TF.to_pil_image((x.clamp(-1, 1) + 1) / 2) + + +def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): + """Apply passed in transforms for HuggingFace Datasets.""" + images = [transform(image.convert(mode)) for image in examples[image_key]] + return {image_key: images} + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + expanded = x[(...,) + (None,) * dims_to_append] + # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. + # https://github.com/pytorch/pytorch/issues/84364 + return expanded.detach().clone() if expanded.device.type == 'mps' else expanded + + +def n_params(module): + """Returns the number of trainable parameters in a module.""" + return sum(p.numel() for p in module.parameters()) + + +def download_file(path, url, digest=None): + """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + if not path.exists(): + with urllib.request.urlopen(url) as response, open(path, 'wb') as f: + shutil.copyfileobj(response, f) + if digest is not None: + file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() + if digest != file_digest: + raise OSError(f'hash of {path} (url: {url}) failed to validate') + return path + + +@contextmanager +def train_mode(model, mode=True): + """A context manager that places a model into training mode and restores + the previous mode on exit.""" + modes = [module.training for module in model.modules()] + try: + yield model.train(mode) + finally: + for i, module in enumerate(model.modules()): + module.training = modes[i] + + +def eval_mode(model): + """A context manager that places a model into evaluation mode and restores + the previous mode on exit.""" + return train_mode(model, False) + + +@torch.no_grad() +def ema_update(model, averaged_model, decay): + """Incorporates updated model parameters into an exponential moving averaged + version of a model. It should be called after each optimizer step.""" + model_params = dict(model.named_parameters()) + averaged_params = dict(averaged_model.named_parameters()) + assert model_params.keys() == averaged_params.keys() + + for name, param in model_params.items(): + averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) + + model_buffers = dict(model.named_buffers()) + averaged_buffers = dict(averaged_model.named_buffers()) + assert model_buffers.keys() == averaged_buffers.keys() + + for name, buf in model_buffers.items(): + averaged_buffers[name].copy_(buf) + + +class EMAWarmup: + """Implements an EMA warmup using an inverse decay schedule. + If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are + good values for models you plan to train for a million or more steps (reaches decay + factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models + you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at + 215.4k steps). + Args: + inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. + power (float): Exponential factor of EMA warmup. Default: 1. + min_value (float): The minimum EMA decay rate. Default: 0. + max_value (float): The maximum EMA decay rate. Default: 1. + start_at (int): The epoch to start averaging at. Default: 0. + last_epoch (int): The index of last epoch. Default: 0. + """ + + def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, + last_epoch=0): + self.inv_gamma = inv_gamma + self.power = power + self.min_value = min_value + self.max_value = max_value + self.start_at = start_at + self.last_epoch = last_epoch + + def state_dict(self): + """Returns the state of the class as a :class:`dict`.""" + return dict(self.__dict__.items()) + + def load_state_dict(self, state_dict): + """Loads the class's state. + Args: + state_dict (dict): scaler state. Should be an object returned + from a call to :meth:`state_dict`. + """ + self.__dict__.update(state_dict) + + def get_value(self): + """Gets the current EMA decay rate.""" + epoch = max(0, self.last_epoch - self.start_at) + value = 1 - (1 + epoch / self.inv_gamma) ** -self.power + return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) + + def step(self): + """Updates the step count.""" + self.last_epoch += 1 + + +class InverseLR(optim.lr_scheduler._LRScheduler): + """Implements an inverse decay learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. + inv_gamma is the number of steps/epochs required for the learning rate to decay to + (1 / 2)**power of its original value. + Args: + optimizer (Optimizer): Wrapped optimizer. + inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. + power (float): Exponential factor of learning rate decay. Default: 1. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.inv_gamma = inv_gamma + self.power = power + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +class ExponentialLR(optim.lr_scheduler._LRScheduler): + """Implements an exponential learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate + continuously by decay (default 0.5) every num_steps steps. + Args: + optimizer (Optimizer): Wrapped optimizer. + num_steps (float): The number of steps to decay the learning rate by decay in. + decay (float): The factor by which to decay the learning rate every num_steps + steps. Default: 0.5. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.num_steps = num_steps + self.decay = decay + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): + """Draws samples from an lognormal distribution.""" + return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() + + +def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from an optionally truncated log-logistic distribution.""" + min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) + max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) + min_cdf = min_value.log().sub(loc).div(scale).sigmoid() + max_cdf = max_value.log().sub(loc).div(scale).sigmoid() + u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf + return u.logit().mul(scale).add(loc).exp().to(dtype) + + +def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): + """Draws samples from an log-uniform distribution.""" + min_value = math.log(min_value) + max_value = math.log(max_value) + return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() + + +def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from a truncated v-diffusion training timestep distribution.""" + min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi + max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi + u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf + return torch.tan(u * math.pi / 2) * sigma_data + + +def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): + """Draws samples from a split lognormal distribution.""" + n = torch.randn(shape, device=device, dtype=dtype).abs() + u = torch.rand(shape, device=device, dtype=dtype) + n_left = n * -scale_1 + loc + n_right = n * scale_2 + loc + ratio = scale_1 / (scale_1 + scale_2) + return torch.where(u < ratio, n_left, n_right).exp() + + +class FolderOfImages(data.Dataset): + """Recursively finds all images in a directory. It does not support + classes/targets.""" + + IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} + + def __init__(self, root, transform=None): + super().__init__() + self.root = Path(root) + self.transform = nn.Identity() if transform is None else transform + self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) + + def __repr__(self): + return f'FolderOfImages(root="{self.root}", len: {len(self)})' + + def __len__(self): + return len(self.paths) + + def __getitem__(self, key): + path = self.paths[key] + with open(path, 'rb') as f: + image = Image.open(f).convert('RGB') + image = self.transform(image) + return image, + + +class CSVLogger: + def __init__(self, filename, columns): + self.filename = Path(filename) + self.columns = columns + if self.filename.exists(): + self.file = open(self.filename, 'a') + else: + self.file = open(self.filename, 'w') + self.write(*self.columns) + + def write(self, *args): + print(*args, sep=',', file=self.file, flush=True) + + +@contextmanager +def tf32_mode(cudnn=None, matmul=None): + """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" + cudnn_old = torch.backends.cudnn.allow_tf32 + matmul_old = torch.backends.cuda.matmul.allow_tf32 + try: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul + yield + finally: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn_old + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul_old diff --git a/comfy/ldm/data/__init__.py b/comfy/ldm/data/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/comfy/ldm/data/util.py b/comfy/ldm/data/util.py new file mode 100644 index 00000000..5b60ceb2 --- /dev/null +++ b/comfy/ldm/data/util.py @@ -0,0 +1,24 @@ +import torch + +from ldm.modules.midas.api import load_midas_transform + + +class AddMiDaS(object): + def __init__(self, model_type): + super().__init__() + self.transform = load_midas_transform(model_type) + + def pt2np(self, x): + x = ((x + 1.0) * .5).detach().cpu().numpy() + return x + + def np2pt(self, x): + x = torch.from_numpy(x) * 2 - 1. + return x + + def __call__(self, sample): + # sample['jpg'] is tensor hwc in [-1, 1] at this point + x = self.pt2np(sample['jpg']) + x = self.transform({"image": x})["image"] + sample['midas_in'] = x + return sample \ No newline at end of file diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py new file mode 100644 index 00000000..bd698621 --- /dev/null +++ b/comfy/ldm/models/autoencoder.py @@ -0,0 +1,223 @@ +import torch +# import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager + +from ldm.modules.diffusionmodules.model import Encoder, Decoder +from ldm.modules.distributions.distributions import DiagonalGaussianDistribution + +from ldm.util import instantiate_from_config +from ldm.modules.ema import LitEma + +# class AutoencoderKL(pl.LightningModule): +class AutoencoderKL(torch.nn.Module): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ema_decay=None, + learn_logvar=False + ): + super().__init__() + self.learn_logvar = learn_logvar + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + + self.use_ema = ema_decay is not None + if self.use_ema: + self.ema_decay = ema_decay + assert 0. < ema_decay < 1. + self.model_ema = LitEma(self, decay=ema_decay) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list()): + if path.lower().endswith(".safetensors"): + import safetensors.torch + sd = safetensors.torch.load_file(path, device="cpu") + else: + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, postfix=""): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, + last_layer=self.get_last_layer(), split="val"+postfix) + + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, + last_layer=self.get_last_layer(), split="val"+postfix) + + self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list( + self.quant_conv.parameters()) + list(self.post_quant_conv.parameters()) + if self.learn_logvar: + print(f"{self.__class__.__name__}: Learning logvar") + ae_params_list.append(self.loss.logvar) + opt_ae = torch.optim.Adam(ae_params_list, + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + if log_ema or self.use_ema: + with self.ema_scope(): + xrec_ema, posterior_ema = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec_ema.shape[1] > 3 + xrec_ema = self.to_rgb(xrec_ema) + log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample())) + log["reconstructions_ema"] = xrec_ema + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x + diff --git a/comfy/ldm/models/diffusion/__init__.py b/comfy/ldm/models/diffusion/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/comfy/ldm/models/diffusion/ddim.py b/comfy/ldm/models/diffusion/ddim.py new file mode 100644 index 00000000..27ead0ea --- /dev/null +++ b/comfy/ldm/models/diffusion/ddim.py @@ -0,0 +1,336 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm + +from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + dynamic_threshold=None, + ucg_schedule=None, + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + + elif isinstance(conditioning, list): + for ctmp in conditioning: + if ctmp.shape[0] != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ucg_schedule=ucg_schedule + ) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None, + ucg_schedule=None): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + if ucg_schedule is not None: + assert len(ucg_schedule) == len(time_range) + unconditional_guidance_scale = ucg_schedule[i] + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold) + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + dynamic_threshold=None): + b, *_, device = *x.shape, x.device + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + model_output = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [torch.cat([ + unconditional_conditioning[k][i], + c[k][i]]) for i in range(len(c[k]))] + else: + c_in[k] = torch.cat([ + unconditional_conditioning[k], + c[k]]) + elif isinstance(c, list): + c_in = list() + assert isinstance(unconditional_conditioning, list) + for i in range(len(c)): + c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) + else: + c_in = torch.cat([unconditional_conditioning, c]) + model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond) + + if self.model.parameterization == "v": + e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) + else: + e_t = model_output + + if score_corrector is not None: + assert self.model.parameterization == "eps", 'not implemented' + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + if self.model.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + + if dynamic_threshold is not None: + raise NotImplementedError() + + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + @torch.no_grad() + def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, + unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None): + num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0] + + assert t_enc <= num_reference_steps + num_steps = t_enc + + if use_original_steps: + alphas_next = self.alphas_cumprod[:num_steps] + alphas = self.alphas_cumprod_prev[:num_steps] + else: + alphas_next = self.ddim_alphas[:num_steps] + alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) + + x_next = x0 + intermediates = [] + inter_steps = [] + for i in tqdm(range(num_steps), desc='Encoding Image'): + t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long) + if unconditional_guidance_scale == 1.: + noise_pred = self.model.apply_model(x_next, t, c) + else: + assert unconditional_conditioning is not None + e_t_uncond, noise_pred = torch.chunk( + self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)), + torch.cat((unconditional_conditioning, c))), 2) + noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond) + + xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next + weighted_noise_pred = alphas_next[i].sqrt() * ( + (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred + x_next = xt_weighted + weighted_noise_pred + if return_intermediates and i % ( + num_steps // return_intermediates) == 0 and i < num_steps - 1: + intermediates.append(x_next) + inter_steps.append(i) + elif return_intermediates and i >= num_steps - 2: + intermediates.append(x_next) + inter_steps.append(i) + if callback: callback(i) + + out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} + if return_intermediates: + out.update({'intermediates': intermediates}) + return x_next, out + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False, callback=None): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + if callback: callback(i) + return x_dec \ No newline at end of file diff --git a/comfy/ldm/models/diffusion/ddpm.py b/comfy/ldm/models/diffusion/ddpm.py new file mode 100644 index 00000000..e297d27e --- /dev/null +++ b/comfy/ldm/models/diffusion/ddpm.py @@ -0,0 +1,1800 @@ +""" +wild mixture of +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/CompVis/taming-transformers +-- merci +""" + +import torch +import torch.nn as nn +import numpy as np +# import pytorch_lightning as pl +from torch.optim.lr_scheduler import LambdaLR +from einops import rearrange, repeat +from contextlib import contextmanager, nullcontext +from functools import partial +import itertools +from tqdm import tqdm +from torchvision.utils import make_grid +# from pytorch_lightning.utilities.distributed import rank_zero_only +from omegaconf import ListConfig + +from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config +from ldm.modules.ema import LitEma +from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution +from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL +from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like +from ldm.models.diffusion.ddim import DDIMSampler + + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def uniform_on_device(r1, r2, shape, device): + return (r1 - r2) * torch.rand(*shape, device=device) + r2 + +# class DDPM(pl.LightningModule): +class DDPM(torch.nn.Module): + # classic DDPM with Gaussian diffusion, in image space + def __init__(self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=[], + load_only_unet=False, + monitor="val/loss", + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1., + conditioning_key=None, + parameterization="eps", # all assuming fixed variance schedules + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0., + make_it_fit=False, + ucg_training=None, + reset_ema=False, + reset_num_ema_updates=False, + ): + super().__init__() + assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"' + self.parameterization = parameterization + print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.image_size = image_size # try conv? + self.channels = channels + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapper(unet_config, conditioning_key) + count_params(self.model, verbose=True) + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self.model) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + self.make_it_fit = make_it_fit + if reset_ema: assert exists(ckpt_path) + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + if reset_ema: + assert self.use_ema + print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.") + self.model_ema = LitEma(self.model) + if reset_num_ema_updates: + print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ") + assert self.use_ema + self.model_ema.reset_num_updates() + + self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, + linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + self.ucg_training = ucg_training or dict() + if self.ucg_training: + self.ucg_prng = np.random.RandomState() + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( + 1. - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) + elif self.parameterization == "v": + lvlb_weights = torch.ones_like(self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))) + else: + raise NotImplementedError("mu not supported") + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + @torch.no_grad() + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + if self.make_it_fit: + n_params = len([name for name, _ in + itertools.chain(self.named_parameters(), + self.named_buffers())]) + for name, param in tqdm( + itertools.chain(self.named_parameters(), + self.named_buffers()), + desc="Fitting old weights to new weights", + total=n_params + ): + if not name in sd: + continue + old_shape = sd[name].shape + new_shape = param.shape + assert len(old_shape) == len(new_shape) + if len(new_shape) > 2: + # we only modify first two axes + assert new_shape[2:] == old_shape[2:] + # assumes first axis corresponds to output dim + if not new_shape == old_shape: + new_param = param.clone() + old_param = sd[name] + if len(new_shape) == 1: + for i in range(new_param.shape[0]): + new_param[i] = old_param[i % old_shape[0]] + elif len(new_shape) >= 2: + for i in range(new_param.shape[0]): + for j in range(new_param.shape[1]): + new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]] + + n_used_old = torch.ones(old_shape[1]) + for j in range(new_param.shape[1]): + n_used_old[j % old_shape[1]] += 1 + n_used_new = torch.zeros(new_shape[1]) + for j in range(new_param.shape[1]): + n_used_new[j] = n_used_old[j % old_shape[1]] + + n_used_new = n_used_new[None, :] + while len(n_used_new.shape) < len(new_shape): + n_used_new = n_used_new.unsqueeze(-1) + new_param /= n_used_new + + sd[name] = new_param + + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys:\n {missing}") + if len(unexpected) > 0: + print(f"\nUnexpected Keys:\n {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def predict_start_from_z_and_v(self, x_t, t, v): + # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v + ) + + def predict_eps_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop((batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def get_v(self, x, noise, t): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x + ) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + elif self.parameterization == "v": + target = self.get_v(x_start, noise, t) + else: + raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported") + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = 'train' if self.training else 'val' + + loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f'{log_prefix}/loss': loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + for k in self.ucg_training: + p = self.ucg_training[k]["p"] + val = self.ucg_training[k]["val"] + if val is None: + val = "" + for i in range(len(batch[k])): + if self.ucg_prng.choice(2, p=[1 - p, p]): + batch[k][i] = val + + loss, loss_dict = self.shared_step(batch) + + self.log_dict(loss_dict, prog_bar=True, + logger=True, on_step=True, on_epoch=True) + + self.log("global_step", self.global_step, + prog_bar=True, logger=True, on_step=True, on_epoch=False) + + if self.use_scheduler: + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + return loss + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + _, loss_dict_no_ema = self.shared_step(batch) + with self.ema_scope(): + _, loss_dict_ema = self.shared_step(batch) + loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} + self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = dict() + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.learn_logvar: + params = params + [self.logvar] + opt = torch.optim.AdamW(params, lr=lr) + return opt + + +class LatentDiffusion(DDPM): + """main class""" + + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="image", + cond_stage_trainable=False, + concat_mode=True, + cond_stage_forward=None, + conditioning_key=None, + scale_factor=1.0, + scale_by_std=False, + force_null_conditioning=False, + *args, **kwargs): + self.force_null_conditioning = force_null_conditioning + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = 'concat' if concat_mode else 'crossattn' + if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning: + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + reset_ema = kwargs.pop("reset_ema", False) + reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + self.concat_mode = concat_mode + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + + # self.instantiate_first_stage(first_stage_config) + # self.instantiate_cond_stage(cond_stage_config) + self.first_stage_config = first_stage_config + self.cond_stage_config = cond_stage_config + + self.cond_stage_forward = cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + if reset_ema: + assert self.use_ema + print( + f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.") + self.model_ema = LitEma(self.model) + if reset_num_ema_updates: + print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ") + assert self.use_ema + self.model_ema.reset_num_updates() + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + # @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule(self, + given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__": + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device), + force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + wtith min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip(L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"]) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, padding=0, + stride=(stride[0] * uf, stride[1] * uf)) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, padding=0, + stride=(stride[0] // df, stride[1] // df)) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, + cond_key=None, return_original_cond=False, bs=None, return_x=False): + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None and not self.force_null_conditioning: + if cond_key is None: + cond_key = self.cond_stage_key + if cond_key != self.first_stage_key: + if cond_key in ['caption', 'coordinates_bbox', "txt"]: + xc = batch[cond_key] + elif cond_key in ['class_label', 'cls']: + xc = batch + else: + xc = super().get_input(batch, cond_key).to(self.device) + else: + xc = x + if not self.cond_stage_trainable or force_c_encode: + if isinstance(xc, dict) or isinstance(xc, list): + c = self.get_learned_conditioning(xc) + else: + c = self.get_learned_conditioning(xc.to(self.device)) + else: + c = xc + if bs is not None: + c = c[:bs] + + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_x: + out.extend([x]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable: + c = self.get_learned_conditioning(c) + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def apply_model(self, x_noisy, t, cond, return_ids=False): + if isinstance(cond, dict): + # hybrid case, cond is expected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + elif self.parameterization == "v": + target = self.get_v(x_start, noise, t) + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, + return_x0=False, score_corrector=None, corrector_kwargs=None): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, + return_codebook_ids=False, quantize_denoised=False, return_x0=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, + img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., + score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, + log_every_t=None): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', + total=timesteps) if verbose else reversed( + range(0, timesteps)) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, return_x0=True, + temperature=temperature[i], noise_dropout=noise_dropout, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: callback(i) + if img_callback: img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, + x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, start_T=None, + log_every_t=None): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( + range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: callback(i) + if img_callback: img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, + verbose=True, timesteps=None, quantize_denoised=False, + mask=None, x0=None, shape=None, **kwargs): + if shape is None: + shape = (batch_size, self.channels, self.image_size, self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, + mask=mask, x0=x0) + + @torch.no_grad() + def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.image_size, self.image_size) + samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, + shape, cond, verbose=False, **kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, + return_intermediates=True, **kwargs) + + return samples, intermediates + + @torch.no_grad() + def get_unconditional_conditioning(self, batch_size, null_label=None): + if null_label is not None: + xc = null_label + if isinstance(xc, ListConfig): + xc = list(xc) + if isinstance(xc, dict) or isinstance(xc, list): + c = self.get_learned_conditioning(xc) + else: + if hasattr(xc, "to"): + xc = xc.to(self.device) + c = self.get_learned_conditioning(xc) + else: + if self.cond_stage_key in ["class_label", "cls"]: + xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device) + return self.get_learned_conditioning(xc) + else: + raise NotImplementedError("todo") + if isinstance(c, list): # in case the encoder gives us a list + for i in range(len(c)): + c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device) + else: + c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device) + return c + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) + log["conditioning"] = xc + elif self.cond_stage_key in ['class_label', "cls"]: + try: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) + log['conditioning'] = xc + except KeyError: + # probably no "human_label" in batch + pass + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( + self.first_stage_model, IdentityFirstStage): + # also display when quantizing x0 while sampling + with ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta, + quantize_denoised=True) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if unconditional_guidance_scale > 1.0: + uc = self.get_unconditional_conditioning(N, unconditional_guidance_label) + if self.model.conditioning_key == "crossattn-adm": + uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]} + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg + + if inpaint: + # make a simple center square + b, h, w = z.shape[0], z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. + mask = mask[:, None, ...] + with ema_scope("Plotting Inpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + mask = 1. - mask + with ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print('Diffusion model optimizing logvar') + params.append(self.logvar) + opt = torch.optim.AdamW(params, lr=lr) + if self.use_scheduler: + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. + return x + + +# class DiffusionWrapper(pl.LightningModule): +class DiffusionWrapper(torch.nn.Module): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False) + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key + assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm'] + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None): + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == 'crossattn': + if not self.sequential_cross_attn: + cc = torch.cat(c_crossattn, 1) + else: + cc = c_crossattn + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'hybrid': + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == 'hybrid-adm': + assert c_adm is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, y=c_adm) + elif self.conditioning_key == 'crossattn-adm': + assert c_adm is not None + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc, y=c_adm) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out + + +class LatentUpscaleDiffusion(LatentDiffusion): + def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs): + super().__init__(*args, **kwargs) + # assumes that neither the cond_stage nor the low_scale_model contain trainable params + assert not self.cond_stage_trainable + self.instantiate_low_stage(low_scale_config) + self.low_scale_key = low_scale_key + self.noise_level_key = noise_level_key + + def instantiate_low_stage(self, config): + model = instantiate_from_config(config) + self.low_scale_model = model.eval() + self.low_scale_model.train = disabled_train + for param in self.low_scale_model.parameters(): + param.requires_grad = False + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): + if not log_mode: + z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) + else: + z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, + force_c_encode=True, return_original_cond=True, bs=bs) + x_low = batch[self.low_scale_key][:bs] + x_low = rearrange(x_low, 'b h w c -> b c h w') + x_low = x_low.to(memory_format=torch.contiguous_format).float() + zx, noise_level = self.low_scale_model(x_low) + if self.noise_level_key is not None: + # get noise level from batch instead, e.g. when extracting a custom noise level for bsr + raise NotImplementedError('TODO') + + all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level} + if log_mode: + # TODO: maybe disable if too expensive + x_low_rec = self.low_scale_model.decode(zx) + return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level + return z, all_conds + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, + unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, + **kwargs): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N, + log_mode=True) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + log["x_lr"] = x_low + log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) + log["conditioning"] = xc + elif self.cond_stage_key in ['class_label', 'cls']: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if unconditional_guidance_scale > 1.0: + uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) + # TODO explore better "unconditional" choices for the other keys + # maybe guide away from empty text label and highest noise level and maximally degraded zx? + uc = dict() + for k in c: + if k == "c_crossattn": + assert isinstance(c[k], list) and len(c[k]) == 1 + uc[k] = [uc_tmp] + elif k == "c_adm": # todo: only run with text-based guidance? + assert isinstance(c[k], torch.Tensor) + #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level + uc[k] = c[k] + elif isinstance(c[k], list): + uc[k] = [c[k][i] for i in range(len(c[k]))] + else: + uc[k] = c[k] + + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg + + if plot_progressive_rows: + with ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + return log + + +class LatentFinetuneDiffusion(LatentDiffusion): + """ + Basis for different finetunas, such as inpainting or depth2image + To disable finetuning mode, set finetune_keys to None + """ + + def __init__(self, + concat_keys: tuple, + finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", + "model_ema.diffusion_modelinput_blocks00weight" + ), + keep_finetune_dims=4, + # if model was trained without concat mode before and we would like to keep these channels + c_concat_log_start=None, # to log reconstruction of c_concat codes + c_concat_log_end=None, + *args, **kwargs + ): + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", list()) + super().__init__(*args, **kwargs) + self.finetune_keys = finetune_keys + self.concat_keys = concat_keys + self.keep_dims = keep_finetune_dims + self.c_concat_log_start = c_concat_log_start + self.c_concat_log_end = c_concat_log_end + if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint' + if exists(ckpt_path): + self.init_from_ckpt(ckpt_path, ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + + # make it explicit, finetune by including extra input channels + if exists(self.finetune_keys) and k in self.finetune_keys: + new_entry = None + for name, param in self.named_parameters(): + if name in self.finetune_keys: + print( + f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only") + new_entry = torch.zeros_like(param) # zero init + assert exists(new_entry), 'did not find matching parameter to modify' + new_entry[:, :self.keep_dims, ...] = sd[k] + sd[k] = new_entry + + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + print(f"Missing Keys: {missing}") + if len(unexpected) > 0: + print(f"Unexpected Keys: {unexpected}") + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, + use_ema_scope=True, + **kwargs): + ema_scope = self.ema_scope if use_ema_scope else nullcontext + use_ddim = ddim_steps is not None + + log = dict() + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True) + c_cat, c = c["c_concat"][0], c["c_crossattn"][0] + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption", "txt"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) + log["conditioning"] = xc + elif self.cond_stage_key in ['class_label', 'cls']: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if not (self.c_concat_log_start is None and self.c_concat_log_end is None): + log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end]) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = list() + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with ema_scope("Sampling"): + samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, + batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if unconditional_guidance_scale > 1.0: + uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label) + uc_cat = c_cat + uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} + with ema_scope("Sampling with classifier-free guidance"): + samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, + batch_size=N, ddim=use_ddim, + ddim_steps=ddim_steps, eta=ddim_eta, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc_full, + ) + x_samples_cfg = self.decode_first_stage(samples_cfg) + log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg + + return log + + +class LatentInpaintDiffusion(LatentFinetuneDiffusion): + """ + can either run as pure inpainting model (only concat mode) or with mixed conditionings, + e.g. mask as concat and text via cross-attn. + To disable finetuning mode, set finetune_keys to None + """ + + def __init__(self, + concat_keys=("mask", "masked_image"), + masked_image_key="masked_image", + *args, **kwargs + ): + super().__init__(concat_keys, *args, **kwargs) + self.masked_image_key = masked_image_key + assert self.masked_image_key in concat_keys + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): + # note: restricted to non-trainable encoders currently + assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting' + z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, + force_c_encode=True, return_original_cond=True, bs=bs) + + assert exists(self.concat_keys) + c_cat = list() + for ck in self.concat_keys: + cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() + if bs is not None: + cc = cc[:bs] + cc = cc.to(self.device) + bchw = z.shape + if ck != self.masked_image_key: + cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) + else: + cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) + c_cat.append(cc) + c_cat = torch.cat(c_cat, dim=1) + all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} + if return_first_stage_outputs: + return z, all_conds, x, xrec, xc + return z, all_conds + + @torch.no_grad() + def log_images(self, *args, **kwargs): + log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs) + log["masked_image"] = rearrange(args[0]["masked_image"], + 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() + return log + + +class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion): + """ + condition on monocular depth estimation + """ + + def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs): + super().__init__(concat_keys=concat_keys, *args, **kwargs) + self.depth_model = instantiate_from_config(depth_stage_config) + self.depth_stage_key = concat_keys[0] + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): + # note: restricted to non-trainable encoders currently + assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img' + z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, + force_c_encode=True, return_original_cond=True, bs=bs) + + assert exists(self.concat_keys) + assert len(self.concat_keys) == 1 + c_cat = list() + for ck in self.concat_keys: + cc = batch[ck] + if bs is not None: + cc = cc[:bs] + cc = cc.to(self.device) + cc = self.depth_model(cc) + cc = torch.nn.functional.interpolate( + cc, + size=z.shape[2:], + mode="bicubic", + align_corners=False, + ) + + depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3], + keepdim=True) + cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1. + c_cat.append(cc) + c_cat = torch.cat(c_cat, dim=1) + all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} + if return_first_stage_outputs: + return z, all_conds, x, xrec, xc + return z, all_conds + + @torch.no_grad() + def log_images(self, *args, **kwargs): + log = super().log_images(*args, **kwargs) + depth = self.depth_model(args[0][self.depth_stage_key]) + depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \ + torch.amax(depth, dim=[1, 2, 3], keepdim=True) + log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1. + return log + + +class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion): + """ + condition on low-res image (and optionally on some spatial noise augmentation) + """ + def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None, + low_scale_config=None, low_scale_key=None, *args, **kwargs): + super().__init__(concat_keys=concat_keys, *args, **kwargs) + self.reshuffle_patch_size = reshuffle_patch_size + self.low_scale_model = None + if low_scale_config is not None: + print("Initializing a low-scale model") + assert exists(low_scale_key) + self.instantiate_low_stage(low_scale_config) + self.low_scale_key = low_scale_key + + def instantiate_low_stage(self, config): + model = instantiate_from_config(config) + self.low_scale_model = model.eval() + self.low_scale_model.train = disabled_train + for param in self.low_scale_model.parameters(): + param.requires_grad = False + + @torch.no_grad() + def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): + # note: restricted to non-trainable encoders currently + assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft' + z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, + force_c_encode=True, return_original_cond=True, bs=bs) + + assert exists(self.concat_keys) + assert len(self.concat_keys) == 1 + # optionally make spatial noise_level here + c_cat = list() + noise_level = None + for ck in self.concat_keys: + cc = batch[ck] + cc = rearrange(cc, 'b h w c -> b c h w') + if exists(self.reshuffle_patch_size): + assert isinstance(self.reshuffle_patch_size, int) + cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w', + p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size) + if bs is not None: + cc = cc[:bs] + cc = cc.to(self.device) + if exists(self.low_scale_model) and ck == self.low_scale_key: + cc, noise_level = self.low_scale_model(cc) + c_cat.append(cc) + c_cat = torch.cat(c_cat, dim=1) + if exists(noise_level): + all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level} + else: + all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} + if return_first_stage_outputs: + return z, all_conds, x, xrec, xc + return z, all_conds + + @torch.no_grad() + def log_images(self, *args, **kwargs): + log = super().log_images(*args, **kwargs) + log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w') + return log diff --git a/comfy/ldm/models/diffusion/dpm_solver/__init__.py b/comfy/ldm/models/diffusion/dpm_solver/__init__.py new file mode 100644 index 00000000..7427f38c --- /dev/null +++ b/comfy/ldm/models/diffusion/dpm_solver/__init__.py @@ -0,0 +1 @@ +from .sampler import DPMSolverSampler \ No newline at end of file diff --git a/comfy/ldm/models/diffusion/dpm_solver/dpm_solver.py b/comfy/ldm/models/diffusion/dpm_solver/dpm_solver.py new file mode 100644 index 00000000..095e5ba3 --- /dev/null +++ b/comfy/ldm/models/diffusion/dpm_solver/dpm_solver.py @@ -0,0 +1,1154 @@ +import torch +import torch.nn.functional as F +import math +from tqdm import tqdm + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + t = self.inverse_lambda(lambda_t) + =============================================================== + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + 1. For discrete-time DPMs: + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + 2. For continuous-time DPMs: + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + =============================================================== + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + Example: + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError( + "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format( + schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * ( + 1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), + self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0 ** 2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), + torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * ( + 1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + We support four types of the diffusion model by setting `model_type`: + 1. "noise": noise prediction model. (Trained by predicting noise). + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + =============================================================== + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + if cond is None: + output = model(x, t_input, **model_kwargs) + else: + output = model(x, t_input, cond, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class DPM_Solver: + def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.): + """Construct a DPM-Solver. + We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0"). + If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver). + If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++). + In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True. + The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales. + Args: + model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]): + `` + def model_fn(x, t_continuous): + return noise + `` + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model. + thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1]. + max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding. + + [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + Args: + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + N: A `int`. The total number of the spacing of the time steps. + device: A torch device. + Returns: + A pytorch tensor of the time steps, with the shape (N + 1,). + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError( + "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast". + Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is: + - If order == 1: + We take `steps` of DPM-Solver-1 (i.e. DDIM). + - If order == 2: + - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of DPM-Solver-2. + - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If order == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2. + ============================================ + Args: + order: A `int`. The max order for the solver (2 or 3). + steps: A `int`. The total number of function evaluations (NFE). + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + device: A torch device. + Returns: + orders: A list of the solver order of each step. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3, ] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3, ] * (K - 1) + [1] + else: + orders = [3, ] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2, ] * K + else: + K = steps // 2 + 1 + orders = [2, ] * (K - 1) + [1] + elif order == 1: + K = 1 + orders = [1, ] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[ + torch.cumsum(torch.tensor([0, ] + orders)).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False): + """ + DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + if self.predict_x0: + phi_1 = torch.expm1(-h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + else: + phi_1 = torch.expm1(h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + + def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, + solver_type='dpm_solver'): + """ + Singlestep solver DPM-Solver-2 from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + r1: A `float`. The hyperparameter of the second-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 0.5 + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + s1 = ns.inverse_lambda(lambda_s1) + log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff( + s1), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t) + alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t) + + if self.predict_x0: + phi_11 = torch.expm1(-r1 * h) + phi_1 = torch.expm1(-h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + expand_dims(sigma_s1 / sigma_s, dims) * x + - expand_dims(alpha_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * ( + model_s1 - model_s) + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_1 = torch.expm1(h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x + - expand_dims(sigma_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s) + ) + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1} + else: + return x_t + + def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None, + return_intermediate=False, solver_type='dpm_solver'): + """ + Singlestep solver DPM-Solver-3 from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + r1: A `float`. The hyperparameter of the third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`). + If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 1. / 3. + if r2 is None: + r2 = 2. / 3. + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + lambda_s2 = lambda_s + r2 * h + s1 = ns.inverse_lambda(lambda_s1) + s2 = ns.inverse_lambda(lambda_s2) + log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff( + s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std( + s2), ns.marginal_std(t) + alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t) + + if self.predict_x0: + phi_11 = torch.expm1(-r1 * h) + phi_12 = torch.expm1(-r2 * h) + phi_1 = torch.expm1(-h) + phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1. + phi_2 = phi_1 / h + 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + expand_dims(sigma_s1 / sigma_s, dims) * x + - expand_dims(alpha_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + expand_dims(sigma_s2 / sigma_s, dims) * x + - expand_dims(alpha_s2 * phi_12, dims) * model_s + + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + expand_dims(alpha_t * phi_2, dims) * D1 + - expand_dims(alpha_t * phi_3, dims) * D2 + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_12 = torch.expm1(r2 * h) + phi_1 = torch.expm1(h) + phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1. + phi_2 = phi_1 / h - 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x + - expand_dims(sigma_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x + - expand_dims(sigma_s2 * phi_12, dims) * model_s + - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - expand_dims(sigma_t * phi_2, dims) * D1 + - expand_dims(sigma_t * phi_3, dims) * D2 + ) + + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2} + else: + return x_t + + def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"): + """ + Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + ns = self.noise_schedule + dims = x.dim() + model_prev_1, model_prev_0 = model_prev_list + t_prev_1, t_prev_0 = t_prev_list + lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda( + t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0 = h_0 / h + D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) + if self.predict_x0: + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0 + ) + else: + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0 + ) + return x_t + + def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'): + """ + Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + model_prev_2, model_prev_1, model_prev_0 = model_prev_list + t_prev_2, t_prev_1, t_prev_0 = t_prev_list + lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda( + t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_1 = lambda_prev_1 - lambda_prev_2 + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0, r1 = h_0 / h, h_1 / h + D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) + D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2) + D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1) + D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1) + if self.predict_x0: + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1 + - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2 + ) + else: + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1 + - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2 + ) + return x_t + + def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, + r2=None): + """ + Singlestep DPM-Solver with the order `order` from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + r1: A `float`. The hyperparameter of the second-order or third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate) + elif order == 2: + return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, + solver_type=solver_type, r1=r1) + elif order == 3: + return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, + solver_type=solver_type, r1=r1, r2=r2) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'): + """ + Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1]) + elif order == 2: + return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + elif order == 3: + return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, + solver_type='dpm_solver'): + """ + The adaptive step size solver based on singlestep DPM-Solver. + Args: + x: A pytorch tensor. The initial value at time `t_T`. + order: A `int`. The (higher) order of the solver. We only support order == 2 or 3. + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + h_init: A `float`. The initial step size (for logSNR). + atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1]. + rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05. + theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1]. + t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the + current time and `t_0` is less than `t_err`. The default setting is 1e-5. + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_0: A pytorch tensor. The approximated solution at time `t_0`. + [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021. + """ + ns = self.noise_schedule + s = t_T * torch.ones((x.shape[0],)).to(x) + lambda_s = ns.marginal_lambda(s) + lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x)) + h = h_init * torch.ones_like(s).to(x) + x_prev = x + nfe = 0 + if order == 2: + r1 = 0.5 + lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, + solver_type=solver_type, + **kwargs) + elif order == 3: + r1, r2 = 1. / 3., 2. / 3. + lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, + return_intermediate=True, + solver_type=solver_type) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, + solver_type=solver_type, + **kwargs) + else: + raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order)) + while torch.abs((s - t_0)).mean() > t_err: + t = ns.inverse_lambda(lambda_s + h) + x_lower, lower_noise_kwargs = lower_update(x, s, t) + x_higher = higher_update(x, s, t, **lower_noise_kwargs) + delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev))) + norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True)) + E = norm_fn((x_higher - x_lower) / delta).max() + if torch.all(E <= 1.): + x = x_higher + s = t + x_prev = x_lower + lambda_s = ns.marginal_lambda(s) + h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s) + nfe += order + print('adaptive solver nfe', nfe) + return x + + def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, + ): + """ + Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. + ===================================================== + We support the following algorithms for both noise prediction model and data prediction model: + - 'singlestep': + Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. + We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps). + The total number of function evaluations (NFE) == `steps`. + Given a fixed NFE == `steps`, the sampling procedure is: + - If `order` == 1: + - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2. + - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If `order` == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2. + - 'multistep': + Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`. + We initialize the first `order` values by lower order multistep solvers. + Given a fixed NFE == `steps`, the sampling procedure is: + Denote K = steps. + - If `order` == 1: + - We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2. + - If `order` == 3: + - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3. + - 'singlestep_fixed': + Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3). + We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE. + - 'adaptive': + Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper). + We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`. + You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs + (NFE) and the sample quality. + - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2. + - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3. + ===================================================== + Some advices for choosing the algorithm: + - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs: + Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`. + e.g. + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False) + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3, + skip_type='time_uniform', method='singlestep') + - For **guided sampling with large guidance scale** by DPMs: + Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`. + e.g. + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True) + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2, + skip_type='time_uniform', method='multistep') + We support three types of `skip_type`: + - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images** + - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**. + - 'time_quadratic': quadratic time for the time steps. + ===================================================== + Args: + x: A pytorch tensor. The initial value at time `t_start` + e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution. + steps: A `int`. The total number of function evaluations (NFE). + t_start: A `float`. The starting time of the sampling. + If `T` is None, we use self.noise_schedule.T (default is 1.0). + t_end: A `float`. The ending time of the sampling. + If `t_end` is None, we use 1. / self.noise_schedule.total_N. + e.g. if total_N == 1000, we have `t_end` == 1e-3. + For discrete-time DPMs: + - We recommend `t_end` == 1. / self.noise_schedule.total_N. + For continuous-time DPMs: + - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15. + order: A `int`. The order of DPM-Solver. + skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'. + method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'. + denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step. + Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1). + This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and + score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID + for diffusion models sampling by diffusion SDEs for low-resolutional images + (such as CIFAR-10). However, we observed that such trick does not matter for + high-resolutional images. As it needs an additional NFE, we do not recommend + it for high-resolutional images. + lower_order_final: A `bool`. Whether to use lower order solvers at the final steps. + Only valid for `method=multistep` and `steps < 15`. We empirically find that + this trick is a key to stabilizing the sampling by DPM-Solver with very few steps + (especially for steps <= 10). So we recommend to set it to be `True`. + solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`. + atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + Returns: + x_end: A pytorch tensor. The approximated solution at time `t_end`. + """ + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + if method == 'adaptive': + with torch.no_grad(): + x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, + solver_type=solver_type) + elif method == 'multistep': + assert steps >= order + timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + with torch.no_grad(): + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + # Init the first `order` values by lower order multistep DPM-Solver. + for init_order in tqdm(range(1, order), desc="DPM init order"): + vec_t = timesteps[init_order].expand(x.shape[0]) + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, + solver_type=solver_type) + model_prev_list.append(self.model_fn(x, vec_t)) + t_prev_list.append(vec_t) + # Compute the remaining values by `order`-th order multistep DPM-Solver. + for step in tqdm(range(order, steps + 1), desc="DPM multistep"): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final and steps < 15: + step_order = min(order, steps + 1 - step) + else: + step_order = order + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, + solver_type=solver_type) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + model_prev_list[-1] = self.model_fn(x, vec_t) + elif method in ['singlestep', 'singlestep_fixed']: + if method == 'singlestep': + timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, + skip_type=skip_type, + t_T=t_T, t_0=t_0, + device=device) + elif method == 'singlestep_fixed': + K = steps // order + orders = [order, ] * K + timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device) + for i, order in enumerate(orders): + t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1] + timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), + N=order, device=device) + lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner) + vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0]) + h = lambda_inner[-1] - lambda_inner[0] + r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h + r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h + x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2) + if denoise_to_zero: + x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,) * (dims - 1)] \ No newline at end of file diff --git a/comfy/ldm/models/diffusion/dpm_solver/sampler.py b/comfy/ldm/models/diffusion/dpm_solver/sampler.py new file mode 100644 index 00000000..7d137b8c --- /dev/null +++ b/comfy/ldm/models/diffusion/dpm_solver/sampler.py @@ -0,0 +1,87 @@ +"""SAMPLING ONLY.""" +import torch + +from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver + + +MODEL_TYPES = { + "eps": "noise", + "v": "v" +} + + +class DPMSolverSampler(object): + def __init__(self, model, **kwargs): + super().__init__() + self.model = model + to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) + self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod)) + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + + print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}') + + device = self.model.betas.device + if x_T is None: + img = torch.randn(size, device=device) + else: + img = x_T + + ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod) + + model_fn = model_wrapper( + lambda x, t, c: self.model.apply_model(x, t, c), + ns, + model_type=MODEL_TYPES[self.model.parameterization], + guidance_type="classifier-free", + condition=conditioning, + unconditional_condition=unconditional_conditioning, + guidance_scale=unconditional_guidance_scale, + ) + + dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False) + x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True) + + return x.to(device), None \ No newline at end of file diff --git a/comfy/ldm/models/diffusion/plms.py b/comfy/ldm/models/diffusion/plms.py new file mode 100644 index 00000000..7002a365 --- /dev/null +++ b/comfy/ldm/models/diffusion/plms.py @@ -0,0 +1,244 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm +from functools import partial + +from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like +from ldm.models.diffusion.sampling_util import norm_thresholding + + +class PLMSSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + if ddim_eta != 0: + raise ValueError('ddim_eta must be 0 for PLMS') + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + dynamic_threshold=None, + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for PLMS sampling is {size}') + + samples, intermediates = self.plms_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ) + return samples, intermediates + + @torch.no_grad() + def plms_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + dynamic_threshold=None): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + print(f"Running PLMS Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) + old_eps = [] + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=old_eps, t_next=ts_next, + dynamic_threshold=dynamic_threshold) + img, pred_x0, e_t = outs + old_eps.append(e_t) + if len(old_eps) >= 4: + old_eps.pop(0) + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, + dynamic_threshold=None): + b, *_, device = *x.shape, x.device + + def get_model_output(x, t): + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + return e_t + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + + def get_x_prev_and_pred_x0(e_t, index): + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + if dynamic_threshold is not None: + pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + e_t = get_model_output(x, t) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) + e_t_next = get_model_output(x_prev, t_next) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) + + return x_prev, pred_x0, e_t diff --git a/comfy/ldm/models/diffusion/sampling_util.py b/comfy/ldm/models/diffusion/sampling_util.py new file mode 100644 index 00000000..7eff02be --- /dev/null +++ b/comfy/ldm/models/diffusion/sampling_util.py @@ -0,0 +1,22 @@ +import torch +import numpy as np + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions. + From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + return x[(...,) + (None,) * dims_to_append] + + +def norm_thresholding(x0, value): + s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim) + return x0 * (value / s) + + +def spatial_norm_thresholding(x0, value): + # b c h w + s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) + return x0 * (value / s) \ No newline at end of file diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py new file mode 100644 index 00000000..67978d4c --- /dev/null +++ b/comfy/ldm/modules/attention.py @@ -0,0 +1,533 @@ +from inspect import isfunction +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat +from typing import Optional, Any + +from ldm.modules.diffusionmodules.util import checkpoint +from .sub_quadratic_attention import efficient_dot_product_attention + +try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + +# CrossAttn precision handling +import os +_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32") + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ + + +class CrossAttentionBirchSan(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + query = self.to_q(x) + context = default(context, x) + key = self.to_k(context) + value = self.to_v(context) + del context, x + + query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1) + key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1) + del key + value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1) + + dtype = query.dtype + # TODO: do we still need to do *everything* in float32, given how we delay the division? + # TODO: do we need to support upcast_softmax too? SD 2.1 seems to work without it + # if self.upcast_attention: + # query = query.float() + # key_t = key_t.float() + + bytes_per_token = torch.finfo(query.dtype).bits//8 + batch_x_heads, q_tokens, _ = query.shape + _, _, k_tokens = key_t.shape + qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens + + stats = torch.cuda.memory_stats(query.device) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD + + kv_chunk_size_min = None + + query_chunk_size_x = 1024 * 4 + kv_chunk_size_min_x = None + kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 1.2) // 1024) * 1024 + if kv_chunk_size_x < 1024: + kv_chunk_size_x = None + + if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes: + # the big matmul fits into our memory limit; do everything in 1 chunk, + # i.e. send it down the unchunked fast-path + query_chunk_size = q_tokens + kv_chunk_size = k_tokens + else: + query_chunk_size = query_chunk_size_x + kv_chunk_size = kv_chunk_size_x + kv_chunk_size_min = kv_chunk_size_min_x + + hidden_states = efficient_dot_product_attention( + query, + key_t, + value, + query_chunk_size=query_chunk_size, + kv_chunk_size=kv_chunk_size, + kv_chunk_size_min=kv_chunk_size_min, + use_checkpoint=self.training, + ) + + hidden_states = hidden_states.to(dtype) + + hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2) + + out_proj, dropout = self.to_out + hidden_states = out_proj(hidden_states) + hidden_states = dropout(hidden_states) + + return hidden_states + + +class CrossAttentionDoggettx(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q_in = self.to_q(x) + context = default(context, x) + k_in = self.to_k(context) + v_in = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device) + + stats = torch.cuda.memory_stats(q.device) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() + modifier = 3 if q.element_size() == 2 else 2.5 + mem_required = tensor_size * modifier + steps = 1 + + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " + # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") + + if steps > 64: + max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 + raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' + f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') + + # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) + first_op_done = False + cleared_cache = False + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + if _ATTN_PRECISION =="fp32": + with torch.autocast(enabled=False, device_type = 'cuda'): + s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale + else: + s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale + first_op_done = True + + s2 = s1.softmax(dim=-1) + del s1 + + r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) + del s2 + break + except torch.cuda.OutOfMemoryError as e: + if first_op_done == False: + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + if cleared_cache == False: + cleared_cache = True + print("out of memory error, emptying cache and trying again") + continue + steps *= 2 + if steps > 64: + raise e + print("out of memory error, increasing steps and trying again", steps) + else: + raise e + + del q, k, v + + r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) + del r1 + + return self.to_out(r2) + +class OriginalCrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + # force cast to fp32 to avoid overflowing + if _ATTN_PRECISION =="fp32": + with torch.autocast(enabled=False, device_type = 'cuda'): + q, k = q.float(), k.float() + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + del q, k + + if exists(mask): + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + +class CrossAttention(CrossAttentionDoggettx): + pass + +class MemoryEfficientCrossAttention(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): + super().__init__() + print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " + f"{heads} heads.") + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + ATTENTION_MODES = { + "softmax": CrossAttention, # vanilla attention + "softmax-xformers": MemoryEfficientCrossAttention + } + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, + disable_self_attn=False): + super().__init__() + attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" + assert attn_mode in self.ATTENTION_MODES + attn_cls = self.ATTENTION_MODES[attn_mode] + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward(self, x, context=None): + return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) + + def _forward(self, x, context=None): + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels) + if not use_linear: + self.proj_in = nn.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = zero_module(nn.Conv2d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context[i]) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + diff --git a/comfy/ldm/modules/diffusionmodules/__init__.py b/comfy/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py new file mode 100644 index 00000000..b089eebb --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,852 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +from typing import Optional, Any + +from ldm.modules.attention import MemoryEfficientCrossAttention + +try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False + print("No module 'xformers'. Proceeding without it.") + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + +class MemoryEfficientAttnBlock(nn.Module): + """ + Uses xformers efficient implementation, + see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + Note: this is a single-head self-attention operation + """ + # + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.attention_op: Optional[Any] = None + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + B, C, H, W = q.shape + q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v)) + + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(B, t.shape[1], 1, C) + .permute(0, 2, 1, 3) + .reshape(B * 1, t.shape[1], C) + .contiguous(), + (q, k, v), + ) + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + + out = ( + out.unsqueeze(0) + .reshape(B, 1, out.shape[1], C) + .permute(0, 2, 1, 3) + .reshape(B, out.shape[1], C) + ) + out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C) + out = self.proj_out(out) + return x+out + + +class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): + def forward(self, x, context=None, mask=None): + b, c, h, w = x.shape + x = rearrange(x, 'b c h w -> b (h w) c') + out = super().forward(x, context=context, mask=mask) + out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c) + return x + out + + +def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): + assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown' + if XFORMERS_IS_AVAILBLE and attn_type == "vanilla": + attn_type = "vanilla-xformers" + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + assert attn_kwargs is None + return AttnBlock(in_channels) + elif attn_type == "vanilla-xformers": + print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") + return MemoryEfficientAttnBlock(in_channels) + elif type == "memory-efficient-cross-attn": + attn_kwargs["query_dim"] = in_channels + return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + raise NotImplementedError() + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, + mid_channels, + kernel_size=3, + stride=1, + padding=1) + self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + + self.conv_out = nn.Conv2d(mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, + z_channels=intermediate_chn, double_z=False, resolution=resolution, + attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, + out_ch=None) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, + mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), + dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + tmp_chn = z_channels*ch_mult[-1] + self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, + resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, + ch_mult=ch_mult, resolution=resolution, ch=ch) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, + out_channels=tmp_chn, depth=rescale_module_depth) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size//in_size))+1 + factor_up = 1.+ (out_size % in_size) + print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") + self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, + out_channels=in_channels) + self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, + attn_resolutions=[], in_channels=None, ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)]) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=4, + stride=2, + padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor==1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 00000000..7df6b5ab --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,786 @@ +from abc import abstractmethod +import math + +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +from ldm.modules.diffusionmodules.util import ( + checkpoint, + conv_nd, + linear, + avg_pool_nd, + zero_module, + normalization, + timestep_embedding, +) +from ldm.modules.attention import SpatialTransformer +from ldm.util import exists + + +# dummy replace +def convert_module_to_f16(x): + pass + +def convert_module_to_f32(x): + pass + + +## go +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads_channels: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + +class TransposedUpsample(nn.Module): + 'Learned 2x upsampling without padding' + def __init__(self, channels, out_channels=None, ks=5): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + + self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) + + def forward(self,x): + return self.up(x) + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! + #return pt_checkpoint(self._forward, x) # pytorch + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial ** 2) * c + model.total_ops += th.DoubleTensor([matmul_ops]) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = th.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) + a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + ): + super().__init__() + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + from omegaconf.listconfig import ListConfig + if type(context_dim) == ListConfig: + context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) + print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set.") + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, 3, padding=1) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or i < num_attention_blocks[level]: + layers.append( + AttentionBlock( + ch, + use_checkpoint=use_checkpoint, + num_heads=num_heads_upsample, + num_head_channels=dim_head, + use_new_attention_order=use_new_attention_order, + ) if not use_spatial_transformer else SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + normalization(ch), + conv_nd(dims, model_channels, n_embed, 1), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def convert_to_fp16(self): + """ + Convert the torso of the model to float16. + """ + self.input_blocks.apply(convert_module_to_f16) + self.middle_block.apply(convert_module_to_f16) + self.output_blocks.apply(convert_module_to_f16) + + def convert_to_fp32(self): + """ + Convert the torso of the model to float32. + """ + self.input_blocks.apply(convert_module_to_f32) + self.middle_block.apply(convert_module_to_f32) + self.output_blocks.apply(convert_module_to_f32) + + def forward(self, x, timesteps=None, context=None, y=None,**kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + for module in self.output_blocks: + h = th.cat([h, hs.pop()], dim=1) + h = module(h, emb, context) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/comfy/ldm/modules/diffusionmodules/upscaling.py b/comfy/ldm/modules/diffusionmodules/upscaling.py new file mode 100644 index 00000000..03816662 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/upscaling.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +import numpy as np +from functools import partial + +from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule +from ldm.util import default + + +class AbstractLowScaleModel(nn.Module): + # for concatenating a downsampled image to the latent representation + def __init__(self, noise_schedule_config=None): + super(AbstractLowScaleModel, self).__init__() + if noise_schedule_config is not None: + self.register_schedule(**noise_schedule_config) + + def register_schedule(self, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def forward(self, x): + return x, None + + def decode(self, x): + return x + + +class SimpleImageConcat(AbstractLowScaleModel): + # no noise level conditioning + def __init__(self): + super(SimpleImageConcat, self).__init__(noise_schedule_config=None) + self.max_noise_level = 0 + + def forward(self, x): + # fix to constant noise level + return x, torch.zeros(x.shape[0], device=x.device).long() + + +class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): + def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): + super().__init__(noise_schedule_config=noise_schedule_config) + self.max_noise_level = max_noise_level + + def forward(self, x, noise_level=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + z = self.q_sample(x, noise_level) + return z, noise_level + + + diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py new file mode 100644 index 00000000..637363df --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,270 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import os +import math +import torch +import torch.nn as nn +import numpy as np +from einops import repeat + +from ldm.util import instantiate_from_config + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), + "dtype": torch.get_autocast_gpu_dtype(), + "cache_enabled": torch.is_autocast_cache_enabled()} + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() \ No newline at end of file diff --git a/comfy/ldm/modules/distributions/__init__.py b/comfy/ldm/modules/distributions/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/comfy/ldm/modules/distributions/distributions.py b/comfy/ldm/modules/distributions/distributions.py new file mode 100644 index 00000000..f2b8ef90 --- /dev/null +++ b/comfy/ldm/modules/distributions/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/comfy/ldm/modules/ema.py b/comfy/ldm/modules/ema.py new file mode 100644 index 00000000..bded2501 --- /dev/null +++ b/comfy/ldm/modules/ema.py @@ -0,0 +1,80 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates + else torch.tensor(-1, dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + # remove as '.'-character is not allowed in buffers + s_name = name.replace('.', '') + self.m_name2s_name.update({name: s_name}) + self.register_buffer(s_name, p.clone().detach().data) + + self.collected_params = [] + + def reset_num_updates(self): + del self.num_updates + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) + + def forward(self, model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/comfy/ldm/modules/encoders/__init__.py b/comfy/ldm/modules/encoders/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/comfy/ldm/modules/encoders/modules.py b/comfy/ldm/modules/encoders/modules.py new file mode 100644 index 00000000..4edd5496 --- /dev/null +++ b/comfy/ldm/modules/encoders/modules.py @@ -0,0 +1,213 @@ +import torch +import torch.nn as nn +from torch.utils.checkpoint import checkpoint + +from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel + +import open_clip +from ldm.util import default, count_params + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class IdentityEncoder(AbstractEncoder): + + def encode(self, x): + return x + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + self.n_classes = n_classes + self.ucg_rate = ucg_rate + + def forward(self, batch, key=None, disable_dropout=False): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + if self.ucg_rate > 0. and not disable_dropout: + mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) + c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1) + c = c.long() + c = self.embedding(c) + return c + + def get_unconditional_conditioning(self, bs, device="cuda"): + uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) + uc = torch.ones((bs,), device=device) * uc_class + uc = {self.key: uc} + return uc + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class FrozenT5Embedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + if freeze: + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +class FrozenCLIPEmbedder(AbstractEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + LAYERS = [ + "last", + "pooled", + "hidden" + ] + def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, + freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + self.tokenizer = CLIPTokenizer.from_pretrained(version) + self.transformer = CLIPTextModel.from_pretrained(version) + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = layer_idx + if layer == "hidden": + assert layer_idx is not None + assert 0 <= abs(layer_idx) <= 12 + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden") + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + return z + + def encode(self, text): + return self(text) + + +class FrozenOpenCLIPEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + #"pooled", + "last", + "penultimate" + ] + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, + freeze=True, layer="last"): + super().__init__() + assert layer in self.LAYERS + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) + del model.visual + self.model = model + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + tokens = open_clip.tokenize(text) + z = self.encode_with_transformer(tokens.to(self.device)) + return z + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + +class FrozenCLIPT5Encoder(AbstractEncoder): + def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", + clip_max_length=77, t5_max_length=77): + super().__init__() + self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) + self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) + print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, " + f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.") + + def encode(self, text): + return self(text) + + def forward(self, text): + clip_z = self.clip_encoder.encode(text) + t5_z = self.t5_encoder.encode(text) + return [clip_z, t5_z] + + diff --git a/comfy/ldm/modules/image_degradation/__init__.py b/comfy/ldm/modules/image_degradation/__init__.py new file mode 100644 index 00000000..7836cada --- /dev/null +++ b/comfy/ldm/modules/image_degradation/__init__.py @@ -0,0 +1,2 @@ +from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr +from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light diff --git a/comfy/ldm/modules/image_degradation/bsrgan.py b/comfy/ldm/modules/image_degradation/bsrgan.py new file mode 100644 index 00000000..32ef5616 --- /dev/null +++ b/comfy/ldm/modules/image_degradation/bsrgan.py @@ -0,0 +1,730 @@ +# -*- coding: utf-8 -*- +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + +import numpy as np +import cv2 +import torch + +from functools import partial +import random +from scipy import ndimage +import scipy +import scipy.stats as ss +from scipy.interpolate import interp2d +from scipy.linalg import orth +import albumentations + +import ldm.modules.image_degradation.utils_image as util + + +def modcrop_np(img, sf): + ''' + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + ''' + w, h = img.shape[:2] + im = np.copy(img) + return im[:w - w % sf, :h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """ generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + ''' + x: image, NxcxHxW + k: kernel, Nx1xhxw + ''' + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): + """" + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], + [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + ''' + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + ''' + if filter_type == 'gaussian': + return fspecial_gaussian(*args, **kwargs) + if filter_type == 'laplacian': + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + ''' + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + ''' + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + ''' blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + ''' bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + ''' + x = bicubic_degradation(x, sf=sf) + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + return x + + +def classical_degradation(x, k, sf=3): + ''' blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + ''' + x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) + else: + k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) + img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: # add noise + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: + img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. + noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(30, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + image = add_blur(image, sf=sf) + + elif i == 1: + image = add_blur(image, sf=sf) + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') + image = image[0::sf, 0::sf, ...] # nearest downsampling + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + example = {"image":image} + return example + + +# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... +def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): + """ + This is an extended degradation model by combining + the degradation models of BSRGAN and Real-ESRGAN + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + use_shuffle: the degradation shuffle + use_sharp: sharpening the img + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + if use_sharp: + img = add_sharpening(img) + hq = img.copy() + + if random.random() < shuffle_prob: + shuffle_order = random.sample(range(13), 13) + else: + shuffle_order = list(range(13)) + # local shuffle for noise, JPEG is always the last one + shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) + shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) + + poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 + + for i in shuffle_order: + if i == 0: + img = add_blur(img, sf=sf) + elif i == 1: + img = add_resize(img, sf=sf) + elif i == 2: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 3: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 4: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 5: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + elif i == 6: + img = add_JPEG_noise(img) + elif i == 7: + img = add_blur(img, sf=sf) + elif i == 8: + img = add_resize(img, sf=sf) + elif i == 9: + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) + elif i == 10: + if random.random() < poisson_prob: + img = add_Poisson_noise(img) + elif i == 11: + if random.random() < speckle_prob: + img = add_speckle_noise(img) + elif i == 12: + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + else: + print('check the shuffle!') + + # resize to desired size + img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), + interpolation=random.choice([1, 2, 3])) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf, lq_patchsize) + + return img, hq + + +if __name__ == '__main__': + print("hey") + img = util.imread_uint('utils/test.png', 3) + print(img) + img = util.uint2single(img) + print(img) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_lq = deg_fn(img) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) + util.imsave(img_concat, str(i) + '.png') + + diff --git a/comfy/ldm/modules/image_degradation/bsrgan_light.py b/comfy/ldm/modules/image_degradation/bsrgan_light.py new file mode 100644 index 00000000..808c7f88 --- /dev/null +++ b/comfy/ldm/modules/image_degradation/bsrgan_light.py @@ -0,0 +1,651 @@ +# -*- coding: utf-8 -*- +import numpy as np +import cv2 +import torch + +from functools import partial +import random +from scipy import ndimage +import scipy +import scipy.stats as ss +from scipy.interpolate import interp2d +from scipy.linalg import orth +import albumentations + +import ldm.modules.image_degradation.utils_image as util + +""" +# -------------------------------------------- +# Super-Resolution +# -------------------------------------------- +# +# Kai Zhang (cskaizhang@gmail.com) +# https://github.com/cszn +# From 2019/03--2021/08 +# -------------------------------------------- +""" + +def modcrop_np(img, sf): + ''' + Args: + img: numpy image, WxH or WxHxC + sf: scale factor + Return: + cropped image + ''' + w, h = img.shape[:2] + im = np.copy(img) + return im[:w - w % sf, :h - h % sf, ...] + + +""" +# -------------------------------------------- +# anisotropic Gaussian kernels +# -------------------------------------------- +""" + + +def analytic_kernel(k): + """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" + k_size = k.shape[0] + # Calculate the big kernels size + big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) + # Loop over the small kernel to fill the big one + for r in range(k_size): + for c in range(k_size): + big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k + # Crop the edges of the big kernel to ignore very small values and increase run time of SR + crop = k_size // 2 + cropped_big_k = big_k[crop:-crop, crop:-crop] + # Normalize to 1 + return cropped_big_k / cropped_big_k.sum() + + +def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): + """ generate an anisotropic Gaussian kernel + Args: + ksize : e.g., 15, kernel size + theta : [0, pi], rotation angle range + l1 : [0.1,50], scaling of eigenvalues + l2 : [0.1,l1], scaling of eigenvalues + If l1 = l2, will get an isotropic Gaussian kernel. + Returns: + k : kernel + """ + + v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) + V = np.array([[v[0], v[1]], [v[1], -v[0]]]) + D = np.array([[l1, 0], [0, l2]]) + Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) + k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) + + return k + + +def gm_blur_kernel(mean, cov, size=15): + center = size / 2.0 + 0.5 + k = np.zeros([size, size]) + for y in range(size): + for x in range(size): + cy = y - center + 1 + cx = x - center + 1 + k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) + + k = k / np.sum(k) + return k + + +def shift_pixel(x, sf, upper_left=True): + """shift pixel for super-resolution with different scale factors + Args: + x: WxHxC or WxH + sf: scale factor + upper_left: shift direction + """ + h, w = x.shape[:2] + shift = (sf - 1) * 0.5 + xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) + if upper_left: + x1 = xv + shift + y1 = yv + shift + else: + x1 = xv - shift + y1 = yv - shift + + x1 = np.clip(x1, 0, w - 1) + y1 = np.clip(y1, 0, h - 1) + + if x.ndim == 2: + x = interp2d(xv, yv, x)(x1, y1) + if x.ndim == 3: + for i in range(x.shape[-1]): + x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) + + return x + + +def blur(x, k): + ''' + x: image, NxcxHxW + k: kernel, Nx1xhxw + ''' + n, c = x.shape[:2] + p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 + x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') + k = k.repeat(1, c, 1, 1) + k = k.view(-1, 1, k.shape[2], k.shape[3]) + x = x.view(1, -1, x.shape[2], x.shape[3]) + x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) + x = x.view(n, c, x.shape[2], x.shape[3]) + + return x + + +def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): + """" + # modified version of https://github.com/assafshocher/BlindSR_dataset_generator + # Kai Zhang + # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var + # max_var = 2.5 * sf + """ + # Set random eigen-vals (lambdas) and angle (theta) for COV matrix + lambda_1 = min_var + np.random.rand() * (max_var - min_var) + lambda_2 = min_var + np.random.rand() * (max_var - min_var) + theta = np.random.rand() * np.pi # random theta + noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 + + # Set COV matrix using Lambdas and Theta + LAMBDA = np.diag([lambda_1, lambda_2]) + Q = np.array([[np.cos(theta), -np.sin(theta)], + [np.sin(theta), np.cos(theta)]]) + SIGMA = Q @ LAMBDA @ Q.T + INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] + + # Set expectation position (shifting kernel for aligned image) + MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) + MU = MU[None, None, :, None] + + # Create meshgrid for Gaussian + [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) + Z = np.stack([X, Y], 2)[:, :, :, None] + + # Calcualte Gaussian for every pixel of the kernel + ZZ = Z - MU + ZZ_t = ZZ.transpose(0, 1, 3, 2) + raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) + + # shift the kernel so it will be centered + # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) + + # Normalize the kernel and return + # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) + kernel = raw_kernel / np.sum(raw_kernel) + return kernel + + +def fspecial_gaussian(hsize, sigma): + hsize = [hsize, hsize] + siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] + std = sigma + [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) + arg = -(x * x + y * y) / (2 * std * std) + h = np.exp(arg) + h[h < scipy.finfo(float).eps * h.max()] = 0 + sumh = h.sum() + if sumh != 0: + h = h / sumh + return h + + +def fspecial_laplacian(alpha): + alpha = max([0, min([alpha, 1])]) + h1 = alpha / (alpha + 1) + h2 = (1 - alpha) / (alpha + 1) + h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] + h = np.array(h) + return h + + +def fspecial(filter_type, *args, **kwargs): + ''' + python code from: + https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py + ''' + if filter_type == 'gaussian': + return fspecial_gaussian(*args, **kwargs) + if filter_type == 'laplacian': + return fspecial_laplacian(*args, **kwargs) + + +""" +# -------------------------------------------- +# degradation models +# -------------------------------------------- +""" + + +def bicubic_degradation(x, sf=3): + ''' + Args: + x: HxWxC image, [0, 1] + sf: down-scale factor + Return: + bicubicly downsampled LR image + ''' + x = util.imresize_np(x, scale=1 / sf) + return x + + +def srmd_degradation(x, k, sf=3): + ''' blur + bicubic downsampling + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2018learning, + title={Learning a single convolutional super-resolution network for multiple degradations}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={3262--3271}, + year={2018} + } + ''' + x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' + x = bicubic_degradation(x, sf=sf) + return x + + +def dpsr_degradation(x, k, sf=3): + ''' bicubic downsampling + blur + Args: + x: HxWxC image, [0, 1] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + Reference: + @inproceedings{zhang2019deep, + title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, + author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + pages={1671--1681}, + year={2019} + } + ''' + x = bicubic_degradation(x, sf=sf) + x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + return x + + +def classical_degradation(x, k, sf=3): + ''' blur + downsampling + Args: + x: HxWxC image, [0, 1]/[0, 255] + k: hxw, double + sf: down-scale factor + Return: + downsampled LR image + ''' + x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') + # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) + st = 0 + return x[st::sf, st::sf, ...] + + +def add_sharpening(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. borrowed from real-ESRGAN + Input image: I; Blurry image: B. + 1. K = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * K + (1 - Mask) * I + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + K = img + weight * residual + K = np.clip(K, 0, 1) + return soft_mask * K + (1 - soft_mask) * img + + +def add_blur(img, sf=4): + wd2 = 4.0 + sf + wd = 2.0 + 0.2 * sf + + wd2 = wd2/4 + wd = wd/4 + + if random.random() < 0.5: + l1 = wd2 * random.random() + l2 = wd2 * random.random() + k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) + else: + k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) + img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror') + + return img + + +def add_resize(img, sf=4): + rnum = np.random.rand() + if rnum > 0.8: # up + sf1 = random.uniform(1, 2) + elif rnum < 0.7: # down + sf1 = random.uniform(0.5 / sf, 1) + else: + sf1 = 1.0 + img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + return img + + +# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): +# noise_level = random.randint(noise_level1, noise_level2) +# rnum = np.random.rand() +# if rnum > 0.6: # add color Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) +# elif rnum < 0.4: # add grayscale Gaussian noise +# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) +# else: # add noise +# L = noise_level2 / 255. +# D = np.diag(np.random.rand(3)) +# U = orth(np.random.rand(3, 3)) +# conv = np.dot(np.dot(np.transpose(U), D), U) +# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) +# img = np.clip(img, 0.0, 1.0) +# return img + +def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + rnum = np.random.rand() + if rnum > 0.6: # add color Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: # add grayscale Gaussian noise + img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: # add noise + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_speckle_noise(img, noise_level1=2, noise_level2=25): + noise_level = random.randint(noise_level1, noise_level2) + img = np.clip(img, 0.0, 1.0) + rnum = random.random() + if rnum > 0.6: + img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) + elif rnum < 0.4: + img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) + else: + L = noise_level2 / 255. + D = np.diag(np.random.rand(3)) + U = orth(np.random.rand(3, 3)) + conv = np.dot(np.dot(np.transpose(U), D), U) + img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) + img = np.clip(img, 0.0, 1.0) + return img + + +def add_Poisson_noise(img): + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = 10 ** (2 * random.random() + 2.0) # [2, 4] + if random.random() < 0.5: + img = np.random.poisson(img * vals).astype(np.float32) / vals + else: + img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) + img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. + noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray + img += noise_gray[:, :, np.newaxis] + img = np.clip(img, 0.0, 1.0) + return img + + +def add_JPEG_noise(img): + quality_factor = random.randint(80, 95) + img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) + result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) + img = cv2.imdecode(encimg, 1) + img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) + return img + + +def random_crop(lq, hq, sf=4, lq_patchsize=64): + h, w = lq.shape[:2] + rnd_h = random.randint(0, h - lq_patchsize) + rnd_w = random.randint(0, w - lq_patchsize) + lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] + + rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) + hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] + return lq, hq + + +def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = img.shape[:2] + img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = img.shape[:2] + + if h < lq_patchsize * sf or w < lq_patchsize * sf: + raise ValueError(f'img size ({h1}X{w1}) is too small!') + + hq = img.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + img = util.imresize_np(img, 1 / 2, True) + img = np.clip(img, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + img = add_blur(img, sf=sf) + + elif i == 1: + img = add_blur(img, sf=sf) + + elif i == 2: + a, b = img.shape[1], img.shape[0] + # downsample2 + if random.random() < 0.75: + sf1 = random.uniform(1, 2 * sf) + img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') + img = img[0::sf, 0::sf, ...] # nearest downsampling + img = np.clip(img, 0.0, 1.0) + + elif i == 3: + # downsample3 + img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + img = np.clip(img, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + img = add_JPEG_noise(img) + + elif i == 6: + # add processed camera sensor noise + if random.random() < isp_prob and isp_model is not None: + with torch.no_grad(): + img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + img = add_JPEG_noise(img) + + # random crop + img, hq = random_crop(img, hq, sf_ori, lq_patchsize) + + return img, hq + + +# todo no isp_model? +def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False): + """ + This is the degradation model of BSRGAN from the paper + "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" + ---------- + sf: scale factor + isp_model: camera ISP model + Returns + ------- + img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] + hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] + """ + image = util.uint2single(image) + isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 + sf_ori = sf + + h1, w1 = image.shape[:2] + image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop + h, w = image.shape[:2] + + hq = image.copy() + + if sf == 4 and random.random() < scale2_prob: # downsample1 + if np.random.rand() < 0.5: + image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + image = util.imresize_np(image, 1 / 2, True) + image = np.clip(image, 0.0, 1.0) + sf = 2 + + shuffle_order = random.sample(range(7), 7) + idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) + if idx1 > idx2: # keep downsample3 last + shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] + + for i in shuffle_order: + + if i == 0: + image = add_blur(image, sf=sf) + + # elif i == 1: + # image = add_blur(image, sf=sf) + + if i == 0: + pass + + elif i == 2: + a, b = image.shape[1], image.shape[0] + # downsample2 + if random.random() < 0.8: + sf1 = random.uniform(1, 2 * sf) + image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), + interpolation=random.choice([1, 2, 3])) + else: + k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) + k_shifted = shift_pixel(k, sf) + k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel + image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') + image = image[0::sf, 0::sf, ...] # nearest downsampling + + image = np.clip(image, 0.0, 1.0) + + elif i == 3: + # downsample3 + image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) + image = np.clip(image, 0.0, 1.0) + + elif i == 4: + # add Gaussian noise + image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) + + elif i == 5: + # add JPEG noise + if random.random() < jpeg_prob: + image = add_JPEG_noise(image) + # + # elif i == 6: + # # add processed camera sensor noise + # if random.random() < isp_prob and isp_model is not None: + # with torch.no_grad(): + # img, hq = isp_model.forward(img.copy(), hq) + + # add final JPEG compression noise + image = add_JPEG_noise(image) + image = util.single2uint(image) + if up: + image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC) # todo: random, as above? want to condition on it then + example = {"image": image} + return example + + + + +if __name__ == '__main__': + print("hey") + img = util.imread_uint('utils/test.png', 3) + img = img[:448, :448] + h = img.shape[0] // 4 + print("resizing to", h) + sf = 4 + deg_fn = partial(degradation_bsrgan_variant, sf=sf) + for i in range(20): + print(i) + img_hq = img + img_lq = deg_fn(img)["image"] + img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) + print(img_lq) + img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] + print(img_lq.shape) + print("bicubic", img_lq_bicubic.shape) + print(img_hq.shape) + lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), + (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), + interpolation=0) + img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) + util.imsave(img_concat, str(i) + '.png') diff --git a/comfy/ldm/modules/image_degradation/utils/test.png b/comfy/ldm/modules/image_degradation/utils/test.png new file mode 100644 index 00000000..4249b43d Binary files /dev/null and b/comfy/ldm/modules/image_degradation/utils/test.png differ diff --git a/comfy/ldm/modules/image_degradation/utils_image.py b/comfy/ldm/modules/image_degradation/utils_image.py new file mode 100644 index 00000000..0175f155 --- /dev/null +++ b/comfy/ldm/modules/image_degradation/utils_image.py @@ -0,0 +1,916 @@ +import os +import math +import random +import numpy as np +import torch +import cv2 +from torchvision.utils import make_grid +from datetime import datetime +#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py + + +os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" + + +''' +# -------------------------------------------- +# Kai Zhang (github: https://github.com/cszn) +# 03/Mar/2019 +# -------------------------------------------- +# https://github.com/twhui/SRGAN-pyTorch +# https://github.com/xinntao/BasicSR +# -------------------------------------------- +''' + + +IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def get_timestamp(): + return datetime.now().strftime('%y%m%d-%H%M%S') + + +def imshow(x, title=None, cbar=False, figsize=None): + plt.figure(figsize=figsize) + plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') + if title: + plt.title(title) + if cbar: + plt.colorbar() + plt.show() + + +def surf(Z, cmap='rainbow', figsize=None): + plt.figure(figsize=figsize) + ax3 = plt.axes(projection='3d') + + w, h = Z.shape[:2] + xx = np.arange(0,w,1) + yy = np.arange(0,h,1) + X, Y = np.meshgrid(xx, yy) + ax3.plot_surface(X,Y,Z,cmap=cmap) + #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) + plt.show() + + +''' +# -------------------------------------------- +# get image pathes +# -------------------------------------------- +''' + + +def get_image_paths(dataroot): + paths = None # return None if dataroot is None + if dataroot is not None: + paths = sorted(_get_paths_from_images(dataroot)) + return paths + + +def _get_paths_from_images(path): + assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) + images = [] + for dirpath, _, fnames in sorted(os.walk(path)): + for fname in sorted(fnames): + if is_image_file(fname): + img_path = os.path.join(dirpath, fname) + images.append(img_path) + assert images, '{:s} has no valid image file'.format(path) + return images + + +''' +# -------------------------------------------- +# split large images into small images +# -------------------------------------------- +''' + + +def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): + w, h = img.shape[:2] + patches = [] + if w > p_max and h > p_max: + w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) + h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) + w1.append(w-p_size) + h1.append(h-p_size) +# print(w1) +# print(h1) + for i in w1: + for j in h1: + patches.append(img[i:i+p_size, j:j+p_size,:]) + else: + patches.append(img) + + return patches + + +def imssave(imgs, img_path): + """ + imgs: list, N images of size WxHxC + """ + img_name, ext = os.path.splitext(os.path.basename(img_path)) + + for i, img in enumerate(imgs): + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') + cv2.imwrite(new_path, img) + + +def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): + """ + split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), + and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) + will be splitted. + Args: + original_dataroot: + taget_dataroot: + p_size: size of small images + p_overlap: patch size in training is a good choice + p_max: images with smaller size than (p_max)x(p_max) keep unchanged. + """ + paths = get_image_paths(original_dataroot) + for img_path in paths: + # img_name, ext = os.path.splitext(os.path.basename(img_path)) + img = imread_uint(img_path, n_channels=n_channels) + patches = patches_from_image(img, p_size, p_overlap, p_max) + imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) + #if original_dataroot == taget_dataroot: + #del img_path + +''' +# -------------------------------------------- +# makedir +# -------------------------------------------- +''' + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) + + +def mkdirs(paths): + if isinstance(paths, str): + mkdir(paths) + else: + for path in paths: + mkdir(path) + + +def mkdir_and_rename(path): + if os.path.exists(path): + new_name = path + '_archived_' + get_timestamp() + print('Path already exists. Rename it to [{:s}]'.format(new_name)) + os.rename(path, new_name) + os.makedirs(path) + + +''' +# -------------------------------------------- +# read image from path +# opencv is fast, but read BGR numpy image +# -------------------------------------------- +''' + + +# -------------------------------------------- +# get uint8 image of size HxWxn_channles (RGB) +# -------------------------------------------- +def imread_uint(path, n_channels=3): + # input: path + # output: HxWx3(RGB or GGG), or HxWx1 (G) + if n_channels == 1: + img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE + img = np.expand_dims(img, axis=2) # HxWx1 + elif n_channels == 3: + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G + if img.ndim == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG + else: + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB + return img + + +# -------------------------------------------- +# matlab's imwrite +# -------------------------------------------- +def imsave(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + +def imwrite(img, img_path): + img = np.squeeze(img) + if img.ndim == 3: + img = img[:, :, [2, 1, 0]] + cv2.imwrite(img_path, img) + + + +# -------------------------------------------- +# get single image of size HxWxn_channles (BGR) +# -------------------------------------------- +def read_img(path): + # read image by cv2 + # return: Numpy float32, HWC, BGR, [0,1] + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE + img = img.astype(np.float32) / 255. + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + # some images have 4 channels + if img.shape[2] > 3: + img = img[:, :, :3] + return img + + +''' +# -------------------------------------------- +# image format conversion +# -------------------------------------------- +# numpy(single) <---> numpy(unit) +# numpy(single) <---> tensor +# numpy(unit) <---> tensor +# -------------------------------------------- +''' + + +# -------------------------------------------- +# numpy(single) [0, 1] <---> numpy(unit) +# -------------------------------------------- + + +def uint2single(img): + + return np.float32(img/255.) + + +def single2uint(img): + + return np.uint8((img.clip(0, 1)*255.).round()) + + +def uint162single(img): + + return np.float32(img/65535.) + + +def single2uint16(img): + + return np.uint16((img.clip(0, 1)*65535.).round()) + + +# -------------------------------------------- +# numpy(unit) (HxWxC or HxW) <---> tensor +# -------------------------------------------- + + +# convert uint to 4-dimensional torch tensor +def uint2tensor4(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) + + +# convert uint to 3-dimensional torch tensor +def uint2tensor3(img): + if img.ndim == 2: + img = np.expand_dims(img, axis=2) + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) + + +# convert 2/3/4-dimensional torch tensor to uint +def tensor2uint(img): + img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + return np.uint8((img*255.0).round()) + + +# -------------------------------------------- +# numpy(single) (HxWxC) <---> tensor +# -------------------------------------------- + + +# convert single (HxWxC) to 3-dimensional torch tensor +def single2tensor3(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() + + +# convert single (HxWxC) to 4-dimensional torch tensor +def single2tensor4(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) + + +# convert torch tensor to single +def tensor2single(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + + return img + +# convert torch tensor to single +def tensor2single3(img): + img = img.data.squeeze().float().cpu().numpy() + if img.ndim == 3: + img = np.transpose(img, (1, 2, 0)) + elif img.ndim == 2: + img = np.expand_dims(img, axis=2) + return img + + +def single2tensor5(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) + + +def single32tensor5(img): + return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) + + +def single42tensor4(img): + return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() + + +# from skimage.io import imread, imsave +def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): + ''' + Converts a torch Tensor into an image Numpy array of BGR channel order + Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order + Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) + ''' + tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp + tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] + n_dim = tensor.dim() + if n_dim == 4: + n_img = len(tensor) + img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 3: + img_np = tensor.numpy() + img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR + elif n_dim == 2: + img_np = tensor.numpy() + else: + raise TypeError( + 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) + if out_type == np.uint8: + img_np = (img_np * 255.0).round() + # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. + return img_np.astype(out_type) + + +''' +# -------------------------------------------- +# Augmentation, flipe and/or rotate +# -------------------------------------------- +# The following two are enough. +# (1) augmet_img: numpy image of WxHxC or WxH +# (2) augment_img_tensor4: tensor image 1xCxWxH +# -------------------------------------------- +''' + + +def augment_img(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + if mode == 0: + return img + elif mode == 1: + return np.flipud(np.rot90(img)) + elif mode == 2: + return np.flipud(img) + elif mode == 3: + return np.rot90(img, k=3) + elif mode == 4: + return np.flipud(np.rot90(img, k=2)) + elif mode == 5: + return np.rot90(img) + elif mode == 6: + return np.rot90(img, k=2) + elif mode == 7: + return np.flipud(np.rot90(img, k=3)) + + +def augment_img_tensor4(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + if mode == 0: + return img + elif mode == 1: + return img.rot90(1, [2, 3]).flip([2]) + elif mode == 2: + return img.flip([2]) + elif mode == 3: + return img.rot90(3, [2, 3]) + elif mode == 4: + return img.rot90(2, [2, 3]).flip([2]) + elif mode == 5: + return img.rot90(1, [2, 3]) + elif mode == 6: + return img.rot90(2, [2, 3]) + elif mode == 7: + return img.rot90(3, [2, 3]).flip([2]) + + +def augment_img_tensor(img, mode=0): + '''Kai Zhang (github: https://github.com/cszn) + ''' + img_size = img.size() + img_np = img.data.cpu().numpy() + if len(img_size) == 3: + img_np = np.transpose(img_np, (1, 2, 0)) + elif len(img_size) == 4: + img_np = np.transpose(img_np, (2, 3, 1, 0)) + img_np = augment_img(img_np, mode=mode) + img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) + if len(img_size) == 3: + img_tensor = img_tensor.permute(2, 0, 1) + elif len(img_size) == 4: + img_tensor = img_tensor.permute(3, 2, 0, 1) + + return img_tensor.type_as(img) + + +def augment_img_np3(img, mode=0): + if mode == 0: + return img + elif mode == 1: + return img.transpose(1, 0, 2) + elif mode == 2: + return img[::-1, :, :] + elif mode == 3: + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 4: + return img[:, ::-1, :] + elif mode == 5: + img = img[:, ::-1, :] + img = img.transpose(1, 0, 2) + return img + elif mode == 6: + img = img[:, ::-1, :] + img = img[::-1, :, :] + return img + elif mode == 7: + img = img[:, ::-1, :] + img = img[::-1, :, :] + img = img.transpose(1, 0, 2) + return img + + +def augment_imgs(img_list, hflip=True, rot=True): + # horizontal flip OR rotate + hflip = hflip and random.random() < 0.5 + vflip = rot and random.random() < 0.5 + rot90 = rot and random.random() < 0.5 + + def _augment(img): + if hflip: + img = img[:, ::-1, :] + if vflip: + img = img[::-1, :, :] + if rot90: + img = img.transpose(1, 0, 2) + return img + + return [_augment(img) for img in img_list] + + +''' +# -------------------------------------------- +# modcrop and shave +# -------------------------------------------- +''' + + +def modcrop(img_in, scale): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + if img.ndim == 2: + H, W = img.shape + H_r, W_r = H % scale, W % scale + img = img[:H - H_r, :W - W_r] + elif img.ndim == 3: + H, W, C = img.shape + H_r, W_r = H % scale, W % scale + img = img[:H - H_r, :W - W_r, :] + else: + raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) + return img + + +def shave(img_in, border=0): + # img_in: Numpy, HWC or HW + img = np.copy(img_in) + h, w = img.shape[:2] + img = img[border:h-border, border:w-border] + return img + + +''' +# -------------------------------------------- +# image processing process on numpy image +# channel_convert(in_c, tar_type, img_list): +# rgb2ycbcr(img, only_y=True): +# bgr2ycbcr(img, only_y=True): +# ycbcr2rgb(img): +# -------------------------------------------- +''' + + +def rgb2ycbcr(img, only_y=True): + '''same as matlab rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + if only_y: + rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 + else: + rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], + [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def ycbcr2rgb(img): + '''same as matlab ycbcr2rgb + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], + [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def bgr2ycbcr(img, only_y=True): + '''bgr version of rgb2ycbcr + only_y: only return Y channel + Input: + uint8, [0, 255] + float, [0, 1] + ''' + in_img_type = img.dtype + img.astype(np.float32) + if in_img_type != np.uint8: + img *= 255. + # convert + if only_y: + rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 + else: + rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], + [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] + if in_img_type == np.uint8: + rlt = rlt.round() + else: + rlt /= 255. + return rlt.astype(in_img_type) + + +def channel_convert(in_c, tar_type, img_list): + # conversion among BGR, gray and y + if in_c == 3 and tar_type == 'gray': # BGR to gray + gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] + return [np.expand_dims(img, axis=2) for img in gray_list] + elif in_c == 3 and tar_type == 'y': # BGR to y + y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] + return [np.expand_dims(img, axis=2) for img in y_list] + elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR + return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] + else: + return img_list + + +''' +# -------------------------------------------- +# metric, PSNR and SSIM +# -------------------------------------------- +''' + + +# -------------------------------------------- +# PSNR +# -------------------------------------------- +def calculate_psnr(img1, img2, border=0): + # img1 and img2 have range [0, 255] + #img1 = img1.squeeze() + #img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError('Input images must have the same dimensions.') + h, w = img1.shape[:2] + img1 = img1[border:h-border, border:w-border] + img2 = img2[border:h-border, border:w-border] + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + mse = np.mean((img1 - img2)**2) + if mse == 0: + return float('inf') + return 20 * math.log10(255.0 / math.sqrt(mse)) + + +# -------------------------------------------- +# SSIM +# -------------------------------------------- +def calculate_ssim(img1, img2, border=0): + '''calculate SSIM + the same outputs as MATLAB's + img1, img2: [0, 255] + ''' + #img1 = img1.squeeze() + #img2 = img2.squeeze() + if not img1.shape == img2.shape: + raise ValueError('Input images must have the same dimensions.') + h, w = img1.shape[:2] + img1 = img1[border:h-border, border:w-border] + img2 = img2[border:h-border, border:w-border] + + if img1.ndim == 2: + return ssim(img1, img2) + elif img1.ndim == 3: + if img1.shape[2] == 3: + ssims = [] + for i in range(3): + ssims.append(ssim(img1[:,:,i], img2[:,:,i])) + return np.array(ssims).mean() + elif img1.shape[2] == 1: + return ssim(np.squeeze(img1), np.squeeze(img2)) + else: + raise ValueError('Wrong input image dimensions.') + + +def ssim(img1, img2): + C1 = (0.01 * 255)**2 + C2 = (0.03 * 255)**2 + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + kernel = cv2.getGaussianKernel(11, 1.5) + window = np.outer(kernel, kernel.transpose()) + + mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid + mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] + mu1_sq = mu1**2 + mu2_sq = mu2**2 + mu1_mu2 = mu1 * mu2 + sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq + sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq + sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 + + ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * + (sigma1_sq + sigma2_sq + C2)) + return ssim_map.mean() + + +''' +# -------------------------------------------- +# matlab's bicubic imresize (numpy and torch) [0, 1] +# -------------------------------------------- +''' + + +# matlab 'imresize' function, now only support 'bicubic' +def cubic(x): + absx = torch.abs(x) + absx2 = absx**2 + absx3 = absx**3 + return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ + (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) + + +def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): + if (scale < 1) and (antialiasing): + # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width + kernel_width = kernel_width / scale + + # Output-space coordinates + x = torch.linspace(1, out_length, out_length) + + # Input-space coordinates. Calculate the inverse mapping such that 0.5 + # in output space maps to 0.5 in input space, and 0.5+scale in output + # space maps to 1.5 in input space. + u = x / scale + 0.5 * (1 - 1 / scale) + + # What is the left-most pixel that can be involved in the computation? + left = torch.floor(u - kernel_width / 2) + + # What is the maximum number of pixels that can be involved in the + # computation? Note: it's OK to use an extra pixel here; if the + # corresponding weights are all zero, it will be eliminated at the end + # of this function. + P = math.ceil(kernel_width) + 2 + + # The indices of the input pixels involved in computing the k-th output + # pixel are in row k of the indices matrix. + indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( + 1, P).expand(out_length, P) + + # The weights used to compute the k-th output pixel are in row k of the + # weights matrix. + distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices + # apply cubic kernel + if (scale < 1) and (antialiasing): + weights = scale * cubic(distance_to_center * scale) + else: + weights = cubic(distance_to_center) + # Normalize the weights matrix so that each row sums to 1. + weights_sum = torch.sum(weights, 1).view(out_length, 1) + weights = weights / weights_sum.expand(out_length, P) + + # If a column in weights is all zero, get rid of it. only consider the first and last column. + weights_zero_tmp = torch.sum((weights == 0), 0) + if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): + indices = indices.narrow(1, 1, P - 2) + weights = weights.narrow(1, 1, P - 2) + if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): + indices = indices.narrow(1, 0, P - 2) + weights = weights.narrow(1, 0, P - 2) + weights = weights.contiguous() + indices = indices.contiguous() + sym_len_s = -indices.min() + 1 + sym_len_e = indices.max() - in_length + indices = indices + sym_len_s - 1 + return weights, indices, int(sym_len_s), int(sym_len_e) + + +# -------------------------------------------- +# imresize for tensor image [0, 1] +# -------------------------------------------- +def imresize(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: pytorch tensor, CHW or HW [0,1] + # output: CHW or HW [0,1] w/o round + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(0) + in_C, in_H, in_W = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = 'cubic' + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) + img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:, :sym_len_Hs, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[:, -sym_len_He:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(in_C, out_H, in_W) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) + out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :, :sym_len_Ws] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, :, -sym_len_We:] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(in_C, out_H, out_W) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + return out_2 + + +# -------------------------------------------- +# imresize for numpy image [0, 1] +# -------------------------------------------- +def imresize_np(img, scale, antialiasing=True): + # Now the scale should be the same for H and W + # input: img: Numpy, HWC or HW [0,1] + # output: HWC or HW [0,1] w/o round + img = torch.from_numpy(img) + need_squeeze = True if img.dim() == 2 else False + if need_squeeze: + img.unsqueeze_(2) + + in_H, in_W, in_C = img.size() + out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) + kernel_width = 4 + kernel = 'cubic' + + # Return the desired dimension order for performing the resize. The + # strategy is to perform the resize first along the dimension with the + # smallest scale factor. + # Now we do not support this. + + # get weights and indices + weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( + in_H, out_H, scale, kernel, kernel_width, antialiasing) + weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( + in_W, out_W, scale, kernel, kernel_width, antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) + img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) + + sym_patch = img[:sym_len_Hs, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) + + sym_patch = img[-sym_len_He:, :, :] + inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(0, inv_idx) + img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(out_H, in_W, in_C) + kernel_width = weights_H.size(1) + for i in range(out_H): + idx = int(indices_H[i][0]) + for j in range(out_C): + out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) + out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) + + sym_patch = out_1[:, :sym_len_Ws, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, -sym_len_We:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(out_H, out_W, in_C) + kernel_width = weights_W.size(1) + for i in range(out_W): + idx = int(indices_W[i][0]) + for j in range(out_C): + out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) + if need_squeeze: + out_2.squeeze_() + + return out_2.numpy() + + +if __name__ == '__main__': + print('---') +# img = imread_uint('test.bmp', 3) +# img = uint2single(img) +# img_bicubic = imresize_np(img, 1/4) \ No newline at end of file diff --git a/comfy/ldm/modules/midas/__init__.py b/comfy/ldm/modules/midas/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/comfy/ldm/modules/midas/api.py b/comfy/ldm/modules/midas/api.py new file mode 100644 index 00000000..b58ebbff --- /dev/null +++ b/comfy/ldm/modules/midas/api.py @@ -0,0 +1,170 @@ +# based on https://github.com/isl-org/MiDaS + +import cv2 +import torch +import torch.nn as nn +from torchvision.transforms import Compose + +from ldm.modules.midas.midas.dpt_depth import DPTDepthModel +from ldm.modules.midas.midas.midas_net import MidasNet +from ldm.modules.midas.midas.midas_net_custom import MidasNet_small +from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet + + +ISL_PATHS = { + "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt", + "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt", + "midas_v21": "", + "midas_v21_small": "", +} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def load_midas_transform(model_type): + # https://github.com/isl-org/MiDaS/blob/master/run.py + # load transform only + if model_type == "dpt_large": # DPT-Large + net_w, net_h = 384, 384 + resize_mode = "minimal" + normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + + elif model_type == "dpt_hybrid": # DPT-Hybrid + net_w, net_h = 384, 384 + resize_mode = "minimal" + normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + + elif model_type == "midas_v21": + net_w, net_h = 384, 384 + resize_mode = "upper_bound" + normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + elif model_type == "midas_v21_small": + net_w, net_h = 256, 256 + resize_mode = "upper_bound" + normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + else: + assert False, f"model_type '{model_type}' not implemented, use: --model_type large" + + transform = Compose( + [ + Resize( + net_w, + net_h, + resize_target=None, + keep_aspect_ratio=True, + ensure_multiple_of=32, + resize_method=resize_mode, + image_interpolation_method=cv2.INTER_CUBIC, + ), + normalization, + PrepareForNet(), + ] + ) + + return transform + + +def load_model(model_type): + # https://github.com/isl-org/MiDaS/blob/master/run.py + # load network + model_path = ISL_PATHS[model_type] + if model_type == "dpt_large": # DPT-Large + model = DPTDepthModel( + path=model_path, + backbone="vitl16_384", + non_negative=True, + ) + net_w, net_h = 384, 384 + resize_mode = "minimal" + normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + + elif model_type == "dpt_hybrid": # DPT-Hybrid + model = DPTDepthModel( + path=model_path, + backbone="vitb_rn50_384", + non_negative=True, + ) + net_w, net_h = 384, 384 + resize_mode = "minimal" + normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + + elif model_type == "midas_v21": + model = MidasNet(model_path, non_negative=True) + net_w, net_h = 384, 384 + resize_mode = "upper_bound" + normalization = NormalizeImage( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + ) + + elif model_type == "midas_v21_small": + model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, + non_negative=True, blocks={'expand': True}) + net_w, net_h = 256, 256 + resize_mode = "upper_bound" + normalization = NormalizeImage( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + ) + + else: + print(f"model_type '{model_type}' not implemented, use: --model_type large") + assert False + + transform = Compose( + [ + Resize( + net_w, + net_h, + resize_target=None, + keep_aspect_ratio=True, + ensure_multiple_of=32, + resize_method=resize_mode, + image_interpolation_method=cv2.INTER_CUBIC, + ), + normalization, + PrepareForNet(), + ] + ) + + return model.eval(), transform + + +class MiDaSInference(nn.Module): + MODEL_TYPES_TORCH_HUB = [ + "DPT_Large", + "DPT_Hybrid", + "MiDaS_small" + ] + MODEL_TYPES_ISL = [ + "dpt_large", + "dpt_hybrid", + "midas_v21", + "midas_v21_small", + ] + + def __init__(self, model_type): + super().__init__() + assert (model_type in self.MODEL_TYPES_ISL) + model, _ = load_model(model_type) + self.model = model + self.model.train = disabled_train + + def forward(self, x): + # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array + # NOTE: we expect that the correct transform has been called during dataloading. + with torch.no_grad(): + prediction = self.model(x) + prediction = torch.nn.functional.interpolate( + prediction.unsqueeze(1), + size=x.shape[2:], + mode="bicubic", + align_corners=False, + ) + assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3]) + return prediction + diff --git a/comfy/ldm/modules/midas/midas/__init__.py b/comfy/ldm/modules/midas/midas/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/comfy/ldm/modules/midas/midas/base_model.py b/comfy/ldm/modules/midas/midas/base_model.py new file mode 100644 index 00000000..5cf43023 --- /dev/null +++ b/comfy/ldm/modules/midas/midas/base_model.py @@ -0,0 +1,16 @@ +import torch + + +class BaseModel(torch.nn.Module): + def load(self, path): + """Load model from file. + + Args: + path (str): file path + """ + parameters = torch.load(path, map_location=torch.device('cpu')) + + if "optimizer" in parameters: + parameters = parameters["model"] + + self.load_state_dict(parameters) diff --git a/comfy/ldm/modules/midas/midas/blocks.py b/comfy/ldm/modules/midas/midas/blocks.py new file mode 100644 index 00000000..2145d18f --- /dev/null +++ b/comfy/ldm/modules/midas/midas/blocks.py @@ -0,0 +1,342 @@ +import torch +import torch.nn as nn + +from .vit import ( + _make_pretrained_vitb_rn50_384, + _make_pretrained_vitl16_384, + _make_pretrained_vitb16_384, + forward_vit, +) + +def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): + if backbone == "vitl16_384": + pretrained = _make_pretrained_vitl16_384( + use_pretrained, hooks=hooks, use_readout=use_readout + ) + scratch = _make_scratch( + [256, 512, 1024, 1024], features, groups=groups, expand=expand + ) # ViT-L/16 - 85.0% Top1 (backbone) + elif backbone == "vitb_rn50_384": + pretrained = _make_pretrained_vitb_rn50_384( + use_pretrained, + hooks=hooks, + use_vit_only=use_vit_only, + use_readout=use_readout, + ) + scratch = _make_scratch( + [256, 512, 768, 768], features, groups=groups, expand=expand + ) # ViT-H/16 - 85.0% Top1 (backbone) + elif backbone == "vitb16_384": + pretrained = _make_pretrained_vitb16_384( + use_pretrained, hooks=hooks, use_readout=use_readout + ) + scratch = _make_scratch( + [96, 192, 384, 768], features, groups=groups, expand=expand + ) # ViT-B/16 - 84.6% Top1 (backbone) + elif backbone == "resnext101_wsl": + pretrained = _make_pretrained_resnext101_wsl(use_pretrained) + scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 + elif backbone == "efficientnet_lite3": + pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) + scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 + else: + print(f"Backbone '{backbone}' not implemented") + assert False + + return pretrained, scratch + + +def _make_scratch(in_shape, out_shape, groups=1, expand=False): + scratch = nn.Module() + + out_shape1 = out_shape + out_shape2 = out_shape + out_shape3 = out_shape + out_shape4 = out_shape + if expand==True: + out_shape1 = out_shape + out_shape2 = out_shape*2 + out_shape3 = out_shape*4 + out_shape4 = out_shape*8 + + scratch.layer1_rn = nn.Conv2d( + in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups + ) + scratch.layer2_rn = nn.Conv2d( + in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups + ) + scratch.layer3_rn = nn.Conv2d( + in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups + ) + scratch.layer4_rn = nn.Conv2d( + in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups + ) + + return scratch + + +def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): + efficientnet = torch.hub.load( + "rwightman/gen-efficientnet-pytorch", + "tf_efficientnet_lite3", + pretrained=use_pretrained, + exportable=exportable + ) + return _make_efficientnet_backbone(efficientnet) + + +def _make_efficientnet_backbone(effnet): + pretrained = nn.Module() + + pretrained.layer1 = nn.Sequential( + effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] + ) + pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) + pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) + pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) + + return pretrained + + +def _make_resnet_backbone(resnet): + pretrained = nn.Module() + pretrained.layer1 = nn.Sequential( + resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 + ) + + pretrained.layer2 = resnet.layer2 + pretrained.layer3 = resnet.layer3 + pretrained.layer4 = resnet.layer4 + + return pretrained + + +def _make_pretrained_resnext101_wsl(use_pretrained): + resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") + return _make_resnet_backbone(resnet) + + + +class Interpolate(nn.Module): + """Interpolation module. + """ + + def __init__(self, scale_factor, mode, align_corners=False): + """Init. + + Args: + scale_factor (float): scaling + mode (str): interpolation mode + """ + super(Interpolate, self).__init__() + + self.interp = nn.functional.interpolate + self.scale_factor = scale_factor + self.mode = mode + self.align_corners = align_corners + + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input + + Returns: + tensor: interpolated data + """ + + x = self.interp( + x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners + ) + + return x + + +class ResidualConvUnit(nn.Module): + """Residual convolution module. + """ + + def __init__(self, features): + """Init. + + Args: + features (int): number of features + """ + super().__init__() + + self.conv1 = nn.Conv2d( + features, features, kernel_size=3, stride=1, padding=1, bias=True + ) + + self.conv2 = nn.Conv2d( + features, features, kernel_size=3, stride=1, padding=1, bias=True + ) + + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input + + Returns: + tensor: output + """ + out = self.relu(x) + out = self.conv1(out) + out = self.relu(out) + out = self.conv2(out) + + return out + x + + +class FeatureFusionBlock(nn.Module): + """Feature fusion block. + """ + + def __init__(self, features): + """Init. + + Args: + features (int): number of features + """ + super(FeatureFusionBlock, self).__init__() + + self.resConfUnit1 = ResidualConvUnit(features) + self.resConfUnit2 = ResidualConvUnit(features) + + def forward(self, *xs): + """Forward pass. + + Returns: + tensor: output + """ + output = xs[0] + + if len(xs) == 2: + output += self.resConfUnit1(xs[1]) + + output = self.resConfUnit2(output) + + output = nn.functional.interpolate( + output, scale_factor=2, mode="bilinear", align_corners=True + ) + + return output + + + + +class ResidualConvUnit_custom(nn.Module): + """Residual convolution module. + """ + + def __init__(self, features, activation, bn): + """Init. + + Args: + features (int): number of features + """ + super().__init__() + + self.bn = bn + + self.groups=1 + + self.conv1 = nn.Conv2d( + features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups + ) + + self.conv2 = nn.Conv2d( + features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups + ) + + if self.bn==True: + self.bn1 = nn.BatchNorm2d(features) + self.bn2 = nn.BatchNorm2d(features) + + self.activation = activation + + self.skip_add = nn.quantized.FloatFunctional() + + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input + + Returns: + tensor: output + """ + + out = self.activation(x) + out = self.conv1(out) + if self.bn==True: + out = self.bn1(out) + + out = self.activation(out) + out = self.conv2(out) + if self.bn==True: + out = self.bn2(out) + + if self.groups > 1: + out = self.conv_merge(out) + + return self.skip_add.add(out, x) + + # return out + x + + +class FeatureFusionBlock_custom(nn.Module): + """Feature fusion block. + """ + + def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): + """Init. + + Args: + features (int): number of features + """ + super(FeatureFusionBlock_custom, self).__init__() + + self.deconv = deconv + self.align_corners = align_corners + + self.groups=1 + + self.expand = expand + out_features = features + if self.expand==True: + out_features = features//2 + + self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) + + self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) + self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) + + self.skip_add = nn.quantized.FloatFunctional() + + def forward(self, *xs): + """Forward pass. + + Returns: + tensor: output + """ + output = xs[0] + + if len(xs) == 2: + res = self.resConfUnit1(xs[1]) + output = self.skip_add.add(output, res) + # output += res + + output = self.resConfUnit2(output) + + output = nn.functional.interpolate( + output, scale_factor=2, mode="bilinear", align_corners=self.align_corners + ) + + output = self.out_conv(output) + + return output + diff --git a/comfy/ldm/modules/midas/midas/dpt_depth.py b/comfy/ldm/modules/midas/midas/dpt_depth.py new file mode 100644 index 00000000..4e9aab5d --- /dev/null +++ b/comfy/ldm/modules/midas/midas/dpt_depth.py @@ -0,0 +1,109 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .base_model import BaseModel +from .blocks import ( + FeatureFusionBlock, + FeatureFusionBlock_custom, + Interpolate, + _make_encoder, + forward_vit, +) + + +def _make_fusion_block(features, use_bn): + return FeatureFusionBlock_custom( + features, + nn.ReLU(False), + deconv=False, + bn=use_bn, + expand=False, + align_corners=True, + ) + + +class DPT(BaseModel): + def __init__( + self, + head, + features=256, + backbone="vitb_rn50_384", + readout="project", + channels_last=False, + use_bn=False, + ): + + super(DPT, self).__init__() + + self.channels_last = channels_last + + hooks = { + "vitb_rn50_384": [0, 1, 8, 11], + "vitb16_384": [2, 5, 8, 11], + "vitl16_384": [5, 11, 17, 23], + } + + # Instantiate backbone and reassemble blocks + self.pretrained, self.scratch = _make_encoder( + backbone, + features, + False, # Set to true of you want to train from scratch, uses ImageNet weights + groups=1, + expand=False, + exportable=False, + hooks=hooks[backbone], + use_readout=readout, + ) + + self.scratch.refinenet1 = _make_fusion_block(features, use_bn) + self.scratch.refinenet2 = _make_fusion_block(features, use_bn) + self.scratch.refinenet3 = _make_fusion_block(features, use_bn) + self.scratch.refinenet4 = _make_fusion_block(features, use_bn) + + self.scratch.output_conv = head + + + def forward(self, x): + if self.channels_last == True: + x.contiguous(memory_format=torch.channels_last) + + layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x) + + layer_1_rn = self.scratch.layer1_rn(layer_1) + layer_2_rn = self.scratch.layer2_rn(layer_2) + layer_3_rn = self.scratch.layer3_rn(layer_3) + layer_4_rn = self.scratch.layer4_rn(layer_4) + + path_4 = self.scratch.refinenet4(layer_4_rn) + path_3 = self.scratch.refinenet3(path_4, layer_3_rn) + path_2 = self.scratch.refinenet2(path_3, layer_2_rn) + path_1 = self.scratch.refinenet1(path_2, layer_1_rn) + + out = self.scratch.output_conv(path_1) + + return out + + +class DPTDepthModel(DPT): + def __init__(self, path=None, non_negative=True, **kwargs): + features = kwargs["features"] if "features" in kwargs else 256 + + head = nn.Sequential( + nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), + Interpolate(scale_factor=2, mode="bilinear", align_corners=True), + nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), + nn.ReLU(True), + nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), + nn.ReLU(True) if non_negative else nn.Identity(), + nn.Identity(), + ) + + super().__init__(head, **kwargs) + + if path is not None: + self.load(path) + + def forward(self, x): + return super().forward(x).squeeze(dim=1) + diff --git a/comfy/ldm/modules/midas/midas/midas_net.py b/comfy/ldm/modules/midas/midas/midas_net.py new file mode 100644 index 00000000..8a954977 --- /dev/null +++ b/comfy/ldm/modules/midas/midas/midas_net.py @@ -0,0 +1,76 @@ +"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. +This file contains code that is adapted from +https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py +""" +import torch +import torch.nn as nn + +from .base_model import BaseModel +from .blocks import FeatureFusionBlock, Interpolate, _make_encoder + + +class MidasNet(BaseModel): + """Network for monocular depth estimation. + """ + + def __init__(self, path=None, features=256, non_negative=True): + """Init. + + Args: + path (str, optional): Path to saved model. Defaults to None. + features (int, optional): Number of features. Defaults to 256. + backbone (str, optional): Backbone network for encoder. Defaults to resnet50 + """ + print("Loading weights: ", path) + + super(MidasNet, self).__init__() + + use_pretrained = False if path is None else True + + self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained) + + self.scratch.refinenet4 = FeatureFusionBlock(features) + self.scratch.refinenet3 = FeatureFusionBlock(features) + self.scratch.refinenet2 = FeatureFusionBlock(features) + self.scratch.refinenet1 = FeatureFusionBlock(features) + + self.scratch.output_conv = nn.Sequential( + nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1), + Interpolate(scale_factor=2, mode="bilinear"), + nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1), + nn.ReLU(True), + nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), + nn.ReLU(True) if non_negative else nn.Identity(), + ) + + if path: + self.load(path) + + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input data (image) + + Returns: + tensor: depth + """ + + layer_1 = self.pretrained.layer1(x) + layer_2 = self.pretrained.layer2(layer_1) + layer_3 = self.pretrained.layer3(layer_2) + layer_4 = self.pretrained.layer4(layer_3) + + layer_1_rn = self.scratch.layer1_rn(layer_1) + layer_2_rn = self.scratch.layer2_rn(layer_2) + layer_3_rn = self.scratch.layer3_rn(layer_3) + layer_4_rn = self.scratch.layer4_rn(layer_4) + + path_4 = self.scratch.refinenet4(layer_4_rn) + path_3 = self.scratch.refinenet3(path_4, layer_3_rn) + path_2 = self.scratch.refinenet2(path_3, layer_2_rn) + path_1 = self.scratch.refinenet1(path_2, layer_1_rn) + + out = self.scratch.output_conv(path_1) + + return torch.squeeze(out, dim=1) diff --git a/comfy/ldm/modules/midas/midas/midas_net_custom.py b/comfy/ldm/modules/midas/midas/midas_net_custom.py new file mode 100644 index 00000000..50e4acb5 --- /dev/null +++ b/comfy/ldm/modules/midas/midas/midas_net_custom.py @@ -0,0 +1,128 @@ +"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. +This file contains code that is adapted from +https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py +""" +import torch +import torch.nn as nn + +from .base_model import BaseModel +from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder + + +class MidasNet_small(BaseModel): + """Network for monocular depth estimation. + """ + + def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True, + blocks={'expand': True}): + """Init. + + Args: + path (str, optional): Path to saved model. Defaults to None. + features (int, optional): Number of features. Defaults to 256. + backbone (str, optional): Backbone network for encoder. Defaults to resnet50 + """ + print("Loading weights: ", path) + + super(MidasNet_small, self).__init__() + + use_pretrained = False if path else True + + self.channels_last = channels_last + self.blocks = blocks + self.backbone = backbone + + self.groups = 1 + + features1=features + features2=features + features3=features + features4=features + self.expand = False + if "expand" in self.blocks and self.blocks['expand'] == True: + self.expand = True + features1=features + features2=features*2 + features3=features*4 + features4=features*8 + + self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable) + + self.scratch.activation = nn.ReLU(False) + + self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) + self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) + self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) + self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners) + + + self.scratch.output_conv = nn.Sequential( + nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups), + Interpolate(scale_factor=2, mode="bilinear"), + nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1), + self.scratch.activation, + nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), + nn.ReLU(True) if non_negative else nn.Identity(), + nn.Identity(), + ) + + if path: + self.load(path) + + + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input data (image) + + Returns: + tensor: depth + """ + if self.channels_last==True: + print("self.channels_last = ", self.channels_last) + x.contiguous(memory_format=torch.channels_last) + + + layer_1 = self.pretrained.layer1(x) + layer_2 = self.pretrained.layer2(layer_1) + layer_3 = self.pretrained.layer3(layer_2) + layer_4 = self.pretrained.layer4(layer_3) + + layer_1_rn = self.scratch.layer1_rn(layer_1) + layer_2_rn = self.scratch.layer2_rn(layer_2) + layer_3_rn = self.scratch.layer3_rn(layer_3) + layer_4_rn = self.scratch.layer4_rn(layer_4) + + + path_4 = self.scratch.refinenet4(layer_4_rn) + path_3 = self.scratch.refinenet3(path_4, layer_3_rn) + path_2 = self.scratch.refinenet2(path_3, layer_2_rn) + path_1 = self.scratch.refinenet1(path_2, layer_1_rn) + + out = self.scratch.output_conv(path_1) + + return torch.squeeze(out, dim=1) + + + +def fuse_model(m): + prev_previous_type = nn.Identity() + prev_previous_name = '' + previous_type = nn.Identity() + previous_name = '' + for name, module in m.named_modules(): + if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU: + # print("FUSED ", prev_previous_name, previous_name, name) + torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True) + elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d: + # print("FUSED ", prev_previous_name, previous_name) + torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True) + # elif previous_type == nn.Conv2d and type(module) == nn.ReLU: + # print("FUSED ", previous_name, name) + # torch.quantization.fuse_modules(m, [previous_name, name], inplace=True) + + prev_previous_type = previous_type + prev_previous_name = previous_name + previous_type = type(module) + previous_name = name \ No newline at end of file diff --git a/comfy/ldm/modules/midas/midas/transforms.py b/comfy/ldm/modules/midas/midas/transforms.py new file mode 100644 index 00000000..350cbc11 --- /dev/null +++ b/comfy/ldm/modules/midas/midas/transforms.py @@ -0,0 +1,234 @@ +import numpy as np +import cv2 +import math + + +def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): + """Rezise the sample to ensure the given size. Keeps aspect ratio. + + Args: + sample (dict): sample + size (tuple): image size + + Returns: + tuple: new size + """ + shape = list(sample["disparity"].shape) + + if shape[0] >= size[0] and shape[1] >= size[1]: + return sample + + scale = [0, 0] + scale[0] = size[0] / shape[0] + scale[1] = size[1] / shape[1] + + scale = max(scale) + + shape[0] = math.ceil(scale * shape[0]) + shape[1] = math.ceil(scale * shape[1]) + + # resize + sample["image"] = cv2.resize( + sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method + ) + + sample["disparity"] = cv2.resize( + sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST + ) + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + tuple(shape[::-1]), + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return tuple(shape) + + +class Resize(object): + """Resize sample to given size (width, height). + """ + + def __init__( + self, + width, + height, + resize_target=True, + keep_aspect_ratio=False, + ensure_multiple_of=1, + resize_method="lower_bound", + image_interpolation_method=cv2.INTER_AREA, + ): + """Init. + + Args: + width (int): desired output width + height (int): desired output height + resize_target (bool, optional): + True: Resize the full sample (image, mask, target). + False: Resize image only. + Defaults to True. + keep_aspect_ratio (bool, optional): + True: Keep the aspect ratio of the input sample. + Output sample might not have the given width and height, and + resize behaviour depends on the parameter 'resize_method'. + Defaults to False. + ensure_multiple_of (int, optional): + Output width and height is constrained to be multiple of this parameter. + Defaults to 1. + resize_method (str, optional): + "lower_bound": Output will be at least as large as the given size. + "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) + "minimal": Scale as least as possible. (Output size might be smaller than given size.) + Defaults to "lower_bound". + """ + self.__width = width + self.__height = height + + self.__resize_target = resize_target + self.__keep_aspect_ratio = keep_aspect_ratio + self.__multiple_of = ensure_multiple_of + self.__resize_method = resize_method + self.__image_interpolation_method = image_interpolation_method + + def constrain_to_multiple_of(self, x, min_val=0, max_val=None): + y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if max_val is not None and y > max_val: + y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int) + + if y < min_val: + y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int) + + return y + + def get_size(self, width, height): + # determine new height and width + scale_height = self.__height / height + scale_width = self.__width / width + + if self.__keep_aspect_ratio: + if self.__resize_method == "lower_bound": + # scale such that output size is lower bound + if scale_width > scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "upper_bound": + # scale such that output size is upper bound + if scale_width < scale_height: + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + elif self.__resize_method == "minimal": + # scale as least as possbile + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + else: + raise ValueError( + f"resize_method {self.__resize_method} not implemented" + ) + + if self.__resize_method == "lower_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, min_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, min_val=self.__width + ) + elif self.__resize_method == "upper_bound": + new_height = self.constrain_to_multiple_of( + scale_height * height, max_val=self.__height + ) + new_width = self.constrain_to_multiple_of( + scale_width * width, max_val=self.__width + ) + elif self.__resize_method == "minimal": + new_height = self.constrain_to_multiple_of(scale_height * height) + new_width = self.constrain_to_multiple_of(scale_width * width) + else: + raise ValueError(f"resize_method {self.__resize_method} not implemented") + + return (new_width, new_height) + + def __call__(self, sample): + width, height = self.get_size( + sample["image"].shape[1], sample["image"].shape[0] + ) + + # resize sample + sample["image"] = cv2.resize( + sample["image"], + (width, height), + interpolation=self.__image_interpolation_method, + ) + + if self.__resize_target: + if "disparity" in sample: + sample["disparity"] = cv2.resize( + sample["disparity"], + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + + if "depth" in sample: + sample["depth"] = cv2.resize( + sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST + ) + + sample["mask"] = cv2.resize( + sample["mask"].astype(np.float32), + (width, height), + interpolation=cv2.INTER_NEAREST, + ) + sample["mask"] = sample["mask"].astype(bool) + + return sample + + +class NormalizeImage(object): + """Normlize image by given mean and std. + """ + + def __init__(self, mean, std): + self.__mean = mean + self.__std = std + + def __call__(self, sample): + sample["image"] = (sample["image"] - self.__mean) / self.__std + + return sample + + +class PrepareForNet(object): + """Prepare sample for usage as network input. + """ + + def __init__(self): + pass + + def __call__(self, sample): + image = np.transpose(sample["image"], (2, 0, 1)) + sample["image"] = np.ascontiguousarray(image).astype(np.float32) + + if "mask" in sample: + sample["mask"] = sample["mask"].astype(np.float32) + sample["mask"] = np.ascontiguousarray(sample["mask"]) + + if "disparity" in sample: + disparity = sample["disparity"].astype(np.float32) + sample["disparity"] = np.ascontiguousarray(disparity) + + if "depth" in sample: + depth = sample["depth"].astype(np.float32) + sample["depth"] = np.ascontiguousarray(depth) + + return sample diff --git a/comfy/ldm/modules/midas/midas/vit.py b/comfy/ldm/modules/midas/midas/vit.py new file mode 100644 index 00000000..ea46b1be --- /dev/null +++ b/comfy/ldm/modules/midas/midas/vit.py @@ -0,0 +1,491 @@ +import torch +import torch.nn as nn +import timm +import types +import math +import torch.nn.functional as F + + +class Slice(nn.Module): + def __init__(self, start_index=1): + super(Slice, self).__init__() + self.start_index = start_index + + def forward(self, x): + return x[:, self.start_index :] + + +class AddReadout(nn.Module): + def __init__(self, start_index=1): + super(AddReadout, self).__init__() + self.start_index = start_index + + def forward(self, x): + if self.start_index == 2: + readout = (x[:, 0] + x[:, 1]) / 2 + else: + readout = x[:, 0] + return x[:, self.start_index :] + readout.unsqueeze(1) + + +class ProjectReadout(nn.Module): + def __init__(self, in_features, start_index=1): + super(ProjectReadout, self).__init__() + self.start_index = start_index + + self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) + + def forward(self, x): + readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :]) + features = torch.cat((x[:, self.start_index :], readout), -1) + + return self.project(features) + + +class Transpose(nn.Module): + def __init__(self, dim0, dim1): + super(Transpose, self).__init__() + self.dim0 = dim0 + self.dim1 = dim1 + + def forward(self, x): + x = x.transpose(self.dim0, self.dim1) + return x + + +def forward_vit(pretrained, x): + b, c, h, w = x.shape + + glob = pretrained.model.forward_flex(x) + + layer_1 = pretrained.activations["1"] + layer_2 = pretrained.activations["2"] + layer_3 = pretrained.activations["3"] + layer_4 = pretrained.activations["4"] + + layer_1 = pretrained.act_postprocess1[0:2](layer_1) + layer_2 = pretrained.act_postprocess2[0:2](layer_2) + layer_3 = pretrained.act_postprocess3[0:2](layer_3) + layer_4 = pretrained.act_postprocess4[0:2](layer_4) + + unflatten = nn.Sequential( + nn.Unflatten( + 2, + torch.Size( + [ + h // pretrained.model.patch_size[1], + w // pretrained.model.patch_size[0], + ] + ), + ) + ) + + if layer_1.ndim == 3: + layer_1 = unflatten(layer_1) + if layer_2.ndim == 3: + layer_2 = unflatten(layer_2) + if layer_3.ndim == 3: + layer_3 = unflatten(layer_3) + if layer_4.ndim == 3: + layer_4 = unflatten(layer_4) + + layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1) + layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2) + layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3) + layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4) + + return layer_1, layer_2, layer_3, layer_4 + + +def _resize_pos_embed(self, posemb, gs_h, gs_w): + posemb_tok, posemb_grid = ( + posemb[:, : self.start_index], + posemb[0, self.start_index :], + ) + + gs_old = int(math.sqrt(len(posemb_grid))) + + posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) + posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") + posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) + + posemb = torch.cat([posemb_tok, posemb_grid], dim=1) + + return posemb + + +def forward_flex(self, x): + b, c, h, w = x.shape + + pos_embed = self._resize_pos_embed( + self.pos_embed, h // self.patch_size[1], w // self.patch_size[0] + ) + + B = x.shape[0] + + if hasattr(self.patch_embed, "backbone"): + x = self.patch_embed.backbone(x) + if isinstance(x, (list, tuple)): + x = x[-1] # last feature if backbone outputs list/tuple of features + + x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) + + if getattr(self, "dist_token", None) is not None: + cls_tokens = self.cls_token.expand( + B, -1, -1 + ) # stole cls_tokens impl from Phil Wang, thanks + dist_token = self.dist_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, dist_token, x), dim=1) + else: + cls_tokens = self.cls_token.expand( + B, -1, -1 + ) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + x = x + pos_embed + x = self.pos_drop(x) + + for blk in self.blocks: + x = blk(x) + + x = self.norm(x) + + return x + + +activations = {} + + +def get_activation(name): + def hook(model, input, output): + activations[name] = output + + return hook + + +def get_readout_oper(vit_features, features, use_readout, start_index=1): + if use_readout == "ignore": + readout_oper = [Slice(start_index)] * len(features) + elif use_readout == "add": + readout_oper = [AddReadout(start_index)] * len(features) + elif use_readout == "project": + readout_oper = [ + ProjectReadout(vit_features, start_index) for out_feat in features + ] + else: + assert ( + False + ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" + + return readout_oper + + +def _make_vit_b16_backbone( + model, + features=[96, 192, 384, 768], + size=[384, 384], + hooks=[2, 5, 8, 11], + vit_features=768, + use_readout="ignore", + start_index=1, +): + pretrained = nn.Module() + + pretrained.model = model + pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) + pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) + pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) + pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) + + pretrained.activations = activations + + readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) + + # 32, 48, 136, 384 + pretrained.act_postprocess1 = nn.Sequential( + readout_oper[0], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[0], + kernel_size=1, + stride=1, + padding=0, + ), + nn.ConvTranspose2d( + in_channels=features[0], + out_channels=features[0], + kernel_size=4, + stride=4, + padding=0, + bias=True, + dilation=1, + groups=1, + ), + ) + + pretrained.act_postprocess2 = nn.Sequential( + readout_oper[1], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[1], + kernel_size=1, + stride=1, + padding=0, + ), + nn.ConvTranspose2d( + in_channels=features[1], + out_channels=features[1], + kernel_size=2, + stride=2, + padding=0, + bias=True, + dilation=1, + groups=1, + ), + ) + + pretrained.act_postprocess3 = nn.Sequential( + readout_oper[2], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[2], + kernel_size=1, + stride=1, + padding=0, + ), + ) + + pretrained.act_postprocess4 = nn.Sequential( + readout_oper[3], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[3], + kernel_size=1, + stride=1, + padding=0, + ), + nn.Conv2d( + in_channels=features[3], + out_channels=features[3], + kernel_size=3, + stride=2, + padding=1, + ), + ) + + pretrained.model.start_index = start_index + pretrained.model.patch_size = [16, 16] + + # We inject this function into the VisionTransformer instances so that + # we can use it with interpolated position embeddings without modifying the library source. + pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) + pretrained.model._resize_pos_embed = types.MethodType( + _resize_pos_embed, pretrained.model + ) + + return pretrained + + +def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): + model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) + + hooks = [5, 11, 17, 23] if hooks == None else hooks + return _make_vit_b16_backbone( + model, + features=[256, 512, 1024, 1024], + hooks=hooks, + vit_features=1024, + use_readout=use_readout, + ) + + +def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): + model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) + + hooks = [2, 5, 8, 11] if hooks == None else hooks + return _make_vit_b16_backbone( + model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout + ) + + +def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None): + model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained) + + hooks = [2, 5, 8, 11] if hooks == None else hooks + return _make_vit_b16_backbone( + model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout + ) + + +def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None): + model = timm.create_model( + "vit_deit_base_distilled_patch16_384", pretrained=pretrained + ) + + hooks = [2, 5, 8, 11] if hooks == None else hooks + return _make_vit_b16_backbone( + model, + features=[96, 192, 384, 768], + hooks=hooks, + use_readout=use_readout, + start_index=2, + ) + + +def _make_vit_b_rn50_backbone( + model, + features=[256, 512, 768, 768], + size=[384, 384], + hooks=[0, 1, 8, 11], + vit_features=768, + use_vit_only=False, + use_readout="ignore", + start_index=1, +): + pretrained = nn.Module() + + pretrained.model = model + + if use_vit_only == True: + pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) + pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) + else: + pretrained.model.patch_embed.backbone.stages[0].register_forward_hook( + get_activation("1") + ) + pretrained.model.patch_embed.backbone.stages[1].register_forward_hook( + get_activation("2") + ) + + pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) + pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) + + pretrained.activations = activations + + readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) + + if use_vit_only == True: + pretrained.act_postprocess1 = nn.Sequential( + readout_oper[0], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[0], + kernel_size=1, + stride=1, + padding=0, + ), + nn.ConvTranspose2d( + in_channels=features[0], + out_channels=features[0], + kernel_size=4, + stride=4, + padding=0, + bias=True, + dilation=1, + groups=1, + ), + ) + + pretrained.act_postprocess2 = nn.Sequential( + readout_oper[1], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[1], + kernel_size=1, + stride=1, + padding=0, + ), + nn.ConvTranspose2d( + in_channels=features[1], + out_channels=features[1], + kernel_size=2, + stride=2, + padding=0, + bias=True, + dilation=1, + groups=1, + ), + ) + else: + pretrained.act_postprocess1 = nn.Sequential( + nn.Identity(), nn.Identity(), nn.Identity() + ) + pretrained.act_postprocess2 = nn.Sequential( + nn.Identity(), nn.Identity(), nn.Identity() + ) + + pretrained.act_postprocess3 = nn.Sequential( + readout_oper[2], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[2], + kernel_size=1, + stride=1, + padding=0, + ), + ) + + pretrained.act_postprocess4 = nn.Sequential( + readout_oper[3], + Transpose(1, 2), + nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), + nn.Conv2d( + in_channels=vit_features, + out_channels=features[3], + kernel_size=1, + stride=1, + padding=0, + ), + nn.Conv2d( + in_channels=features[3], + out_channels=features[3], + kernel_size=3, + stride=2, + padding=1, + ), + ) + + pretrained.model.start_index = start_index + pretrained.model.patch_size = [16, 16] + + # We inject this function into the VisionTransformer instances so that + # we can use it with interpolated position embeddings without modifying the library source. + pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) + + # We inject this function into the VisionTransformer instances so that + # we can use it with interpolated position embeddings without modifying the library source. + pretrained.model._resize_pos_embed = types.MethodType( + _resize_pos_embed, pretrained.model + ) + + return pretrained + + +def _make_pretrained_vitb_rn50_384( + pretrained, use_readout="ignore", hooks=None, use_vit_only=False +): + model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) + + hooks = [0, 1, 8, 11] if hooks == None else hooks + return _make_vit_b_rn50_backbone( + model, + features=[256, 512, 768, 768], + size=[384, 384], + hooks=hooks, + use_vit_only=use_vit_only, + use_readout=use_readout, + ) diff --git a/comfy/ldm/modules/midas/utils.py b/comfy/ldm/modules/midas/utils.py new file mode 100644 index 00000000..9a9d3b5b --- /dev/null +++ b/comfy/ldm/modules/midas/utils.py @@ -0,0 +1,189 @@ +"""Utils for monoDepth.""" +import sys +import re +import numpy as np +import cv2 +import torch + + +def read_pfm(path): + """Read pfm file. + + Args: + path (str): path to file + + Returns: + tuple: (data, scale) + """ + with open(path, "rb") as file: + + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().rstrip() + if header.decode("ascii") == "PF": + color = True + elif header.decode("ascii") == "Pf": + color = False + else: + raise Exception("Not a PFM file: " + path) + + dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii")) + if dim_match: + width, height = list(map(int, dim_match.groups())) + else: + raise Exception("Malformed PFM header.") + + scale = float(file.readline().decode("ascii").rstrip()) + if scale < 0: + # little-endian + endian = "<" + scale = -scale + else: + # big-endian + endian = ">" + + data = np.fromfile(file, endian + "f") + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + + return data, scale + + +def write_pfm(path, image, scale=1): + """Write pfm file. + + Args: + path (str): pathto file + image (array): data + scale (int, optional): Scale. Defaults to 1. + """ + + with open(path, "wb") as file: + color = None + + if image.dtype.name != "float32": + raise Exception("Image dtype must be float32.") + + image = np.flipud(image) + + if len(image.shape) == 3 and image.shape[2] == 3: # color image + color = True + elif ( + len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1 + ): # greyscale + color = False + else: + raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.") + + file.write("PF\n" if color else "Pf\n".encode()) + file.write("%d %d\n".encode() % (image.shape[1], image.shape[0])) + + endian = image.dtype.byteorder + + if endian == "<" or endian == "=" and sys.byteorder == "little": + scale = -scale + + file.write("%f\n".encode() % scale) + + image.tofile(file) + + +def read_image(path): + """Read image and output RGB image (0-1). + + Args: + path (str): path to file + + Returns: + array: RGB image (0-1) + """ + img = cv2.imread(path) + + if img.ndim == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 + + return img + + +def resize_image(img): + """Resize image and make it fit for network. + + Args: + img (array): image + + Returns: + tensor: data ready for network + """ + height_orig = img.shape[0] + width_orig = img.shape[1] + + if width_orig > height_orig: + scale = width_orig / 384 + else: + scale = height_orig / 384 + + height = (np.ceil(height_orig / scale / 32) * 32).astype(int) + width = (np.ceil(width_orig / scale / 32) * 32).astype(int) + + img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA) + + img_resized = ( + torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float() + ) + img_resized = img_resized.unsqueeze(0) + + return img_resized + + +def resize_depth(depth, width, height): + """Resize depth map and bring to CPU (numpy). + + Args: + depth (tensor): depth + width (int): image width + height (int): image height + + Returns: + array: processed depth + """ + depth = torch.squeeze(depth[0, :, :, :]).to("cpu") + + depth_resized = cv2.resize( + depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC + ) + + return depth_resized + +def write_depth(path, depth, bits=1): + """Write depth map to pfm and png file. + + Args: + path (str): filepath without extension + depth (array): depth + """ + write_pfm(path + ".pfm", depth.astype(np.float32)) + + depth_min = depth.min() + depth_max = depth.max() + + max_val = (2**(8*bits))-1 + + if depth_max - depth_min > np.finfo("float").eps: + out = max_val * (depth - depth_min) / (depth_max - depth_min) + else: + out = np.zeros(depth.shape, dtype=depth.type) + + if bits == 1: + cv2.imwrite(path + ".png", out.astype("uint8")) + elif bits == 2: + cv2.imwrite(path + ".png", out.astype("uint16")) + + return diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py new file mode 100644 index 00000000..fe9bb82c --- /dev/null +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -0,0 +1,204 @@ +# original source: +# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py +# license: +# MIT +# credit: +# Amin Rezaei (original author) +# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) +# implementation of: +# Self-attention Does Not Need O(n2) Memory": +# https://arxiv.org/abs/2112.05682v2 + +from functools import partial +import torch +from torch import Tensor +from torch.utils.checkpoint import checkpoint +import math +from typing import Optional, NamedTuple, Protocol, List + +from torch import Tensor +from typing import List + +def dynamic_slice( + x: Tensor, + starts: List[int], + sizes: List[int], +) -> Tensor: + slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] + return x[slicing] + +class AttnChunk(NamedTuple): + exp_values: Tensor + exp_weights_sum: Tensor + max_score: Tensor + +class SummarizeChunk(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> AttnChunk: ... + +class ComputeQueryChunkAttn(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> Tensor: ... + +def _summarize_chunk( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, +) -> AttnChunk: + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + max_score, _ = torch.max(attn_weights, -1, keepdim=True) + max_score = max_score.detach() + exp_weights = torch.exp(attn_weights - max_score) + exp_values = torch.bmm(exp_weights, value) + max_score = max_score.squeeze(-1) + return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) + +def _query_chunk_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + summarize_chunk: SummarizeChunk, + kv_chunk_size: int, +) -> Tensor: + batch_x_heads, k_channels_per_head, k_tokens = key_t.shape + _, _, v_channels_per_head = value.shape + + def chunk_scanner(chunk_idx: int) -> AttnChunk: + key_chunk = dynamic_slice( + key_t, + (0, 0, chunk_idx), + (batch_x_heads, k_channels_per_head, kv_chunk_size) + ) + value_chunk = dynamic_slice( + value, + (0, chunk_idx, 0), + (batch_x_heads, kv_chunk_size, v_channels_per_head) + ) + return summarize_chunk(query, key_chunk, value_chunk) + + chunks: List[AttnChunk] = [ + chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size) + ] + acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) + chunk_values, chunk_weights, chunk_max = acc_chunk + + global_max, _ = torch.max(chunk_max, 0, keepdim=True) + max_diffs = torch.exp(chunk_max - global_max) + chunk_values *= torch.unsqueeze(max_diffs, -1) + chunk_weights *= max_diffs + + all_values = chunk_values.sum(dim=0) + all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) + return all_values / all_weights + +# TODO: refactor CrossAttention#get_attention_scores to share code with this +def _get_attention_scores_no_kv_chunking( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, +) -> Tensor: + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + attn_probs = attn_scores.softmax(dim=-1) + del attn_scores + hidden_states_slice = torch.bmm(attn_probs, value) + return hidden_states_slice + +class ScannedChunk(NamedTuple): + chunk_idx: int + attn_chunk: AttnChunk + +def efficient_dot_product_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + query_chunk_size=1024, + kv_chunk_size: Optional[int] = None, + kv_chunk_size_min: Optional[int] = None, + use_checkpoint=True, +): + """Computes efficient dot-product attention given query, transposed key, and value. + This is efficient version of attention presented in + https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. + Args: + query: queries for calculating attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + key_t: keys for calculating attention with shape of + `[batch * num_heads, channels_per_head, tokens]`. + value: values to be used in attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + query_chunk_size: int: query chunks size + kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) + kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). + use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) + Returns: + Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. + """ + batch_x_heads, q_tokens, q_channels_per_head = query.shape + _, _, k_tokens = key_t.shape + scale = q_channels_per_head ** -0.5 + + kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) + if kv_chunk_size_min is not None: + kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) + + def get_query_chunk(chunk_idx: int) -> Tensor: + return dynamic_slice( + query, + (0, chunk_idx, 0), + (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) + ) + + summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale) + summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk + compute_query_chunk_attn: ComputeQueryChunkAttn = partial( + _get_attention_scores_no_kv_chunking, + scale=scale + ) if k_tokens <= kv_chunk_size else ( + # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) + partial( + _query_chunk_attention, + kv_chunk_size=kv_chunk_size, + summarize_chunk=summarize_chunk, + ) + ) + + if q_tokens <= query_chunk_size: + # fast-path for when there's just 1 query chunk + return compute_query_chunk_attn( + query=query, + key_t=key_t, + value=value, + ) + + # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, + # and pass slices to be mutated, instead of torch.cat()ing the returned slices + res = torch.cat([ + compute_query_chunk_attn( + query=get_query_chunk(i * query_chunk_size), + key_t=key_t, + value=value, + ) for i in range(math.ceil(q_tokens / query_chunk_size)) + ], dim=1) + return res diff --git a/comfy/ldm/util.py b/comfy/ldm/util.py new file mode 100644 index 00000000..8c09ca1c --- /dev/null +++ b/comfy/ldm/util.py @@ -0,0 +1,197 @@ +import importlib + +import torch +from torch import optim +import numpy as np + +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +class AdamWwithEMAandWings(optim.Optimizer): + # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 + def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using + weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code + ema_power=1., param_names=()): + """AdamW that saves EMA versions of the parameters.""" + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= weight_decay: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + if not 0.0 <= ema_decay <= 1.0: + raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) + defaults = dict(lr=lr, betas=betas, eps=eps, + weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, + ema_power=ema_power, param_names=param_names) + super().__init__(params, defaults) + + def __setstate__(self, state): + super().__setstate__(state) + for group in self.param_groups: + group.setdefault('amsgrad', False) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + params_with_grad = [] + grads = [] + exp_avgs = [] + exp_avg_sqs = [] + ema_params_with_grad = [] + state_sums = [] + max_exp_avg_sqs = [] + state_steps = [] + amsgrad = group['amsgrad'] + beta1, beta2 = group['betas'] + ema_decay = group['ema_decay'] + ema_power = group['ema_power'] + + for p in group['params']: + if p.grad is None: + continue + params_with_grad.append(p) + if p.grad.is_sparse: + raise RuntimeError('AdamW does not support sparse gradients') + grads.append(p.grad) + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of parameter values + state['param_exp_avg'] = p.detach().float().clone() + + exp_avgs.append(state['exp_avg']) + exp_avg_sqs.append(state['exp_avg_sq']) + ema_params_with_grad.append(state['param_exp_avg']) + + if amsgrad: + max_exp_avg_sqs.append(state['max_exp_avg_sq']) + + # update the steps for each param group update + state['step'] += 1 + # record the step after step update + state_steps.append(state['step']) + + optim._functional.adamw(params_with_grad, + grads, + exp_avgs, + exp_avg_sqs, + max_exp_avg_sqs, + state_steps, + amsgrad=amsgrad, + beta1=beta1, + beta2=beta2, + lr=group['lr'], + weight_decay=group['weight_decay'], + eps=group['eps'], + maximize=False) + + cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) + for param, ema_param in zip(params_with_grad, ema_params_with_grad): + ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) + + return loss \ No newline at end of file diff --git a/comfy/samplers.py b/comfy/samplers.py new file mode 100644 index 00000000..cc750e96 --- /dev/null +++ b/comfy/samplers.py @@ -0,0 +1,114 @@ +import k_diffusion.sampling +import k_diffusion.external +import torch +import contextlib + +class CFGDenoiser(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + + def forward(self, x, sigma, uncond, cond, cond_scale): + if len(uncond[0]) == len(cond[0]) and x.shape[0] * x.shape[2] * x.shape[3] <= (96 * 96): #TODO check memory instead + x_in = torch.cat([x] * 2) + sigma_in = torch.cat([sigma] * 2) + cond_in = torch.cat([uncond, cond]) + uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2) + else: + cond = self.inner_model(x, sigma, cond=cond) + uncond = self.inner_model(x, sigma, cond=uncond) + return uncond + (cond - uncond) * cond_scale + + +def simple_scheduler(model, steps): + sigs = [] + ss = len(model.sigmas) / steps + for x in range(steps): + sigs += [float(model.sigmas[-(1 + int(x * ss))])] + sigs += [0.0] + return torch.FloatTensor(sigs) + + +class KSampler: + SCHEDULERS = ["karras", "normal", "simple"] + SAMPLERS = ["sample_euler", "sample_euler_ancestral", "sample_heun", "sample_dpm_2", "sample_dpm_2_ancestral", + "sample_lms", "sample_dpm_fast", "sample_dpm_adaptive", "sample_dpmpp_2s_ancestral", "sample_dpmpp_sde", + "sample_dpmpp_2m"] + + def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None): + self.model = model + if self.model.parameterization == "v": + self.model_wrap = k_diffusion.external.CompVisVDenoiser(self.model, quantize=True) + else: + self.model_wrap = k_diffusion.external.CompVisDenoiser(self.model, quantize=True) + self.model_k = CFGDenoiser(self.model_wrap) + self.device = device + if scheduler not in self.SCHEDULERS: + scheduler = self.SCHEDULERS[0] + if sampler not in self.SAMPLERS: + sampler = self.SAMPLERS[0] + self.scheduler = scheduler + self.sampler = sampler + self.sigma_min=float(self.model_wrap.sigmas[0]) + self.sigma_max=float(self.model_wrap.sigmas[-1]) + self.set_steps(steps, denoise) + + def _calculate_sigmas(self, steps): + sigmas = None + + discard_penultimate_sigma = False + if self.sampler in ['sample_dpm_2', 'sample_dpm_2_ancestral']: + steps += 1 + discard_penultimate_sigma = True + + if self.scheduler == "karras": + sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max, device=self.device) + elif self.scheduler == "normal": + sigmas = self.model_wrap.get_sigmas(steps).to(self.device) + elif self.scheduler == "simple": + sigmas = simple_scheduler(self.model_wrap, steps).to(self.device) + else: + print("error invalid scheduler", self.scheduler) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + def set_steps(self, steps, denoise=None): + self.steps = steps + if denoise is None: + self.sigmas = self._calculate_sigmas(steps) + else: + new_steps = int(steps/denoise) + sigmas = self._calculate_sigmas(new_steps) + self.sigmas = sigmas[-(steps + 1):] + + + def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None): + sigmas = self.sigmas + sigma_min = self.sigma_min + + if last_step is not None: + sigma_min = sigmas[last_step] + sigmas = sigmas[:last_step + 1] + if start_step is not None: + sigmas = sigmas[start_step:] + + + noise *= sigmas[0] + if latent_image is not None: + noise += latent_image + + if self.model.model.diffusion_model.dtype == torch.float16: + precision_scope = torch.autocast + else: + precision_scope = contextlib.nullcontext + + with precision_scope(self.device): + if self.sampler == "sample_dpm_fast": + samples = k_diffusion.sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) + elif self.sampler == "sample_dpm_adaptive": + samples = k_diffusion.sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) + else: + samples = getattr(k_diffusion.sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) + return samples.to(torch.float32) diff --git a/comfy/sd.py b/comfy/sd.py new file mode 100644 index 00000000..dafa6f52 --- /dev/null +++ b/comfy/sd.py @@ -0,0 +1,124 @@ +import torch + +import sd1_clip +import sd2_clip +from ldm.util import instantiate_from_config +from ldm.models.autoencoder import AutoencoderKL +from omegaconf import OmegaConf + + +def load_model_from_config(config, ckpt, verbose=False, load_state_dict_to=[]): + print(f"Loading model from {ckpt}") + + if ckpt.lower().endswith(".safetensors"): + import safetensors.torch + sd = safetensors.torch.load_file(ckpt, device="cpu") + else: + pl_sd = torch.load(ckpt, map_location="cpu") + if "global_step" in pl_sd: + print(f"Global Step: {pl_sd['global_step']}") + sd = pl_sd["state_dict"] + model = instantiate_from_config(config.model) + + m, u = model.load_state_dict(sd, strict=False) + + k = list(sd.keys()) + for x in k: + # print(x) + if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): + y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") + sd[y] = sd.pop(x) + + for x in load_state_dict_to: + x.load_state_dict(sd, strict=False) + + if len(m) > 0 and verbose: + print("missing keys:") + print(m) + if len(u) > 0 and verbose: + print("unexpected keys:") + print(u) + + model.eval() + return model + + + +class CLIP: + def __init__(self, config): + self.target_clip = config["target"] + if self.target_clip == "ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder": + clip = sd2_clip.SD2ClipModel + tokenizer = sd2_clip.SD2Tokenizer + elif self.target_clip == "ldm.modules.encoders.modules.FrozenCLIPEmbedder": + clip = sd1_clip.SD1ClipModel + tokenizer = sd1_clip.SD1Tokenizer + if "params" in config: + self.cond_stage_model = clip(**(config["params"])) + else: + self.cond_stage_model = clip() + self.tokenizer = tokenizer() + + def encode(self, text): + tokens = self.tokenizer.tokenize_with_weights(text) + cond = self.cond_stage_model.encode_token_weights(tokens) + return cond + + +class VAE: + def __init__(self, ckpt_path=None, scale_factor=0.18215, device="cuda", config=None): + if config is None: + #default SD1.x/SD2.x VAE parameters + ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss", ckpt_path=ckpt_path) + else: + self.first_stage_model = AutoencoderKL(**(config['params']), ckpt_path=ckpt_path) + self.first_stage_model = self.first_stage_model.eval() + self.scale_factor = scale_factor + self.device = device + + def decode(self, samples): + self.first_stage_model = self.first_stage_model.to(self.device) + samples = samples.to(self.device) + pixel_samples = self.first_stage_model.decode(1. / self.scale_factor * samples) + pixel_samples = torch.clamp((pixel_samples + 1.0) / 2.0, min=0.0, max=1.0) + self.first_stage_model = self.first_stage_model.cpu() + pixel_samples = pixel_samples.cpu().movedim(1,-1) + return pixel_samples + + def encode(self, pixel_samples): + self.first_stage_model = self.first_stage_model.to(self.device) + pixel_samples = pixel_samples.movedim(-1,1).to(self.device) + samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor + self.first_stage_model = self.first_stage_model.cpu() + samples = samples.cpu() + return samples + + +def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True): + config = OmegaConf.load(config_path) + model_config_params = config['model']['params'] + clip_config = model_config_params['cond_stage_config'] + scale_factor = model_config_params['scale_factor'] + vae_config = model_config_params['first_stage_config'] + + clip = None + vae = None + + class WeightsLoader(torch.nn.Module): + pass + + w = WeightsLoader() + load_state_dict_to = [] + if output_vae: + vae = VAE(scale_factor=scale_factor, config=vae_config) + w.first_stage_model = vae.first_stage_model + load_state_dict_to = [w] + + if output_clip: + clip = CLIP(config=clip_config) + w.cond_stage_model = clip.cond_stage_model + load_state_dict_to = [w] + + model = load_model_from_config(config, ckpt_path, verbose=False, load_state_dict_to=load_state_dict_to) + return (model, clip, vae) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py new file mode 100644 index 00000000..2a881832 --- /dev/null +++ b/comfy/sd1_clip.py @@ -0,0 +1,178 @@ +import os + +from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig +import torch + +class ClipTokenWeightEncoder: + def encode_token_weights(self, token_weight_pairs): + z_empty = self.encode(self.empty_tokens) + output = [] + for x in token_weight_pairs: + tokens = [list(map(lambda a: a[0], x))] + z = self.encode(tokens) + for i in range(len(z)): + for j in range(len(z[i])): + weight = x[j][1] + z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j] + output += [z] + if (len(output) == 0): + return self.encode(self.empty_tokens) + return torch.cat(output, dim=-2) + +class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + LAYERS = [ + "last", + "pooled", + "hidden" + ] + def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, + freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, textmodel_path=None): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + if textmodel_path is not None: + self.transformer = CLIPTextModel.from_pretrained(textmodel_path) + else: + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") + config = CLIPTextConfig.from_json_file(textmodel_json_config) + self.transformer = CLIPTextModel(config) + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = None + self.empty_tokens = [[49406] + [49407] * 76] + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) <= 12 + self.clip_layer(layer_idx) + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def clip_layer(self, layer_idx): + if abs(layer_idx) >= 12: + self.layer = "last" + else: + self.layer = "hidden" + self.layer_idx = layer_idx + + def forward(self, tokens): + tokens = torch.LongTensor(tokens).to(self.device) + outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden") + + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + z = self.transformer.text_model.final_layer_norm(z) + + return z + + def encode(self, tokens): + return self(tokens) + +def parse_parentheses(string): + result = [] + current_item = "" + nesting_level = 0 + for char in string: + if char == "(": + if nesting_level == 0: + if current_item: + result.append(current_item) + current_item = "(" + else: + current_item = "(" + else: + current_item += char + nesting_level += 1 + elif char == ")": + nesting_level -= 1 + if nesting_level == 0: + result.append(current_item + ")") + current_item = "" + else: + current_item += char + else: + current_item += char + if current_item: + result.append(current_item) + return result + +def token_weights(string, current_weight): + a = parse_parentheses(string) + out = [] + for x in a: + weight = current_weight + if len(x) >= 2 and x[-1] == ')' and x[0] == '(': + x = x[1:-1] + xx = x.rfind(":") + weight *= 1.1 + if xx > 0: + try: + weight = float(x[xx+1:]) + x = x[:xx] + except: + pass + out += token_weights(x, weight) + else: + out += [(x, current_weight)] + return out + +def escape_important(text): + text = text.replace("\\)", "\0\1") + text = text.replace("\\(", "\0\2") + return text + +def unescape_important(text): + text = text.replace("\0\1", ")") + text = text.replace("\0\2", "(") + return text + +class SD1Tokenizer: + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True): + if tokenizer_path is None: + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.max_length = max_length + empty = self.tokenizer('')["input_ids"] + self.start_token = empty[0] + self.end_token = empty[1] + self.pad_with_end = pad_with_end + vocab = self.tokenizer.get_vocab() + self.inv_vocab = {v: k for k, v in vocab.items()} + + def tokenize_with_weights(self, text): + text = escape_important(text) + parsed_weights = token_weights(text, 1.0) + + tokens = [] + for t in parsed_weights: + tt = self.tokenizer(unescape_important(t[0]))["input_ids"][1:-1] + for x in tt: + tokens += [(x, t[1])] + + out_tokens = [] + for x in range(0, len(tokens), self.max_length - 2): + o_token = [(self.start_token, 1.0)] + tokens[x:min(self.max_length - 2 + x, len(tokens))] + o_token += [(self.end_token, 1.0)] + if self.pad_with_end: + o_token +=[(self.end_token, 1.0)] * (self.max_length - len(o_token)) + else: + o_token +=[(0, 1.0)] * (self.max_length - len(o_token)) + + out_tokens += [o_token] + + return out_tokens + + def untokenize(self, token_weight_pair): + return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json new file mode 100644 index 00000000..0158a1fd --- /dev/null +++ b/comfy/sd1_clip_config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/comfy/sd1_tokenizer/merges.txt b/comfy/sd1_tokenizer/merges.txt new file mode 100644 index 00000000..76e821f1 --- /dev/null +++ b/comfy/sd1_tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c om +s e +er s +m e +m e +al l +c on +m o +k e +g e +ou t +en t +c o +f e +v er +a r +f ro +a u +p o +c e +gh t +ar e +s s +fro m +c h +t r +ou n +on e +b y +d o +t h +w or +er e +k e +p ro +f or +d s +b o +t a +w e +g o +h e +t er +in g +d e +b e +ati on +m or +a y +e x +il l +p e +k s +s c +l u +f u +q u +v er +ðŁ ĺ +j u +m u +at e +an d +v e +k ing +m ar +o p +h i +.. . +p re +a d +r u +th at +j o +o f +c e +ne w +a m +a p +g re +s s +d u +no w +y e +t ing +y our +it y +n i +c i +p ar +g u +f i +a f +p er +t er +u p +s o +g i +on s +g r +g e +b r +p l +' t +m i +in e +we e +b i +u s +sh o +ha ve +to day +a v +m an +en t +ac k +ur e +ou r +â Ģ +c u +l d +lo o +i m +ic e +s om +f in +re d +re n +oo d +w as +ti on +p i +i r +th er +t y +p h +ar d +e c +! ! +m on +mor e +w ill +t ra +c an +c ol +p u +t e +w n +m b +s o +it i +ju st +n ing +h ere +t u +p a +p r +bu t +wh at +al ly +f ir +m in +c a +an t +s a +t ed +e v +m ent +f a +ge t +am e +ab out +g ra +no t +ha pp +ay s +m an +h is +ti me +li ke +g h +ha s +th an +lo ve +ar t +st e +d ing +h e +c re +w s +w at +d er +it e +s er +ac e +ag e +en d +st r +a w +st or +r e +c ar +el l +al l +p s +f ri +p ho +p or +d o +a k +w i +f re +wh o +sh i +b oo +s on +el l +wh en +il l +ho w +gre at +w in +e l +b l +s si +al i +som 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sd1_clip.ClipTokenWeightEncoder): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + #"pooled", + "last", + "penultimate", + "hidden" + ] + #version="laion2b_s32b_b79k" + def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, + freeze=True, layer="penultimate", layer_idx=None): + super().__init__() + assert layer in self.LAYERS + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu')) + del model.visual + self.model = model + + self.device = device + self.max_length = max_length + self.empty_tokens = [[49406] + [49407] + [0] * 75] + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + elif self.layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) < 24 + self.clip_layer(layer_idx) + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def clip_layer(self, layer_idx): + #layer_idx should have the same logic as the one for SD1 + if abs(layer_idx) >= 24: + self.layer_idx = 0 + else: + if layer_idx < 0: + self.layer_idx = -(layer_idx + 1) + else: + self.layer_idx = 24 - (layer_idx + 1) + + def forward(self, tokens): + tokens = torch.LongTensor(tokens).to(self.device) + z = self.encode_with_transformer(tokens) + return z + + def encode_with_transformer(self, tokens): + x = self.model.token_embedding(tokens) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, tokens): + return self(tokens) + + + +class SD2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, tokenizer_path=None): + super().__init__(tokenizer_path, pad_with_end=False) + + diff --git a/comfyui_screenshot.png b/comfyui_screenshot.png new file mode 100644 index 00000000..72642438 Binary files /dev/null and b/comfyui_screenshot.png differ diff --git a/main.py b/main.py new file mode 100644 index 00000000..a8f376cc --- /dev/null +++ b/main.py @@ -0,0 +1,314 @@ +import os +import sys +import copy +import json +import threading +import queue +import traceback + +import torch + +import nodes + + +def recursive_execute(prompt, outputs, current_item, extra_data={}): + unique_id = current_item + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + c_obj = nodes.NODE_CLASS_MAPPINGS[class_type] + valid_inputs = c_obj.INPUT_TYPES() + if unique_id in outputs: + return [] + + executed = [] + + for x in inputs: + input_data = inputs[x] + + if isinstance(input_data, list): + input_unique_id = input_data[0] + output_index = input_data[1] + if input_unique_id not in outputs: + executed += recursive_execute(prompt, outputs, input_unique_id, extra_data) + + input_data_all = {} + for x in inputs: + input_data = inputs[x] + if isinstance(input_data, list): + input_unique_id = input_data[0] + output_index = input_data[1] + obj = outputs[input_unique_id][output_index] + input_data_all[x] = obj + else: + if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]): + input_data_all[x] = input_data + + + obj = c_obj() + if "hidden" in valid_inputs: + h = valid_inputs["hidden"] + for x in h: + if h[x] == "PROMPT": + input_data_all[x] = prompt + if h[x] == "EXTRA_PNGINFO": + if "extra_pnginfo" in extra_data: + input_data_all[x] = extra_data['extra_pnginfo'] + + outputs[unique_id] = getattr(obj, obj.FUNCTION)(**input_data_all) + return executed + [unique_id] + +def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item): + unique_id = current_item + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + + if unique_id not in outputs: + return True + + to_delete = False + if unique_id not in old_prompt: + to_delete = True + elif inputs == old_prompt[unique_id]['inputs']: + for x in inputs: + input_data = inputs[x] + + if isinstance(input_data, list): + input_unique_id = input_data[0] + output_index = input_data[1] + if input_unique_id in outputs: + to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id) + else: + to_delete = True + if to_delete: + break + else: + to_delete = True + + if to_delete: + print("deleted", unique_id) + d = outputs.pop(unique_id) + del d + return to_delete + +class PromptExecutor: + def __init__(self): + self.outputs = {} + self.old_prompt = {} + + def execute(self, prompt, extra_data={}): + with torch.no_grad(): + for x in prompt: + recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x) + + current_outputs = set(self.outputs.keys()) + executed = [] + try: + for x in prompt: + class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']] + if hasattr(class_, 'OUTPUT_NODE'): + if class_.OUTPUT_NODE == True: + valid = False + try: + m = validate_inputs(prompt, x) + valid = m[0] + except: + valid = False + if valid: + executed += recursive_execute(prompt, self.outputs, x, extra_data) + except Exception as e: + print(traceback.format_exc()) + to_delete = [] + for o in self.outputs: + if o not in current_outputs: + to_delete += [o] + if o in self.old_prompt: + d = self.old_prompt.pop(o) + del d + for o in to_delete: + d = self.outputs.pop(o) + del d + else: + executed = set(executed) + for x in executed: + self.old_prompt[x] = copy.deepcopy(prompt[x]) + +def validate_inputs(prompt, item): + unique_id = item + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] + + class_inputs = obj_class.INPUT_TYPES() + required_inputs = class_inputs['required'] + for x in required_inputs: + if x not in inputs: + return (False, "Required input is missing. {}, {}".format(class_type, x)) + val = inputs[x] + info = required_inputs[x] + type_input = info[0] + if isinstance(val, list): + if len(val) != 2: + return (False, "Bad Input. {}, {}".format(class_type, x)) + o_id = val[0] + o_class_type = prompt[o_id]['class_type'] + r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES + if r[val[1]] != type_input: + return (False, "Return type mismatch. {}, {}".format(class_type, x)) + r = validate_inputs(prompt, o_id) + if r[0] == False: + return r + else: + if type_input == "INT": + val = int(val) + inputs[x] = val + if type_input == "FLOAT": + val = float(val) + inputs[x] = val + if type_input == "STRING": + val = str(val) + inputs[x] = val + + if len(info) > 1: + if "min" in info[1] and val < info[1]["min"]: + return (False, "Value smaller than min. {}, {}".format(class_type, x)) + if "max" in info[1] and val > info[1]["max"]: + return (False, "Value bigger than max. {}, {}".format(class_type, x)) + + if isinstance(type_input, list): + if val not in type_input: + return (False, "Value not in list. {}, {}".format(class_type, x)) + return (True, "") + +def validate_prompt(prompt): + outputs = set() + for x in prompt: + class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']] + if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True: + outputs.add(x) + + if len(outputs) == 0: + return (False, "Prompt has no outputs") + + good_outputs = set() + for o in outputs: + valid = False + reason = "" + try: + m = validate_inputs(prompt, o) + valid = m[0] + reason = m[1] + except: + valid = False + reason = "Parsing error" + + if valid == True: + good_outputs.add(x) + else: + print("Failed to validate prompt for output {} {}".format(o, reason)) + print("output will be ignored") + + if len(good_outputs) == 0: + return (False, "Prompt has no properly connected outputs") + + return (True, "") + +def prompt_worker(q): + e = PromptExecutor() + while True: + item = q.get() + e.execute(item[-2], item[-1]) + q.task_done() + + +from http.server import BaseHTTPRequestHandler, HTTPServer + +class PromptServer(BaseHTTPRequestHandler): + def _set_headers(self, code=200, ct='text/html'): + self.send_response(code) + self.send_header('Content-type', ct) + self.end_headers() + def log_message(self, format, *args): + pass + def do_GET(self): + if self.path == "/prompt": + self._set_headers(ct='application/json') + prompt_info = {} + exec_info = {} + exec_info['queue_remaining'] = self.server.prompt_queue.unfinished_tasks + prompt_info['exec_info'] = exec_info + self.wfile.write(json.dumps(prompt_info).encode('utf-8')) + elif self.path == "/object_info": + self._set_headers(ct='application/json') + out = {} + for x in nodes.NODE_CLASS_MAPPINGS: + obj_class = nodes.NODE_CLASS_MAPPINGS[x] + info = {} + info['input'] = obj_class.INPUT_TYPES() + info['output'] = obj_class.RETURN_TYPES + info['name'] = x #TODO + info['description'] = '' + out[x] = info + self.wfile.write(json.dumps(out).encode('utf-8')) + elif self.path[1:] in os.listdir(self.server.server_dir): + self._set_headers() + with open(os.path.join(self.server.server_dir, self.path[1:]), "rb") as f: + self.wfile.write(f.read()) + else: + self._set_headers() + with open(os.path.join(self.server.server_dir, "index.html"), "rb") as f: + self.wfile.write(f.read()) + + def do_HEAD(self): + self._set_headers() + + def do_POST(self): + resp_code = 200 + out_string = "" + if self.path == "/prompt": + print("got prompt") + self.data_string = self.rfile.read(int(self.headers['Content-Length'])) + json_data = json.loads(self.data_string) + if "number" in json_data: + number = float(json_data['number']) + else: + number = self.server.number + self.server.number += 1 + if "prompt" in json_data: + prompt = json_data["prompt"] + valid = validate_prompt(prompt) + extra_data = {} + if "extra_data" in json_data: + extra_data = json_data["extra_data"] + if valid[0]: + self.server.prompt_queue.put((number, id(prompt), prompt, extra_data)) + else: + resp_code = 400 + out_string = valid[1] + print("invalid prompt:", valid[1]) + self._set_headers(code=resp_code) + self.end_headers() + self.wfile.write(out_string.encode('utf8')) + return + + +def run(prompt_queue, address='', port=8188): + server_address = (address, port) + httpd = HTTPServer(server_address, PromptServer) + httpd.server_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "webshit") + httpd.prompt_queue = prompt_queue + httpd.number = 0 + if server_address[0] == '': + addr = '0.0.0.0' + else: + addr = server_address[0] + print("Starting server\n") + print("To see the GUI go to: http://{}:{}".format(addr, server_address[1])) + httpd.serve_forever() + + +if __name__ == "__main__": + q = queue.PriorityQueue() + threading.Thread(target=prompt_worker, daemon=True, args=(q,)).start() + run(q, address='127.0.0.1', port=8188) + + diff --git a/models/checkpoints/put_checkpoints_here b/models/checkpoints/put_checkpoints_here new file mode 100644 index 00000000..e69de29b diff --git a/models/configs/anything_v3.yaml b/models/configs/anything_v3.yaml new file mode 100644 index 00000000..8bcfe584 --- /dev/null +++ b/models/configs/anything_v3.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference.yaml b/models/configs/v1-inference.yaml new file mode 100644 index 00000000..d4effe56 --- /dev/null +++ b/models/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inference_clip_skip_2.yaml b/models/configs/v1-inference_clip_skip_2.yaml new file mode 100644 index 00000000..8bcfe584 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v2-inference-v.yaml b/models/configs/v2-inference-v.yaml new file mode 100644 index 00000000..8ec8dfbf --- /dev/null +++ b/models/configs/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference-v_fp32.yaml b/models/configs/v2-inference-v_fp32.yaml new file mode 100644 index 00000000..d5c9b9cb --- /dev/null +++ b/models/configs/v2-inference-v_fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/vae/put_vae_here b/models/vae/put_vae_here new file mode 100644 index 00000000..e69de29b diff --git a/nodes.py b/nodes.py new file mode 100644 index 00000000..a297fc3b --- /dev/null +++ b/nodes.py @@ -0,0 +1,221 @@ +import torch + +import os +import sys +import json + +from PIL import Image +from PIL.PngImagePlugin import PngInfo +import numpy as np + +sys.path.append(os.path.join(sys.path[0], "comfy")) + + +import comfy.samplers +import comfy.sd + +supported_ckpt_extensions = ['.ckpt'] +try: + import safetensors.torch + supported_ckpt_extensions += ['.safetensors'] +except: + print("Could not import safetensors, safetensors support disabled.") + +def filter_files_extensions(files, extensions): + return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files))) + +class CLIPTextEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": {"text": ("STRING", ), "clip": ("CLIP", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + def encode(self, clip, text): + return (clip.encode(text), ) + +class VAEDecode: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + def decode(self, vae, samples): + return (vae.decode(samples), ) + +class VAEEncode: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + def encode(self, vae, pixels): + return (vae.encode(pixels), ) + +class CheckpointLoader: + models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") + config_dir = os.path.join(models_dir, "configs") + ckpt_dir = os.path.join(models_dir, "checkpoints") + + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (filter_files_extensions(os.listdir(s.config_dir), '.yaml'), ), + "ckpt_name": (filter_files_extensions(os.listdir(s.ckpt_dir), supported_ckpt_extensions), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): + config_path = os.path.join(self.config_dir, config_name) + ckpt_path = os.path.join(self.ckpt_dir, ckpt_name) + return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True) + +class VAELoader: + models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") + vae_dir = os.path.join(models_dir, "vae") + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae_name": (filter_files_extensions(os.listdir(s.vae_dir), supported_ckpt_extensions), )}} + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + + #TODO: scale factor? + def load_vae(self, vae_name): + vae_path = os.path.join(self.vae_dir, vae_name) + vae = comfy.sd.VAE(ckpt_path=vae_path) + return (vae,) + +class EmptyLatentImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (latent, ) + +class LatentUpscale: + upscale_methods = ["nearest-exact", "bilinear", "area"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + def upscale(self, samples, upscale_method, width, height): + s = torch.nn.functional.interpolate(samples, size=(height // 8, width // 8), mode=upscale_method) + return (s,) + +class KSampler: + def __init__(self, device="cuda"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): + noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu") + model = model.to(self.device) + noise = noise.to(self.device) + latent_image = latent_image.to(self.device) + + if positive.shape[0] < noise.shape[0]: + positive = torch.cat([positive] * noise.shape[0]) + + if negative.shape[0] < noise.shape[0]: + negative = torch.cat([negative] * noise.shape[0]) + + positive = positive.to(self.device) + negative = negative.to(self.device) + + if sampler_name in comfy.samplers.KSampler.SAMPLERS: + sampler = comfy.samplers.KSampler(model, steps=steps, device=self.device, sampler=sampler_name, scheduler=scheduler, denoise=denoise) + else: + #other samplers + pass + + samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image) + samples = samples.cpu() + model = model.cpu() + return (samples, ) + + +class SaveImage: + def __init__(self): + self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") + try: + self.counter = int(max(filter(lambda a: 'ComfyUI_' in a, os.listdir(self.output_dir))).split('_')[1]) + 1 + except: + self.counter = 1 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", )}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + def save_images(self, images, prompt=None, extra_pnginfo=None): + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(i.astype(np.uint8)) + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + img.save(f"output/ComfyUI_{self.counter:05}_.png", pnginfo=metadata, optimize=True) + self.counter += 1 + + +NODE_CLASS_MAPPINGS = { + "KSampler": KSampler, + "CheckpointLoader": CheckpointLoader, + "CLIPTextEncode": CLIPTextEncode, + "VAEDecode": VAEDecode, + "VAEEncode": VAEEncode, + "VAELoader": VAELoader, + "EmptyLatentImage": EmptyLatentImage, + "LatentUpscale": LatentUpscale, + "SaveImage": SaveImage, +} + + diff --git a/output/_output_images_will_be_put_here b/output/_output_images_will_be_put_here new file mode 100644 index 00000000..e69de29b diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..64cc3fc2 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +torch +torchdiffeq +torchsde +omegaconf +einops +open-clip-torch +transformers +safetensors +pytorch_lightning + diff --git a/webshit/index.html b/webshit/index.html new file mode 100644 index 00000000..ef31afb9 --- /dev/null +++ b/webshit/index.html @@ -0,0 +1,465 @@ + + + + + + + + + + +Queue size: X
+
+
+
+
+
+
+
+ + + diff --git a/webshit/litegraph.core.js b/webshit/litegraph.core.js new file mode 100644 index 00000000..ef992909 --- /dev/null +++ b/webshit/litegraph.core.js @@ -0,0 +1,14132 @@ +//packer version + + +(function(global) { + // ************************************************************* + // LiteGraph CLASS ******* + // ************************************************************* + + /** + * The Global Scope. It contains all the registered node classes. + * + * @class LiteGraph + * @constructor + */ + + var LiteGraph = (global.LiteGraph = { + VERSION: 0.4, + + CANVAS_GRID_SIZE: 10, + + NODE_TITLE_HEIGHT: 30, + NODE_TITLE_TEXT_Y: 20, + NODE_SLOT_HEIGHT: 20, + NODE_WIDGET_HEIGHT: 20, + NODE_WIDTH: 140, + NODE_MIN_WIDTH: 50, + NODE_COLLAPSED_RADIUS: 10, + NODE_COLLAPSED_WIDTH: 80, + NODE_TITLE_COLOR: "#999", + NODE_SELECTED_TITLE_COLOR: "#FFF", + NODE_TEXT_SIZE: 14, + NODE_TEXT_COLOR: "#AAA", + NODE_SUBTEXT_SIZE: 12, + NODE_DEFAULT_COLOR: "#333", + NODE_DEFAULT_BGCOLOR: "#353535", + NODE_DEFAULT_BOXCOLOR: "#666", + NODE_DEFAULT_SHAPE: "box", + NODE_BOX_OUTLINE_COLOR: "#FFF", + DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)", + DEFAULT_GROUP_FONT: 24, + + WIDGET_BGCOLOR: "#222", + WIDGET_OUTLINE_COLOR: "#666", + WIDGET_TEXT_COLOR: "#DDD", + WIDGET_SECONDARY_TEXT_COLOR: "#999", + + LINK_COLOR: "#9A9", + EVENT_LINK_COLOR: "#A86", + CONNECTING_LINK_COLOR: "#AFA", + + MAX_NUMBER_OF_NODES: 1000, //avoid infinite loops + DEFAULT_POSITION: [100, 100], //default node position + VALID_SHAPES: ["default", "box", "round", "card"], //,"circle" + + //shapes are used for nodes but also for slots + BOX_SHAPE: 1, + ROUND_SHAPE: 2, + CIRCLE_SHAPE: 3, + CARD_SHAPE: 4, + ARROW_SHAPE: 5, + GRID_SHAPE: 6, // intended for slot arrays + + //enums + INPUT: 1, + OUTPUT: 2, + + EVENT: -1, //for outputs + ACTION: -1, //for inputs + + NODE_MODES: ["Always", "On Event", "Never", "On Trigger"], // helper, will add "On Request" and more in the future + NODE_MODES_COLORS:["#666","#422","#333","#224","#626"], // use with node_box_coloured_by_mode + ALWAYS: 0, + ON_EVENT: 1, + NEVER: 2, + ON_TRIGGER: 3, + + UP: 1, + DOWN: 2, + LEFT: 3, + RIGHT: 4, + CENTER: 5, + + LINK_RENDER_MODES: ["Straight", "Linear", "Spline"], // helper + STRAIGHT_LINK: 0, + LINEAR_LINK: 1, + SPLINE_LINK: 2, + + NORMAL_TITLE: 0, + NO_TITLE: 1, + TRANSPARENT_TITLE: 2, + AUTOHIDE_TITLE: 3, + + proxy: null, //used to redirect calls + node_images_path: "", + + debug: false, + catch_exceptions: true, + throw_errors: true, + allow_scripts: false, //if set to true some nodes like Formula would be allowed to evaluate code that comes from unsafe sources (like node configuration), which could lead to exploits + registered_node_types: {}, //nodetypes by string + node_types_by_file_extension: {}, //used for dropping files in the canvas + Nodes: {}, //node types by classname + Globals: {}, //used to store vars between graphs + + searchbox_extras: {}, //used to add extra features to the search box + auto_sort_node_types: false, // [true!] If set to true, will automatically sort node types / categories in the context menus + + node_box_coloured_when_on: false, // [true!] this make the nodes box (top left circle) coloured when triggered (execute/action), visual feedback + node_box_coloured_by_mode: false, // [true!] nodebox based on node mode, visual feedback + + dialog_close_on_mouse_leave: true, // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false + dialog_close_on_mouse_leave_delay: 500, + + shift_click_do_break_link_from: false, // [false!] prefer false if results too easy to break links - implement with ALT or TODO custom keys + click_do_break_link_to: false, // [false!]prefer false, way too easy to break links + + search_hide_on_mouse_leave: true, // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false + search_filter_enabled: false, // [true!] enable filtering slots type in the search widget, !requires auto_load_slot_types or manual set registered_slot_[in/out]_types and slot_types_[in/out] + search_show_all_on_open: true, // [true!] opens the results list when opening the search widget + + auto_load_slot_types: false, // [if want false, use true, run, get vars values to be statically set, than disable] nodes types and nodeclass association with node types need to be calculated, if dont want this, calculate once and set registered_slot_[in/out]_types and slot_types_[in/out] + + // set these values if not using auto_load_slot_types + registered_slot_in_types: {}, // slot types for nodeclass + registered_slot_out_types: {}, // slot types for nodeclass + slot_types_in: [], // slot types IN + slot_types_out: [], // slot types OUT + slot_types_default_in: [], // specify for each IN slot type a(/many) deafult node(s), use single string, array, or object (with node, title, parameters, ..) like for search + slot_types_default_out: [], // specify for each OUT slot type a(/many) deafult node(s), use single string, array, or object (with node, title, parameters, ..) like for search + + alt_drag_do_clone_nodes: false, // [true!] very handy, ALT click to clone and drag the new node + + do_add_triggers_slots: false, // [true!] will create and connect event slots when using action/events connections, !WILL CHANGE node mode when using onTrigger (enable mode colors), onExecuted does not need this + + allow_multi_output_for_events: true, // [false!] being events, it is strongly reccomended to use them sequentually, one by one + + middle_click_slot_add_default_node: false, //[true!] allows to create and connect a ndoe clicking with the third button (wheel) + + release_link_on_empty_shows_menu: false, //[true!] dragging a link to empty space will open a menu, add from list, search or defaults + + pointerevents_method: "mouse", // "mouse"|"pointer" use mouse for retrocompatibility issues? (none found @ now) + // TODO implement pointercancel, gotpointercapture, lostpointercapture, (pointerover, pointerout if necessary) + + /** + * Register a node class so it can be listed when the user wants to create a new one + * @method registerNodeType + * @param {String} type name of the node and path + * @param {Class} base_class class containing the structure of a node + */ + + registerNodeType: function(type, base_class) { + if (!base_class.prototype) { + throw "Cannot register a simple object, it must be a class with a prototype"; + } + base_class.type = type; + + if (LiteGraph.debug) { + console.log("Node registered: " + type); + } + + var categories = type.split("/"); + var classname = base_class.name; + + var pos = type.lastIndexOf("/"); + base_class.category = type.substr(0, pos); + + if (!base_class.title) { + base_class.title = classname; + } + //info.name = name.substr(pos+1,name.length - pos); + + //extend class + if (base_class.prototype) { + //is a class + for (var i in LGraphNode.prototype) { + if (!base_class.prototype[i]) { + base_class.prototype[i] = LGraphNode.prototype[i]; + } + } + } + + var prev = this.registered_node_types[type]; + if(prev) + console.log("replacing node type: " + type); + else + { + if( !Object.hasOwnProperty( base_class.prototype, "shape") ) + Object.defineProperty(base_class.prototype, "shape", { + set: function(v) { + switch (v) { + case "default": + delete this._shape; + break; + case "box": + this._shape = LiteGraph.BOX_SHAPE; + break; + case "round": + this._shape = LiteGraph.ROUND_SHAPE; + break; + case "circle": + this._shape = LiteGraph.CIRCLE_SHAPE; + break; + case "card": + this._shape = LiteGraph.CARD_SHAPE; + break; + default: + this._shape = v; + } + }, + get: function(v) { + return this._shape; + }, + enumerable: true, + configurable: true + }); + + //warnings + if (base_class.prototype.onPropertyChange) { + console.warn( + "LiteGraph node class " + + type + + " has onPropertyChange method, it must be called onPropertyChanged with d at the end" + ); + } + + //used to know which nodes create when dragging files to the canvas + if (base_class.supported_extensions) { + for (var i in base_class.supported_extensions) { + var ext = base_class.supported_extensions[i]; + if(ext && ext.constructor === String) + this.node_types_by_file_extension[ ext.toLowerCase() ] = base_class; + } + } + } + + this.registered_node_types[type] = base_class; + if (base_class.constructor.name) { + this.Nodes[classname] = base_class; + } + if (LiteGraph.onNodeTypeRegistered) { + LiteGraph.onNodeTypeRegistered(type, base_class); + } + if (prev && LiteGraph.onNodeTypeReplaced) { + LiteGraph.onNodeTypeReplaced(type, base_class, prev); + } + + //warnings + if (base_class.prototype.onPropertyChange) { + console.warn( + "LiteGraph node class " + + type + + " has onPropertyChange method, it must be called onPropertyChanged with d at the end" + ); + } + + //used to know which nodes create when dragging files to the canvas + if (base_class.supported_extensions) { + for (var i=0; i < base_class.supported_extensions.length; i++) { + var ext = base_class.supported_extensions[i]; + if(ext && ext.constructor === String) + this.node_types_by_file_extension[ ext.toLowerCase() ] = base_class; + } + } + + // TODO one would want to know input and ouput :: this would allow trought registerNodeAndSlotType to get all the slots types + //console.debug("Registering "+type); + if (this.auto_load_slot_types) nodeTmp = new base_class(base_class.title || "tmpnode"); + }, + + /** + * removes a node type from the system + * @method unregisterNodeType + * @param {String|Object} type name of the node or the node constructor itself + */ + unregisterNodeType: function(type) { + var base_class = type.constructor === String ? this.registered_node_types[type] : type; + if(!base_class) + throw("node type not found: " + type ); + delete this.registered_node_types[base_class.type]; + if(base_class.constructor.name) + delete this.Nodes[base_class.constructor.name]; + }, + + /** + * Save a slot type and his node + * @method registerSlotType + * @param {String|Object} type name of the node or the node constructor itself + * @param {String} slot_type name of the slot type (variable type), eg. string, number, array, boolean, .. + */ + registerNodeAndSlotType: function(type,slot_type,out){ + out = out || false; + var base_class = type.constructor === String && this.registered_node_types[type] !== "anonymous" ? this.registered_node_types[type] : type; + + var sCN = base_class.constructor.type; + + if (typeof slot_type == "string"){ + var aTypes = slot_type.split(","); + }else if (slot_type == this.EVENT || slot_type == this.ACTION){ + var aTypes = ["_event_"]; + }else{ + var aTypes = ["*"]; + } + + for (var i = 0; i < aTypes.length; ++i) { + var sT = aTypes[i]; //.toLowerCase(); + if (sT === ""){ + sT = "*"; + } + var registerTo = out ? "registered_slot_out_types" : "registered_slot_in_types"; + if (typeof this[registerTo][sT] == "undefined") this[registerTo][sT] = {nodes: []}; + this[registerTo][sT].nodes.push(sCN); + + // check if is a new type + if (!out){ + if (!this.slot_types_in.includes(sT.toLowerCase())){ + this.slot_types_in.push(sT.toLowerCase()); + this.slot_types_in.sort(); + } + }else{ + if (!this.slot_types_out.includes(sT.toLowerCase())){ + this.slot_types_out.push(sT.toLowerCase()); + this.slot_types_out.sort(); + } + } + } + }, + + /** + * Create a new nodetype by passing a function, it wraps it with a proper class and generates inputs according to the parameters of the function. + * Useful to wrap simple methods that do not require properties, and that only process some input to generate an output. + * @method wrapFunctionAsNode + * @param {String} name node name with namespace (p.e.: 'math/sum') + * @param {Function} func + * @param {Array} param_types [optional] an array containing the type of every parameter, otherwise parameters will accept any type + * @param {String} return_type [optional] string with the return type, otherwise it will be generic + * @param {Object} properties [optional] properties to be configurable + */ + wrapFunctionAsNode: function( + name, + func, + param_types, + return_type, + properties + ) { + var params = Array(func.length); + var code = ""; + var names = LiteGraph.getParameterNames(func); + for (var i = 0; i < names.length; ++i) { + code += + "this.addInput('" + + names[i] + + "'," + + (param_types && param_types[i] + ? "'" + param_types[i] + "'" + : "0") + + ");\n"; + } + code += + "this.addOutput('out'," + + (return_type ? "'" + return_type + "'" : 0) + + ");\n"; + if (properties) { + code += + "this.properties = " + JSON.stringify(properties) + ";\n"; + } + var classobj = Function(code); + classobj.title = name.split("/").pop(); + classobj.desc = "Generated from " + func.name; + classobj.prototype.onExecute = function onExecute() { + for (var i = 0; i < params.length; ++i) { + params[i] = this.getInputData(i); + } + var r = func.apply(this, params); + this.setOutputData(0, r); + }; + this.registerNodeType(name, classobj); + }, + + /** + * Removes all previously registered node's types + */ + clearRegisteredTypes: function() { + this.registered_node_types = {}; + this.node_types_by_file_extension = {}; + this.Nodes = {}; + this.searchbox_extras = {}; + }, + + /** + * Adds this method to all nodetypes, existing and to be created + * (You can add it to LGraphNode.prototype but then existing node types wont have it) + * @method addNodeMethod + * @param {Function} func + */ + addNodeMethod: function(name, func) { + LGraphNode.prototype[name] = func; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if (type.prototype[name]) { + type.prototype["_" + name] = type.prototype[name]; + } //keep old in case of replacing + type.prototype[name] = func; + } + }, + + /** + * Create a node of a given type with a name. The node is not attached to any graph yet. + * @method createNode + * @param {String} type full name of the node class. p.e. "math/sin" + * @param {String} name a name to distinguish from other nodes + * @param {Object} options to set options + */ + + createNode: function(type, title, options) { + var base_class = this.registered_node_types[type]; + if (!base_class) { + if (LiteGraph.debug) { + console.log( + 'GraphNode type "' + type + '" not registered.' + ); + } + return null; + } + + var prototype = base_class.prototype || base_class; + + title = title || base_class.title || type; + + var node = null; + + if (LiteGraph.catch_exceptions) { + try { + node = new base_class(title); + } catch (err) { + console.error(err); + return null; + } + } else { + node = new base_class(title); + } + + node.type = type; + + if (!node.title && title) { + node.title = title; + } + if (!node.properties) { + node.properties = {}; + } + if (!node.properties_info) { + node.properties_info = []; + } + if (!node.flags) { + node.flags = {}; + } + if (!node.size) { + node.size = node.computeSize(); + //call onresize? + } + if (!node.pos) { + node.pos = LiteGraph.DEFAULT_POSITION.concat(); + } + if (!node.mode) { + node.mode = LiteGraph.ALWAYS; + } + + //extra options + if (options) { + for (var i in options) { + node[i] = options[i]; + } + } + + // callback + if ( node.onNodeCreated ) { + node.onNodeCreated(); + } + + return node; + }, + + /** + * Returns a registered node type with a given name + * @method getNodeType + * @param {String} type full name of the node class. p.e. "math/sin" + * @return {Class} the node class + */ + getNodeType: function(type) { + return this.registered_node_types[type]; + }, + + /** + * Returns a list of node types matching one category + * @method getNodeType + * @param {String} category category name + * @return {Array} array with all the node classes + */ + + getNodeTypesInCategory: function(category, filter) { + var r = []; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if (type.filter != filter) { + continue; + } + + if (category == "") { + if (type.category == null) { + r.push(type); + } + } else if (type.category == category) { + r.push(type); + } + } + + if (this.auto_sort_node_types) { + r.sort(function(a,b){return a.title.localeCompare(b.title)}); + } + + return r; + }, + + /** + * Returns a list with all the node type categories + * @method getNodeTypesCategories + * @param {String} filter only nodes with ctor.filter equal can be shown + * @return {Array} array with all the names of the categories + */ + getNodeTypesCategories: function( filter ) { + var categories = { "": 1 }; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if ( type.category && !type.skip_list ) + { + if(type.filter != filter) + continue; + categories[type.category] = 1; + } + } + var result = []; + for (var i in categories) { + result.push(i); + } + return this.auto_sort_node_types ? result.sort() : result; + }, + + //debug purposes: reloads all the js scripts that matches a wildcard + reloadNodes: function(folder_wildcard) { + var tmp = document.getElementsByTagName("script"); + //weird, this array changes by its own, so we use a copy + var script_files = []; + for (var i=0; i < tmp.length; i++) { + script_files.push(tmp[i]); + } + + var docHeadObj = document.getElementsByTagName("head")[0]; + folder_wildcard = document.location.href + folder_wildcard; + + for (var i=0; i < script_files.length; i++) { + var src = script_files[i].src; + if ( + !src || + src.substr(0, folder_wildcard.length) != folder_wildcard + ) { + continue; + } + + try { + if (LiteGraph.debug) { + console.log("Reloading: " + src); + } + var dynamicScript = document.createElement("script"); + dynamicScript.type = "text/javascript"; + dynamicScript.src = src; + docHeadObj.appendChild(dynamicScript); + docHeadObj.removeChild(script_files[i]); + } catch (err) { + if (LiteGraph.throw_errors) { + throw err; + } + if (LiteGraph.debug) { + console.log("Error while reloading " + src); + } + } + } + + if (LiteGraph.debug) { + console.log("Nodes reloaded"); + } + }, + + //separated just to improve if it doesn't work + cloneObject: function(obj, target) { + if (obj == null) { + return null; + } + var r = JSON.parse(JSON.stringify(obj)); + if (!target) { + return r; + } + + for (var i in r) { + target[i] = r[i]; + } + return target; + }, + + /** + * Returns if the types of two slots are compatible (taking into account wildcards, etc) + * @method isValidConnection + * @param {String} type_a + * @param {String} type_b + * @return {Boolean} true if they can be connected + */ + isValidConnection: function(type_a, type_b) { + if (type_a=="" || type_a==="*") type_a = 0; + if (type_b=="" || type_b==="*") type_b = 0; + if ( + !type_a //generic output + || !type_b // generic input + || type_a == type_b //same type (is valid for triggers) + || (type_a == LiteGraph.EVENT && type_b == LiteGraph.ACTION) + ) { + return true; + } + + // Enforce string type to handle toLowerCase call (-1 number not ok) + type_a = String(type_a); + type_b = String(type_b); + type_a = type_a.toLowerCase(); + type_b = type_b.toLowerCase(); + + // For nodes supporting multiple connection types + if (type_a.indexOf(",") == -1 && type_b.indexOf(",") == -1) { + return type_a == type_b; + } + + // Check all permutations to see if one is valid + var supported_types_a = type_a.split(","); + var supported_types_b = type_b.split(","); + for (var i = 0; i < supported_types_a.length; ++i) { + for (var j = 0; j < supported_types_b.length; ++j) { + if(this.isValidConnection(supported_types_a[i],supported_types_b[j])){ + //if (supported_types_a[i] == supported_types_b[j]) { + return true; + } + } + } + + return false; + }, + + /** + * Register a string in the search box so when the user types it it will recommend this node + * @method registerSearchboxExtra + * @param {String} node_type the node recommended + * @param {String} description text to show next to it + * @param {Object} data it could contain info of how the node should be configured + * @return {Boolean} true if they can be connected + */ + registerSearchboxExtra: function(node_type, description, data) { + this.searchbox_extras[description.toLowerCase()] = { + type: node_type, + desc: description, + data: data + }; + }, + + /** + * Wrapper to load files (from url using fetch or from file using FileReader) + * @method fetchFile + * @param {String|File|Blob} url the url of the file (or the file itself) + * @param {String} type an string to know how to fetch it: "text","arraybuffer","json","blob" + * @param {Function} on_complete callback(data) + * @param {Function} on_error in case of an error + * @return {FileReader|Promise} returns the object used to + */ + fetchFile: function( url, type, on_complete, on_error ) { + var that = this; + if(!url) + return null; + + type = type || "text"; + if( url.constructor === String ) + { + if (url.substr(0, 4) == "http" && LiteGraph.proxy) { + url = LiteGraph.proxy + url.substr(url.indexOf(":") + 3); + } + return fetch(url) + .then(function(response) { + if(!response.ok) + throw new Error("File not found"); //it will be catch below + if(type == "arraybuffer") + return response.arrayBuffer(); + else if(type == "text" || type == "string") + return response.text(); + else if(type == "json") + return response.json(); + else if(type == "blob") + return response.blob(); + }) + .then(function(data) { + if(on_complete) + on_complete(data); + }) + .catch(function(error) { + console.error("error fetching file:",url); + if(on_error) + on_error(error); + }); + } + else if( url.constructor === File || url.constructor === Blob) + { + var reader = new FileReader(); + reader.onload = function(e) + { + var v = e.target.result; + if( type == "json" ) + v = JSON.parse(v); + if(on_complete) + on_complete(v); + } + if(type == "arraybuffer") + return reader.readAsArrayBuffer(url); + else if(type == "text" || type == "json") + return reader.readAsText(url); + else if(type == "blob") + return reader.readAsBinaryString(url); + } + return null; + } + }); + + //timer that works everywhere + if (typeof performance != "undefined") { + LiteGraph.getTime = performance.now.bind(performance); + } else if (typeof Date != "undefined" && Date.now) { + LiteGraph.getTime = Date.now.bind(Date); + } else if (typeof process != "undefined") { + LiteGraph.getTime = function() { + var t = process.hrtime(); + return t[0] * 0.001 + t[1] * 1e-6; + }; + } else { + LiteGraph.getTime = function getTime() { + return new Date().getTime(); + }; + } + + //********************************************************************************* + // LGraph CLASS + //********************************************************************************* + + /** + * LGraph is the class that contain a full graph. We instantiate one and add nodes to it, and then we can run the execution loop. + * supported callbacks: + + onNodeAdded: when a new node is added to the graph + + onNodeRemoved: when a node inside this graph is removed + + onNodeConnectionChange: some connection has changed in the graph (connected or disconnected) + * + * @class LGraph + * @constructor + * @param {Object} o data from previous serialization [optional] + */ + + function LGraph(o) { + if (LiteGraph.debug) { + console.log("Graph created"); + } + this.list_of_graphcanvas = null; + this.clear(); + + if (o) { + this.configure(o); + } + } + + global.LGraph = LiteGraph.LGraph = LGraph; + + //default supported types + LGraph.supported_types = ["number", "string", "boolean"]; + + //used to know which types of connections support this graph (some graphs do not allow certain types) + LGraph.prototype.getSupportedTypes = function() { + return this.supported_types || LGraph.supported_types; + }; + + LGraph.STATUS_STOPPED = 1; + LGraph.STATUS_RUNNING = 2; + + /** + * Removes all nodes from this graph + * @method clear + */ + + LGraph.prototype.clear = function() { + this.stop(); + this.status = LGraph.STATUS_STOPPED; + + this.last_node_id = 0; + this.last_link_id = 0; + + this._version = -1; //used to detect changes + + //safe clear + if (this._nodes) { + for (var i = 0; i < this._nodes.length; ++i) { + var node = this._nodes[i]; + if (node.onRemoved) { + node.onRemoved(); + } + } + } + + //nodes + this._nodes = []; + this._nodes_by_id = {}; + this._nodes_in_order = []; //nodes sorted in execution order + this._nodes_executable = null; //nodes that contain onExecute sorted in execution order + + //other scene stuff + this._groups = []; + + //links + this.links = {}; //container with all the links + + //iterations + this.iteration = 0; + + //custom data + this.config = {}; + this.vars = {}; + this.extra = {}; //to store custom data + + //timing + this.globaltime = 0; + this.runningtime = 0; + this.fixedtime = 0; + this.fixedtime_lapse = 0.01; + this.elapsed_time = 0.01; + this.last_update_time = 0; + this.starttime = 0; + + this.catch_errors = true; + + this.nodes_executing = []; + this.nodes_actioning = []; + this.nodes_executedAction = []; + + //subgraph_data + this.inputs = {}; + this.outputs = {}; + + //notify canvas to redraw + this.change(); + + this.sendActionToCanvas("clear"); + }; + + /** + * Attach Canvas to this graph + * @method attachCanvas + * @param {GraphCanvas} graph_canvas + */ + + LGraph.prototype.attachCanvas = function(graphcanvas) { + if (graphcanvas.constructor != LGraphCanvas) { + throw "attachCanvas expects a LGraphCanvas instance"; + } + if (graphcanvas.graph && graphcanvas.graph != this) { + graphcanvas.graph.detachCanvas(graphcanvas); + } + + graphcanvas.graph = this; + + if (!this.list_of_graphcanvas) { + this.list_of_graphcanvas = []; + } + this.list_of_graphcanvas.push(graphcanvas); + }; + + /** + * Detach Canvas from this graph + * @method detachCanvas + * @param {GraphCanvas} graph_canvas + */ + LGraph.prototype.detachCanvas = function(graphcanvas) { + if (!this.list_of_graphcanvas) { + return; + } + + var pos = this.list_of_graphcanvas.indexOf(graphcanvas); + if (pos == -1) { + return; + } + graphcanvas.graph = null; + this.list_of_graphcanvas.splice(pos, 1); + }; + + /** + * Starts running this graph every interval milliseconds. + * @method start + * @param {number} interval amount of milliseconds between executions, if 0 then it renders to the monitor refresh rate + */ + + LGraph.prototype.start = function(interval) { + if (this.status == LGraph.STATUS_RUNNING) { + return; + } + this.status = LGraph.STATUS_RUNNING; + + if (this.onPlayEvent) { + this.onPlayEvent(); + } + + this.sendEventToAllNodes("onStart"); + + //launch + this.starttime = LiteGraph.getTime(); + this.last_update_time = this.starttime; + interval = interval || 0; + var that = this; + + //execute once per frame + if ( interval == 0 && typeof window != "undefined" && window.requestAnimationFrame ) { + function on_frame() { + if (that.execution_timer_id != -1) { + return; + } + window.requestAnimationFrame(on_frame); + if(that.onBeforeStep) + that.onBeforeStep(); + that.runStep(1, !that.catch_errors); + if(that.onAfterStep) + that.onAfterStep(); + } + this.execution_timer_id = -1; + on_frame(); + } else { //execute every 'interval' ms + this.execution_timer_id = setInterval(function() { + //execute + if(that.onBeforeStep) + that.onBeforeStep(); + that.runStep(1, !that.catch_errors); + if(that.onAfterStep) + that.onAfterStep(); + }, interval); + } + }; + + /** + * Stops the execution loop of the graph + * @method stop execution + */ + + LGraph.prototype.stop = function() { + if (this.status == LGraph.STATUS_STOPPED) { + return; + } + + this.status = LGraph.STATUS_STOPPED; + + if (this.onStopEvent) { + this.onStopEvent(); + } + + if (this.execution_timer_id != null) { + if (this.execution_timer_id != -1) { + clearInterval(this.execution_timer_id); + } + this.execution_timer_id = null; + } + + this.sendEventToAllNodes("onStop"); + }; + + /** + * Run N steps (cycles) of the graph + * @method runStep + * @param {number} num number of steps to run, default is 1 + * @param {Boolean} do_not_catch_errors [optional] if you want to try/catch errors + * @param {number} limit max number of nodes to execute (used to execute from start to a node) + */ + + LGraph.prototype.runStep = function(num, do_not_catch_errors, limit ) { + num = num || 1; + + var start = LiteGraph.getTime(); + this.globaltime = 0.001 * (start - this.starttime); + + var nodes = this._nodes_executable + ? this._nodes_executable + : this._nodes; + if (!nodes) { + return; + } + + limit = limit || nodes.length; + + if (do_not_catch_errors) { + //iterations + for (var i = 0; i < num; i++) { + for (var j = 0; j < limit; ++j) { + var node = nodes[j]; + if (node.mode == LiteGraph.ALWAYS && node.onExecute) { + //wrap node.onExecute(); + node.doExecute(); + } + } + + this.fixedtime += this.fixedtime_lapse; + if (this.onExecuteStep) { + this.onExecuteStep(); + } + } + + if (this.onAfterExecute) { + this.onAfterExecute(); + } + } else { + try { + //iterations + for (var i = 0; i < num; i++) { + for (var j = 0; j < limit; ++j) { + var node = nodes[j]; + if (node.mode == LiteGraph.ALWAYS && node.onExecute) { + node.onExecute(); + } + } + + this.fixedtime += this.fixedtime_lapse; + if (this.onExecuteStep) { + this.onExecuteStep(); + } + } + + if (this.onAfterExecute) { + this.onAfterExecute(); + } + this.errors_in_execution = false; + } catch (err) { + this.errors_in_execution = true; + if (LiteGraph.throw_errors) { + throw err; + } + if (LiteGraph.debug) { + console.log("Error during execution: " + err); + } + this.stop(); + } + } + + var now = LiteGraph.getTime(); + var elapsed = now - start; + if (elapsed == 0) { + elapsed = 1; + } + this.execution_time = 0.001 * elapsed; + this.globaltime += 0.001 * elapsed; + this.iteration += 1; + this.elapsed_time = (now - this.last_update_time) * 0.001; + this.last_update_time = now; + this.nodes_executing = []; + this.nodes_actioning = []; + this.nodes_executedAction = []; + }; + + /** + * Updates the graph execution order according to relevance of the nodes (nodes with only outputs have more relevance than + * nodes with only inputs. + * @method updateExecutionOrder + */ + LGraph.prototype.updateExecutionOrder = function() { + this._nodes_in_order = this.computeExecutionOrder(false); + this._nodes_executable = []; + for (var i = 0; i < this._nodes_in_order.length; ++i) { + if (this._nodes_in_order[i].onExecute) { + this._nodes_executable.push(this._nodes_in_order[i]); + } + } + }; + + //This is more internal, it computes the executable nodes in order and returns it + LGraph.prototype.computeExecutionOrder = function( + only_onExecute, + set_level + ) { + var L = []; + var S = []; + var M = {}; + var visited_links = {}; //to avoid repeating links + var remaining_links = {}; //to a + + //search for the nodes without inputs (starting nodes) + for (var i = 0, l = this._nodes.length; i < l; ++i) { + var node = this._nodes[i]; + if (only_onExecute && !node.onExecute) { + continue; + } + + M[node.id] = node; //add to pending nodes + + var num = 0; //num of input connections + if (node.inputs) { + for (var j = 0, l2 = node.inputs.length; j < l2; j++) { + if (node.inputs[j] && node.inputs[j].link != null) { + num += 1; + } + } + } + + if (num == 0) { + //is a starting node + S.push(node); + if (set_level) { + node._level = 1; + } + } //num of input links + else { + if (set_level) { + node._level = 0; + } + remaining_links[node.id] = num; + } + } + + while (true) { + if (S.length == 0) { + break; + } + + //get an starting node + var node = S.shift(); + L.push(node); //add to ordered list + delete M[node.id]; //remove from the pending nodes + + if (!node.outputs) { + continue; + } + + //for every output + for (var i = 0; i < node.outputs.length; i++) { + var output = node.outputs[i]; + //not connected + if ( + output == null || + output.links == null || + output.links.length == 0 + ) { + continue; + } + + //for every connection + for (var j = 0; j < output.links.length; j++) { + var link_id = output.links[j]; + var link = this.links[link_id]; + if (!link) { + continue; + } + + //already visited link (ignore it) + if (visited_links[link.id]) { + continue; + } + + var target_node = this.getNodeById(link.target_id); + if (target_node == null) { + visited_links[link.id] = true; + continue; + } + + if ( + set_level && + (!target_node._level || + target_node._level <= node._level) + ) { + target_node._level = node._level + 1; + } + + visited_links[link.id] = true; //mark as visited + remaining_links[target_node.id] -= 1; //reduce the number of links remaining + if (remaining_links[target_node.id] == 0) { + S.push(target_node); + } //if no more links, then add to starters array + } + } + } + + //the remaining ones (loops) + for (var i in M) { + L.push(M[i]); + } + + if (L.length != this._nodes.length && LiteGraph.debug) { + console.warn("something went wrong, nodes missing"); + } + + var l = L.length; + + //save order number in the node + for (var i = 0; i < l; ++i) { + L[i].order = i; + } + + //sort now by priority + L = L.sort(function(A, B) { + var Ap = A.constructor.priority || A.priority || 0; + var Bp = B.constructor.priority || B.priority || 0; + if (Ap == Bp) { + //if same priority, sort by order + return A.order - B.order; + } + return Ap - Bp; //sort by priority + }); + + //save order number in the node, again... + for (var i = 0; i < l; ++i) { + L[i].order = i; + } + + return L; + }; + + /** + * Returns all the nodes that could affect this one (ancestors) by crawling all the inputs recursively. + * It doesn't include the node itself + * @method getAncestors + * @return {Array} an array with all the LGraphNodes that affect this node, in order of execution + */ + LGraph.prototype.getAncestors = function(node) { + var ancestors = []; + var pending = [node]; + var visited = {}; + + while (pending.length) { + var current = pending.shift(); + if (!current.inputs) { + continue; + } + if (!visited[current.id] && current != node) { + visited[current.id] = true; + ancestors.push(current); + } + + for (var i = 0; i < current.inputs.length; ++i) { + var input = current.getInputNode(i); + if (input && ancestors.indexOf(input) == -1) { + pending.push(input); + } + } + } + + ancestors.sort(function(a, b) { + return a.order - b.order; + }); + return ancestors; + }; + + /** + * Positions every node in a more readable manner + * @method arrange + */ + LGraph.prototype.arrange = function(margin) { + margin = margin || 100; + + var nodes = this.computeExecutionOrder(false, true); + var columns = []; + for (var i = 0; i < nodes.length; ++i) { + var node = nodes[i]; + var col = node._level || 1; + if (!columns[col]) { + columns[col] = []; + } + columns[col].push(node); + } + + var x = margin; + + for (var i = 0; i < columns.length; ++i) { + var column = columns[i]; + if (!column) { + continue; + } + var max_size = 100; + var y = margin + LiteGraph.NODE_TITLE_HEIGHT; + for (var j = 0; j < column.length; ++j) { + var node = column[j]; + node.pos[0] = x; + node.pos[1] = y; + if (node.size[0] > max_size) { + max_size = node.size[0]; + } + y += node.size[1] + margin + LiteGraph.NODE_TITLE_HEIGHT; + } + x += max_size + margin; + } + + this.setDirtyCanvas(true, true); + }; + + /** + * Returns the amount of time the graph has been running in milliseconds + * @method getTime + * @return {number} number of milliseconds the graph has been running + */ + LGraph.prototype.getTime = function() { + return this.globaltime; + }; + + /** + * Returns the amount of time accumulated using the fixedtime_lapse var. This is used in context where the time increments should be constant + * @method getFixedTime + * @return {number} number of milliseconds the graph has been running + */ + + LGraph.prototype.getFixedTime = function() { + return this.fixedtime; + }; + + /** + * Returns the amount of time it took to compute the latest iteration. Take into account that this number could be not correct + * if the nodes are using graphical actions + * @method getElapsedTime + * @return {number} number of milliseconds it took the last cycle + */ + + LGraph.prototype.getElapsedTime = function() { + return this.elapsed_time; + }; + + /** + * Sends an event to all the nodes, useful to trigger stuff + * @method sendEventToAllNodes + * @param {String} eventname the name of the event (function to be called) + * @param {Array} params parameters in array format + */ + LGraph.prototype.sendEventToAllNodes = function(eventname, params, mode) { + mode = mode || LiteGraph.ALWAYS; + + var nodes = this._nodes_in_order ? this._nodes_in_order : this._nodes; + if (!nodes) { + return; + } + + for (var j = 0, l = nodes.length; j < l; ++j) { + var node = nodes[j]; + + if ( + node.constructor === LiteGraph.Subgraph && + eventname != "onExecute" + ) { + if (node.mode == mode) { + node.sendEventToAllNodes(eventname, params, mode); + } + continue; + } + + if (!node[eventname] || node.mode != mode) { + continue; + } + if (params === undefined) { + node[eventname](); + } else if (params && params.constructor === Array) { + node[eventname].apply(node, params); + } else { + node[eventname](params); + } + } + }; + + LGraph.prototype.sendActionToCanvas = function(action, params) { + if (!this.list_of_graphcanvas) { + return; + } + + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var c = this.list_of_graphcanvas[i]; + if (c[action]) { + c[action].apply(c, params); + } + } + }; + + /** + * Adds a new node instance to this graph + * @method add + * @param {LGraphNode} node the instance of the node + */ + + LGraph.prototype.add = function(node, skip_compute_order) { + if (!node) { + return; + } + + //groups + if (node.constructor === LGraphGroup) { + this._groups.push(node); + this.setDirtyCanvas(true); + this.change(); + node.graph = this; + this._version++; + return; + } + + //nodes + if (node.id != -1 && this._nodes_by_id[node.id] != null) { + console.warn( + "LiteGraph: there is already a node with this ID, changing it" + ); + node.id = ++this.last_node_id; + } + + if (this._nodes.length >= LiteGraph.MAX_NUMBER_OF_NODES) { + throw "LiteGraph: max number of nodes in a graph reached"; + } + + //give him an id + if (node.id == null || node.id == -1) { + node.id = ++this.last_node_id; + } else if (this.last_node_id < node.id) { + this.last_node_id = node.id; + } + + node.graph = this; + this._version++; + + this._nodes.push(node); + this._nodes_by_id[node.id] = node; + + if (node.onAdded) { + node.onAdded(this); + } + + if (this.config.align_to_grid) { + node.alignToGrid(); + } + + if (!skip_compute_order) { + this.updateExecutionOrder(); + } + + if (this.onNodeAdded) { + this.onNodeAdded(node); + } + + this.setDirtyCanvas(true); + this.change(); + + return node; //to chain actions + }; + + /** + * Removes a node from the graph + * @method remove + * @param {LGraphNode} node the instance of the node + */ + + LGraph.prototype.remove = function(node) { + if (node.constructor === LiteGraph.LGraphGroup) { + var index = this._groups.indexOf(node); + if (index != -1) { + this._groups.splice(index, 1); + } + node.graph = null; + this._version++; + this.setDirtyCanvas(true, true); + this.change(); + return; + } + + if (this._nodes_by_id[node.id] == null) { + return; + } //not found + + if (node.ignore_remove) { + return; + } //cannot be removed + + this.beforeChange(); //sure? - almost sure is wrong + + //disconnect inputs + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + if (slot.link != null) { + node.disconnectInput(i); + } + } + } + + //disconnect outputs + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + if (slot.links != null && slot.links.length) { + node.disconnectOutput(i); + } + } + } + + //node.id = -1; //why? + + //callback + if (node.onRemoved) { + node.onRemoved(); + } + + node.graph = null; + this._version++; + + //remove from canvas render + if (this.list_of_graphcanvas) { + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var canvas = this.list_of_graphcanvas[i]; + if (canvas.selected_nodes[node.id]) { + delete canvas.selected_nodes[node.id]; + } + if (canvas.node_dragged == node) { + canvas.node_dragged = null; + } + } + } + + //remove from containers + var pos = this._nodes.indexOf(node); + if (pos != -1) { + this._nodes.splice(pos, 1); + } + delete this._nodes_by_id[node.id]; + + if (this.onNodeRemoved) { + this.onNodeRemoved(node); + } + + //close panels + this.sendActionToCanvas("checkPanels"); + + this.setDirtyCanvas(true, true); + this.afterChange(); //sure? - almost sure is wrong + this.change(); + + this.updateExecutionOrder(); + }; + + /** + * Returns a node by its id. + * @method getNodeById + * @param {Number} id + */ + + LGraph.prototype.getNodeById = function(id) { + if (id == null) { + return null; + } + return this._nodes_by_id[id]; + }; + + /** + * Returns a list of nodes that matches a class + * @method findNodesByClass + * @param {Class} classObject the class itself (not an string) + * @return {Array} a list with all the nodes of this type + */ + LGraph.prototype.findNodesByClass = function(classObject, result) { + result = result || []; + result.length = 0; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].constructor === classObject) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns a list of nodes that matches a type + * @method findNodesByType + * @param {String} type the name of the node type + * @return {Array} a list with all the nodes of this type + */ + LGraph.prototype.findNodesByType = function(type, result) { + var type = type.toLowerCase(); + result = result || []; + result.length = 0; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].type.toLowerCase() == type) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns the first node that matches a name in its title + * @method findNodeByTitle + * @param {String} name the name of the node to search + * @return {Node} the node or null + */ + LGraph.prototype.findNodeByTitle = function(title) { + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].title == title) { + return this._nodes[i]; + } + } + return null; + }; + + /** + * Returns a list of nodes that matches a name + * @method findNodesByTitle + * @param {String} name the name of the node to search + * @return {Array} a list with all the nodes with this name + */ + LGraph.prototype.findNodesByTitle = function(title) { + var result = []; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].title == title) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns the top-most node in this position of the canvas + * @method getNodeOnPos + * @param {number} x the x coordinate in canvas space + * @param {number} y the y coordinate in canvas space + * @param {Array} nodes_list a list with all the nodes to search from, by default is all the nodes in the graph + * @return {LGraphNode} the node at this position or null + */ + LGraph.prototype.getNodeOnPos = function(x, y, nodes_list, margin) { + nodes_list = nodes_list || this._nodes; + var nRet = null; + for (var i = nodes_list.length - 1; i >= 0; i--) { + var n = nodes_list[i]; + if (n.isPointInside(x, y, margin)) { + // check for lesser interest nodes (TODO check for overlapping, use the top) + /*if (typeof n == "LGraphGroup"){ + nRet = n; + }else{*/ + return n; + /*}*/ + } + } + return nRet; + }; + + /** + * Returns the top-most group in that position + * @method getGroupOnPos + * @param {number} x the x coordinate in canvas space + * @param {number} y the y coordinate in canvas space + * @return {LGraphGroup} the group or null + */ + LGraph.prototype.getGroupOnPos = function(x, y) { + for (var i = this._groups.length - 1; i >= 0; i--) { + var g = this._groups[i]; + if (g.isPointInside(x, y, 2, true)) { + return g; + } + } + return null; + }; + + /** + * Checks that the node type matches the node type registered, used when replacing a nodetype by a newer version during execution + * this replaces the ones using the old version with the new version + * @method checkNodeTypes + */ + LGraph.prototype.checkNodeTypes = function() { + var changes = false; + for (var i = 0; i < this._nodes.length; i++) { + var node = this._nodes[i]; + var ctor = LiteGraph.registered_node_types[node.type]; + if (node.constructor == ctor) { + continue; + } + console.log("node being replaced by newer version: " + node.type); + var newnode = LiteGraph.createNode(node.type); + changes = true; + this._nodes[i] = newnode; + newnode.configure(node.serialize()); + newnode.graph = this; + this._nodes_by_id[newnode.id] = newnode; + if (node.inputs) { + newnode.inputs = node.inputs.concat(); + } + if (node.outputs) { + newnode.outputs = node.outputs.concat(); + } + } + this.updateExecutionOrder(); + }; + + // ********** GLOBALS ***************** + + LGraph.prototype.onAction = function(action, param, options) { + this._input_nodes = this.findNodesByClass( + LiteGraph.GraphInput, + this._input_nodes + ); + for (var i = 0; i < this._input_nodes.length; ++i) { + var node = this._input_nodes[i]; + if (node.properties.name != action) { + continue; + } + //wrap node.onAction(action, param); + node.actionDo(action, param, options); + break; + } + }; + + LGraph.prototype.trigger = function(action, param) { + if (this.onTrigger) { + this.onTrigger(action, param); + } + }; + + /** + * Tell this graph it has a global graph input of this type + * @method addGlobalInput + * @param {String} name + * @param {String} type + * @param {*} value [optional] + */ + LGraph.prototype.addInput = function(name, type, value) { + var input = this.inputs[name]; + if (input) { + //already exist + return; + } + + this.beforeChange(); + this.inputs[name] = { name: name, type: type, value: value }; + this._version++; + this.afterChange(); + + if (this.onInputAdded) { + this.onInputAdded(name, type); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Assign a data to the global graph input + * @method setGlobalInputData + * @param {String} name + * @param {*} data + */ + LGraph.prototype.setInputData = function(name, data) { + var input = this.inputs[name]; + if (!input) { + return; + } + input.value = data; + }; + + /** + * Returns the current value of a global graph input + * @method getInputData + * @param {String} name + * @return {*} the data + */ + LGraph.prototype.getInputData = function(name) { + var input = this.inputs[name]; + if (!input) { + return null; + } + return input.value; + }; + + /** + * Changes the name of a global graph input + * @method renameInput + * @param {String} old_name + * @param {String} new_name + */ + LGraph.prototype.renameInput = function(old_name, name) { + if (name == old_name) { + return; + } + + if (!this.inputs[old_name]) { + return false; + } + + if (this.inputs[name]) { + console.error("there is already one input with that name"); + return false; + } + + this.inputs[name] = this.inputs[old_name]; + delete this.inputs[old_name]; + this._version++; + + if (this.onInputRenamed) { + this.onInputRenamed(old_name, name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Changes the type of a global graph input + * @method changeInputType + * @param {String} name + * @param {String} type + */ + LGraph.prototype.changeInputType = function(name, type) { + if (!this.inputs[name]) { + return false; + } + + if ( + this.inputs[name].type && + String(this.inputs[name].type).toLowerCase() == + String(type).toLowerCase() + ) { + return; + } + + this.inputs[name].type = type; + this._version++; + if (this.onInputTypeChanged) { + this.onInputTypeChanged(name, type); + } + }; + + /** + * Removes a global graph input + * @method removeInput + * @param {String} name + * @param {String} type + */ + LGraph.prototype.removeInput = function(name) { + if (!this.inputs[name]) { + return false; + } + + delete this.inputs[name]; + this._version++; + + if (this.onInputRemoved) { + this.onInputRemoved(name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + return true; + }; + + /** + * Creates a global graph output + * @method addOutput + * @param {String} name + * @param {String} type + * @param {*} value + */ + LGraph.prototype.addOutput = function(name, type, value) { + this.outputs[name] = { name: name, type: type, value: value }; + this._version++; + + if (this.onOutputAdded) { + this.onOutputAdded(name, type); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Assign a data to the global output + * @method setOutputData + * @param {String} name + * @param {String} value + */ + LGraph.prototype.setOutputData = function(name, value) { + var output = this.outputs[name]; + if (!output) { + return; + } + output.value = value; + }; + + /** + * Returns the current value of a global graph output + * @method getOutputData + * @param {String} name + * @return {*} the data + */ + LGraph.prototype.getOutputData = function(name) { + var output = this.outputs[name]; + if (!output) { + return null; + } + return output.value; + }; + + /** + * Renames a global graph output + * @method renameOutput + * @param {String} old_name + * @param {String} new_name + */ + LGraph.prototype.renameOutput = function(old_name, name) { + if (!this.outputs[old_name]) { + return false; + } + + if (this.outputs[name]) { + console.error("there is already one output with that name"); + return false; + } + + this.outputs[name] = this.outputs[old_name]; + delete this.outputs[old_name]; + this._version++; + + if (this.onOutputRenamed) { + this.onOutputRenamed(old_name, name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Changes the type of a global graph output + * @method changeOutputType + * @param {String} name + * @param {String} type + */ + LGraph.prototype.changeOutputType = function(name, type) { + if (!this.outputs[name]) { + return false; + } + + if ( + this.outputs[name].type && + String(this.outputs[name].type).toLowerCase() == + String(type).toLowerCase() + ) { + return; + } + + this.outputs[name].type = type; + this._version++; + if (this.onOutputTypeChanged) { + this.onOutputTypeChanged(name, type); + } + }; + + /** + * Removes a global graph output + * @method removeOutput + * @param {String} name + */ + LGraph.prototype.removeOutput = function(name) { + if (!this.outputs[name]) { + return false; + } + delete this.outputs[name]; + this._version++; + + if (this.onOutputRemoved) { + this.onOutputRemoved(name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + return true; + }; + + LGraph.prototype.triggerInput = function(name, value) { + var nodes = this.findNodesByTitle(name); + for (var i = 0; i < nodes.length; ++i) { + nodes[i].onTrigger(value); + } + }; + + LGraph.prototype.setCallback = function(name, func) { + var nodes = this.findNodesByTitle(name); + for (var i = 0; i < nodes.length; ++i) { + nodes[i].setTrigger(func); + } + }; + + //used for undo, called before any change is made to the graph + LGraph.prototype.beforeChange = function(info) { + if (this.onBeforeChange) { + this.onBeforeChange(this,info); + } + this.sendActionToCanvas("onBeforeChange", this); + }; + + //used to resend actions, called after any change is made to the graph + LGraph.prototype.afterChange = function(info) { + if (this.onAfterChange) { + this.onAfterChange(this,info); + } + this.sendActionToCanvas("onAfterChange", this); + }; + + LGraph.prototype.connectionChange = function(node, link_info) { + this.updateExecutionOrder(); + if (this.onConnectionChange) { + this.onConnectionChange(node); + } + this._version++; + this.sendActionToCanvas("onConnectionChange"); + }; + + /** + * returns if the graph is in live mode + * @method isLive + */ + + LGraph.prototype.isLive = function() { + if (!this.list_of_graphcanvas) { + return false; + } + + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var c = this.list_of_graphcanvas[i]; + if (c.live_mode) { + return true; + } + } + return false; + }; + + /** + * clears the triggered slot animation in all links (stop visual animation) + * @method clearTriggeredSlots + */ + LGraph.prototype.clearTriggeredSlots = function() { + for (var i in this.links) { + var link_info = this.links[i]; + if (!link_info) { + continue; + } + if (link_info._last_time) { + link_info._last_time = 0; + } + } + }; + + /* Called when something visually changed (not the graph!) */ + LGraph.prototype.change = function() { + if (LiteGraph.debug) { + console.log("Graph changed"); + } + this.sendActionToCanvas("setDirty", [true, true]); + if (this.on_change) { + this.on_change(this); + } + }; + + LGraph.prototype.setDirtyCanvas = function(fg, bg) { + this.sendActionToCanvas("setDirty", [fg, bg]); + }; + + /** + * Destroys a link + * @method removeLink + * @param {Number} link_id + */ + LGraph.prototype.removeLink = function(link_id) { + var link = this.links[link_id]; + if (!link) { + return; + } + var node = this.getNodeById(link.target_id); + if (node) { + node.disconnectInput(link.target_slot); + } + }; + + //save and recover app state *************************************** + /** + * Creates a Object containing all the info about this graph, it can be serialized + * @method serialize + * @return {Object} value of the node + */ + LGraph.prototype.serialize = function() { + var nodes_info = []; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + nodes_info.push(this._nodes[i].serialize()); + } + + //pack link info into a non-verbose format + var links = []; + for (var i in this.links) { + //links is an OBJECT + var link = this.links[i]; + if (!link.serialize) { + //weird bug I havent solved yet + console.warn( + "weird LLink bug, link info is not a LLink but a regular object" + ); + var link2 = new LLink(); + for (var j in link) { + link2[j] = link[j]; + } + this.links[i] = link2; + link = link2; + } + + links.push(link.serialize()); + } + + var groups_info = []; + for (var i = 0; i < this._groups.length; ++i) { + groups_info.push(this._groups[i].serialize()); + } + + var data = { + last_node_id: this.last_node_id, + last_link_id: this.last_link_id, + nodes: nodes_info, + links: links, + groups: groups_info, + config: this.config, + extra: this.extra, + version: LiteGraph.VERSION + }; + + if(this.onSerialize) + this.onSerialize(data); + + return data; + }; + + /** + * Configure a graph from a JSON string + * @method configure + * @param {String} str configure a graph from a JSON string + * @param {Boolean} returns if there was any error parsing + */ + LGraph.prototype.configure = function(data, keep_old) { + if (!data) { + return; + } + + if (!keep_old) { + this.clear(); + } + + var nodes = data.nodes; + + //decode links info (they are very verbose) + if (data.links && data.links.constructor === Array) { + var links = []; + for (var i = 0; i < data.links.length; ++i) { + var link_data = data.links[i]; + if(!link_data) //weird bug + { + console.warn("serialized graph link data contains errors, skipping."); + continue; + } + var link = new LLink(); + link.configure(link_data); + links[link.id] = link; + } + data.links = links; + } + + //copy all stored fields + for (var i in data) { + if(i == "nodes" || i == "groups" ) //links must be accepted + continue; + this[i] = data[i]; + } + + var error = false; + + //create nodes + this._nodes = []; + if (nodes) { + for (var i = 0, l = nodes.length; i < l; ++i) { + var n_info = nodes[i]; //stored info + var node = LiteGraph.createNode(n_info.type, n_info.title); + if (!node) { + if (LiteGraph.debug) { + console.log( + "Node not found or has errors: " + n_info.type + ); + } + + //in case of error we create a replacement node to avoid losing info + node = new LGraphNode(); + node.last_serialization = n_info; + node.has_errors = true; + error = true; + //continue; + } + + node.id = n_info.id; //id it or it will create a new id + this.add(node, true); //add before configure, otherwise configure cannot create links + } + + //configure nodes afterwards so they can reach each other + for (var i = 0, l = nodes.length; i < l; ++i) { + var n_info = nodes[i]; + var node = this.getNodeById(n_info.id); + if (node) { + node.configure(n_info); + } + } + } + + //groups + this._groups.length = 0; + if (data.groups) { + for (var i = 0; i < data.groups.length; ++i) { + var group = new LiteGraph.LGraphGroup(); + group.configure(data.groups[i]); + this.add(group); + } + } + + this.updateExecutionOrder(); + + this.extra = data.extra || {}; + + if(this.onConfigure) + this.onConfigure(data); + + this._version++; + this.setDirtyCanvas(true, true); + return error; + }; + + LGraph.prototype.load = function(url, callback) { + var that = this; + + //from file + if(url.constructor === File || url.constructor === Blob) + { + var reader = new FileReader(); + reader.addEventListener('load', function(event) { + var data = JSON.parse(event.target.result); + that.configure(data); + if(callback) + callback(); + }); + + reader.readAsText(url); + return; + } + + //is a string, then an URL + var req = new XMLHttpRequest(); + req.open("GET", url, true); + req.send(null); + req.onload = function(oEvent) { + if (req.status !== 200) { + console.error("Error loading graph:", req.status, req.response); + return; + } + var data = JSON.parse( req.response ); + that.configure(data); + if(callback) + callback(); + }; + req.onerror = function(err) { + console.error("Error loading graph:", err); + }; + }; + + LGraph.prototype.onNodeTrace = function(node, msg, color) { + //TODO + }; + + //this is the class in charge of storing link information + function LLink(id, type, origin_id, origin_slot, target_id, target_slot) { + this.id = id; + this.type = type; + this.origin_id = origin_id; + this.origin_slot = origin_slot; + this.target_id = target_id; + this.target_slot = target_slot; + + this._data = null; + this._pos = new Float32Array(2); //center + } + + LLink.prototype.configure = function(o) { + if (o.constructor === Array) { + this.id = o[0]; + this.origin_id = o[1]; + this.origin_slot = o[2]; + this.target_id = o[3]; + this.target_slot = o[4]; + this.type = o[5]; + } else { + this.id = o.id; + this.type = o.type; + this.origin_id = o.origin_id; + this.origin_slot = o.origin_slot; + this.target_id = o.target_id; + this.target_slot = o.target_slot; + } + }; + + LLink.prototype.serialize = function() { + return [ + this.id, + this.origin_id, + this.origin_slot, + this.target_id, + this.target_slot, + this.type + ]; + }; + + LiteGraph.LLink = LLink; + + // ************************************************************* + // Node CLASS ******* + // ************************************************************* + + /* + title: string + pos: [x,y] + size: [x,y] + + input|output: every connection + + { name:string, type:string, pos: [x,y]=Optional, direction: "input"|"output", links: Array }); + + general properties: + + clip_area: if you render outside the node, it will be clipped + + unsafe_execution: not allowed for safe execution + + skip_repeated_outputs: when adding new outputs, it wont show if there is one already connected + + resizable: if set to false it wont be resizable with the mouse + + horizontal: slots are distributed horizontally + + widgets_start_y: widgets start at y distance from the top of the node + + flags object: + + collapsed: if it is collapsed + + supported callbacks: + + onAdded: when added to graph (warning: this is called BEFORE the node is configured when loading) + + onRemoved: when removed from graph + + onStart: when the graph starts playing + + onStop: when the graph stops playing + + onDrawForeground: render the inside widgets inside the node + + onDrawBackground: render the background area inside the node (only in edit mode) + + onMouseDown + + onMouseMove + + onMouseUp + + onMouseEnter + + onMouseLeave + + onExecute: execute the node + + onPropertyChanged: when a property is changed in the panel (return true to skip default behaviour) + + onGetInputs: returns an array of possible inputs + + onGetOutputs: returns an array of possible outputs + + onBounding: in case this node has a bigger bounding than the node itself (the callback receives the bounding as [x,y,w,h]) + + onDblClick: double clicked in the node + + onInputDblClick: input slot double clicked (can be used to automatically create a node connected) + + onOutputDblClick: output slot double clicked (can be used to automatically create a node connected) + + onConfigure: called after the node has been configured + + onSerialize: to add extra info when serializing (the callback receives the object that should be filled with the data) + + onSelected + + onDeselected + + onDropItem : DOM item dropped over the node + + onDropFile : file dropped over the node + + onConnectInput : if returns false the incoming connection will be canceled + + onConnectionsChange : a connection changed (new one or removed) (LiteGraph.INPUT or LiteGraph.OUTPUT, slot, true if connected, link_info, input_info ) + + onAction: action slot triggered + + getExtraMenuOptions: to add option to context menu +*/ + + /** + * Base Class for all the node type classes + * @class LGraphNode + * @param {String} name a name for the node + */ + + function LGraphNode(title) { + this._ctor(title); + } + + global.LGraphNode = LiteGraph.LGraphNode = LGraphNode; + + LGraphNode.prototype._ctor = function(title) { + this.title = title || "Unnamed"; + this.size = [LiteGraph.NODE_WIDTH, 60]; + this.graph = null; + + this._pos = new Float32Array(10, 10); + + Object.defineProperty(this, "pos", { + set: function(v) { + if (!v || v.length < 2) { + return; + } + this._pos[0] = v[0]; + this._pos[1] = v[1]; + }, + get: function() { + return this._pos; + }, + enumerable: true + }); + + this.id = -1; //not know till not added + this.type = null; + + //inputs available: array of inputs + this.inputs = []; + this.outputs = []; + this.connections = []; + + //local data + this.properties = {}; //for the values + this.properties_info = []; //for the info + + this.flags = {}; + }; + + /** + * configure a node from an object containing the serialized info + * @method configure + */ + LGraphNode.prototype.configure = function(info) { + if (this.graph) { + this.graph._version++; + } + for (var j in info) { + if (j == "properties") { + //i don't want to clone properties, I want to reuse the old container + for (var k in info.properties) { + this.properties[k] = info.properties[k]; + if (this.onPropertyChanged) { + this.onPropertyChanged( k, info.properties[k] ); + } + } + continue; + } + + if (info[j] == null) { + continue; + } else if (typeof info[j] == "object") { + //object + if (this[j] && this[j].configure) { + this[j].configure(info[j]); + } else { + this[j] = LiteGraph.cloneObject(info[j], this[j]); + } + } //value + else { + this[j] = info[j]; + } + } + + if (!info.title) { + this.title = this.constructor.title; + } + + if (this.onConnectionsChange) { + if (this.inputs) { + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + var link_info = this.graph + ? this.graph.links[input.link] + : null; + this.onConnectionsChange( + LiteGraph.INPUT, + i, + true, + link_info, + input + ); //link_info has been created now, so its updated + } + } + + if (this.outputs) { + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + if (!output.links) { + continue; + } + for (var j = 0; j < output.links.length; ++j) { + var link_info = this.graph + ? this.graph.links[output.links[j]] + : null; + this.onConnectionsChange( + LiteGraph.OUTPUT, + i, + true, + link_info, + output + ); //link_info has been created now, so its updated + } + } + } + } + + if( this.widgets ) + { + for (var i = 0; i < this.widgets.length; ++i) + { + var w = this.widgets[i]; + if(!w) + continue; + if(w.options && w.options.property && this.properties[ w.options.property ]) + w.value = JSON.parse( JSON.stringify( this.properties[ w.options.property ] ) ); + } + if (info.widgets_values) { + for (var i = 0; i < info.widgets_values.length; ++i) { + if (this.widgets[i]) { + this.widgets[i].value = info.widgets_values[i]; + } + } + } + } + + if (this.onConfigure) { + this.onConfigure(info); + } + }; + + /** + * serialize the content + * @method serialize + */ + + LGraphNode.prototype.serialize = function() { + //create serialization object + var o = { + id: this.id, + type: this.type, + pos: this.pos, + size: this.size, + flags: LiteGraph.cloneObject(this.flags), + order: this.order, + mode: this.mode + }; + + //special case for when there were errors + if (this.constructor === LGraphNode && this.last_serialization) { + return this.last_serialization; + } + + if (this.inputs) { + o.inputs = this.inputs; + } + + if (this.outputs) { + //clear outputs last data (because data in connections is never serialized but stored inside the outputs info) + for (var i = 0; i < this.outputs.length; i++) { + delete this.outputs[i]._data; + } + o.outputs = this.outputs; + } + + if (this.title && this.title != this.constructor.title) { + o.title = this.title; + } + + if (this.properties) { + o.properties = LiteGraph.cloneObject(this.properties); + } + + if (this.widgets && this.serialize_widgets) { + o.widgets_values = []; + for (var i = 0; i < this.widgets.length; ++i) { + if(this.widgets[i]) + o.widgets_values[i] = this.widgets[i].value; + else + o.widgets_values[i] = null; + } + } + + if (!o.type) { + o.type = this.constructor.type; + } + + if (this.color) { + o.color = this.color; + } + if (this.bgcolor) { + o.bgcolor = this.bgcolor; + } + if (this.boxcolor) { + o.boxcolor = this.boxcolor; + } + if (this.shape) { + o.shape = this.shape; + } + + if (this.onSerialize) { + if (this.onSerialize(o)) { + console.warn( + "node onSerialize shouldnt return anything, data should be stored in the object pass in the first parameter" + ); + } + } + + return o; + }; + + /* Creates a clone of this node */ + LGraphNode.prototype.clone = function() { + var node = LiteGraph.createNode(this.type); + if (!node) { + return null; + } + + //we clone it because serialize returns shared containers + var data = LiteGraph.cloneObject(this.serialize()); + + //remove links + if (data.inputs) { + for (var i = 0; i < data.inputs.length; ++i) { + data.inputs[i].link = null; + } + } + + if (data.outputs) { + for (var i = 0; i < data.outputs.length; ++i) { + if (data.outputs[i].links) { + data.outputs[i].links.length = 0; + } + } + } + + delete data["id"]; + //remove links + node.configure(data); + + return node; + }; + + /** + * serialize and stringify + * @method toString + */ + + LGraphNode.prototype.toString = function() { + return JSON.stringify(this.serialize()); + }; + //LGraphNode.prototype.deserialize = function(info) {} //this cannot be done from within, must be done in LiteGraph + + /** + * get the title string + * @method getTitle + */ + + LGraphNode.prototype.getTitle = function() { + return this.title || this.constructor.title; + }; + + /** + * sets the value of a property + * @method setProperty + * @param {String} name + * @param {*} value + */ + LGraphNode.prototype.setProperty = function(name, value) { + if (!this.properties) { + this.properties = {}; + } + if( value === this.properties[name] ) + return; + var prev_value = this.properties[name]; + this.properties[name] = value; + if (this.onPropertyChanged) { + if( this.onPropertyChanged(name, value, prev_value) === false ) //abort change + this.properties[name] = prev_value; + } + if(this.widgets) //widgets could be linked to properties + for(var i = 0; i < this.widgets.length; ++i) + { + var w = this.widgets[i]; + if(!w) + continue; + if(w.options.property == name) + { + w.value = value; + break; + } + } + }; + + // Execution ************************* + /** + * sets the output data + * @method setOutputData + * @param {number} slot + * @param {*} data + */ + LGraphNode.prototype.setOutputData = function(slot, data) { + if (!this.outputs) { + return; + } + + //this maybe slow and a niche case + //if(slot && slot.constructor === String) + // slot = this.findOutputSlot(slot); + + if (slot == -1 || slot >= this.outputs.length) { + return; + } + + var output_info = this.outputs[slot]; + if (!output_info) { + return; + } + + //store data in the output itself in case we want to debug + output_info._data = data; + + //if there are connections, pass the data to the connections + if (this.outputs[slot].links) { + for (var i = 0; i < this.outputs[slot].links.length; i++) { + var link_id = this.outputs[slot].links[i]; + var link = this.graph.links[link_id]; + if(link) + link.data = data; + } + } + }; + + /** + * sets the output data type, useful when you want to be able to overwrite the data type + * @method setOutputDataType + * @param {number} slot + * @param {String} datatype + */ + LGraphNode.prototype.setOutputDataType = function(slot, type) { + if (!this.outputs) { + return; + } + if (slot == -1 || slot >= this.outputs.length) { + return; + } + var output_info = this.outputs[slot]; + if (!output_info) { + return; + } + //store data in the output itself in case we want to debug + output_info.type = type; + + //if there are connections, pass the data to the connections + if (this.outputs[slot].links) { + for (var i = 0; i < this.outputs[slot].links.length; i++) { + var link_id = this.outputs[slot].links[i]; + this.graph.links[link_id].type = type; + } + } + }; + + /** + * Retrieves the input data (data traveling through the connection) from one slot + * @method getInputData + * @param {number} slot + * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link + * @return {*} data or if it is not connected returns undefined + */ + LGraphNode.prototype.getInputData = function(slot, force_update) { + if (!this.inputs) { + return; + } //undefined; + + if (slot >= this.inputs.length || this.inputs[slot].link == null) { + return; + } + + var link_id = this.inputs[slot].link; + var link = this.graph.links[link_id]; + if (!link) { + //bug: weird case but it happens sometimes + return null; + } + + if (!force_update) { + return link.data; + } + + //special case: used to extract data from the incoming connection before the graph has been executed + var node = this.graph.getNodeById(link.origin_id); + if (!node) { + return link.data; + } + + if (node.updateOutputData) { + node.updateOutputData(link.origin_slot); + } else if (node.onExecute) { + node.onExecute(); + } + + return link.data; + }; + + /** + * Retrieves the input data type (in case this supports multiple input types) + * @method getInputDataType + * @param {number} slot + * @return {String} datatype in string format + */ + LGraphNode.prototype.getInputDataType = function(slot) { + if (!this.inputs) { + return null; + } //undefined; + + if (slot >= this.inputs.length || this.inputs[slot].link == null) { + return null; + } + var link_id = this.inputs[slot].link; + var link = this.graph.links[link_id]; + if (!link) { + //bug: weird case but it happens sometimes + return null; + } + var node = this.graph.getNodeById(link.origin_id); + if (!node) { + return link.type; + } + var output_info = node.outputs[link.origin_slot]; + if (output_info) { + return output_info.type; + } + return null; + }; + + /** + * Retrieves the input data from one slot using its name instead of slot number + * @method getInputDataByName + * @param {String} slot_name + * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link + * @return {*} data or if it is not connected returns null + */ + LGraphNode.prototype.getInputDataByName = function( + slot_name, + force_update + ) { + var slot = this.findInputSlot(slot_name); + if (slot == -1) { + return null; + } + return this.getInputData(slot, force_update); + }; + + /** + * tells you if there is a connection in one input slot + * @method isInputConnected + * @param {number} slot + * @return {boolean} + */ + LGraphNode.prototype.isInputConnected = function(slot) { + if (!this.inputs) { + return false; + } + return slot < this.inputs.length && this.inputs[slot].link != null; + }; + + /** + * tells you info about an input connection (which node, type, etc) + * @method getInputInfo + * @param {number} slot + * @return {Object} object or null { link: id, name: string, type: string or 0 } + */ + LGraphNode.prototype.getInputInfo = function(slot) { + if (!this.inputs) { + return null; + } + if (slot < this.inputs.length) { + return this.inputs[slot]; + } + return null; + }; + + /** + * Returns the link info in the connection of an input slot + * @method getInputLink + * @param {number} slot + * @return {LLink} object or null + */ + LGraphNode.prototype.getInputLink = function(slot) { + if (!this.inputs) { + return null; + } + if (slot < this.inputs.length) { + var slot_info = this.inputs[slot]; + return this.graph.links[ slot_info.link ]; + } + return null; + }; + + /** + * returns the node connected in the input slot + * @method getInputNode + * @param {number} slot + * @return {LGraphNode} node or null + */ + LGraphNode.prototype.getInputNode = function(slot) { + if (!this.inputs) { + return null; + } + if (slot >= this.inputs.length) { + return null; + } + var input = this.inputs[slot]; + if (!input || input.link === null) { + return null; + } + var link_info = this.graph.links[input.link]; + if (!link_info) { + return null; + } + return this.graph.getNodeById(link_info.origin_id); + }; + + /** + * returns the value of an input with this name, otherwise checks if there is a property with that name + * @method getInputOrProperty + * @param {string} name + * @return {*} value + */ + LGraphNode.prototype.getInputOrProperty = function(name) { + if (!this.inputs || !this.inputs.length) { + return this.properties ? this.properties[name] : null; + } + + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input_info = this.inputs[i]; + if (name == input_info.name && input_info.link != null) { + var link = this.graph.links[input_info.link]; + if (link) { + return link.data; + } + } + } + return this.properties[name]; + }; + + /** + * tells you the last output data that went in that slot + * @method getOutputData + * @param {number} slot + * @return {Object} object or null + */ + LGraphNode.prototype.getOutputData = function(slot) { + if (!this.outputs) { + return null; + } + if (slot >= this.outputs.length) { + return null; + } + + var info = this.outputs[slot]; + return info._data; + }; + + /** + * tells you info about an output connection (which node, type, etc) + * @method getOutputInfo + * @param {number} slot + * @return {Object} object or null { name: string, type: string, links: [ ids of links in number ] } + */ + LGraphNode.prototype.getOutputInfo = function(slot) { + if (!this.outputs) { + return null; + } + if (slot < this.outputs.length) { + return this.outputs[slot]; + } + return null; + }; + + /** + * tells you if there is a connection in one output slot + * @method isOutputConnected + * @param {number} slot + * @return {boolean} + */ + LGraphNode.prototype.isOutputConnected = function(slot) { + if (!this.outputs) { + return false; + } + return ( + slot < this.outputs.length && + this.outputs[slot].links && + this.outputs[slot].links.length + ); + }; + + /** + * tells you if there is any connection in the output slots + * @method isAnyOutputConnected + * @return {boolean} + */ + LGraphNode.prototype.isAnyOutputConnected = function() { + if (!this.outputs) { + return false; + } + for (var i = 0; i < this.outputs.length; ++i) { + if (this.outputs[i].links && this.outputs[i].links.length) { + return true; + } + } + return false; + }; + + /** + * retrieves all the nodes connected to this output slot + * @method getOutputNodes + * @param {number} slot + * @return {array} + */ + LGraphNode.prototype.getOutputNodes = function(slot) { + if (!this.outputs || this.outputs.length == 0) { + return null; + } + + if (slot >= this.outputs.length) { + return null; + } + + var output = this.outputs[slot]; + if (!output.links || output.links.length == 0) { + return null; + } + + var r = []; + for (var i = 0; i < output.links.length; i++) { + var link_id = output.links[i]; + var link = this.graph.links[link_id]; + if (link) { + var target_node = this.graph.getNodeById(link.target_id); + if (target_node) { + r.push(target_node); + } + } + } + return r; + }; + + LGraphNode.prototype.addOnTriggerInput = function(){ + var trigS = this.findInputSlot("onTrigger"); + if (trigS == -1){ //!trigS || + var input = this.addInput("onTrigger", LiteGraph.EVENT, {optional: true, nameLocked: true}); + return this.findInputSlot("onTrigger"); + } + return trigS; + } + + LGraphNode.prototype.addOnExecutedOutput = function(){ + var trigS = this.findOutputSlot("onExecuted"); + if (trigS == -1){ //!trigS || + var output = this.addOutput("onExecuted", LiteGraph.ACTION, {optional: true, nameLocked: true}); + return this.findOutputSlot("onExecuted"); + } + return trigS; + } + + LGraphNode.prototype.onAfterExecuteNode = function(param, options){ + var trigS = this.findOutputSlot("onExecuted"); + if (trigS != -1){ + + //console.debug(this.id+":"+this.order+" triggering slot onAfterExecute"); + //console.debug(param); + //console.debug(options); + this.triggerSlot(trigS, param, null, options); + + } + } + + LGraphNode.prototype.changeMode = function(modeTo){ + switch(modeTo){ + case LiteGraph.ON_EVENT: + // this.addOnExecutedOutput(); + break; + + case LiteGraph.ON_TRIGGER: + this.addOnTriggerInput(); + this.addOnExecutedOutput(); + break; + + case LiteGraph.NEVER: + break; + + case LiteGraph.ALWAYS: + break; + + case LiteGraph.ON_REQUEST: + break; + + default: + return false; + break; + } + this.mode = modeTo; + return true; + }; + + /** + * Triggers the node code execution, place a boolean/counter to mark the node as being executed + * @method execute + * @param {*} param + * @param {*} options + */ + LGraphNode.prototype.doExecute = function(param, options) { + options = options || {}; + if (this.onExecute){ + + // enable this to give the event an ID + if (!options.action_call) options.action_call = this.id+"_exec_"+Math.floor(Math.random()*9999); + + this.graph.nodes_executing[this.id] = true; //.push(this.id); + + this.onExecute(param, options); + + this.graph.nodes_executing[this.id] = false; //.pop(); + + // save execution/action ref + this.exec_version = this.graph.iteration; + if(options && options.action_call){ + this.action_call = options.action_call; // if (param) + this.graph.nodes_executedAction[this.id] = options.action_call; + } + } + this.execute_triggered = 2; // the nFrames it will be used (-- each step), means "how old" is the event + if(this.onAfterExecuteNode) this.onAfterExecuteNode(param, options); // callback + }; + + /** + * Triggers an action, wrapped by logics to control execution flow + * @method actionDo + * @param {String} action name + * @param {*} param + */ + LGraphNode.prototype.actionDo = function(action, param, options) { + options = options || {}; + if (this.onAction){ + + // enable this to give the event an ID + if (!options.action_call) options.action_call = this.id+"_"+(action?action:"action")+"_"+Math.floor(Math.random()*9999); + + this.graph.nodes_actioning[this.id] = (action?action:"actioning"); //.push(this.id); + + this.onAction(action, param, options); + + this.graph.nodes_actioning[this.id] = false; //.pop(); + + // save execution/action ref + if(options && options.action_call){ + this.action_call = options.action_call; // if (param) + this.graph.nodes_executedAction[this.id] = options.action_call; + } + } + this.action_triggered = 2; // the nFrames it will be used (-- each step), means "how old" is the event + if(this.onAfterExecuteNode) this.onAfterExecuteNode(param, options); + }; + + /** + * Triggers an event in this node, this will trigger any output with the same name + * @method trigger + * @param {String} event name ( "on_play", ... ) if action is equivalent to false then the event is send to all + * @param {*} param + */ + LGraphNode.prototype.trigger = function(action, param, options) { + if (!this.outputs || !this.outputs.length) { + return; + } + + if (this.graph) + this.graph._last_trigger_time = LiteGraph.getTime(); + + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + if ( !output || output.type !== LiteGraph.EVENT || (action && output.name != action) ) + continue; + this.triggerSlot(i, param, null, options); + } + }; + + /** + * Triggers a slot event in this node: cycle output slots and launch execute/action on connected nodes + * @method triggerSlot + * @param {Number} slot the index of the output slot + * @param {*} param + * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot + */ + LGraphNode.prototype.triggerSlot = function(slot, param, link_id, options) { + options = options || {}; + if (!this.outputs) { + return; + } + + var output = this.outputs[slot]; + if (!output) { + return; + } + + var links = output.links; + if (!links || !links.length) { + return; + } + + if (this.graph) { + this.graph._last_trigger_time = LiteGraph.getTime(); + } + + //for every link attached here + for (var k = 0; k < links.length; ++k) { + var id = links[k]; + if (link_id != null && link_id != id) { + //to skip links + continue; + } + var link_info = this.graph.links[links[k]]; + if (!link_info) { + //not connected + continue; + } + link_info._last_time = LiteGraph.getTime(); + var node = this.graph.getNodeById(link_info.target_id); + if (!node) { + //node not found? + continue; + } + + //used to mark events in graph + var target_connection = node.inputs[link_info.target_slot]; + + if (node.mode === LiteGraph.ON_TRIGGER) + { + // generate unique trigger ID if not present + if (!options.action_call) options.action_call = this.id+"_trigg_"+Math.floor(Math.random()*9999); + if (node.onExecute) { + // -- wrapping node.onExecute(param); -- + node.doExecute(param, options); + } + } + else if (node.onAction) { + // generate unique action ID if not present + if (!options.action_call) options.action_call = this.id+"_act_"+Math.floor(Math.random()*9999); + //pass the action name + var target_connection = node.inputs[link_info.target_slot]; + // wrap node.onAction(target_connection.name, param); + node.actionDo(target_connection.name, param, options); + } + } + }; + + /** + * clears the trigger slot animation + * @method clearTriggeredSlot + * @param {Number} slot the index of the output slot + * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot + */ + LGraphNode.prototype.clearTriggeredSlot = function(slot, link_id) { + if (!this.outputs) { + return; + } + + var output = this.outputs[slot]; + if (!output) { + return; + } + + var links = output.links; + if (!links || !links.length) { + return; + } + + //for every link attached here + for (var k = 0; k < links.length; ++k) { + var id = links[k]; + if (link_id != null && link_id != id) { + //to skip links + continue; + } + var link_info = this.graph.links[links[k]]; + if (!link_info) { + //not connected + continue; + } + link_info._last_time = 0; + } + }; + + /** + * changes node size and triggers callback + * @method setSize + * @param {vec2} size + */ + LGraphNode.prototype.setSize = function(size) + { + this.size = size; + if(this.onResize) + this.onResize(this.size); + } + + /** + * add a new property to this node + * @method addProperty + * @param {string} name + * @param {*} default_value + * @param {string} type string defining the output type ("vec3","number",...) + * @param {Object} extra_info this can be used to have special properties of the property (like values, etc) + */ + LGraphNode.prototype.addProperty = function( + name, + default_value, + type, + extra_info + ) { + var o = { name: name, type: type, default_value: default_value }; + if (extra_info) { + for (var i in extra_info) { + o[i] = extra_info[i]; + } + } + if (!this.properties_info) { + this.properties_info = []; + } + this.properties_info.push(o); + if (!this.properties) { + this.properties = {}; + } + this.properties[name] = default_value; + return o; + }; + + //connections + + /** + * add a new output slot to use in this node + * @method addOutput + * @param {string} name + * @param {string} type string defining the output type ("vec3","number",...) + * @param {Object} extra_info this can be used to have special properties of an output (label, special color, position, etc) + */ + LGraphNode.prototype.addOutput = function(name, type, extra_info) { + var o = { name: name, type: type, links: null }; + if (extra_info) { + for (var i in extra_info) { + o[i] = extra_info[i]; + } + } + + if (!this.outputs) { + this.outputs = []; + } + this.outputs.push(o); + if (this.onOutputAdded) { + this.onOutputAdded(o); + } + + if (LiteGraph.auto_load_slot_types) LiteGraph.registerNodeAndSlotType(this,type,true); + + this.setSize( this.computeSize() ); + this.setDirtyCanvas(true, true); + return o; + }; + + /** + * add a new output slot to use in this node + * @method addOutputs + * @param {Array} array of triplets like [[name,type,extra_info],[...]] + */ + LGraphNode.prototype.addOutputs = function(array) { + for (var i = 0; i < array.length; ++i) { + var info = array[i]; + var o = { name: info[0], type: info[1], link: null }; + if (array[2]) { + for (var j in info[2]) { + o[j] = info[2][j]; + } + } + + if (!this.outputs) { + this.outputs = []; + } + this.outputs.push(o); + if (this.onOutputAdded) { + this.onOutputAdded(o); + } + + if (LiteGraph.auto_load_slot_types) LiteGraph.registerNodeAndSlotType(this,info[1],true); + + } + + this.setSize( this.computeSize() ); + this.setDirtyCanvas(true, true); + }; + + /** + * remove an existing output slot + * @method removeOutput + * @param {number} slot + */ + LGraphNode.prototype.removeOutput = function(slot) { + this.disconnectOutput(slot); + this.outputs.splice(slot, 1); + for (var i = slot; i < this.outputs.length; ++i) { + if (!this.outputs[i] || !this.outputs[i].links) { + continue; + } + var links = this.outputs[i].links; + for (var j = 0; j < links.length; ++j) { + var link = this.graph.links[links[j]]; + if (!link) { + continue; + } + link.origin_slot -= 1; + } + } + + this.setSize( this.computeSize() ); + if (this.onOutputRemoved) { + this.onOutputRemoved(slot); + } + this.setDirtyCanvas(true, true); + }; + + /** + * add a new input slot to use in this node + * @method addInput + * @param {string} name + * @param {string} type string defining the input type ("vec3","number",...), it its a generic one use 0 + * @param {Object} extra_info this can be used to have special properties of an input (label, color, position, etc) + */ + LGraphNode.prototype.addInput = function(name, type, extra_info) { + type = type || 0; + var o = { name: name, type: type, link: null }; + if (extra_info) { + for (var i in extra_info) { + o[i] = extra_info[i]; + } + } + + if (!this.inputs) { + this.inputs = []; + } + + this.inputs.push(o); + this.setSize( this.computeSize() ); + + if (this.onInputAdded) { + this.onInputAdded(o); + } + + LiteGraph.registerNodeAndSlotType(this,type); + + this.setDirtyCanvas(true, true); + return o; + }; + + /** + * add several new input slots in this node + * @method addInputs + * @param {Array} array of triplets like [[name,type,extra_info],[...]] + */ + LGraphNode.prototype.addInputs = function(array) { + for (var i = 0; i < array.length; ++i) { + var info = array[i]; + var o = { name: info[0], type: info[1], link: null }; + if (array[2]) { + for (var j in info[2]) { + o[j] = info[2][j]; + } + } + + if (!this.inputs) { + this.inputs = []; + } + this.inputs.push(o); + if (this.onInputAdded) { + this.onInputAdded(o); + } + + LiteGraph.registerNodeAndSlotType(this,info[1]); + } + + this.setSize( this.computeSize() ); + this.setDirtyCanvas(true, true); + }; + + /** + * remove an existing input slot + * @method removeInput + * @param {number} slot + */ + LGraphNode.prototype.removeInput = function(slot) { + this.disconnectInput(slot); + var slot_info = this.inputs.splice(slot, 1); + for (var i = slot; i < this.inputs.length; ++i) { + if (!this.inputs[i]) { + continue; + } + var link = this.graph.links[this.inputs[i].link]; + if (!link) { + continue; + } + link.target_slot -= 1; + } + this.setSize( this.computeSize() ); + if (this.onInputRemoved) { + this.onInputRemoved(slot, slot_info[0] ); + } + this.setDirtyCanvas(true, true); + }; + + /** + * add an special connection to this node (used for special kinds of graphs) + * @method addConnection + * @param {string} name + * @param {string} type string defining the input type ("vec3","number",...) + * @param {[x,y]} pos position of the connection inside the node + * @param {string} direction if is input or output + */ + LGraphNode.prototype.addConnection = function(name, type, pos, direction) { + var o = { + name: name, + type: type, + pos: pos, + direction: direction, + links: null + }; + this.connections.push(o); + return o; + }; + + /** + * computes the minimum size of a node according to its inputs and output slots + * @method computeSize + * @param {number} minHeight + * @return {number} the total size + */ + LGraphNode.prototype.computeSize = function(out) { + if (this.constructor.size) { + return this.constructor.size.concat(); + } + + var rows = Math.max( + this.inputs ? this.inputs.length : 1, + this.outputs ? this.outputs.length : 1 + ); + var size = out || new Float32Array([0, 0]); + rows = Math.max(rows, 1); + var font_size = LiteGraph.NODE_TEXT_SIZE; //although it should be graphcanvas.inner_text_font size + + var title_width = compute_text_size(this.title); + var input_width = 0; + var output_width = 0; + + if (this.inputs) { + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input = this.inputs[i]; + var text = input.label || input.name || ""; + var text_width = compute_text_size(text); + if (input_width < text_width) { + input_width = text_width; + } + } + } + + if (this.outputs) { + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + var text = output.label || output.name || ""; + var text_width = compute_text_size(text); + if (output_width < text_width) { + output_width = text_width; + } + } + } + + size[0] = Math.max(input_width + output_width + 10, title_width); + size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH); + if (this.widgets && this.widgets.length) { + size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH * 1.5); + } + + size[1] = (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT; + + var widgets_height = 0; + if (this.widgets && this.widgets.length) { + for (var i = 0, l = this.widgets.length; i < l; ++i) { + if (this.widgets[i].computeSize) + widgets_height += this.widgets[i].computeSize(size[0])[1] + 4; + else + widgets_height += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + widgets_height += 8; + } + + //compute height using widgets height + if( this.widgets_up ) + size[1] = Math.max( size[1], widgets_height ); + else if( this.widgets_start_y != null ) + size[1] = Math.max( size[1], widgets_height + this.widgets_start_y ); + else + size[1] += widgets_height; + + function compute_text_size(text) { + if (!text) { + return 0; + } + return font_size * text.length * 0.6; + } + + if ( + this.constructor.min_height && + size[1] < this.constructor.min_height + ) { + size[1] = this.constructor.min_height; + } + + size[1] += 6; //margin + + return size; + }; + + /** + * returns all the info available about a property of this node. + * + * @method getPropertyInfo + * @param {String} property name of the property + * @return {Object} the object with all the available info + */ + LGraphNode.prototype.getPropertyInfo = function( property ) + { + var info = null; + + //there are several ways to define info about a property + //legacy mode + if (this.properties_info) { + for (var i = 0; i < this.properties_info.length; ++i) { + if (this.properties_info[i].name == property) { + info = this.properties_info[i]; + break; + } + } + } + //litescene mode using the constructor + if(this.constructor["@" + property]) + info = this.constructor["@" + property]; + + if(this.constructor.widgets_info && this.constructor.widgets_info[property]) + info = this.constructor.widgets_info[property]; + + //litescene mode using the constructor + if (!info && this.onGetPropertyInfo) { + info = this.onGetPropertyInfo(property); + } + + if (!info) + info = {}; + if(!info.type) + info.type = typeof this.properties[property]; + if(info.widget == "combo") + info.type = "enum"; + + return info; + } + + /** + * Defines a widget inside the node, it will be rendered on top of the node, you can control lots of properties + * + * @method addWidget + * @param {String} type the widget type (could be "number","string","combo" + * @param {String} name the text to show on the widget + * @param {String} value the default value + * @param {Function|String} callback function to call when it changes (optionally, it can be the name of the property to modify) + * @param {Object} options the object that contains special properties of this widget + * @return {Object} the created widget object + */ + LGraphNode.prototype.addWidget = function( type, name, value, callback, options ) + { + if (!this.widgets) { + this.widgets = []; + } + + if(!options && callback && callback.constructor === Object) + { + options = callback; + callback = null; + } + + if(options && options.constructor === String) //options can be the property name + options = { property: options }; + + if(callback && callback.constructor === String) //callback can be the property name + { + if(!options) + options = {}; + options.property = callback; + callback = null; + } + + if(callback && callback.constructor !== Function) + { + console.warn("addWidget: callback must be a function"); + callback = null; + } + + var w = { + type: type.toLowerCase(), + name: name, + value: value, + callback: callback, + options: options || {} + }; + + if (w.options.y !== undefined) { + w.y = w.options.y; + } + + if (!callback && !w.options.callback && !w.options.property) { + console.warn("LiteGraph addWidget(...) without a callback or property assigned"); + } + if (type == "combo" && !w.options.values) { + throw "LiteGraph addWidget('combo',...) requires to pass values in options: { values:['red','blue'] }"; + } + this.widgets.push(w); + this.setSize( this.computeSize() ); + return w; + }; + + LGraphNode.prototype.addCustomWidget = function(custom_widget) { + if (!this.widgets) { + this.widgets = []; + } + this.widgets.push(custom_widget); + return custom_widget; + }; + + /** + * returns the bounding of the object, used for rendering purposes + * bounding is: [topleft_cornerx, topleft_cornery, width, height] + * @method getBounding + * @return {Float32Array[4]} the total size + */ + LGraphNode.prototype.getBounding = function(out) { + out = out || new Float32Array(4); + out[0] = this.pos[0] - 4; + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + out[2] = this.size[0] + 4; + out[3] = this.flags.collapsed ? LiteGraph.NODE_TITLE_HEIGHT : this.size[1] + LiteGraph.NODE_TITLE_HEIGHT; + + if (this.onBounding) { + this.onBounding(out); + } + return out; + }; + + /** + * checks if a point is inside the shape of a node + * @method isPointInside + * @param {number} x + * @param {number} y + * @return {boolean} + */ + LGraphNode.prototype.isPointInside = function(x, y, margin, skip_title) { + margin = margin || 0; + + var margin_top = this.graph && this.graph.isLive() ? 0 : LiteGraph.NODE_TITLE_HEIGHT; + if (skip_title) { + margin_top = 0; + } + if (this.flags && this.flags.collapsed) { + //if ( distance([x,y], [this.pos[0] + this.size[0]*0.5, this.pos[1] + this.size[1]*0.5]) < LiteGraph.NODE_COLLAPSED_RADIUS) + if ( + isInsideRectangle( + x, + y, + this.pos[0] - margin, + this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT - margin, + (this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH) + + 2 * margin, + LiteGraph.NODE_TITLE_HEIGHT + 2 * margin + ) + ) { + return true; + } + } else if ( + this.pos[0] - 4 - margin < x && + this.pos[0] + this.size[0] + 4 + margin > x && + this.pos[1] - margin_top - margin < y && + this.pos[1] + this.size[1] + margin > y + ) { + return true; + } + return false; + }; + + /** + * checks if a point is inside a node slot, and returns info about which slot + * @method getSlotInPosition + * @param {number} x + * @param {number} y + * @return {Object} if found the object contains { input|output: slot object, slot: number, link_pos: [x,y] } + */ + LGraphNode.prototype.getSlotInPosition = function(x, y) { + //search for inputs + var link_pos = new Float32Array(2); + if (this.inputs) { + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input = this.inputs[i]; + this.getConnectionPos(true, i, link_pos); + if ( + isInsideRectangle( + x, + y, + link_pos[0] - 10, + link_pos[1] - 5, + 20, + 10 + ) + ) { + return { input: input, slot: i, link_pos: link_pos }; + } + } + } + + if (this.outputs) { + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + this.getConnectionPos(false, i, link_pos); + if ( + isInsideRectangle( + x, + y, + link_pos[0] - 10, + link_pos[1] - 5, + 20, + 10 + ) + ) { + return { output: output, slot: i, link_pos: link_pos }; + } + } + } + + return null; + }; + + /** + * returns the input slot with a given name (used for dynamic slots), -1 if not found + * @method findInputSlot + * @param {string} name the name of the slot + * @param {boolean} returnObj if the obj itself wanted + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findInputSlot = function(name, returnObj) { + if (!this.inputs) { + return -1; + } + for (var i = 0, l = this.inputs.length; i < l; ++i) { + if (name == this.inputs[i].name) { + return !returnObj ? i : this.inputs[i]; + } + } + return -1; + }; + + /** + * returns the output slot with a given name (used for dynamic slots), -1 if not found + * @method findOutputSlot + * @param {string} name the name of the slot + * @param {boolean} returnObj if the obj itself wanted + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findOutputSlot = function(name, returnObj) { + returnObj = returnObj || false; + if (!this.outputs) { + return -1; + } + for (var i = 0, l = this.outputs.length; i < l; ++i) { + if (name == this.outputs[i].name) { + return !returnObj ? i : this.outputs[i]; + } + } + return -1; + }; + + // TODO refactor: USE SINGLE findInput/findOutput functions! :: merge options + + /** + * returns the first free input slot + * @method findInputSlotFree + * @param {object} options + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findInputSlotFree = function(optsIn) { + var optsIn = optsIn || {}; + var optsDef = {returnObj: false + ,typesNotAccepted: [] + }; + var opts = Object.assign(optsDef,optsIn); + if (!this.inputs) { + return -1; + } + for (var i = 0, l = this.inputs.length; i < l; ++i) { + if (this.inputs[i].link && this.inputs[i].link != null) { + continue; + } + if (opts.typesNotAccepted && opts.typesNotAccepted.includes && opts.typesNotAccepted.includes(this.inputs[i].type)){ + continue; + } + return !opts.returnObj ? i : this.inputs[i]; + } + return -1; + }; + + /** + * returns the first output slot free + * @method findOutputSlotFree + * @param {object} options + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findOutputSlotFree = function(optsIn) { + var optsIn = optsIn || {}; + var optsDef = { returnObj: false + ,typesNotAccepted: [] + }; + var opts = Object.assign(optsDef,optsIn); + if (!this.outputs) { + return -1; + } + for (var i = 0, l = this.outputs.length; i < l; ++i) { + if (this.outputs[i].links && this.outputs[i].links != null) { + continue; + } + if (opts.typesNotAccepted && opts.typesNotAccepted.includes && opts.typesNotAccepted.includes(this.outputs[i].type)){ + continue; + } + return !opts.returnObj ? i : this.outputs[i]; + } + return -1; + }; + + /** + * findSlotByType for INPUTS + */ + LGraphNode.prototype.findInputSlotByType = function(type, returnObj, preferFreeSlot, doNotUseOccupied) { + return this.findSlotByType(true, type, returnObj, preferFreeSlot, doNotUseOccupied); + }; + + /** + * findSlotByType for OUTPUTS + */ + LGraphNode.prototype.findOutputSlotByType = function(type, returnObj, preferFreeSlot, doNotUseOccupied) { + return this.findSlotByType(false, type, returnObj, preferFreeSlot, doNotUseOccupied); + }; + + /** + * returns the output (or input) slot with a given type, -1 if not found + * @method findSlotByType + * @param {boolean} input uise inputs instead of outputs + * @param {string} type the type of the slot + * @param {boolean} returnObj if the obj itself wanted + * @param {boolean} preferFreeSlot if we want a free slot (if not found, will return the first of the type anyway) + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findSlotByType = function(input, type, returnObj, preferFreeSlot, doNotUseOccupied) { + input = input || false; + returnObj = returnObj || false; + preferFreeSlot = preferFreeSlot || false; + doNotUseOccupied = doNotUseOccupied || false; + var aSlots = input ? this.inputs : this.outputs; + if (!aSlots) { + return -1; + } + // !! empty string type is considered 0, * !! + if (type == "" || type == "*") type = 0; + for (var i = 0, l = aSlots.length; i < l; ++i) { + var tFound = false; + var aSource = (type+"").toLowerCase().split(","); + var aDest = aSlots[i].type=="0"||aSlots[i].type=="*"?"0":aSlots[i].type; + aDest = (aDest+"").toLowerCase().split(","); + for(sI=0;sI= 0 && target_slot !== null){ + //console.debug("CONNbyTYPE type "+target_slotType+" for "+target_slot) + return this.connect(slot, target_node, target_slot); + }else{ + //console.log("type "+target_slotType+" not found or not free?") + if (opts.createEventInCase && target_slotType == LiteGraph.EVENT){ + // WILL CREATE THE onTrigger IN SLOT + //console.debug("connect WILL CREATE THE onTrigger "+target_slotType+" to "+target_node); + return this.connect(slot, target_node, -1); + } + // connect to the first general output slot if not found a specific type and + if (opts.generalTypeInCase){ + var target_slot = target_node.findInputSlotByType(0, false, true, true); + //console.debug("connect TO a general type (*, 0), if not found the specific type ",target_slotType," to ",target_node,"RES_SLOT:",target_slot); + if (target_slot >= 0){ + return this.connect(slot, target_node, target_slot); + } + } + // connect to the first free input slot if not found a specific type and this output is general + if (opts.firstFreeIfOutputGeneralInCase && (target_slotType == 0 || target_slotType == "*" || target_slotType == "")){ + var target_slot = target_node.findInputSlotFree({typesNotAccepted: [LiteGraph.EVENT] }); + //console.debug("connect TO TheFirstFREE ",target_slotType," to ",target_node,"RES_SLOT:",target_slot); + if (target_slot >= 0){ + return this.connect(slot, target_node, target_slot); + } + } + + console.debug("no way to connect type: ",target_slotType," to targetNODE ",target_node); + //TODO filter + + return null; + } + } + + /** + * connect this node input to the output of another node BY TYPE + * @method connectByType + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} node the target node + * @param {string} target_type the output slot type of the target node + * @return {Object} the link_info is created, otherwise null + */ + LGraphNode.prototype.connectByTypeOutput = function(slot, source_node, source_slotType, optsIn) { + var optsIn = optsIn || {}; + var optsDef = { createEventInCase: true + ,firstFreeIfInputGeneralInCase: true + ,generalTypeInCase: true + }; + var opts = Object.assign(optsDef,optsIn); + if (source_node && source_node.constructor === Number) { + source_node = this.graph.getNodeById(source_node); + } + source_slot = source_node.findOutputSlotByType(source_slotType, false, true); + if (source_slot >= 0 && source_slot !== null){ + //console.debug("CONNbyTYPE OUT! type "+source_slotType+" for "+source_slot) + return source_node.connect(source_slot, this, slot); + }else{ + + // connect to the first general output slot if not found a specific type and + if (opts.generalTypeInCase){ + var source_slot = source_node.findOutputSlotByType(0, false, true, true); + if (source_slot >= 0){ + return source_node.connect(source_slot, this, slot); + } + } + + if (opts.createEventInCase && source_slotType == LiteGraph.EVENT){ + // WILL CREATE THE onExecuted OUT SLOT + if (LiteGraph.do_add_triggers_slots){ + var source_slot = source_node.addOnExecutedOutput(); + return source_node.connect(source_slot, this, slot); + } + } + // connect to the first free output slot if not found a specific type and this input is general + if (opts.firstFreeIfInputGeneralInCase && (source_slotType == 0 || source_slotType == "*" || source_slotType == "")){ + var source_slot = source_node.findOutputSlotFree({typesNotAccepted: [LiteGraph.EVENT] }); + if (source_slot >= 0){ + return source_node.connect(source_slot, this, slot); + } + } + + console.debug("no way to connect byOUT type: ",source_slotType," to sourceNODE ",source_node); + //TODO filter + + //console.log("type OUT! "+source_slotType+" not found or not free?") + return null; + } + } + + /** + * connect this node output to the input of another node + * @method connect + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} node the target node + * @param {number_or_string} target_slot the input slot of the target node (could be the number of the slot or the string with the name of the slot, or -1 to connect a trigger) + * @return {Object} the link_info is created, otherwise null + */ + LGraphNode.prototype.connect = function(slot, target_node, target_slot) { + target_slot = target_slot || 0; + + if (!this.graph) { + //could be connected before adding it to a graph + console.log( + "Connect: Error, node doesn't belong to any graph. Nodes must be added first to a graph before connecting them." + ); //due to link ids being associated with graphs + return null; + } + + //seek for the output slot + if (slot.constructor === String) { + slot = this.findOutputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return null; + } + } else if (!this.outputs || slot >= this.outputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return null; + } + + if (target_node && target_node.constructor === Number) { + target_node = this.graph.getNodeById(target_node); + } + if (!target_node) { + throw "target node is null"; + } + + //avoid loopback + if (target_node == this) { + return null; + } + + //you can specify the slot by name + if (target_slot.constructor === String) { + target_slot = target_node.findInputSlot(target_slot); + if (target_slot == -1) { + if (LiteGraph.debug) { + console.log( + "Connect: Error, no slot of name " + target_slot + ); + } + return null; + } + } else if (target_slot === LiteGraph.EVENT) { + + if (LiteGraph.do_add_triggers_slots){ + //search for first slot with event? :: NO this is done outside + //console.log("Connect: Creating triggerEvent"); + // force mode + target_node.changeMode(LiteGraph.ON_TRIGGER); + target_slot = target_node.findInputSlot("onTrigger"); + }else{ + return null; // -- break -- + } + } else if ( + !target_node.inputs || + target_slot >= target_node.inputs.length + ) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return null; + } + + var changed = false; + + var input = target_node.inputs[target_slot]; + var link_info = null; + var output = this.outputs[slot]; + + if (!this.outputs[slot]){ + /*console.debug("Invalid slot passed: "+slot); + console.debug(this.outputs);*/ + return null; + } + + // allow target node to change slot + if (target_node.onBeforeConnectInput) { + // This way node can choose another slot (or make a new one?) + target_slot = target_node.onBeforeConnectInput(target_slot); //callback + } + + //check target_slot and check connection types + if (target_slot===false || target_slot===null || !LiteGraph.isValidConnection(output.type, input.type)) + { + this.setDirtyCanvas(false, true); + if(changed) + this.graph.connectionChange(this, link_info); + return null; + }else{ + //console.debug("valid connection",output.type, input.type); + } + + //allows nodes to block connection, callback + if (target_node.onConnectInput) { + if ( target_node.onConnectInput(target_slot, output.type, output, this, slot) === false ) { + return null; + } + } + if (this.onConnectOutput) { // callback + if ( this.onConnectOutput(slot, input.type, input, target_node, target_slot) === false ) { + return null; + } + } + + //if there is something already plugged there, disconnect + if (target_node.inputs[target_slot] && target_node.inputs[target_slot].link != null) { + this.graph.beforeChange(); + target_node.disconnectInput(target_slot, {doProcessChange: false}); + changed = true; + } + if (output.links !== null && output.links.length){ + switch(output.type){ + case LiteGraph.EVENT: + if (!LiteGraph.allow_multi_output_for_events){ + this.graph.beforeChange(); + this.disconnectOutput(slot, false, {doProcessChange: false}); // Input(target_slot, {doProcessChange: false}); + changed = true; + } + break; + default: + break; + } + } + + //create link class + link_info = new LLink( + ++this.graph.last_link_id, + input.type || output.type, + this.id, + slot, + target_node.id, + target_slot + ); + + //add to graph links list + this.graph.links[link_info.id] = link_info; + + //connect in output + if (output.links == null) { + output.links = []; + } + output.links.push(link_info.id); + //connect in input + target_node.inputs[target_slot].link = link_info.id; + if (this.graph) { + this.graph._version++; + } + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + true, + link_info, + output + ); + } //link_info has been created now, so its updated + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + target_slot, + true, + link_info, + input + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + target_slot, + this, + slot + ); + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot, + target_node, + target_slot + ); + } + + this.setDirtyCanvas(false, true); + this.graph.afterChange(); + this.graph.connectionChange(this, link_info); + + return link_info; + }; + + /** + * disconnect one output to an specific node + * @method disconnectOutput + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} target_node the target node to which this slot is connected [Optional, if not target_node is specified all nodes will be disconnected] + * @return {boolean} if it was disconnected successfully + */ + LGraphNode.prototype.disconnectOutput = function(slot, target_node) { + if (slot.constructor === String) { + slot = this.findOutputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return false; + } + } else if (!this.outputs || slot >= this.outputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return false; + } + + //get output slot + var output = this.outputs[slot]; + if (!output || !output.links || output.links.length == 0) { + return false; + } + + //one of the output links in this slot + if (target_node) { + if (target_node.constructor === Number) { + target_node = this.graph.getNodeById(target_node); + } + if (!target_node) { + throw "Target Node not found"; + } + + for (var i = 0, l = output.links.length; i < l; i++) { + var link_id = output.links[i]; + var link_info = this.graph.links[link_id]; + + //is the link we are searching for... + if (link_info.target_id == target_node.id) { + output.links.splice(i, 1); //remove here + var input = target_node.inputs[link_info.target_slot]; + input.link = null; //remove there + delete this.graph.links[link_id]; //remove the link from the links pool + if (this.graph) { + this.graph._version++; + } + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + link_info.target_slot, + false, + link_info, + input + ); + } //link_info hasn't been modified so its ok + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + false, + link_info, + output + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot + ); + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot + ); + } + break; + } + } + } //all the links in this output slot + else { + for (var i = 0, l = output.links.length; i < l; i++) { + var link_id = output.links[i]; + var link_info = this.graph.links[link_id]; + if (!link_info) { + //bug: it happens sometimes + continue; + } + + var target_node = this.graph.getNodeById(link_info.target_id); + var input = null; + if (this.graph) { + this.graph._version++; + } + if (target_node) { + input = target_node.inputs[link_info.target_slot]; + input.link = null; //remove other side link + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + link_info.target_slot, + false, + link_info, + input + ); + } //link_info hasn't been modified so its ok + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot + ); + } + } + delete this.graph.links[link_id]; //remove the link from the links pool + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + false, + link_info, + output + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot + ); + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot + ); + } + } + output.links = null; + } + + this.setDirtyCanvas(false, true); + this.graph.connectionChange(this); + return true; + }; + + /** + * disconnect one input + * @method disconnectInput + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @return {boolean} if it was disconnected successfully + */ + LGraphNode.prototype.disconnectInput = function(slot) { + //seek for the output slot + if (slot.constructor === String) { + slot = this.findInputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return false; + } + } else if (!this.inputs || slot >= this.inputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return false; + } + + var input = this.inputs[slot]; + if (!input) { + return false; + } + + var link_id = this.inputs[slot].link; + if(link_id != null) + { + this.inputs[slot].link = null; + + //remove other side + var link_info = this.graph.links[link_id]; + if (link_info) { + var target_node = this.graph.getNodeById(link_info.origin_id); + if (!target_node) { + return false; + } + + var output = target_node.outputs[link_info.origin_slot]; + if (!output || !output.links || output.links.length == 0) { + return false; + } + + //search in the inputs list for this link + for (var i = 0, l = output.links.length; i < l; i++) { + if (output.links[i] == link_id) { + output.links.splice(i, 1); + break; + } + } + + delete this.graph.links[link_id]; //remove from the pool + if (this.graph) { + this.graph._version++; + } + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.INPUT, + slot, + false, + link_info, + input + ); + } + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.OUTPUT, + i, + false, + link_info, + output + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + target_node, + i + ); + this.graph.onNodeConnectionChange(LiteGraph.INPUT, this, slot); + } + } + } //link != null + + this.setDirtyCanvas(false, true); + if(this.graph) + this.graph.connectionChange(this); + return true; + }; + + /** + * returns the center of a connection point in canvas coords + * @method getConnectionPos + * @param {boolean} is_input true if if a input slot, false if it is an output + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {vec2} out [optional] a place to store the output, to free garbage + * @return {[x,y]} the position + **/ + LGraphNode.prototype.getConnectionPos = function( + is_input, + slot_number, + out + ) { + out = out || new Float32Array(2); + var num_slots = 0; + if (is_input && this.inputs) { + num_slots = this.inputs.length; + } + if (!is_input && this.outputs) { + num_slots = this.outputs.length; + } + + var offset = LiteGraph.NODE_SLOT_HEIGHT * 0.5; + + if (this.flags.collapsed) { + var w = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + if (this.horizontal) { + out[0] = this.pos[0] + w * 0.5; + if (is_input) { + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + } else { + out[1] = this.pos[1]; + } + } else { + if (is_input) { + out[0] = this.pos[0]; + } else { + out[0] = this.pos[0] + w; + } + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5; + } + return out; + } + + //weird feature that never got finished + if (is_input && slot_number == -1) { + out[0] = this.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * 0.5; + out[1] = this.pos[1] + LiteGraph.NODE_TITLE_HEIGHT * 0.5; + return out; + } + + //hard-coded pos + if ( + is_input && + num_slots > slot_number && + this.inputs[slot_number].pos + ) { + out[0] = this.pos[0] + this.inputs[slot_number].pos[0]; + out[1] = this.pos[1] + this.inputs[slot_number].pos[1]; + return out; + } else if ( + !is_input && + num_slots > slot_number && + this.outputs[slot_number].pos + ) { + out[0] = this.pos[0] + this.outputs[slot_number].pos[0]; + out[1] = this.pos[1] + this.outputs[slot_number].pos[1]; + return out; + } + + //horizontal distributed slots + if (this.horizontal) { + out[0] = + this.pos[0] + (slot_number + 0.5) * (this.size[0] / num_slots); + if (is_input) { + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + } else { + out[1] = this.pos[1] + this.size[1]; + } + return out; + } + + //default vertical slots + if (is_input) { + out[0] = this.pos[0] + offset; + } else { + out[0] = this.pos[0] + this.size[0] + 1 - offset; + } + out[1] = + this.pos[1] + + (slot_number + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + + (this.constructor.slot_start_y || 0); + return out; + }; + + /* Force align to grid */ + LGraphNode.prototype.alignToGrid = function() { + this.pos[0] = + LiteGraph.CANVAS_GRID_SIZE * + Math.round(this.pos[0] / LiteGraph.CANVAS_GRID_SIZE); + this.pos[1] = + LiteGraph.CANVAS_GRID_SIZE * + Math.round(this.pos[1] / LiteGraph.CANVAS_GRID_SIZE); + }; + + /* Console output */ + LGraphNode.prototype.trace = function(msg) { + if (!this.console) { + this.console = []; + } + + this.console.push(msg); + if (this.console.length > LGraphNode.MAX_CONSOLE) { + this.console.shift(); + } + + if(this.graph.onNodeTrace) + this.graph.onNodeTrace(this, msg); + }; + + /* Forces to redraw or the main canvas (LGraphNode) or the bg canvas (links) */ + LGraphNode.prototype.setDirtyCanvas = function( + dirty_foreground, + dirty_background + ) { + if (!this.graph) { + return; + } + this.graph.sendActionToCanvas("setDirty", [ + dirty_foreground, + dirty_background + ]); + }; + + LGraphNode.prototype.loadImage = function(url) { + var img = new Image(); + img.src = LiteGraph.node_images_path + url; + img.ready = false; + + var that = this; + img.onload = function() { + this.ready = true; + that.setDirtyCanvas(true); + }; + return img; + }; + + //safe LGraphNode action execution (not sure if safe) + /* +LGraphNode.prototype.executeAction = function(action) +{ + if(action == "") return false; + + if( action.indexOf(";") != -1 || action.indexOf("}") != -1) + { + this.trace("Error: Action contains unsafe characters"); + return false; + } + + var tokens = action.split("("); + var func_name = tokens[0]; + if( typeof(this[func_name]) != "function") + { + this.trace("Error: Action not found on node: " + func_name); + return false; + } + + var code = action; + + try + { + var _foo = eval; + eval = null; + (new Function("with(this) { " + code + "}")).call(this); + eval = _foo; + } + catch (err) + { + this.trace("Error executing action {" + action + "} :" + err); + return false; + } + + return true; +} +*/ + + /* Allows to get onMouseMove and onMouseUp events even if the mouse is out of focus */ + LGraphNode.prototype.captureInput = function(v) { + if (!this.graph || !this.graph.list_of_graphcanvas) { + return; + } + + var list = this.graph.list_of_graphcanvas; + + for (var i = 0; i < list.length; ++i) { + var c = list[i]; + //releasing somebody elses capture?! + if (!v && c.node_capturing_input != this) { + continue; + } + + //change + c.node_capturing_input = v ? this : null; + } + }; + + /** + * Collapse the node to make it smaller on the canvas + * @method collapse + **/ + LGraphNode.prototype.collapse = function(force) { + this.graph._version++; + if (this.constructor.collapsable === false && !force) { + return; + } + if (!this.flags.collapsed) { + this.flags.collapsed = true; + } else { + this.flags.collapsed = false; + } + this.setDirtyCanvas(true, true); + }; + + /** + * Forces the node to do not move or realign on Z + * @method pin + **/ + + LGraphNode.prototype.pin = function(v) { + this.graph._version++; + if (v === undefined) { + this.flags.pinned = !this.flags.pinned; + } else { + this.flags.pinned = v; + } + }; + + LGraphNode.prototype.localToScreen = function(x, y, graphcanvas) { + return [ + (x + this.pos[0]) * graphcanvas.scale + graphcanvas.offset[0], + (y + this.pos[1]) * graphcanvas.scale + graphcanvas.offset[1] + ]; + }; + + function LGraphGroup(title) { + this._ctor(title); + } + + global.LGraphGroup = LiteGraph.LGraphGroup = LGraphGroup; + + LGraphGroup.prototype._ctor = function(title) { + this.title = title || "Group"; + this.font_size = 24; + this.color = LGraphCanvas.node_colors.pale_blue + ? LGraphCanvas.node_colors.pale_blue.groupcolor + : "#AAA"; + this._bounding = new Float32Array([10, 10, 140, 80]); + this._pos = this._bounding.subarray(0, 2); + this._size = this._bounding.subarray(2, 4); + this._nodes = []; + this.graph = null; + + Object.defineProperty(this, "pos", { + set: function(v) { + if (!v || v.length < 2) { + return; + } + this._pos[0] = v[0]; + this._pos[1] = v[1]; + }, + get: function() { + return this._pos; + }, + enumerable: true + }); + + Object.defineProperty(this, "size", { + set: function(v) { + if (!v || v.length < 2) { + return; + } + this._size[0] = Math.max(140, v[0]); + this._size[1] = Math.max(80, v[1]); + }, + get: function() { + return this._size; + }, + enumerable: true + }); + }; + + LGraphGroup.prototype.configure = function(o) { + this.title = o.title; + this._bounding.set(o.bounding); + this.color = o.color; + this.font = o.font; + }; + + LGraphGroup.prototype.serialize = function() { + var b = this._bounding; + return { + title: this.title, + bounding: [ + Math.round(b[0]), + Math.round(b[1]), + Math.round(b[2]), + Math.round(b[3]) + ], + color: this.color, + font: this.font + }; + }; + + LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) { + this._pos[0] += deltax; + this._pos[1] += deltay; + if (ignore_nodes) { + return; + } + for (var i = 0; i < this._nodes.length; ++i) { + var node = this._nodes[i]; + node.pos[0] += deltax; + node.pos[1] += deltay; + } + }; + + LGraphGroup.prototype.recomputeInsideNodes = function() { + this._nodes.length = 0; + var nodes = this.graph._nodes; + var node_bounding = new Float32Array(4); + + for (var i = 0; i < nodes.length; ++i) { + var node = nodes[i]; + node.getBounding(node_bounding); + if (!overlapBounding(this._bounding, node_bounding)) { + continue; + } //out of the visible area + this._nodes.push(node); + } + }; + + LGraphGroup.prototype.isPointInside = LGraphNode.prototype.isPointInside; + LGraphGroup.prototype.setDirtyCanvas = LGraphNode.prototype.setDirtyCanvas; + + //**************************************** + + //Scale and Offset + function DragAndScale(element, skip_events) { + this.offset = new Float32Array([0, 0]); + this.scale = 1; + this.max_scale = 10; + this.min_scale = 0.1; + this.onredraw = null; + this.enabled = true; + this.last_mouse = [0, 0]; + this.element = null; + this.visible_area = new Float32Array(4); + + if (element) { + this.element = element; + if (!skip_events) { + this.bindEvents(element); + } + } + } + + LiteGraph.DragAndScale = DragAndScale; + + DragAndScale.prototype.bindEvents = function(element) { + this.last_mouse = new Float32Array(2); + + this._binded_mouse_callback = this.onMouse.bind(this); + + LiteGraph.pointerListenerAdd(element,"down", this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(element,"move", this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(element,"up", this._binded_mouse_callback); + + element.addEventListener( + "mousewheel", + this._binded_mouse_callback, + false + ); + element.addEventListener("wheel", this._binded_mouse_callback, false); + }; + + DragAndScale.prototype.computeVisibleArea = function( viewport ) { + if (!this.element) { + this.visible_area[0] = this.visible_area[1] = this.visible_area[2] = this.visible_area[3] = 0; + return; + } + var width = this.element.width; + var height = this.element.height; + var startx = -this.offset[0]; + var starty = -this.offset[1]; + if( viewport ) + { + startx += viewport[0] / this.scale; + starty += viewport[1] / this.scale; + width = viewport[2]; + height = viewport[3]; + } + var endx = startx + width / this.scale; + var endy = starty + height / this.scale; + this.visible_area[0] = startx; + this.visible_area[1] = starty; + this.visible_area[2] = endx - startx; + this.visible_area[3] = endy - starty; + }; + + DragAndScale.prototype.onMouse = function(e) { + if (!this.enabled) { + return; + } + + var canvas = this.element; + var rect = canvas.getBoundingClientRect(); + var x = e.clientX - rect.left; + var y = e.clientY - rect.top; + e.canvasx = x; + e.canvasy = y; + e.dragging = this.dragging; + + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + + //console.log("pointerevents: DragAndScale onMouse "+e.type+" "+is_inside); + + var ignore = false; + if (this.onmouse) { + ignore = this.onmouse(e); + } + + if (e.type == LiteGraph.pointerevents_method+"down" && is_inside) { + this.dragging = true; + LiteGraph.pointerListenerRemove(canvas,"move",this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(document,"move",this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(document,"up",this._binded_mouse_callback); + } else if (e.type == LiteGraph.pointerevents_method+"move") { + if (!ignore) { + var deltax = x - this.last_mouse[0]; + var deltay = y - this.last_mouse[1]; + if (this.dragging) { + this.mouseDrag(deltax, deltay); + } + } + } else if (e.type == LiteGraph.pointerevents_method+"up") { + this.dragging = false; + LiteGraph.pointerListenerRemove(document,"move",this._binded_mouse_callback); + LiteGraph.pointerListenerRemove(document,"up",this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(canvas,"move",this._binded_mouse_callback); + } else if ( is_inside && + (e.type == "mousewheel" || + e.type == "wheel" || + e.type == "DOMMouseScroll") + ) { + e.eventType = "mousewheel"; + if (e.type == "wheel") { + e.wheel = -e.deltaY; + } else { + e.wheel = + e.wheelDeltaY != null ? e.wheelDeltaY : e.detail * -60; + } + + //from stack overflow + e.delta = e.wheelDelta + ? e.wheelDelta / 40 + : e.deltaY + ? -e.deltaY / 3 + : 0; + this.changeDeltaScale(1.0 + e.delta * 0.05); + } + + this.last_mouse[0] = x; + this.last_mouse[1] = y; + + if(is_inside) + { + e.preventDefault(); + e.stopPropagation(); + return false; + } + }; + + DragAndScale.prototype.toCanvasContext = function(ctx) { + ctx.scale(this.scale, this.scale); + ctx.translate(this.offset[0], this.offset[1]); + }; + + DragAndScale.prototype.convertOffsetToCanvas = function(pos) { + //return [pos[0] / this.scale - this.offset[0], pos[1] / this.scale - this.offset[1]]; + return [ + (pos[0] + this.offset[0]) * this.scale, + (pos[1] + this.offset[1]) * this.scale + ]; + }; + + DragAndScale.prototype.convertCanvasToOffset = function(pos, out) { + out = out || [0, 0]; + out[0] = pos[0] / this.scale - this.offset[0]; + out[1] = pos[1] / this.scale - this.offset[1]; + return out; + }; + + DragAndScale.prototype.mouseDrag = function(x, y) { + this.offset[0] += x / this.scale; + this.offset[1] += y / this.scale; + + if (this.onredraw) { + this.onredraw(this); + } + }; + + DragAndScale.prototype.changeScale = function(value, zooming_center) { + if (value < this.min_scale) { + value = this.min_scale; + } else if (value > this.max_scale) { + value = this.max_scale; + } + + if (value == this.scale) { + return; + } + + if (!this.element) { + return; + } + + var rect = this.element.getBoundingClientRect(); + if (!rect) { + return; + } + + zooming_center = zooming_center || [ + rect.width * 0.5, + rect.height * 0.5 + ]; + var center = this.convertCanvasToOffset(zooming_center); + this.scale = value; + if (Math.abs(this.scale - 1) < 0.01) { + this.scale = 1; + } + + var new_center = this.convertCanvasToOffset(zooming_center); + var delta_offset = [ + new_center[0] - center[0], + new_center[1] - center[1] + ]; + + this.offset[0] += delta_offset[0]; + this.offset[1] += delta_offset[1]; + + if (this.onredraw) { + this.onredraw(this); + } + }; + + DragAndScale.prototype.changeDeltaScale = function(value, zooming_center) { + this.changeScale(this.scale * value, zooming_center); + }; + + DragAndScale.prototype.reset = function() { + this.scale = 1; + this.offset[0] = 0; + this.offset[1] = 0; + }; + + //********************************************************************************* + // LGraphCanvas: LGraph renderer CLASS + //********************************************************************************* + + /** + * This class is in charge of rendering one graph inside a canvas. And provides all the interaction required. + * Valid callbacks are: onNodeSelected, onNodeDeselected, onShowNodePanel, onNodeDblClicked + * + * @class LGraphCanvas + * @constructor + * @param {HTMLCanvas} canvas the canvas where you want to render (it accepts a selector in string format or the canvas element itself) + * @param {LGraph} graph [optional] + * @param {Object} options [optional] { skip_rendering, autoresize, viewport } + */ + function LGraphCanvas(canvas, graph, options) { + this.options = options = options || {}; + + //if(graph === undefined) + // throw ("No graph assigned"); + this.background_image = LGraphCanvas.DEFAULT_BACKGROUND_IMAGE; + + if (canvas && canvas.constructor === String) { + canvas = document.querySelector(canvas); + } + + this.ds = new DragAndScale(); + this.zoom_modify_alpha = true; //otherwise it generates ugly patterns when scaling down too much + + this.title_text_font = "" + LiteGraph.NODE_TEXT_SIZE + "px Arial"; + this.inner_text_font = + "normal " + LiteGraph.NODE_SUBTEXT_SIZE + "px Arial"; + this.node_title_color = LiteGraph.NODE_TITLE_COLOR; + this.default_link_color = LiteGraph.LINK_COLOR; + this.default_connection_color = { + input_off: "#778", + input_on: "#7F7", //"#BBD" + output_off: "#778", + output_on: "#7F7" //"#BBD" + }; + this.default_connection_color_byType = { + /*number: "#7F7", + string: "#77F", + boolean: "#F77",*/ + } + this.default_connection_color_byTypeOff = { + /*number: "#474", + string: "#447", + boolean: "#744",*/ + }; + + this.highquality_render = true; + this.use_gradients = false; //set to true to render titlebar with gradients + this.editor_alpha = 1; //used for transition + this.pause_rendering = false; + this.clear_background = true; + + this.read_only = false; //if set to true users cannot modify the graph + this.render_only_selected = true; + this.live_mode = false; + this.show_info = true; + this.allow_dragcanvas = true; + this.allow_dragnodes = true; + this.allow_interaction = true; //allow to control widgets, buttons, collapse, etc + this.allow_searchbox = true; + this.allow_reconnect_links = true; //allows to change a connection with having to redo it again + this.align_to_grid = false; //snap to grid + + this.drag_mode = false; + this.dragging_rectangle = null; + + this.filter = null; //allows to filter to only accept some type of nodes in a graph + + this.set_canvas_dirty_on_mouse_event = true; //forces to redraw the canvas if the mouse does anything + this.always_render_background = false; + this.render_shadows = true; + this.render_canvas_border = true; + this.render_connections_shadows = false; //too much cpu + this.render_connections_border = true; + this.render_curved_connections = false; + this.render_connection_arrows = false; + this.render_collapsed_slots = true; + this.render_execution_order = false; + this.render_title_colored = true; + this.render_link_tooltip = true; + + this.links_render_mode = LiteGraph.SPLINE_LINK; + + this.mouse = [0, 0]; //mouse in canvas coordinates, where 0,0 is the top-left corner of the blue rectangle + this.graph_mouse = [0, 0]; //mouse in graph coordinates, where 0,0 is the top-left corner of the blue rectangle + this.canvas_mouse = this.graph_mouse; //LEGACY: REMOVE THIS, USE GRAPH_MOUSE INSTEAD + + //to personalize the search box + this.onSearchBox = null; + this.onSearchBoxSelection = null; + + //callbacks + this.onMouse = null; + this.onDrawBackground = null; //to render background objects (behind nodes and connections) in the canvas affected by transform + this.onDrawForeground = null; //to render foreground objects (above nodes and connections) in the canvas affected by transform + this.onDrawOverlay = null; //to render foreground objects not affected by transform (for GUIs) + this.onDrawLinkTooltip = null; //called when rendering a tooltip + this.onNodeMoved = null; //called after moving a node + this.onSelectionChange = null; //called if the selection changes + this.onConnectingChange = null; //called before any link changes + this.onBeforeChange = null; //called before modifying the graph + this.onAfterChange = null; //called after modifying the graph + + this.connections_width = 3; + this.round_radius = 8; + + this.current_node = null; + this.node_widget = null; //used for widgets + this.over_link_center = null; + this.last_mouse_position = [0, 0]; + this.visible_area = this.ds.visible_area; + this.visible_links = []; + + this.viewport = options.viewport || null; //to constraint render area to a portion of the canvas + + //link canvas and graph + if (graph) { + graph.attachCanvas(this); + } + + this.setCanvas(canvas,options.skip_events); + this.clear(); + + if (!options.skip_render) { + this.startRendering(); + } + + this.autoresize = options.autoresize; + } + + global.LGraphCanvas = LiteGraph.LGraphCanvas = LGraphCanvas; + + LGraphCanvas.DEFAULT_BACKGROUND_IMAGE = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII="; + + LGraphCanvas.link_type_colors = { + "-1": LiteGraph.EVENT_LINK_COLOR, + number: "#AAA", + node: "#DCA" + }; + LGraphCanvas.gradients = {}; //cache of gradients + + /** + * clears all the data inside + * + * @method clear + */ + LGraphCanvas.prototype.clear = function() { + this.frame = 0; + this.last_draw_time = 0; + this.render_time = 0; + this.fps = 0; + + //this.scale = 1; + //this.offset = [0,0]; + + this.dragging_rectangle = null; + + this.selected_nodes = {}; + this.selected_group = null; + + this.visible_nodes = []; + this.node_dragged = null; + this.node_over = null; + this.node_capturing_input = null; + this.connecting_node = null; + this.highlighted_links = {}; + + this.dragging_canvas = false; + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.dirty_area = null; + + this.node_in_panel = null; + this.node_widget = null; + + this.last_mouse = [0, 0]; + this.last_mouseclick = 0; + this.pointer_is_down = false; + this.pointer_is_double = false; + this.visible_area.set([0, 0, 0, 0]); + + if (this.onClear) { + this.onClear(); + } + }; + + /** + * assigns a graph, you can reassign graphs to the same canvas + * + * @method setGraph + * @param {LGraph} graph + */ + LGraphCanvas.prototype.setGraph = function(graph, skip_clear) { + if (this.graph == graph) { + return; + } + + if (!skip_clear) { + this.clear(); + } + + if (!graph && this.graph) { + this.graph.detachCanvas(this); + return; + } + + graph.attachCanvas(this); + + //remove the graph stack in case a subgraph was open + if (this._graph_stack) + this._graph_stack = null; + + this.setDirty(true, true); + }; + + /** + * returns the top level graph (in case there are subgraphs open on the canvas) + * + * @method getTopGraph + * @return {LGraph} graph + */ + LGraphCanvas.prototype.getTopGraph = function() + { + if(this._graph_stack.length) + return this._graph_stack[0]; + return this.graph; + } + + /** + * opens a graph contained inside a node in the current graph + * + * @method openSubgraph + * @param {LGraph} graph + */ + LGraphCanvas.prototype.openSubgraph = function(graph) { + if (!graph) { + throw "graph cannot be null"; + } + + if (this.graph == graph) { + throw "graph cannot be the same"; + } + + this.clear(); + + if (this.graph) { + if (!this._graph_stack) { + this._graph_stack = []; + } + this._graph_stack.push(this.graph); + } + + graph.attachCanvas(this); + this.checkPanels(); + this.setDirty(true, true); + }; + + /** + * closes a subgraph contained inside a node + * + * @method closeSubgraph + * @param {LGraph} assigns a graph + */ + LGraphCanvas.prototype.closeSubgraph = function() { + if (!this._graph_stack || this._graph_stack.length == 0) { + return; + } + var subgraph_node = this.graph._subgraph_node; + var graph = this._graph_stack.pop(); + this.selected_nodes = {}; + this.highlighted_links = {}; + graph.attachCanvas(this); + this.setDirty(true, true); + if (subgraph_node) { + this.centerOnNode(subgraph_node); + this.selectNodes([subgraph_node]); + } + // when close sub graph back to offset [0, 0] scale 1 + this.ds.offset = [0, 0] + this.ds.scale = 1 + }; + + /** + * returns the visualy active graph (in case there are more in the stack) + * @method getCurrentGraph + * @return {LGraph} the active graph + */ + LGraphCanvas.prototype.getCurrentGraph = function() { + return this.graph; + }; + + /** + * assigns a canvas + * + * @method setCanvas + * @param {Canvas} assigns a canvas (also accepts the ID of the element (not a selector) + */ + LGraphCanvas.prototype.setCanvas = function(canvas, skip_events) { + var that = this; + + if (canvas) { + if (canvas.constructor === String) { + canvas = document.getElementById(canvas); + if (!canvas) { + throw "Error creating LiteGraph canvas: Canvas not found"; + } + } + } + + if (canvas === this.canvas) { + return; + } + + if (!canvas && this.canvas) { + //maybe detach events from old_canvas + if (!skip_events) { + this.unbindEvents(); + } + } + + this.canvas = canvas; + this.ds.element = canvas; + + if (!canvas) { + return; + } + + //this.canvas.tabindex = "1000"; + canvas.className += " lgraphcanvas"; + canvas.data = this; + canvas.tabindex = "1"; //to allow key events + + //bg canvas: used for non changing stuff + this.bgcanvas = null; + if (!this.bgcanvas) { + this.bgcanvas = document.createElement("canvas"); + this.bgcanvas.width = this.canvas.width; + this.bgcanvas.height = this.canvas.height; + } + + if (canvas.getContext == null) { + if (canvas.localName != "canvas") { + throw "Element supplied for LGraphCanvas must be a element, you passed a " + + canvas.localName; + } + throw "This browser doesn't support Canvas"; + } + + var ctx = (this.ctx = canvas.getContext("2d")); + if (ctx == null) { + if (!canvas.webgl_enabled) { + console.warn( + "This canvas seems to be WebGL, enabling WebGL renderer" + ); + } + this.enableWebGL(); + } + + //input: (move and up could be unbinded) + // why here? this._mousemove_callback = this.processMouseMove.bind(this); + // why here? this._mouseup_callback = this.processMouseUp.bind(this); + + if (!skip_events) { + this.bindEvents(); + } + }; + + //used in some events to capture them + LGraphCanvas.prototype._doNothing = function doNothing(e) { + //console.log("pointerevents: _doNothing "+e.type); + e.preventDefault(); + return false; + }; + LGraphCanvas.prototype._doReturnTrue = function doNothing(e) { + e.preventDefault(); + return true; + }; + + /** + * binds mouse, keyboard, touch and drag events to the canvas + * @method bindEvents + **/ + LGraphCanvas.prototype.bindEvents = function() { + if (this._events_binded) { + console.warn("LGraphCanvas: events already binded"); + return; + } + + //console.log("pointerevents: bindEvents"); + + var canvas = this.canvas; + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; //hack used when moving canvas between windows + + this._mousedown_callback = this.processMouseDown.bind(this); + this._mousewheel_callback = this.processMouseWheel.bind(this); + // why mousemove and mouseup were not binded here? + this._mousemove_callback = this.processMouseMove.bind(this); + this._mouseup_callback = this.processMouseUp.bind(this); + + //touch events -- TODO IMPLEMENT + //this._touch_callback = this.touchHandler.bind(this); + + LiteGraph.pointerListenerAdd(canvas,"down", this._mousedown_callback, true); //down do not need to store the binded + canvas.addEventListener("mousewheel", this._mousewheel_callback, false); + + LiteGraph.pointerListenerAdd(canvas,"up", this._mouseup_callback, true); // CHECK: ??? binded or not + LiteGraph.pointerListenerAdd(canvas,"move", this._mousemove_callback); + + canvas.addEventListener("contextmenu", this._doNothing); + canvas.addEventListener( + "DOMMouseScroll", + this._mousewheel_callback, + false + ); + + //touch events -- THIS WAY DOES NOT WORK, finish implementing pointerevents, than clean the touchevents + /*if( 'touchstart' in document.documentElement ) + { + canvas.addEventListener("touchstart", this._touch_callback, true); + canvas.addEventListener("touchmove", this._touch_callback, true); + canvas.addEventListener("touchend", this._touch_callback, true); + canvas.addEventListener("touchcancel", this._touch_callback, true); + }*/ + + //Keyboard ****************** + this._key_callback = this.processKey.bind(this); + + canvas.addEventListener("keydown", this._key_callback, true); + document.addEventListener("keyup", this._key_callback, true); //in document, otherwise it doesn't fire keyup + + //Dropping Stuff over nodes ************************************ + this._ondrop_callback = this.processDrop.bind(this); + + canvas.addEventListener("dragover", this._doNothing, false); + canvas.addEventListener("dragend", this._doNothing, false); + canvas.addEventListener("drop", this._ondrop_callback, false); + canvas.addEventListener("dragenter", this._doReturnTrue, false); + + this._events_binded = true; + }; + + /** + * unbinds mouse events from the canvas + * @method unbindEvents + **/ + LGraphCanvas.prototype.unbindEvents = function() { + if (!this._events_binded) { + console.warn("LGraphCanvas: no events binded"); + return; + } + + //console.log("pointerevents: unbindEvents"); + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; + + LiteGraph.pointerListenerRemove(this.canvas,"move", this._mousedown_callback); + LiteGraph.pointerListenerRemove(this.canvas,"up", this._mousedown_callback); + LiteGraph.pointerListenerRemove(this.canvas,"down", this._mousedown_callback); + this.canvas.removeEventListener( + "mousewheel", + this._mousewheel_callback + ); + this.canvas.removeEventListener( + "DOMMouseScroll", + this._mousewheel_callback + ); + this.canvas.removeEventListener("keydown", this._key_callback); + document.removeEventListener("keyup", this._key_callback); + this.canvas.removeEventListener("contextmenu", this._doNothing); + this.canvas.removeEventListener("drop", this._ondrop_callback); + this.canvas.removeEventListener("dragenter", this._doReturnTrue); + + //touch events -- THIS WAY DOES NOT WORK, finish implementing pointerevents, than clean the touchevents + /*this.canvas.removeEventListener("touchstart", this._touch_callback ); + this.canvas.removeEventListener("touchmove", this._touch_callback ); + this.canvas.removeEventListener("touchend", this._touch_callback ); + this.canvas.removeEventListener("touchcancel", this._touch_callback );*/ + + this._mousedown_callback = null; + this._mousewheel_callback = null; + this._key_callback = null; + this._ondrop_callback = null; + + this._events_binded = false; + }; + + LGraphCanvas.getFileExtension = function(url) { + var question = url.indexOf("?"); + if (question != -1) { + url = url.substr(0, question); + } + var point = url.lastIndexOf("."); + if (point == -1) { + return ""; + } + return url.substr(point + 1).toLowerCase(); + }; + + /** + * this function allows to render the canvas using WebGL instead of Canvas2D + * this is useful if you plant to render 3D objects inside your nodes, it uses litegl.js for webgl and canvas2DtoWebGL to emulate the Canvas2D calls in webGL + * @method enableWebGL + **/ + LGraphCanvas.prototype.enableWebGL = function() { + if (typeof GL === undefined) { + throw "litegl.js must be included to use a WebGL canvas"; + } + if (typeof enableWebGLCanvas === undefined) { + throw "webglCanvas.js must be included to use this feature"; + } + + this.gl = this.ctx = enableWebGLCanvas(this.canvas); + this.ctx.webgl = true; + this.bgcanvas = this.canvas; + this.bgctx = this.gl; + this.canvas.webgl_enabled = true; + + /* + GL.create({ canvas: this.bgcanvas }); + this.bgctx = enableWebGLCanvas( this.bgcanvas ); + window.gl = this.gl; + */ + }; + + /** + * marks as dirty the canvas, this way it will be rendered again + * + * @class LGraphCanvas + * @method setDirty + * @param {bool} fgcanvas if the foreground canvas is dirty (the one containing the nodes) + * @param {bool} bgcanvas if the background canvas is dirty (the one containing the wires) + */ + LGraphCanvas.prototype.setDirty = function(fgcanvas, bgcanvas) { + if (fgcanvas) { + this.dirty_canvas = true; + } + if (bgcanvas) { + this.dirty_bgcanvas = true; + } + }; + + /** + * Used to attach the canvas in a popup + * + * @method getCanvasWindow + * @return {window} returns the window where the canvas is attached (the DOM root node) + */ + LGraphCanvas.prototype.getCanvasWindow = function() { + if (!this.canvas) { + return window; + } + var doc = this.canvas.ownerDocument; + return doc.defaultView || doc.parentWindow; + }; + + /** + * starts rendering the content of the canvas when needed + * + * @method startRendering + */ + LGraphCanvas.prototype.startRendering = function() { + if (this.is_rendering) { + return; + } //already rendering + + this.is_rendering = true; + renderFrame.call(this); + + function renderFrame() { + if (!this.pause_rendering) { + this.draw(); + } + + var window = this.getCanvasWindow(); + if (this.is_rendering) { + window.requestAnimationFrame(renderFrame.bind(this)); + } + } + }; + + /** + * stops rendering the content of the canvas (to save resources) + * + * @method stopRendering + */ + LGraphCanvas.prototype.stopRendering = function() { + this.is_rendering = false; + /* + if(this.rendering_timer_id) + { + clearInterval(this.rendering_timer_id); + this.rendering_timer_id = null; + } + */ + }; + + /* LiteGraphCanvas input */ + + //used to block future mouse events (because of im gui) + LGraphCanvas.prototype.blockClick = function() + { + this.block_click = true; + this.last_mouseclick = 0; + } + + LGraphCanvas.prototype.processMouseDown = function(e) { + + if( this.set_canvas_dirty_on_mouse_event ) + this.dirty_canvas = true; + + if (!this.graph) { + return; + } + + this.adjustMouseEvent(e); + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; + LGraphCanvas.active_canvas = this; + var that = this; + + var x = e.clientX; + var y = e.clientY; + //console.log(y,this.viewport); + //console.log("pointerevents: processMouseDown pointerId:"+e.pointerId+" which:"+e.which+" isPrimary:"+e.isPrimary+" :: x y "+x+" "+y); + + this.ds.viewport = this.viewport; + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + + //move mouse move event to the window in case it drags outside of the canvas + if(!this.options.skip_events) + { + LiteGraph.pointerListenerRemove(this.canvas,"move", this._mousemove_callback); + LiteGraph.pointerListenerAdd(ref_window.document,"move", this._mousemove_callback,true); //catch for the entire window + LiteGraph.pointerListenerAdd(ref_window.document,"up", this._mouseup_callback,true); + } + + if(!is_inside){ + return; + } + + var node = this.graph.getNodeOnPos( e.canvasX, e.canvasY, this.visible_nodes, 5 ); + var skip_dragging = false; + var skip_action = false; + var now = LiteGraph.getTime(); + var is_primary = (e.isPrimary === undefined || !e.isPrimary); + var is_double_click = (now - this.last_mouseclick < 300) && is_primary; + this.mouse[0] = e.clientX; + this.mouse[1] = e.clientY; + this.graph_mouse[0] = e.canvasX; + this.graph_mouse[1] = e.canvasY; + this.last_click_position = [this.mouse[0],this.mouse[1]]; + + if (this.pointer_is_down && is_primary ){ + this.pointer_is_double = true; + //console.log("pointerevents: pointer_is_double start"); + }else{ + this.pointer_is_double = false; + } + this.pointer_is_down = true; + + + this.canvas.focus(); + + LiteGraph.closeAllContextMenus(ref_window); + + if (this.onMouse) + { + if (this.onMouse(e) == true) + return; + } + + //left button mouse / single finger + if (e.which == 1 && !this.pointer_is_double) + { + if (e.ctrlKey) + { + this.dragging_rectangle = new Float32Array(4); + this.dragging_rectangle[0] = e.canvasX; + this.dragging_rectangle[1] = e.canvasY; + this.dragging_rectangle[2] = 1; + this.dragging_rectangle[3] = 1; + skip_action = true; + } + + // clone node ALT dragging + if (LiteGraph.alt_drag_do_clone_nodes && e.altKey && node && this.allow_interaction && !skip_action && !this.read_only) + { + if (cloned = node.clone()){ + cloned.pos[0] += 5; + cloned.pos[1] += 5; + this.graph.add(cloned,false,{doCalcSize: false}); + node = cloned; + skip_action = true; + if (!block_drag_node) { + if (this.allow_dragnodes) { + this.graph.beforeChange(); + this.node_dragged = node; + } + if (!this.selected_nodes[node.id]) { + this.processNodeSelected(node, e); + } + } + } + } + + var clicking_canvas_bg = false; + + //when clicked on top of a node + //and it is not interactive + if (node && this.allow_interaction && !skip_action && !this.read_only) { + if (!this.live_mode && !node.flags.pinned) { + this.bringToFront(node); + } //if it wasn't selected? + + //not dragging mouse to connect two slots + if ( !this.connecting_node && !node.flags.collapsed && !this.live_mode ) { + //Search for corner for resize + if ( !skip_action && + node.resizable !== false && + isInsideRectangle( e.canvasX, + e.canvasY, + node.pos[0] + node.size[0] - 5, + node.pos[1] + node.size[1] - 5, + 10, + 10 + ) + ) { + this.graph.beforeChange(); + this.resizing_node = node; + this.canvas.style.cursor = "se-resize"; + skip_action = true; + } else { + //search for outputs + if (node.outputs) { + for ( var i = 0, l = node.outputs.length; i < l; ++i ) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20 + ) + ) { + this.connecting_node = node; + this.connecting_output = output; + this.connecting_output.slot_index = i; + this.connecting_pos = node.getConnectionPos( false, i ); + this.connecting_slot = i; + + if (LiteGraph.shift_click_do_break_link_from){ + if (e.shiftKey) { + node.disconnectOutput(i); + } + } + + if (is_double_click) { + if (node.onOutputDblClick) { + node.onOutputDblClick(i, e); + } + } else { + if (node.onOutputClick) { + node.onOutputClick(i, e); + } + } + + skip_action = true; + break; + } + } + } + + //search for inputs + if (node.inputs) { + for ( var i = 0, l = node.inputs.length; i < l; ++i ) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20 + ) + ) { + if (is_double_click) { + if (node.onInputDblClick) { + node.onInputDblClick(i, e); + } + } else { + if (node.onInputClick) { + node.onInputClick(i, e); + } + } + + if (input.link !== null) { + var link_info = this.graph.links[ + input.link + ]; //before disconnecting + if (LiteGraph.click_do_break_link_to){ + node.disconnectInput(i); + this.dirty_bgcanvas = true; + skip_action = true; + }else{ + // do same action as has not node ? + } + + if ( + this.allow_reconnect_links || + //this.move_destination_link_without_shift || + e.shiftKey + ) { + if (!LiteGraph.click_do_break_link_to){ + node.disconnectInput(i); + } + this.connecting_node = this.graph._nodes_by_id[ + link_info.origin_id + ]; + this.connecting_slot = + link_info.origin_slot; + this.connecting_output = this.connecting_node.outputs[ + this.connecting_slot + ]; + this.connecting_pos = this.connecting_node.getConnectionPos( false, this.connecting_slot ); + + this.dirty_bgcanvas = true; + skip_action = true; + } + + + }else{ + // has not node + } + + if (!skip_action){ + // connect from in to out, from to to from + this.connecting_node = node; + this.connecting_input = input; + this.connecting_input.slot_index = i; + this.connecting_pos = node.getConnectionPos( true, i ); + this.connecting_slot = i; + + this.dirty_bgcanvas = true; + skip_action = true; + } + } + } + } + } //not resizing + } + + //it wasn't clicked on the links boxes + if (!skip_action) { + var block_drag_node = false; + var pos = [e.canvasX - node.pos[0], e.canvasY - node.pos[1]]; + + //widgets + var widget = this.processNodeWidgets( node, this.graph_mouse, e ); + if (widget) { + block_drag_node = true; + this.node_widget = [node, widget]; + } + + //double clicking + if (is_double_click && this.selected_nodes[node.id]) { + //double click node + if (node.onDblClick) { + node.onDblClick( e, pos, this ); + } + this.processNodeDblClicked(node); + block_drag_node = true; + } + + //if do not capture mouse + if ( node.onMouseDown && node.onMouseDown( e, pos, this ) ) { + block_drag_node = true; + } else { + //open subgraph button + if(node.subgraph && !node.skip_subgraph_button) + { + if ( !node.flags.collapsed && pos[0] > node.size[0] - LiteGraph.NODE_TITLE_HEIGHT && pos[1] < 0 ) { + var that = this; + setTimeout(function() { + that.openSubgraph(node.subgraph); + }, 10); + } + } + + if (this.live_mode) { + clicking_canvas_bg = true; + block_drag_node = true; + } + } + + if (!block_drag_node) { + if (this.allow_dragnodes) { + this.graph.beforeChange(); + this.node_dragged = node; + } + if (!this.selected_nodes[node.id]) { + this.processNodeSelected(node, e); + } + } + + this.dirty_canvas = true; + } + } //clicked outside of nodes + else { + if (!skip_action){ + //search for link connector + if(!this.read_only) { + for (var i = 0; i < this.visible_links.length; ++i) { + var link = this.visible_links[i]; + var center = link._pos; + if ( + !center || + e.canvasX < center[0] - 4 || + e.canvasX > center[0] + 4 || + e.canvasY < center[1] - 4 || + e.canvasY > center[1] + 4 + ) { + continue; + } + //link clicked + this.showLinkMenu(link, e); + this.over_link_center = null; //clear tooltip + break; + } + } + + this.selected_group = this.graph.getGroupOnPos( e.canvasX, e.canvasY ); + this.selected_group_resizing = false; + if (this.selected_group && !this.read_only ) { + if (e.ctrlKey) { + this.dragging_rectangle = null; + } + + var dist = distance( [e.canvasX, e.canvasY], [ this.selected_group.pos[0] + this.selected_group.size[0], this.selected_group.pos[1] + this.selected_group.size[1] ] ); + if (dist * this.ds.scale < 10) { + this.selected_group_resizing = true; + } else { + this.selected_group.recomputeInsideNodes(); + } + } + + if (is_double_click && !this.read_only && this.allow_searchbox) { + this.showSearchBox(e); + e.preventDefault(); + e.stopPropagation(); + } + + clicking_canvas_bg = true; + } + } + + if (!skip_action && clicking_canvas_bg && this.allow_dragcanvas) { + //console.log("pointerevents: dragging_canvas start"); + this.dragging_canvas = true; + } + + } else if (e.which == 2) { + //middle button + + if (LiteGraph.middle_click_slot_add_default_node){ + if (node && this.allow_interaction && !skip_action && !this.read_only){ + //not dragging mouse to connect two slots + if ( + !this.connecting_node && + !node.flags.collapsed && + !this.live_mode + ) { + var mClikSlot = false; + var mClikSlot_index = false; + var mClikSlot_isOut = false; + //search for outputs + if (node.outputs) { + for ( var i = 0, l = node.outputs.length; i < l; ++i ) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + if (isInsideRectangle(e.canvasX,e.canvasY,link_pos[0] - 15,link_pos[1] - 10,30,20)) { + mClikSlot = output; + mClikSlot_index = i; + mClikSlot_isOut = true; + break; + } + } + } + + //search for inputs + if (node.inputs) { + for ( var i = 0, l = node.inputs.length; i < l; ++i ) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + if (isInsideRectangle(e.canvasX,e.canvasY,link_pos[0] - 15,link_pos[1] - 10,30,20)) { + mClikSlot = input; + mClikSlot_index = i; + mClikSlot_isOut = false; + break; + } + } + } + //console.log("middleClickSlots? "+mClikSlot+" & "+(mClikSlot_index!==false)); + if (mClikSlot && mClikSlot_index!==false){ + + var alphaPosY = 0.5-((mClikSlot_index+1)/((mClikSlot_isOut?node.outputs.length:node.inputs.length))); + var node_bounding = node.getBounding(); + // estimate a position: this is a bad semi-bad-working mess .. REFACTOR with a correct autoplacement that knows about the others slots and nodes + var posRef = [ (!mClikSlot_isOut?node_bounding[0]:node_bounding[0]+node_bounding[2])// + node_bounding[0]/this.canvas.width*150 + ,e.canvasY-80// + node_bounding[0]/this.canvas.width*66 // vertical "derive" + ]; + var nodeCreated = this.createDefaultNodeForSlot({ nodeFrom: !mClikSlot_isOut?null:node + ,slotFrom: !mClikSlot_isOut?null:mClikSlot_index + ,nodeTo: !mClikSlot_isOut?node:null + ,slotTo: !mClikSlot_isOut?mClikSlot_index:null + ,position: posRef //,e: e + ,nodeType: "AUTO" //nodeNewType + ,posAdd:[!mClikSlot_isOut?-30:30, -alphaPosY*130] //-alphaPosY*30] + ,posSizeFix:[!mClikSlot_isOut?-1:0, 0] //-alphaPosY*2*/ + }); + + } + } + } + } + + } else if (e.which == 3 || this.pointer_is_double) { + + //right button + if (this.allow_interaction && !skip_action && !this.read_only){ + + // is it hover a node ? + if (node){ + if(Object.keys(this.selected_nodes).length + && (this.selected_nodes[node.id] || e.shiftKey || e.ctrlKey || e.metaKey) + ){ + // is multiselected or using shift to include the now node + if (!this.selected_nodes[node.id]) this.selectNodes([node],true); // add this if not present + }else{ + // update selection + this.selectNodes([node]); + } + } + + // show menu on this node + this.processContextMenu(node, e); + } + + } + + //TODO + //if(this.node_selected != prev_selected) + // this.onNodeSelectionChange(this.node_selected); + + this.last_mouse[0] = e.clientX; + this.last_mouse[1] = e.clientY; + this.last_mouseclick = LiteGraph.getTime(); + this.last_mouse_dragging = true; + + /* + if( (this.dirty_canvas || this.dirty_bgcanvas) && this.rendering_timer_id == null) + this.draw(); + */ + + this.graph.change(); + + //this is to ensure to defocus(blur) if a text input element is on focus + if ( + !ref_window.document.activeElement || + (ref_window.document.activeElement.nodeName.toLowerCase() != + "input" && + ref_window.document.activeElement.nodeName.toLowerCase() != + "textarea") + ) { + e.preventDefault(); + } + e.stopPropagation(); + + if (this.onMouseDown) { + this.onMouseDown(e); + } + + return false; + }; + + /** + * Called when a mouse move event has to be processed + * @method processMouseMove + **/ + LGraphCanvas.prototype.processMouseMove = function(e) { + if (this.autoresize) { + this.resize(); + } + + if( this.set_canvas_dirty_on_mouse_event ) + this.dirty_canvas = true; + + if (!this.graph) { + return; + } + + LGraphCanvas.active_canvas = this; + this.adjustMouseEvent(e); + var mouse = [e.clientX, e.clientY]; + this.mouse[0] = mouse[0]; + this.mouse[1] = mouse[1]; + var delta = [ + mouse[0] - this.last_mouse[0], + mouse[1] - this.last_mouse[1] + ]; + this.last_mouse = mouse; + this.graph_mouse[0] = e.canvasX; + this.graph_mouse[1] = e.canvasY; + + //console.log("pointerevents: processMouseMove "+e.pointerId+" "+e.isPrimary); + + if(this.block_click) + { + //console.log("pointerevents: processMouseMove block_click"); + e.preventDefault(); + return false; + } + + e.dragging = this.last_mouse_dragging; + + if (this.node_widget) { + this.processNodeWidgets( + this.node_widget[0], + this.graph_mouse, + e, + this.node_widget[1] + ); + this.dirty_canvas = true; + } + + if (this.dragging_rectangle) + { + this.dragging_rectangle[2] = e.canvasX - this.dragging_rectangle[0]; + this.dragging_rectangle[3] = e.canvasY - this.dragging_rectangle[1]; + this.dirty_canvas = true; + } + else if (this.selected_group && !this.read_only) + { + //moving/resizing a group + if (this.selected_group_resizing) { + this.selected_group.size = [ + e.canvasX - this.selected_group.pos[0], + e.canvasY - this.selected_group.pos[1] + ]; + } else { + var deltax = delta[0] / this.ds.scale; + var deltay = delta[1] / this.ds.scale; + this.selected_group.move(deltax, deltay, e.ctrlKey); + if (this.selected_group._nodes.length) { + this.dirty_canvas = true; + } + } + this.dirty_bgcanvas = true; + } else if (this.dragging_canvas) { + ////console.log("pointerevents: processMouseMove is dragging_canvas"); + this.ds.offset[0] += delta[0] / this.ds.scale; + this.ds.offset[1] += delta[1] / this.ds.scale; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } else if (this.allow_interaction && !this.read_only) { + if (this.connecting_node) { + this.dirty_canvas = true; + } + + //get node over + var node = this.graph.getNodeOnPos(e.canvasX,e.canvasY,this.visible_nodes); + + //remove mouseover flag + for (var i = 0, l = this.graph._nodes.length; i < l; ++i) { + if (this.graph._nodes[i].mouseOver && node != this.graph._nodes[i] ) { + //mouse leave + this.graph._nodes[i].mouseOver = false; + if (this.node_over && this.node_over.onMouseLeave) { + this.node_over.onMouseLeave(e); + } + this.node_over = null; + this.dirty_canvas = true; + } + } + + //mouse over a node + if (node) { + + if(node.redraw_on_mouse) + this.dirty_canvas = true; + + //this.canvas.style.cursor = "move"; + if (!node.mouseOver) { + //mouse enter + node.mouseOver = true; + this.node_over = node; + this.dirty_canvas = true; + + if (node.onMouseEnter) { + node.onMouseEnter(e); + } + } + + //in case the node wants to do something + if (node.onMouseMove) { + node.onMouseMove( e, [e.canvasX - node.pos[0], e.canvasY - node.pos[1]], this ); + } + + //if dragging a link + if (this.connecting_node) { + + if (this.connecting_output){ + + var pos = this._highlight_input || [0, 0]; //to store the output of isOverNodeInput + + //on top of input + if (this.isOverNodeBox(node, e.canvasX, e.canvasY)) { + //mouse on top of the corner box, don't know what to do + } else { + //check if I have a slot below de mouse + var slot = this.isOverNodeInput( node, e.canvasX, e.canvasY, pos ); + if (slot != -1 && node.inputs[slot]) { + var slot_type = node.inputs[slot].type; + if ( LiteGraph.isValidConnection( this.connecting_output.type, slot_type ) ) { + this._highlight_input = pos; + this._highlight_input_slot = node.inputs[slot]; // XXX CHECK THIS + } + } else { + this._highlight_input = null; + this._highlight_input_slot = null; // XXX CHECK THIS + } + } + + }else if(this.connecting_input){ + + var pos = this._highlight_output || [0, 0]; //to store the output of isOverNodeOutput + + //on top of output + if (this.isOverNodeBox(node, e.canvasX, e.canvasY)) { + //mouse on top of the corner box, don't know what to do + } else { + //check if I have a slot below de mouse + var slot = this.isOverNodeOutput( node, e.canvasX, e.canvasY, pos ); + if (slot != -1 && node.outputs[slot]) { + var slot_type = node.outputs[slot].type; + if ( LiteGraph.isValidConnection( this.connecting_input.type, slot_type ) ) { + this._highlight_output = pos; + } + } else { + this._highlight_output = null; + } + } + } + } + + //Search for corner + if (this.canvas) { + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + node.pos[0] + node.size[0] - 5, + node.pos[1] + node.size[1] - 5, + 5, + 5 + ) + ) { + this.canvas.style.cursor = "se-resize"; + } else { + this.canvas.style.cursor = "crosshair"; + } + } + } else { //not over a node + + //search for link connector + var over_link = null; + for (var i = 0; i < this.visible_links.length; ++i) { + var link = this.visible_links[i]; + var center = link._pos; + if ( + !center || + e.canvasX < center[0] - 4 || + e.canvasX > center[0] + 4 || + e.canvasY < center[1] - 4 || + e.canvasY > center[1] + 4 + ) { + continue; + } + over_link = link; + break; + } + if( over_link != this.over_link_center ) + { + this.over_link_center = over_link; + this.dirty_canvas = true; + } + + if (this.canvas) { + this.canvas.style.cursor = ""; + } + } //end + + //send event to node if capturing input (used with widgets that allow drag outside of the area of the node) + if ( this.node_capturing_input && this.node_capturing_input != node && this.node_capturing_input.onMouseMove ) { + this.node_capturing_input.onMouseMove(e,[e.canvasX - this.node_capturing_input.pos[0],e.canvasY - this.node_capturing_input.pos[1]], this); + } + + //node being dragged + if (this.node_dragged && !this.live_mode) { + //console.log("draggin!",this.selected_nodes); + for (var i in this.selected_nodes) { + var n = this.selected_nodes[i]; + n.pos[0] += delta[0] / this.ds.scale; + n.pos[1] += delta[1] / this.ds.scale; + } + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } + + if (this.resizing_node && !this.live_mode) { + //convert mouse to node space + var desired_size = [ e.canvasX - this.resizing_node.pos[0], e.canvasY - this.resizing_node.pos[1] ]; + var min_size = this.resizing_node.computeSize(); + desired_size[0] = Math.max( min_size[0], desired_size[0] ); + desired_size[1] = Math.max( min_size[1], desired_size[1] ); + this.resizing_node.setSize( desired_size ); + + this.canvas.style.cursor = "se-resize"; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } + } + + e.preventDefault(); + return false; + }; + + /** + * Called when a mouse up event has to be processed + * @method processMouseUp + **/ + LGraphCanvas.prototype.processMouseUp = function(e) { + + var is_primary = ( e.isPrimary === undefined || e.isPrimary ); + + //early exit for extra pointer + if(!is_primary){ + /*e.stopPropagation(); + e.preventDefault();*/ + //console.log("pointerevents: processMouseUp pointerN_stop "+e.pointerId+" "+e.isPrimary); + return false; + } + + //console.log("pointerevents: processMouseUp "+e.pointerId+" "+e.isPrimary+" :: "+e.clientX+" "+e.clientY); + + if( this.set_canvas_dirty_on_mouse_event ) + this.dirty_canvas = true; + + if (!this.graph) + return; + + var window = this.getCanvasWindow(); + var document = window.document; + LGraphCanvas.active_canvas = this; + + //restore the mousemove event back to the canvas + if(!this.options.skip_events) + { + //console.log("pointerevents: processMouseUp adjustEventListener"); + LiteGraph.pointerListenerRemove(document,"move", this._mousemove_callback,true); + LiteGraph.pointerListenerAdd(this.canvas,"move", this._mousemove_callback,true); + LiteGraph.pointerListenerRemove(document,"up", this._mouseup_callback,true); + } + + this.adjustMouseEvent(e); + var now = LiteGraph.getTime(); + e.click_time = now - this.last_mouseclick; + this.last_mouse_dragging = false; + this.last_click_position = null; + + if(this.block_click) + { + //console.log("pointerevents: processMouseUp block_clicks"); + this.block_click = false; //used to avoid sending twice a click in a immediate button + } + + //console.log("pointerevents: processMouseUp which: "+e.which); + + if (e.which == 1) { + + if( this.node_widget ) + { + this.processNodeWidgets( this.node_widget[0], this.graph_mouse, e ); + } + + //left button + this.node_widget = null; + + if (this.selected_group) { + var diffx = + this.selected_group.pos[0] - + Math.round(this.selected_group.pos[0]); + var diffy = + this.selected_group.pos[1] - + Math.round(this.selected_group.pos[1]); + this.selected_group.move(diffx, diffy, e.ctrlKey); + this.selected_group.pos[0] = Math.round( + this.selected_group.pos[0] + ); + this.selected_group.pos[1] = Math.round( + this.selected_group.pos[1] + ); + if (this.selected_group._nodes.length) { + this.dirty_canvas = true; + } + this.selected_group = null; + } + this.selected_group_resizing = false; + + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes + ); + + if (this.dragging_rectangle) { + if (this.graph) { + var nodes = this.graph._nodes; + var node_bounding = new Float32Array(4); + + //compute bounding and flip if left to right + var w = Math.abs(this.dragging_rectangle[2]); + var h = Math.abs(this.dragging_rectangle[3]); + var startx = + this.dragging_rectangle[2] < 0 + ? this.dragging_rectangle[0] - w + : this.dragging_rectangle[0]; + var starty = + this.dragging_rectangle[3] < 0 + ? this.dragging_rectangle[1] - h + : this.dragging_rectangle[1]; + this.dragging_rectangle[0] = startx; + this.dragging_rectangle[1] = starty; + this.dragging_rectangle[2] = w; + this.dragging_rectangle[3] = h; + + // test dragging rect size, if minimun simulate a click + if (!node || (w > 10 && h > 10 )){ + //test against all nodes (not visible because the rectangle maybe start outside + var to_select = []; + for (var i = 0; i < nodes.length; ++i) { + var nodeX = nodes[i]; + nodeX.getBounding(node_bounding); + if ( + !overlapBounding( + this.dragging_rectangle, + node_bounding + ) + ) { + continue; + } //out of the visible area + to_select.push(nodeX); + } + if (to_select.length) { + this.selectNodes(to_select,e.shiftKey); // add to selection with shift + } + }else{ + // will select of update selection + this.selectNodes([node],e.shiftKey||e.ctrlKey); // add to selection add to selection with ctrlKey or shiftKey + } + + } + this.dragging_rectangle = null; + } else if (this.connecting_node) { + //dragging a connection + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + + var connInOrOut = this.connecting_output || this.connecting_input; + var connType = connInOrOut.type; + + //node below mouse + if (node) { + + /* no need to condition on event type.. just another type + if ( + connType == LiteGraph.EVENT && + this.isOverNodeBox(node, e.canvasX, e.canvasY) + ) { + + this.connecting_node.connect( + this.connecting_slot, + node, + LiteGraph.EVENT + ); + + } else {*/ + + //slot below mouse? connect + + if (this.connecting_output){ + + var slot = this.isOverNodeInput( + node, + e.canvasX, + e.canvasY + ); + if (slot != -1) { + this.connecting_node.connect(this.connecting_slot, node, slot); + } else { + //not on top of an input + // look for a good slot + this.connecting_node.connectByType(this.connecting_slot,node,connType); + } + + }else if (this.connecting_input){ + + var slot = this.isOverNodeOutput( + node, + e.canvasX, + e.canvasY + ); + + if (slot != -1) { + node.connect(slot, this.connecting_node, this.connecting_slot); // this is inverted has output-input nature like + } else { + //not on top of an input + // look for a good slot + this.connecting_node.connectByTypeOutput(this.connecting_slot,node,connType); + } + + } + + + //} + + }else{ + + // add menu when releasing link in empty space + if (LiteGraph.release_link_on_empty_shows_menu){ + if (e.shiftKey && this.allow_searchbox){ + if(this.connecting_output){ + this.showSearchBox(e,{node_from: this.connecting_node, slot_from: this.connecting_output, type_filter_in: this.connecting_output.type}); + }else if(this.connecting_input){ + this.showSearchBox(e,{node_to: this.connecting_node, slot_from: this.connecting_input, type_filter_out: this.connecting_input.type}); + } + }else{ + if(this.connecting_output){ + this.showConnectionMenu({nodeFrom: this.connecting_node, slotFrom: this.connecting_output, e: e}); + }else if(this.connecting_input){ + this.showConnectionMenu({nodeTo: this.connecting_node, slotTo: this.connecting_input, e: e}); + } + } + } + } + + this.connecting_output = null; + this.connecting_input = null; + this.connecting_pos = null; + this.connecting_node = null; + this.connecting_slot = -1; + } //not dragging connection + else if (this.resizing_node) { + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.graph.afterChange(this.resizing_node); + this.resizing_node = null; + } else if (this.node_dragged) { + //node being dragged? + var node = this.node_dragged; + if ( + node && + e.click_time < 300 && + isInsideRectangle( e.canvasX, e.canvasY, node.pos[0], node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT, LiteGraph.NODE_TITLE_HEIGHT, LiteGraph.NODE_TITLE_HEIGHT ) + ) { + node.collapse(); + } + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.node_dragged.pos[0] = Math.round(this.node_dragged.pos[0]); + this.node_dragged.pos[1] = Math.round(this.node_dragged.pos[1]); + if (this.graph.config.align_to_grid || this.align_to_grid ) { + this.node_dragged.alignToGrid(); + } + if( this.onNodeMoved ) + this.onNodeMoved( this.node_dragged ); + this.graph.afterChange(this.node_dragged); + this.node_dragged = null; + } //no node being dragged + else { + //get node over + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes + ); + + if (!node && e.click_time < 300) { + this.deselectAllNodes(); + } + + this.dirty_canvas = true; + this.dragging_canvas = false; + + if (this.node_over && this.node_over.onMouseUp) { + this.node_over.onMouseUp( e, [ e.canvasX - this.node_over.pos[0], e.canvasY - this.node_over.pos[1] ], this ); + } + if ( + this.node_capturing_input && + this.node_capturing_input.onMouseUp + ) { + this.node_capturing_input.onMouseUp(e, [ + e.canvasX - this.node_capturing_input.pos[0], + e.canvasY - this.node_capturing_input.pos[1] + ]); + } + } + } else if (e.which == 2) { + //middle button + //trace("middle"); + this.dirty_canvas = true; + this.dragging_canvas = false; + } else if (e.which == 3) { + //right button + //trace("right"); + this.dirty_canvas = true; + this.dragging_canvas = false; + } + + /* + if((this.dirty_canvas || this.dirty_bgcanvas) && this.rendering_timer_id == null) + this.draw(); + */ + + if (is_primary) + { + this.pointer_is_down = false; + this.pointer_is_double = false; + } + + this.graph.change(); + + //console.log("pointerevents: processMouseUp stopPropagation"); + e.stopPropagation(); + e.preventDefault(); + return false; + }; + + /** + * Called when a mouse wheel event has to be processed + * @method processMouseWheel + **/ + LGraphCanvas.prototype.processMouseWheel = function(e) { + if (!this.graph || !this.allow_dragcanvas) { + return; + } + + var delta = e.wheelDeltaY != null ? e.wheelDeltaY : e.detail * -60; + + this.adjustMouseEvent(e); + + var x = e.clientX; + var y = e.clientY; + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + if(!is_inside) + return; + + var scale = this.ds.scale; + + if (delta > 0) { + scale *= 1.1; + } else if (delta < 0) { + scale *= 1 / 1.1; + } + + //this.setZoom( scale, [ e.clientX, e.clientY ] ); + this.ds.changeScale(scale, [e.clientX, e.clientY]); + + this.graph.change(); + + e.preventDefault(); + return false; // prevent default + }; + + /** + * returns true if a position (in graph space) is on top of a node little corner box + * @method isOverNodeBox + **/ + LGraphCanvas.prototype.isOverNodeBox = function(node, canvasx, canvasy) { + var title_height = LiteGraph.NODE_TITLE_HEIGHT; + if ( + isInsideRectangle( + canvasx, + canvasy, + node.pos[0] + 2, + node.pos[1] + 2 - title_height, + title_height - 4, + title_height - 4 + ) + ) { + return true; + } + return false; + }; + + /** + * returns the INDEX if a position (in graph space) is on top of a node input slot + * @method isOverNodeInput + **/ + LGraphCanvas.prototype.isOverNodeInput = function( + node, + canvasx, + canvasy, + slot_pos + ) { + if (node.inputs) { + for (var i = 0, l = node.inputs.length; i < l; ++i) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + var is_inside = false; + if (node.horizontal) { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 5, + link_pos[1] - 10, + 10, + 20 + ); + } else { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 10, + link_pos[1] - 5, + 40, + 10 + ); + } + if (is_inside) { + if (slot_pos) { + slot_pos[0] = link_pos[0]; + slot_pos[1] = link_pos[1]; + } + return i; + } + } + } + return -1; + }; + + /** + * returns the INDEX if a position (in graph space) is on top of a node output slot + * @method isOverNodeOuput + **/ + LGraphCanvas.prototype.isOverNodeOutput = function( + node, + canvasx, + canvasy, + slot_pos + ) { + if (node.outputs) { + for (var i = 0, l = node.outputs.length; i < l; ++i) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + var is_inside = false; + if (node.horizontal) { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 5, + link_pos[1] - 10, + 10, + 20 + ); + } else { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 10, + link_pos[1] - 5, + 40, + 10 + ); + } + if (is_inside) { + if (slot_pos) { + slot_pos[0] = link_pos[0]; + slot_pos[1] = link_pos[1]; + } + return i; + } + } + } + return -1; + }; + + /** + * process a key event + * @method processKey + **/ + LGraphCanvas.prototype.processKey = function(e) { + if (!this.graph) { + return; + } + + var block_default = false; + //console.log(e); //debug + + if (e.target.localName == "input") { + return; + } + + if (e.type == "keydown") { + if (e.keyCode == 32) { + //space + this.dragging_canvas = true; + block_default = true; + } + + if (e.keyCode == 27) { + //esc + if(this.node_panel) this.node_panel.close(); + if(this.options_panel) this.options_panel.close(); + block_default = true; + } + + //select all Control A + if (e.keyCode == 65 && e.ctrlKey) { + this.selectNodes(); + block_default = true; + } + + if (e.code == "KeyC" && (e.metaKey || e.ctrlKey) && !e.shiftKey) { + //copy + if (this.selected_nodes) { + this.copyToClipboard(); + block_default = true; + } + } + + if (e.code == "KeyV" && (e.metaKey || e.ctrlKey) && !e.shiftKey) { + //paste + this.pasteFromClipboard(); + } + + //delete or backspace + if (e.keyCode == 46 || e.keyCode == 8) { + if ( + e.target.localName != "input" && + e.target.localName != "textarea" + ) { + this.deleteSelectedNodes(); + block_default = true; + } + } + + //collapse + //... + + //TODO + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].onKeyDown) { + this.selected_nodes[i].onKeyDown(e); + } + } + } + } else if (e.type == "keyup") { + if (e.keyCode == 32) { + // space + this.dragging_canvas = false; + } + + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].onKeyUp) { + this.selected_nodes[i].onKeyUp(e); + } + } + } + } + + this.graph.change(); + + if (block_default) { + e.preventDefault(); + e.stopImmediatePropagation(); + return false; + } + }; + + LGraphCanvas.prototype.copyToClipboard = function() { + var clipboard_info = { + nodes: [], + links: [] + }; + var index = 0; + var selected_nodes_array = []; + for (var i in this.selected_nodes) { + var node = this.selected_nodes[i]; + node._relative_id = index; + selected_nodes_array.push(node); + index += 1; + } + + for (var i = 0; i < selected_nodes_array.length; ++i) { + var node = selected_nodes_array[i]; + var cloned = node.clone(); + if(!cloned) + { + console.warn("node type not found: " + node.type ); + continue; + } + clipboard_info.nodes.push(cloned.serialize()); + if (node.inputs && node.inputs.length) { + for (var j = 0; j < node.inputs.length; ++j) { + var input = node.inputs[j]; + if (!input || input.link == null) { + continue; + } + var link_info = this.graph.links[input.link]; + if (!link_info) { + continue; + } + var target_node = this.graph.getNodeById( + link_info.origin_id + ); + if (!target_node || !this.selected_nodes[target_node.id]) { + //improve this by allowing connections to non-selected nodes + continue; + } //not selected + clipboard_info.links.push([ + target_node._relative_id, + link_info.origin_slot, //j, + node._relative_id, + link_info.target_slot + ]); + } + } + } + localStorage.setItem( + "litegrapheditor_clipboard", + JSON.stringify(clipboard_info) + ); + }; + + LGraphCanvas.prototype.pasteFromClipboard = function() { + var data = localStorage.getItem("litegrapheditor_clipboard"); + if (!data) { + return; + } + + this.graph.beforeChange(); + + //create nodes + var clipboard_info = JSON.parse(data); + // calculate top-left node, could work without this processing but using diff with last node pos :: clipboard_info.nodes[clipboard_info.nodes.length-1].pos + var posMin = false; + var posMinIndexes = false; + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + if (posMin){ + if(posMin[0]>clipboard_info.nodes[i].pos[0]){ + posMin[0] = clipboard_info.nodes[i].pos[0]; + posMinIndexes[0] = i; + } + if(posMin[1]>clipboard_info.nodes[i].pos[1]){ + posMin[1] = clipboard_info.nodes[i].pos[1]; + posMinIndexes[1] = i; + } + } + else{ + posMin = [clipboard_info.nodes[i].pos[0], clipboard_info.nodes[i].pos[1]]; + posMinIndexes = [i, i]; + } + } + var nodes = []; + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + var node_data = clipboard_info.nodes[i]; + var node = LiteGraph.createNode(node_data.type); + if (node) { + node.configure(node_data); + + //paste in last known mouse position + node.pos[0] += this.graph_mouse[0] - posMin[0]; //+= 5; + node.pos[1] += this.graph_mouse[1] - posMin[1]; //+= 5; + + this.graph.add(node,{doProcessChange:false}); + + nodes.push(node); + } + } + + //create links + for (var i = 0; i < clipboard_info.links.length; ++i) { + var link_info = clipboard_info.links[i]; + var origin_node = nodes[link_info[0]]; + var target_node = nodes[link_info[2]]; + if( origin_node && target_node ) + origin_node.connect(link_info[1], target_node, link_info[3]); + else + console.warn("Warning, nodes missing on pasting"); + } + + this.selectNodes(nodes); + + this.graph.afterChange(); + }; + + /** + * process a item drop event on top the canvas + * @method processDrop + **/ + LGraphCanvas.prototype.processDrop = function(e) { + e.preventDefault(); + this.adjustMouseEvent(e); + var x = e.clientX; + var y = e.clientY; + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + if(!is_inside){ + return; + // --- BREAK --- + } + + var pos = [e.canvasX, e.canvasY]; + + + var node = this.graph ? this.graph.getNodeOnPos(pos[0], pos[1]) : null; + + if (!node) { + var r = null; + if (this.onDropItem) { + r = this.onDropItem(event); + } + if (!r) { + this.checkDropItem(e); + } + return; + } + + if (node.onDropFile || node.onDropData) { + var files = e.dataTransfer.files; + if (files && files.length) { + for (var i = 0; i < files.length; i++) { + var file = e.dataTransfer.files[0]; + var filename = file.name; + var ext = LGraphCanvas.getFileExtension(filename); + //console.log(file); + + if (node.onDropFile) { + node.onDropFile(file); + } + + if (node.onDropData) { + //prepare reader + var reader = new FileReader(); + reader.onload = function(event) { + //console.log(event.target); + var data = event.target.result; + node.onDropData(data, filename, file); + }; + + //read data + var type = file.type.split("/")[0]; + if (type == "text" || type == "") { + reader.readAsText(file); + } else if (type == "image") { + reader.readAsDataURL(file); + } else { + reader.readAsArrayBuffer(file); + } + } + } + } + } + + if (node.onDropItem) { + if (node.onDropItem(event)) { + return true; + } + } + + if (this.onDropItem) { + return this.onDropItem(event); + } + + return false; + }; + + //called if the graph doesn't have a default drop item behaviour + LGraphCanvas.prototype.checkDropItem = function(e) { + if (e.dataTransfer.files.length) { + var file = e.dataTransfer.files[0]; + var ext = LGraphCanvas.getFileExtension(file.name).toLowerCase(); + var nodetype = LiteGraph.node_types_by_file_extension[ext]; + if (nodetype) { + this.graph.beforeChange(); + var node = LiteGraph.createNode(nodetype.type); + node.pos = [e.canvasX, e.canvasY]; + this.graph.add(node); + if (node.onDropFile) { + node.onDropFile(file); + } + this.graph.afterChange(); + } + } + }; + + LGraphCanvas.prototype.processNodeDblClicked = function(n) { + if (this.onShowNodePanel) { + this.onShowNodePanel(n); + } + else + { + this.showShowNodePanel(n); + } + + if (this.onNodeDblClicked) { + this.onNodeDblClicked(n); + } + + this.setDirty(true); + }; + + LGraphCanvas.prototype.processNodeSelected = function(node, e) { + this.selectNode(node, e && (e.shiftKey||e.ctrlKey)); + if (this.onNodeSelected) { + this.onNodeSelected(node); + } + }; + + /** + * selects a given node (or adds it to the current selection) + * @method selectNode + **/ + LGraphCanvas.prototype.selectNode = function( + node, + add_to_current_selection + ) { + if (node == null) { + this.deselectAllNodes(); + } else { + this.selectNodes([node], add_to_current_selection); + } + }; + + /** + * selects several nodes (or adds them to the current selection) + * @method selectNodes + **/ + LGraphCanvas.prototype.selectNodes = function( nodes, add_to_current_selection ) + { + if (!add_to_current_selection) { + this.deselectAllNodes(); + } + + nodes = nodes || this.graph._nodes; + if (typeof nodes == "string") nodes = [nodes]; + for (var i in nodes) { + var node = nodes[i]; + if (node.is_selected) { + continue; + } + + if (!node.is_selected && node.onSelected) { + node.onSelected(); + } + node.is_selected = true; + this.selected_nodes[node.id] = node; + + if (node.inputs) { + for (var j = 0; j < node.inputs.length; ++j) { + this.highlighted_links[node.inputs[j].link] = true; + } + } + if (node.outputs) { + for (var j = 0; j < node.outputs.length; ++j) { + var out = node.outputs[j]; + if (out.links) { + for (var k = 0; k < out.links.length; ++k) { + this.highlighted_links[out.links[k]] = true; + } + } + } + } + } + + if( this.onSelectionChange ) + this.onSelectionChange( this.selected_nodes ); + + this.setDirty(true); + }; + + /** + * removes a node from the current selection + * @method deselectNode + **/ + LGraphCanvas.prototype.deselectNode = function(node) { + if (!node.is_selected) { + return; + } + if (node.onDeselected) { + node.onDeselected(); + } + node.is_selected = false; + + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + + //remove highlighted + if (node.inputs) { + for (var i = 0; i < node.inputs.length; ++i) { + delete this.highlighted_links[node.inputs[i].link]; + } + } + if (node.outputs) { + for (var i = 0; i < node.outputs.length; ++i) { + var out = node.outputs[i]; + if (out.links) { + for (var j = 0; j < out.links.length; ++j) { + delete this.highlighted_links[out.links[j]]; + } + } + } + } + }; + + /** + * removes all nodes from the current selection + * @method deselectAllNodes + **/ + LGraphCanvas.prototype.deselectAllNodes = function() { + if (!this.graph) { + return; + } + var nodes = this.graph._nodes; + for (var i = 0, l = nodes.length; i < l; ++i) { + var node = nodes[i]; + if (!node.is_selected) { + continue; + } + if (node.onDeselected) { + node.onDeselected(); + } + node.is_selected = false; + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + } + this.selected_nodes = {}; + this.current_node = null; + this.highlighted_links = {}; + if( this.onSelectionChange ) + this.onSelectionChange( this.selected_nodes ); + this.setDirty(true); + }; + + /** + * deletes all nodes in the current selection from the graph + * @method deleteSelectedNodes + **/ + LGraphCanvas.prototype.deleteSelectedNodes = function() { + + this.graph.beforeChange(); + + for (var i in this.selected_nodes) { + var node = this.selected_nodes[i]; + + if(node.block_delete) + continue; + + //autoconnect when possible (very basic, only takes into account first input-output) + if(node.inputs && node.inputs.length && node.outputs && node.outputs.length && LiteGraph.isValidConnection( node.inputs[0].type, node.outputs[0].type ) && node.inputs[0].link && node.outputs[0].links && node.outputs[0].links.length ) + { + var input_link = node.graph.links[ node.inputs[0].link ]; + var output_link = node.graph.links[ node.outputs[0].links[0] ]; + var input_node = node.getInputNode(0); + var output_node = node.getOutputNodes(0)[0]; + if(input_node && output_node) + input_node.connect( input_link.origin_slot, output_node, output_link.target_slot ); + } + this.graph.remove(node); + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + } + this.selected_nodes = {}; + this.current_node = null; + this.highlighted_links = {}; + this.setDirty(true); + this.graph.afterChange(); + }; + + /** + * centers the camera on a given node + * @method centerOnNode + **/ + LGraphCanvas.prototype.centerOnNode = function(node) { + this.ds.offset[0] = + -node.pos[0] - + node.size[0] * 0.5 + + (this.canvas.width * 0.5) / this.ds.scale; + this.ds.offset[1] = + -node.pos[1] - + node.size[1] * 0.5 + + (this.canvas.height * 0.5) / this.ds.scale; + this.setDirty(true, true); + }; + + /** + * adds some useful properties to a mouse event, like the position in graph coordinates + * @method adjustMouseEvent + **/ + LGraphCanvas.prototype.adjustMouseEvent = function(e) { + var clientX_rel = 0; + var clientY_rel = 0; + + if (this.canvas) { + var b = this.canvas.getBoundingClientRect(); + clientX_rel = e.clientX - b.left; + clientY_rel = e.clientY - b.top; + } else { + clientX_rel = e.clientX; + clientY_rel = e.clientY; + } + + e.deltaX = clientX_rel - this.last_mouse_position[0]; + e.deltaY = clientY_rel- this.last_mouse_position[1]; + + this.last_mouse_position[0] = clientX_rel; + this.last_mouse_position[1] = clientY_rel; + + e.canvasX = clientX_rel / this.ds.scale - this.ds.offset[0]; + e.canvasY = clientY_rel / this.ds.scale - this.ds.offset[1]; + + //console.log("pointerevents: adjustMouseEvent "+e.clientX+":"+e.clientY+" "+clientX_rel+":"+clientY_rel+" "+e.canvasX+":"+e.canvasY); + }; + + /** + * changes the zoom level of the graph (default is 1), you can pass also a place used to pivot the zoom + * @method setZoom + **/ + LGraphCanvas.prototype.setZoom = function(value, zooming_center) { + this.ds.changeScale(value, zooming_center); + /* + if(!zooming_center && this.canvas) + zooming_center = [this.canvas.width * 0.5,this.canvas.height * 0.5]; + + var center = this.convertOffsetToCanvas( zooming_center ); + + this.ds.scale = value; + + if(this.scale > this.max_zoom) + this.scale = this.max_zoom; + else if(this.scale < this.min_zoom) + this.scale = this.min_zoom; + + var new_center = this.convertOffsetToCanvas( zooming_center ); + var delta_offset = [new_center[0] - center[0], new_center[1] - center[1]]; + + this.offset[0] += delta_offset[0]; + this.offset[1] += delta_offset[1]; + */ + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + }; + + /** + * converts a coordinate from graph coordinates to canvas2D coordinates + * @method convertOffsetToCanvas + **/ + LGraphCanvas.prototype.convertOffsetToCanvas = function(pos, out) { + return this.ds.convertOffsetToCanvas(pos, out); + }; + + /** + * converts a coordinate from Canvas2D coordinates to graph space + * @method convertCanvasToOffset + **/ + LGraphCanvas.prototype.convertCanvasToOffset = function(pos, out) { + return this.ds.convertCanvasToOffset(pos, out); + }; + + //converts event coordinates from canvas2D to graph coordinates + LGraphCanvas.prototype.convertEventToCanvasOffset = function(e) { + var rect = this.canvas.getBoundingClientRect(); + return this.convertCanvasToOffset([ + e.clientX - rect.left, + e.clientY - rect.top + ]); + }; + + /** + * brings a node to front (above all other nodes) + * @method bringToFront + **/ + LGraphCanvas.prototype.bringToFront = function(node) { + var i = this.graph._nodes.indexOf(node); + if (i == -1) { + return; + } + + this.graph._nodes.splice(i, 1); + this.graph._nodes.push(node); + }; + + /** + * sends a node to the back (below all other nodes) + * @method sendToBack + **/ + LGraphCanvas.prototype.sendToBack = function(node) { + var i = this.graph._nodes.indexOf(node); + if (i == -1) { + return; + } + + this.graph._nodes.splice(i, 1); + this.graph._nodes.unshift(node); + }; + + /* Interaction */ + + /* LGraphCanvas render */ + var temp = new Float32Array(4); + + /** + * checks which nodes are visible (inside the camera area) + * @method computeVisibleNodes + **/ + LGraphCanvas.prototype.computeVisibleNodes = function(nodes, out) { + var visible_nodes = out || []; + visible_nodes.length = 0; + nodes = nodes || this.graph._nodes; + for (var i = 0, l = nodes.length; i < l; ++i) { + var n = nodes[i]; + + //skip rendering nodes in live mode + if (this.live_mode && !n.onDrawBackground && !n.onDrawForeground) { + continue; + } + + if (!overlapBounding(this.visible_area, n.getBounding(temp))) { + continue; + } //out of the visible area + + visible_nodes.push(n); + } + return visible_nodes; + }; + + /** + * renders the whole canvas content, by rendering in two separated canvas, one containing the background grid and the connections, and one containing the nodes) + * @method draw + **/ + LGraphCanvas.prototype.draw = function(force_canvas, force_bgcanvas) { + if (!this.canvas || this.canvas.width == 0 || this.canvas.height == 0) { + return; + } + + //fps counting + var now = LiteGraph.getTime(); + this.render_time = (now - this.last_draw_time) * 0.001; + this.last_draw_time = now; + + if (this.graph) { + this.ds.computeVisibleArea(this.viewport); + } + + if ( + this.dirty_bgcanvas || + force_bgcanvas || + this.always_render_background || + (this.graph && + this.graph._last_trigger_time && + now - this.graph._last_trigger_time < 1000) + ) { + this.drawBackCanvas(); + } + + if (this.dirty_canvas || force_canvas) { + this.drawFrontCanvas(); + } + + this.fps = this.render_time ? 1.0 / this.render_time : 0; + this.frame += 1; + }; + + /** + * draws the front canvas (the one containing all the nodes) + * @method drawFrontCanvas + **/ + LGraphCanvas.prototype.drawFrontCanvas = function() { + this.dirty_canvas = false; + + if (!this.ctx) { + this.ctx = this.bgcanvas.getContext("2d"); + } + var ctx = this.ctx; + if (!ctx) { + //maybe is using webgl... + return; + } + + var canvas = this.canvas; + if ( ctx.start2D && !this.viewport ) { + ctx.start2D(); + ctx.restore(); + ctx.setTransform(1, 0, 0, 1, 0, 0); + } + + //clip dirty area if there is one, otherwise work in full canvas + var area = this.viewport || this.dirty_area; + if (area) { + ctx.save(); + ctx.beginPath(); + ctx.rect( area[0],area[1],area[2],area[3] ); + ctx.clip(); + } + + //clear + //canvas.width = canvas.width; + if (this.clear_background) { + if(area) + ctx.clearRect( area[0],area[1],area[2],area[3] ); + else + ctx.clearRect(0, 0, canvas.width, canvas.height); + } + + //draw bg canvas + if (this.bgcanvas == this.canvas) { + this.drawBackCanvas(); + } else { + ctx.drawImage( this.bgcanvas, 0, 0 ); + } + + //rendering + if (this.onRender) { + this.onRender(canvas, ctx); + } + + //info widget + if (this.show_info) { + this.renderInfo(ctx, area ? area[0] : 0, area ? area[1] : 0 ); + } + + if (this.graph) { + //apply transformations + ctx.save(); + this.ds.toCanvasContext(ctx); + + //draw nodes + var drawn_nodes = 0; + var visible_nodes = this.computeVisibleNodes( + null, + this.visible_nodes + ); + + for (var i = 0; i < visible_nodes.length; ++i) { + var node = visible_nodes[i]; + + //transform coords system + ctx.save(); + ctx.translate(node.pos[0], node.pos[1]); + + //Draw + this.drawNode(node, ctx); + drawn_nodes += 1; + + //Restore + ctx.restore(); + } + + //on top (debug) + if (this.render_execution_order) { + this.drawExecutionOrder(ctx); + } + + //connections ontop? + if (this.graph.config.links_ontop) { + if (!this.live_mode) { + this.drawConnections(ctx); + } + } + + //current connection (the one being dragged by the mouse) + if (this.connecting_pos != null) { + ctx.lineWidth = this.connections_width; + var link_color = null; + + var connInOrOut = this.connecting_output || this.connecting_input; + + var connType = connInOrOut.type; + var connDir = connInOrOut.dir; + if(connDir == null) + { + if (this.connecting_output) + connDir = this.connecting_node.horizontal ? LiteGraph.DOWN : LiteGraph.RIGHT; + else + connDir = this.connecting_node.horizontal ? LiteGraph.UP : LiteGraph.LEFT; + } + var connShape = connInOrOut.shape; + + switch (connType) { + case LiteGraph.EVENT: + link_color = LiteGraph.EVENT_LINK_COLOR; + break; + default: + link_color = LiteGraph.CONNECTING_LINK_COLOR; + } + + //the connection being dragged by the mouse + this.renderLink( + ctx, + this.connecting_pos, + [this.graph_mouse[0], this.graph_mouse[1]], + null, + false, + null, + link_color, + connDir, + LiteGraph.CENTER + ); + + ctx.beginPath(); + if ( + connType === LiteGraph.EVENT || + connShape === LiteGraph.BOX_SHAPE + ) { + ctx.rect( + this.connecting_pos[0] - 6 + 0.5, + this.connecting_pos[1] - 5 + 0.5, + 14, + 10 + ); + ctx.fill(); + ctx.beginPath(); + ctx.rect( + this.graph_mouse[0] - 6 + 0.5, + this.graph_mouse[1] - 5 + 0.5, + 14, + 10 + ); + } else if (connShape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(this.connecting_pos[0] + 8, this.connecting_pos[1] + 0.5); + ctx.lineTo(this.connecting_pos[0] - 4, this.connecting_pos[1] + 6 + 0.5); + ctx.lineTo(this.connecting_pos[0] - 4, this.connecting_pos[1] - 6 + 0.5); + ctx.closePath(); + } + else { + ctx.arc( + this.connecting_pos[0], + this.connecting_pos[1], + 4, + 0, + Math.PI * 2 + ); + ctx.fill(); + ctx.beginPath(); + ctx.arc( + this.graph_mouse[0], + this.graph_mouse[1], + 4, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + + ctx.fillStyle = "#ffcc00"; + if (this._highlight_input) { + ctx.beginPath(); + var shape = this._highlight_input_slot.shape; + if (shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(this._highlight_input[0] + 8, this._highlight_input[1] + 0.5); + ctx.lineTo(this._highlight_input[0] - 4, this._highlight_input[1] + 6 + 0.5); + ctx.lineTo(this._highlight_input[0] - 4, this._highlight_input[1] - 6 + 0.5); + ctx.closePath(); + } else { + ctx.arc( + this._highlight_input[0], + this._highlight_input[1], + 6, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + } + if (this._highlight_output) { + ctx.beginPath(); + if (shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(this._highlight_output[0] + 8, this._highlight_output[1] + 0.5); + ctx.lineTo(this._highlight_output[0] - 4, this._highlight_output[1] + 6 + 0.5); + ctx.lineTo(this._highlight_output[0] - 4, this._highlight_output[1] - 6 + 0.5); + ctx.closePath(); + } else { + ctx.arc( + this._highlight_output[0], + this._highlight_output[1], + 6, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + } + } + + //the selection rectangle + if (this.dragging_rectangle) { + ctx.strokeStyle = "#FFF"; + ctx.strokeRect( + this.dragging_rectangle[0], + this.dragging_rectangle[1], + this.dragging_rectangle[2], + this.dragging_rectangle[3] + ); + } + + //on top of link center + if(this.over_link_center && this.render_link_tooltip) + this.drawLinkTooltip( ctx, this.over_link_center ); + else + if(this.onDrawLinkTooltip) //to remove + this.onDrawLinkTooltip(ctx,null); + + //custom info + if (this.onDrawForeground) { + this.onDrawForeground(ctx, this.visible_rect); + } + + ctx.restore(); + } + + //draws panel in the corner + if (this._graph_stack && this._graph_stack.length) { + this.drawSubgraphPanel( ctx ); + } + + + if (this.onDrawOverlay) { + this.onDrawOverlay(ctx); + } + + if (area){ + ctx.restore(); + } + + if (ctx.finish2D) { + //this is a function I use in webgl renderer + ctx.finish2D(); + } + }; + + /** + * draws the panel in the corner that shows subgraph properties + * @method drawSubgraphPanel + **/ + LGraphCanvas.prototype.drawSubgraphPanel = function (ctx) { + var subgraph = this.graph; + var subnode = subgraph._subgraph_node; + if (!subnode) { + console.warn("subgraph without subnode"); + return; + } + this.drawSubgraphPanelLeft(subgraph, subnode, ctx) + this.drawSubgraphPanelRight(subgraph, subnode, ctx) + } + + LGraphCanvas.prototype.drawSubgraphPanelLeft = function (subgraph, subnode, ctx) { + var num = subnode.inputs ? subnode.inputs.length : 0; + var w = 200; + var h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6); + + ctx.fillStyle = "#111"; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + ctx.roundRect(10, 10, w, (num + 1) * h + 50, [8]); + ctx.fill(); + ctx.globalAlpha = 1; + + ctx.fillStyle = "#888"; + ctx.font = "14px Arial"; + ctx.textAlign = "left"; + ctx.fillText("Graph Inputs", 20, 34); + // var pos = this.mouse; + + if (this.drawButton(w - 20, 20, 20, 20, "X", "#151515")) { + this.closeSubgraph(); + return; + } + + var y = 50; + ctx.font = "14px Arial"; + if (subnode.inputs) + for (var i = 0; i < subnode.inputs.length; ++i) { + var input = subnode.inputs[i]; + if (input.not_subgraph_input) + continue; + + //input button clicked + if (this.drawButton(20, y + 2, w - 20, h - 2)) { + var type = subnode.constructor.input_node_type || "graph/input"; + this.graph.beforeChange(); + var newnode = LiteGraph.createNode(type); + if (newnode) { + subgraph.add(newnode); + this.block_click = false; + this.last_click_position = null; + this.selectNodes([newnode]); + this.node_dragged = newnode; + this.dragging_canvas = false; + newnode.setProperty("name", input.name); + newnode.setProperty("type", input.type); + this.node_dragged.pos[0] = this.graph_mouse[0] - 5; + this.node_dragged.pos[1] = this.graph_mouse[1] - 5; + this.graph.afterChange(); + } + else + console.error("graph input node not found:", type); + } + ctx.fillStyle = "#9C9"; + ctx.beginPath(); + ctx.arc(w - 16, y + h * 0.5, 5, 0, 2 * Math.PI); + ctx.fill(); + ctx.fillStyle = "#AAA"; + ctx.fillText(input.name, 30, y + h * 0.75); + // var tw = ctx.measureText(input.name); + ctx.fillStyle = "#777"; + ctx.fillText(input.type, 130, y + h * 0.75); + y += h; + } + //add + button + if (this.drawButton(20, y + 2, w - 20, h - 2, "+", "#151515", "#222")) { + this.showSubgraphPropertiesDialog(subnode); + } + } + LGraphCanvas.prototype.drawSubgraphPanelRight = function (subgraph, subnode, ctx) { + var num = subnode.outputs ? subnode.outputs.length : 0; + var canvas_w = this.bgcanvas.width + var w = 200; + var h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6); + + ctx.fillStyle = "#111"; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + ctx.roundRect(canvas_w - w - 10, 10, w, (num + 1) * h + 50, [8]); + ctx.fill(); + ctx.globalAlpha = 1; + + ctx.fillStyle = "#888"; + ctx.font = "14px Arial"; + ctx.textAlign = "left"; + var title_text = "Graph Outputs" + var tw = ctx.measureText(title_text).width + ctx.fillText(title_text, (canvas_w - tw) - 20, 34); + // var pos = this.mouse; + if (this.drawButton(canvas_w - w, 20, 20, 20, "X", "#151515")) { + this.closeSubgraph(); + return; + } + + var y = 50; + ctx.font = "14px Arial"; + if (subnode.outputs) + for (var i = 0; i < subnode.outputs.length; ++i) { + var output = subnode.outputs[i]; + if (output.not_subgraph_input) + continue; + + //output button clicked + if (this.drawButton(canvas_w - w, y + 2, w - 20, h - 2)) { + var type = subnode.constructor.output_node_type || "graph/output"; + this.graph.beforeChange(); + var newnode = LiteGraph.createNode(type); + if (newnode) { + subgraph.add(newnode); + this.block_click = false; + this.last_click_position = null; + this.selectNodes([newnode]); + this.node_dragged = newnode; + this.dragging_canvas = false; + newnode.setProperty("name", output.name); + newnode.setProperty("type", output.type); + this.node_dragged.pos[0] = this.graph_mouse[0] - 5; + this.node_dragged.pos[1] = this.graph_mouse[1] - 5; + this.graph.afterChange(); + } + else + console.error("graph input node not found:", type); + } + ctx.fillStyle = "#9C9"; + ctx.beginPath(); + ctx.arc(canvas_w - w + 16, y + h * 0.5, 5, 0, 2 * Math.PI); + ctx.fill(); + ctx.fillStyle = "#AAA"; + ctx.fillText(output.name, canvas_w - w + 30, y + h * 0.75); + // var tw = ctx.measureText(input.name); + ctx.fillStyle = "#777"; + ctx.fillText(output.type, canvas_w - w + 130, y + h * 0.75); + y += h; + } + //add + button + if (this.drawButton(canvas_w - w, y + 2, w - 20, h - 2, "+", "#151515", "#222")) { + this.showSubgraphPropertiesDialogRight(subnode); + } + } + //Draws a button into the canvas overlay and computes if it was clicked using the immediate gui paradigm + LGraphCanvas.prototype.drawButton = function( x,y,w,h, text, bgcolor, hovercolor, textcolor ) + { + var ctx = this.ctx; + bgcolor = bgcolor || LiteGraph.NODE_DEFAULT_COLOR; + hovercolor = hovercolor || "#555"; + textcolor = textcolor || LiteGraph.NODE_TEXT_COLOR; + var yFix = y + LiteGraph.NODE_TITLE_HEIGHT + 2; // fix the height with the title + var pos = this.mouse; + var hover = LiteGraph.isInsideRectangle( pos[0], pos[1], x,yFix,w,h ); + pos = this.last_click_position; + var clicked = pos && LiteGraph.isInsideRectangle( pos[0], pos[1], x,yFix,w,h ); + + ctx.fillStyle = hover ? hovercolor : bgcolor; + if(clicked) + ctx.fillStyle = "#AAA"; + ctx.beginPath(); + ctx.roundRect(x,y,w,h,[4] ); + ctx.fill(); + + if(text != null) + { + if(text.constructor == String) + { + ctx.fillStyle = textcolor; + ctx.textAlign = "center"; + ctx.font = ((h * 0.65)|0) + "px Arial"; + ctx.fillText( text, x + w * 0.5,y + h * 0.75 ); + ctx.textAlign = "left"; + } + } + + var was_clicked = clicked && !this.block_click; + if(clicked) + this.blockClick(); + return was_clicked; + } + + LGraphCanvas.prototype.isAreaClicked = function( x,y,w,h, hold_click ) + { + var pos = this.mouse; + var hover = LiteGraph.isInsideRectangle( pos[0], pos[1], x,y,w,h ); + pos = this.last_click_position; + var clicked = pos && LiteGraph.isInsideRectangle( pos[0], pos[1], x,y,w,h ); + var was_clicked = clicked && !this.block_click; + if(clicked && hold_click) + this.blockClick(); + return was_clicked; + } + + /** + * draws some useful stats in the corner of the canvas + * @method renderInfo + **/ + LGraphCanvas.prototype.renderInfo = function(ctx, x, y) { + x = x || 10; + y = y || this.canvas.height - 80; + + ctx.save(); + ctx.translate(x, y); + + ctx.font = "10px Arial"; + ctx.fillStyle = "#888"; + ctx.textAlign = "left"; + if (this.graph) { + ctx.fillText( "T: " + this.graph.globaltime.toFixed(2) + "s", 5, 13 * 1 ); + ctx.fillText("I: " + this.graph.iteration, 5, 13 * 2 ); + ctx.fillText("N: " + this.graph._nodes.length + " [" + this.visible_nodes.length + "]", 5, 13 * 3 ); + ctx.fillText("V: " + this.graph._version, 5, 13 * 4); + ctx.fillText("FPS:" + this.fps.toFixed(2), 5, 13 * 5); + } else { + ctx.fillText("No graph selected", 5, 13 * 1); + } + ctx.restore(); + }; + + /** + * draws the back canvas (the one containing the background and the connections) + * @method drawBackCanvas + **/ + LGraphCanvas.prototype.drawBackCanvas = function() { + var canvas = this.bgcanvas; + if ( + canvas.width != this.canvas.width || + canvas.height != this.canvas.height + ) { + canvas.width = this.canvas.width; + canvas.height = this.canvas.height; + } + + if (!this.bgctx) { + this.bgctx = this.bgcanvas.getContext("2d"); + } + var ctx = this.bgctx; + if (ctx.start) { + ctx.start(); + } + + var viewport = this.viewport || [0,0,ctx.canvas.width,ctx.canvas.height]; + + //clear + if (this.clear_background) { + ctx.clearRect( viewport[0], viewport[1], viewport[2], viewport[3] ); + } + + //show subgraph stack header + if (this._graph_stack && this._graph_stack.length) { + ctx.save(); + var parent_graph = this._graph_stack[this._graph_stack.length - 1]; + var subgraph_node = this.graph._subgraph_node; + ctx.strokeStyle = subgraph_node.bgcolor; + ctx.lineWidth = 10; + ctx.strokeRect(1, 1, canvas.width - 2, canvas.height - 2); + ctx.lineWidth = 1; + ctx.font = "40px Arial"; + ctx.textAlign = "center"; + ctx.fillStyle = subgraph_node.bgcolor || "#AAA"; + var title = ""; + for (var i = 1; i < this._graph_stack.length; ++i) { + title += + this._graph_stack[i]._subgraph_node.getTitle() + " >> "; + } + ctx.fillText( + title + subgraph_node.getTitle(), + canvas.width * 0.5, + 40 + ); + ctx.restore(); + } + + var bg_already_painted = false; + if (this.onRenderBackground) { + bg_already_painted = this.onRenderBackground(canvas, ctx); + } + + //reset in case of error + if ( !this.viewport ) + { + ctx.restore(); + ctx.setTransform(1, 0, 0, 1, 0, 0); + } + this.visible_links.length = 0; + + if (this.graph) { + //apply transformations + ctx.save(); + this.ds.toCanvasContext(ctx); + + //render BG + if ( + this.background_image && + this.ds.scale > 0.5 && + !bg_already_painted + ) { + if (this.zoom_modify_alpha) { + ctx.globalAlpha = + (1.0 - 0.5 / this.ds.scale) * this.editor_alpha; + } else { + ctx.globalAlpha = this.editor_alpha; + } + ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = false; // ctx.mozImageSmoothingEnabled = + if ( + !this._bg_img || + this._bg_img.name != this.background_image + ) { + this._bg_img = new Image(); + this._bg_img.name = this.background_image; + this._bg_img.src = this.background_image; + var that = this; + this._bg_img.onload = function() { + that.draw(true, true); + }; + } + + var pattern = null; + if (this._pattern == null && this._bg_img.width > 0) { + pattern = ctx.createPattern(this._bg_img, "repeat"); + this._pattern_img = this._bg_img; + this._pattern = pattern; + } else { + pattern = this._pattern; + } + if (pattern) { + ctx.fillStyle = pattern; + ctx.fillRect( + this.visible_area[0], + this.visible_area[1], + this.visible_area[2], + this.visible_area[3] + ); + ctx.fillStyle = "transparent"; + } + + ctx.globalAlpha = 1.0; + ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = true; //= ctx.mozImageSmoothingEnabled + } + + //groups + if (this.graph._groups.length && !this.live_mode) { + this.drawGroups(canvas, ctx); + } + + if (this.onDrawBackground) { + this.onDrawBackground(ctx, this.visible_area); + } + if (this.onBackgroundRender) { + //LEGACY + console.error( + "WARNING! onBackgroundRender deprecated, now is named onDrawBackground " + ); + this.onBackgroundRender = null; + } + + //DEBUG: show clipping area + //ctx.fillStyle = "red"; + //ctx.fillRect( this.visible_area[0] + 10, this.visible_area[1] + 10, this.visible_area[2] - 20, this.visible_area[3] - 20); + + //bg + if (this.render_canvas_border) { + ctx.strokeStyle = "#235"; + ctx.strokeRect(0, 0, canvas.width, canvas.height); + } + + if (this.render_connections_shadows) { + ctx.shadowColor = "#000"; + ctx.shadowOffsetX = 0; + ctx.shadowOffsetY = 0; + ctx.shadowBlur = 6; + } else { + ctx.shadowColor = "rgba(0,0,0,0)"; + } + + //draw connections + if (!this.live_mode) { + this.drawConnections(ctx); + } + + ctx.shadowColor = "rgba(0,0,0,0)"; + + //restore state + ctx.restore(); + } + + if (ctx.finish) { + ctx.finish(); + } + + this.dirty_bgcanvas = false; + this.dirty_canvas = true; //to force to repaint the front canvas with the bgcanvas + }; + + var temp_vec2 = new Float32Array(2); + + /** + * draws the given node inside the canvas + * @method drawNode + **/ + LGraphCanvas.prototype.drawNode = function(node, ctx) { + var glow = false; + this.current_node = node; + + var color = node.color || node.constructor.color || LiteGraph.NODE_DEFAULT_COLOR; + var bgcolor = node.bgcolor || node.constructor.bgcolor || LiteGraph.NODE_DEFAULT_BGCOLOR; + + //shadow and glow + if (node.mouseOver) { + glow = true; + } + + var low_quality = this.ds.scale < 0.6; //zoomed out + + //only render if it forces it to do it + if (this.live_mode) { + if (!node.flags.collapsed) { + ctx.shadowColor = "transparent"; + if (node.onDrawForeground) { + node.onDrawForeground(ctx, this, this.canvas); + } + } + return; + } + + var editor_alpha = this.editor_alpha; + ctx.globalAlpha = editor_alpha; + + if (this.render_shadows && !low_quality) { + ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR; + ctx.shadowOffsetX = 2 * this.ds.scale; + ctx.shadowOffsetY = 2 * this.ds.scale; + ctx.shadowBlur = 3 * this.ds.scale; + } else { + ctx.shadowColor = "transparent"; + } + + //custom draw collapsed method (draw after shadows because they are affected) + if ( + node.flags.collapsed && + node.onDrawCollapsed && + node.onDrawCollapsed(ctx, this) == true + ) { + return; + } + + //clip if required (mask) + var shape = node._shape || LiteGraph.BOX_SHAPE; + var size = temp_vec2; + temp_vec2.set(node.size); + var horizontal = node.horizontal; // || node.flags.horizontal; + + if (node.flags.collapsed) { + ctx.font = this.inner_text_font; + var title = node.getTitle ? node.getTitle() : node.title; + if (title != null) { + node._collapsed_width = Math.min( + node.size[0], + ctx.measureText(title).width + + LiteGraph.NODE_TITLE_HEIGHT * 2 + ); //LiteGraph.NODE_COLLAPSED_WIDTH; + size[0] = node._collapsed_width; + size[1] = 0; + } + } + + if (node.clip_area) { + //Start clipping + ctx.save(); + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE) { + ctx.rect(0, 0, size[0], size[1]); + } else if (shape == LiteGraph.ROUND_SHAPE) { + ctx.roundRect(0, 0, size[0], size[1], [10]); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc( + size[0] * 0.5, + size[1] * 0.5, + size[0] * 0.5, + 0, + Math.PI * 2 + ); + } + ctx.clip(); + } + + //draw shape + if (node.has_errors) { + bgcolor = "red"; + } + this.drawNodeShape( + node, + ctx, + size, + color, + bgcolor, + node.is_selected, + node.mouseOver + ); + ctx.shadowColor = "transparent"; + + //draw foreground + if (node.onDrawForeground) { + node.onDrawForeground(ctx, this, this.canvas); + } + + //connection slots + ctx.textAlign = horizontal ? "center" : "left"; + ctx.font = this.inner_text_font; + + var render_text = !low_quality; + + var out_slot = this.connecting_output; + var in_slot = this.connecting_input; + ctx.lineWidth = 1; + + var max_y = 0; + var slot_pos = new Float32Array(2); //to reuse + + //render inputs and outputs + if (!node.flags.collapsed) { + //input connection slots + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + + var slot_type = slot.type; + var slot_shape = slot.shape; + + ctx.globalAlpha = editor_alpha; + //change opacity of incompatible slots when dragging a connection + if ( this.connecting_output && !LiteGraph.isValidConnection( slot.type , out_slot.type) ) { + ctx.globalAlpha = 0.4 * editor_alpha; + } + + ctx.fillStyle = + slot.link != null + ? slot.color_on || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.input_on + : slot.color_off || + this.default_connection_color_byTypeOff[slot_type] || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.input_off; + + var pos = node.getConnectionPos(true, i, slot_pos); + pos[0] -= node.pos[0]; + pos[1] -= node.pos[1]; + if (max_y < pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) { + max_y = pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5; + } + + ctx.beginPath(); + + if (slot_type == "array"){ + slot_shape = LiteGraph.GRID_SHAPE; // place in addInput? addOutput instead? + } + + var doStroke = true; + + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + if (horizontal) { + ctx.rect( + pos[0] - 5 + 0.5, + pos[1] - 8 + 0.5, + 10, + 14 + ); + } else { + ctx.rect( + pos[0] - 6 + 0.5, + pos[1] - 5 + 0.5, + 14, + 10 + ); + } + } else if (slot_shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(pos[0] + 8, pos[1] + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] + 6 + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] - 6 + 0.5); + ctx.closePath(); + } else if (slot_shape === LiteGraph.GRID_SHAPE) { + ctx.rect(pos[0] - 4, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 4, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 4, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 1, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 4, pos[1] + 2, 2, 2); + ctx.rect(pos[0] - 1, pos[1] + 2, 2, 2); + ctx.rect(pos[0] + 2, pos[1] + 2, 2, 2); + doStroke = false; + } else { + if(low_quality) + ctx.rect(pos[0] - 4, pos[1] - 4, 8, 8 ); //faster + else + ctx.arc(pos[0], pos[1], 4, 0, Math.PI * 2); + } + ctx.fill(); + + //render name + if (render_text) { + var text = slot.label != null ? slot.label : slot.name; + if (text) { + ctx.fillStyle = LiteGraph.NODE_TEXT_COLOR; + if (horizontal || slot.dir == LiteGraph.UP) { + ctx.fillText(text, pos[0], pos[1] - 10); + } else { + ctx.fillText(text, pos[0] + 10, pos[1] + 5); + } + } + } + } + } + + //output connection slots + + ctx.textAlign = horizontal ? "center" : "right"; + ctx.strokeStyle = "black"; + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + + var slot_type = slot.type; + var slot_shape = slot.shape; + + //change opacity of incompatible slots when dragging a connection + if (this.connecting_input && !LiteGraph.isValidConnection( slot_type , in_slot.type) ) { + ctx.globalAlpha = 0.4 * editor_alpha; + } + + var pos = node.getConnectionPos(false, i, slot_pos); + pos[0] -= node.pos[0]; + pos[1] -= node.pos[1]; + if (max_y < pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) { + max_y = pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5; + } + + ctx.fillStyle = + slot.links && slot.links.length + ? slot.color_on || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.output_on + : slot.color_off || + this.default_connection_color_byTypeOff[slot_type] || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.output_off; + ctx.beginPath(); + //ctx.rect( node.size[0] - 14,i*14,10,10); + + if (slot_type == "array"){ + slot_shape = LiteGraph.GRID_SHAPE; + } + + var doStroke = true; + + if ( + slot_type === LiteGraph.EVENT || + slot_shape === LiteGraph.BOX_SHAPE + ) { + if (horizontal) { + ctx.rect( + pos[0] - 5 + 0.5, + pos[1] - 8 + 0.5, + 10, + 14 + ); + } else { + ctx.rect( + pos[0] - 6 + 0.5, + pos[1] - 5 + 0.5, + 14, + 10 + ); + } + } else if (slot_shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(pos[0] + 8, pos[1] + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] + 6 + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] - 6 + 0.5); + ctx.closePath(); + } else if (slot_shape === LiteGraph.GRID_SHAPE) { + ctx.rect(pos[0] - 4, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 4, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 4, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 1, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 4, pos[1] + 2, 2, 2); + ctx.rect(pos[0] - 1, pos[1] + 2, 2, 2); + ctx.rect(pos[0] + 2, pos[1] + 2, 2, 2); + doStroke = false; + } else { + if(low_quality) + ctx.rect(pos[0] - 4, pos[1] - 4, 8, 8 ); + else + ctx.arc(pos[0], pos[1], 4, 0, Math.PI * 2); + } + + //trigger + //if(slot.node_id != null && slot.slot == -1) + // ctx.fillStyle = "#F85"; + + //if(slot.links != null && slot.links.length) + ctx.fill(); + if(!low_quality && doStroke) + ctx.stroke(); + + //render output name + if (render_text) { + var text = slot.label != null ? slot.label : slot.name; + if (text) { + ctx.fillStyle = LiteGraph.NODE_TEXT_COLOR; + if (horizontal || slot.dir == LiteGraph.DOWN) { + ctx.fillText(text, pos[0], pos[1] - 8); + } else { + ctx.fillText(text, pos[0] - 10, pos[1] + 5); + } + } + } + } + } + + ctx.textAlign = "left"; + ctx.globalAlpha = 1; + + if (node.widgets) { + var widgets_y = max_y; + if (horizontal || node.widgets_up) { + widgets_y = 2; + } + if( node.widgets_start_y != null ) + widgets_y = node.widgets_start_y; + this.drawNodeWidgets( + node, + widgets_y, + ctx, + this.node_widget && this.node_widget[0] == node + ? this.node_widget[1] + : null + ); + } + } else if (this.render_collapsed_slots) { + //if collapsed + var input_slot = null; + var output_slot = null; + + //get first connected slot to render + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + if (slot.link == null) { + continue; + } + input_slot = slot; + break; + } + } + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + if (!slot.links || !slot.links.length) { + continue; + } + output_slot = slot; + } + } + + if (input_slot) { + var x = 0; + var y = LiteGraph.NODE_TITLE_HEIGHT * -0.5; //center + if (horizontal) { + x = node._collapsed_width * 0.5; + y = -LiteGraph.NODE_TITLE_HEIGHT; + } + ctx.fillStyle = "#686"; + ctx.beginPath(); + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + ctx.rect(x - 7 + 0.5, y - 4, 14, 8); + } else if (slot.shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(x + 8, y); + ctx.lineTo(x + -4, y - 4); + ctx.lineTo(x + -4, y + 4); + ctx.closePath(); + } else { + ctx.arc(x, y, 4, 0, Math.PI * 2); + } + ctx.fill(); + } + + if (output_slot) { + var x = node._collapsed_width; + var y = LiteGraph.NODE_TITLE_HEIGHT * -0.5; //center + if (horizontal) { + x = node._collapsed_width * 0.5; + y = 0; + } + ctx.fillStyle = "#686"; + ctx.strokeStyle = "black"; + ctx.beginPath(); + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + ctx.rect(x - 7 + 0.5, y - 4, 14, 8); + } else if (slot.shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(x + 6, y); + ctx.lineTo(x - 6, y - 4); + ctx.lineTo(x - 6, y + 4); + ctx.closePath(); + } else { + ctx.arc(x, y, 4, 0, Math.PI * 2); + } + ctx.fill(); + //ctx.stroke(); + } + } + + if (node.clip_area) { + ctx.restore(); + } + + ctx.globalAlpha = 1.0; + }; + + //used by this.over_link_center + LGraphCanvas.prototype.drawLinkTooltip = function( ctx, link ) + { + var pos = link._pos; + ctx.fillStyle = "black"; + ctx.beginPath(); + ctx.arc( pos[0], pos[1], 3, 0, Math.PI * 2 ); + ctx.fill(); + + if(link.data == null) + return; + + if(this.onDrawLinkTooltip) + if( this.onDrawLinkTooltip(ctx,link,this) == true ) + return; + + var data = link.data; + var text = null; + + if( data.constructor === Number ) + text = data.toFixed(2); + else if( data.constructor === String ) + text = "\"" + data + "\""; + else if( data.constructor === Boolean ) + text = String(data); + else if (data.toToolTip) + text = data.toToolTip(); + else + text = "[" + data.constructor.name + "]"; + + if(text == null) + return; + text = text.substr(0,30); //avoid weird + + ctx.font = "14px Courier New"; + var info = ctx.measureText(text); + var w = info.width + 20; + var h = 24; + ctx.shadowColor = "black"; + ctx.shadowOffsetX = 2; + ctx.shadowOffsetY = 2; + ctx.shadowBlur = 3; + ctx.fillStyle = "#454"; + ctx.beginPath(); + ctx.roundRect( pos[0] - w*0.5, pos[1] - 15 - h, w, h, [3]); + ctx.moveTo( pos[0] - 10, pos[1] - 15 ); + ctx.lineTo( pos[0] + 10, pos[1] - 15 ); + ctx.lineTo( pos[0], pos[1] - 5 ); + ctx.fill(); + ctx.shadowColor = "transparent"; + ctx.textAlign = "center"; + ctx.fillStyle = "#CEC"; + ctx.fillText(text, pos[0], pos[1] - 15 - h * 0.3); + } + + /** + * draws the shape of the given node in the canvas + * @method drawNodeShape + **/ + var tmp_area = new Float32Array(4); + + LGraphCanvas.prototype.drawNodeShape = function( + node, + ctx, + size, + fgcolor, + bgcolor, + selected, + mouse_over + ) { + //bg rect + ctx.strokeStyle = fgcolor; + ctx.fillStyle = bgcolor; + + var title_height = LiteGraph.NODE_TITLE_HEIGHT; + var low_quality = this.ds.scale < 0.5; + + //render node area depending on shape + var shape = + node._shape || node.constructor.shape || LiteGraph.ROUND_SHAPE; + + var title_mode = node.constructor.title_mode; + + var render_title = true; + if (title_mode == LiteGraph.TRANSPARENT_TITLE || title_mode == LiteGraph.NO_TITLE) { + render_title = false; + } else if (title_mode == LiteGraph.AUTOHIDE_TITLE && mouse_over) { + render_title = true; + } + + var area = tmp_area; + area[0] = 0; //x + area[1] = render_title ? -title_height : 0; //y + area[2] = size[0] + 1; //w + area[3] = render_title ? size[1] + title_height : size[1]; //h + + var old_alpha = ctx.globalAlpha; + + //full node shape + //if(node.flags.collapsed) + { + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE || low_quality) { + ctx.fillRect(area[0], area[1], area[2], area[3]); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + shape == LiteGraph.CARD_SHAPE + ) { + ctx.roundRect( + area[0], + area[1], + area[2], + area[3], + shape == LiteGraph.CARD_SHAPE ? [this.round_radius,this.round_radius,0,0] : [this.round_radius] + ); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc( + size[0] * 0.5, + size[1] * 0.5, + size[0] * 0.5, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + + //separator + if(!node.flags.collapsed && render_title) + { + ctx.shadowColor = "transparent"; + ctx.fillStyle = "rgba(0,0,0,0.2)"; + ctx.fillRect(0, -1, area[2], 2); + } + } + ctx.shadowColor = "transparent"; + + if (node.onDrawBackground) { + node.onDrawBackground(ctx, this, this.canvas, this.graph_mouse ); + } + + //title bg (remember, it is rendered ABOVE the node) + if (render_title || title_mode == LiteGraph.TRANSPARENT_TITLE) { + //title bar + if (node.onDrawTitleBar) { + node.onDrawTitleBar( ctx, title_height, size, this.ds.scale, fgcolor ); + } else if ( + title_mode != LiteGraph.TRANSPARENT_TITLE && + (node.constructor.title_color || this.render_title_colored) + ) { + var title_color = node.constructor.title_color || fgcolor; + + if (node.flags.collapsed) { + ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR; + } + + //* gradient test + if (this.use_gradients) { + var grad = LGraphCanvas.gradients[title_color]; + if (!grad) { + grad = LGraphCanvas.gradients[ title_color ] = ctx.createLinearGradient(0, 0, 400, 0); + grad.addColorStop(0, title_color); // TODO refactor: validate color !! prevent DOMException + grad.addColorStop(1, "#000"); + } + ctx.fillStyle = grad; + } else { + ctx.fillStyle = title_color; + } + + //ctx.globalAlpha = 0.5 * old_alpha; + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE || low_quality) { + ctx.rect(0, -title_height, size[0] + 1, title_height); + } else if ( shape == LiteGraph.ROUND_SHAPE || shape == LiteGraph.CARD_SHAPE ) { + ctx.roundRect( + 0, + -title_height, + size[0] + 1, + title_height, + node.flags.collapsed ? [this.round_radius] : [this.round_radius,this.round_radius,0,0] + ); + } + ctx.fill(); + ctx.shadowColor = "transparent"; + } + + var colState = false; + if (LiteGraph.node_box_coloured_by_mode){ + if(LiteGraph.NODE_MODES_COLORS[node.mode]){ + colState = LiteGraph.NODE_MODES_COLORS[node.mode]; + } + } + if (LiteGraph.node_box_coloured_when_on){ + colState = node.action_triggered ? "#FFF" : (node.execute_triggered ? "#AAA" : colState); + } + + //title box + var box_size = 10; + if (node.onDrawTitleBox) { + node.onDrawTitleBox(ctx, title_height, size, this.ds.scale); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + shape == LiteGraph.CIRCLE_SHAPE || + shape == LiteGraph.CARD_SHAPE + ) { + if (low_quality) { + ctx.fillStyle = "black"; + ctx.beginPath(); + ctx.arc( + title_height * 0.5, + title_height * -0.5, + box_size * 0.5 + 1, + 0, + Math.PI * 2 + ); + ctx.fill(); + } + + ctx.fillStyle = node.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR; + if(low_quality) + ctx.fillRect( title_height * 0.5 - box_size *0.5, title_height * -0.5 - box_size *0.5, box_size , box_size ); + else + { + ctx.beginPath(); + ctx.arc( + title_height * 0.5, + title_height * -0.5, + box_size * 0.5, + 0, + Math.PI * 2 + ); + ctx.fill(); + } + } else { + if (low_quality) { + ctx.fillStyle = "black"; + ctx.fillRect( + (title_height - box_size) * 0.5 - 1, + (title_height + box_size) * -0.5 - 1, + box_size + 2, + box_size + 2 + ); + } + ctx.fillStyle = node.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR; + ctx.fillRect( + (title_height - box_size) * 0.5, + (title_height + box_size) * -0.5, + box_size, + box_size + ); + } + ctx.globalAlpha = old_alpha; + + //title text + if (node.onDrawTitleText) { + node.onDrawTitleText( + ctx, + title_height, + size, + this.ds.scale, + this.title_text_font, + selected + ); + } + if (!low_quality) { + ctx.font = this.title_text_font; + var title = String(node.getTitle()); + if (title) { + if (selected) { + ctx.fillStyle = LiteGraph.NODE_SELECTED_TITLE_COLOR; + } else { + ctx.fillStyle = + node.constructor.title_text_color || + this.node_title_color; + } + if (node.flags.collapsed) { + ctx.textAlign = "left"; + var measure = ctx.measureText(title); + ctx.fillText( + title.substr(0,20), //avoid urls too long + title_height,// + measure.width * 0.5, + LiteGraph.NODE_TITLE_TEXT_Y - title_height + ); + ctx.textAlign = "left"; + } else { + ctx.textAlign = "left"; + ctx.fillText( + title, + title_height, + LiteGraph.NODE_TITLE_TEXT_Y - title_height + ); + } + } + } + + //subgraph box + if (!node.flags.collapsed && node.subgraph && !node.skip_subgraph_button) { + var w = LiteGraph.NODE_TITLE_HEIGHT; + var x = node.size[0] - w; + var over = LiteGraph.isInsideRectangle( this.graph_mouse[0] - node.pos[0], this.graph_mouse[1] - node.pos[1], x+2, -w+2, w-4, w-4 ); + ctx.fillStyle = over ? "#888" : "#555"; + if( shape == LiteGraph.BOX_SHAPE || low_quality) + ctx.fillRect(x+2, -w+2, w-4, w-4); + else + { + ctx.beginPath(); + ctx.roundRect(x+2, -w+2, w-4, w-4,[4]); + ctx.fill(); + } + ctx.fillStyle = "#333"; + ctx.beginPath(); + ctx.moveTo(x + w * 0.2, -w * 0.6); + ctx.lineTo(x + w * 0.8, -w * 0.6); + ctx.lineTo(x + w * 0.5, -w * 0.3); + ctx.fill(); + } + + //custom title render + if (node.onDrawTitle) { + node.onDrawTitle(ctx); + } + } + + //render selection marker + if (selected) { + if (node.onBounding) { + node.onBounding(area); + } + + if (title_mode == LiteGraph.TRANSPARENT_TITLE) { + area[1] -= title_height; + area[3] += title_height; + } + ctx.lineWidth = 1; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE) { + ctx.rect( + -6 + area[0], + -6 + area[1], + 12 + area[2], + 12 + area[3] + ); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + (shape == LiteGraph.CARD_SHAPE && node.flags.collapsed) + ) { + ctx.roundRect( + -6 + area[0], + -6 + area[1], + 12 + area[2], + 12 + area[3], + [this.round_radius * 2] + ); + } else if (shape == LiteGraph.CARD_SHAPE) { + ctx.roundRect( + -6 + area[0], + -6 + area[1], + 12 + area[2], + 12 + area[3], + [this.round_radius * 2,2,this.round_radius * 2,2] + ); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc( + size[0] * 0.5, + size[1] * 0.5, + size[0] * 0.5 + 6, + 0, + Math.PI * 2 + ); + } + ctx.strokeStyle = LiteGraph.NODE_BOX_OUTLINE_COLOR; + ctx.stroke(); + ctx.strokeStyle = fgcolor; + ctx.globalAlpha = 1; + } + + // these counter helps in conditioning drawing based on if the node has been executed or an action occurred + if (node.execute_triggered>0) node.execute_triggered--; + if (node.action_triggered>0) node.action_triggered--; + }; + + var margin_area = new Float32Array(4); + var link_bounding = new Float32Array(4); + var tempA = new Float32Array(2); + var tempB = new Float32Array(2); + + /** + * draws every connection visible in the canvas + * OPTIMIZE THIS: pre-catch connections position instead of recomputing them every time + * @method drawConnections + **/ + LGraphCanvas.prototype.drawConnections = function(ctx) { + var now = LiteGraph.getTime(); + var visible_area = this.visible_area; + margin_area[0] = visible_area[0] - 20; + margin_area[1] = visible_area[1] - 20; + margin_area[2] = visible_area[2] + 40; + margin_area[3] = visible_area[3] + 40; + + //draw connections + ctx.lineWidth = this.connections_width; + + ctx.fillStyle = "#AAA"; + ctx.strokeStyle = "#AAA"; + ctx.globalAlpha = this.editor_alpha; + //for every node + var nodes = this.graph._nodes; + for (var n = 0, l = nodes.length; n < l; ++n) { + var node = nodes[n]; + //for every input (we render just inputs because it is easier as every slot can only have one input) + if (!node.inputs || !node.inputs.length) { + continue; + } + + for (var i = 0; i < node.inputs.length; ++i) { + var input = node.inputs[i]; + if (!input || input.link == null) { + continue; + } + var link_id = input.link; + var link = this.graph.links[link_id]; + if (!link) { + continue; + } + + //find link info + var start_node = this.graph.getNodeById(link.origin_id); + if (start_node == null) { + continue; + } + var start_node_slot = link.origin_slot; + var start_node_slotpos = null; + if (start_node_slot == -1) { + start_node_slotpos = [ + start_node.pos[0] + 10, + start_node.pos[1] + 10 + ]; + } else { + start_node_slotpos = start_node.getConnectionPos( + false, + start_node_slot, + tempA + ); + } + var end_node_slotpos = node.getConnectionPos(true, i, tempB); + + //compute link bounding + link_bounding[0] = start_node_slotpos[0]; + link_bounding[1] = start_node_slotpos[1]; + link_bounding[2] = end_node_slotpos[0] - start_node_slotpos[0]; + link_bounding[3] = end_node_slotpos[1] - start_node_slotpos[1]; + if (link_bounding[2] < 0) { + link_bounding[0] += link_bounding[2]; + link_bounding[2] = Math.abs(link_bounding[2]); + } + if (link_bounding[3] < 0) { + link_bounding[1] += link_bounding[3]; + link_bounding[3] = Math.abs(link_bounding[3]); + } + + //skip links outside of the visible area of the canvas + if (!overlapBounding(link_bounding, margin_area)) { + continue; + } + + var start_slot = start_node.outputs[start_node_slot]; + var end_slot = node.inputs[i]; + if (!start_slot || !end_slot) { + continue; + } + var start_dir = + start_slot.dir || + (start_node.horizontal ? LiteGraph.DOWN : LiteGraph.RIGHT); + var end_dir = + end_slot.dir || + (node.horizontal ? LiteGraph.UP : LiteGraph.LEFT); + + this.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + link, + false, + 0, + null, + start_dir, + end_dir + ); + + //event triggered rendered on top + if (link && link._last_time && now - link._last_time < 1000) { + var f = 2.0 - (now - link._last_time) * 0.002; + var tmp = ctx.globalAlpha; + ctx.globalAlpha = tmp * f; + this.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + link, + true, + f, + "white", + start_dir, + end_dir + ); + ctx.globalAlpha = tmp; + } + } + } + ctx.globalAlpha = 1; + }; + + /** + * draws a link between two points + * @method renderLink + * @param {vec2} a start pos + * @param {vec2} b end pos + * @param {Object} link the link object with all the link info + * @param {boolean} skip_border ignore the shadow of the link + * @param {boolean} flow show flow animation (for events) + * @param {string} color the color for the link + * @param {number} start_dir the direction enum + * @param {number} end_dir the direction enum + * @param {number} num_sublines number of sublines (useful to represent vec3 or rgb) + **/ + LGraphCanvas.prototype.renderLink = function( + ctx, + a, + b, + link, + skip_border, + flow, + color, + start_dir, + end_dir, + num_sublines + ) { + if (link) { + this.visible_links.push(link); + } + + //choose color + if (!color && link) { + color = link.color || LGraphCanvas.link_type_colors[link.type]; + } + if (!color) { + color = this.default_link_color; + } + if (link != null && this.highlighted_links[link.id]) { + color = "#FFF"; + } + + start_dir = start_dir || LiteGraph.RIGHT; + end_dir = end_dir || LiteGraph.LEFT; + + var dist = distance(a, b); + + if (this.render_connections_border && this.ds.scale > 0.6) { + ctx.lineWidth = this.connections_width + 4; + } + ctx.lineJoin = "round"; + num_sublines = num_sublines || 1; + if (num_sublines > 1) { + ctx.lineWidth = 0.5; + } + + //begin line shape + ctx.beginPath(); + for (var i = 0; i < num_sublines; i += 1) { + var offsety = (i - (num_sublines - 1) * 0.5) * 5; + + if (this.links_render_mode == LiteGraph.SPLINE_LINK) { + ctx.moveTo(a[0], a[1] + offsety); + var start_offset_x = 0; + var start_offset_y = 0; + var end_offset_x = 0; + var end_offset_y = 0; + switch (start_dir) { + case LiteGraph.LEFT: + start_offset_x = dist * -0.25; + break; + case LiteGraph.RIGHT: + start_offset_x = dist * 0.25; + break; + case LiteGraph.UP: + start_offset_y = dist * -0.25; + break; + case LiteGraph.DOWN: + start_offset_y = dist * 0.25; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + end_offset_x = dist * -0.25; + break; + case LiteGraph.RIGHT: + end_offset_x = dist * 0.25; + break; + case LiteGraph.UP: + end_offset_y = dist * -0.25; + break; + case LiteGraph.DOWN: + end_offset_y = dist * 0.25; + break; + } + ctx.bezierCurveTo( + a[0] + start_offset_x, + a[1] + start_offset_y + offsety, + b[0] + end_offset_x, + b[1] + end_offset_y + offsety, + b[0], + b[1] + offsety + ); + } else if (this.links_render_mode == LiteGraph.LINEAR_LINK) { + ctx.moveTo(a[0], a[1] + offsety); + var start_offset_x = 0; + var start_offset_y = 0; + var end_offset_x = 0; + var end_offset_y = 0; + switch (start_dir) { + case LiteGraph.LEFT: + start_offset_x = -1; + break; + case LiteGraph.RIGHT: + start_offset_x = 1; + break; + case LiteGraph.UP: + start_offset_y = -1; + break; + case LiteGraph.DOWN: + start_offset_y = 1; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + end_offset_x = -1; + break; + case LiteGraph.RIGHT: + end_offset_x = 1; + break; + case LiteGraph.UP: + end_offset_y = -1; + break; + case LiteGraph.DOWN: + end_offset_y = 1; + break; + } + var l = 15; + ctx.lineTo( + a[0] + start_offset_x * l, + a[1] + start_offset_y * l + offsety + ); + ctx.lineTo( + b[0] + end_offset_x * l, + b[1] + end_offset_y * l + offsety + ); + ctx.lineTo(b[0], b[1] + offsety); + } else if (this.links_render_mode == LiteGraph.STRAIGHT_LINK) { + ctx.moveTo(a[0], a[1]); + var start_x = a[0]; + var start_y = a[1]; + var end_x = b[0]; + var end_y = b[1]; + if (start_dir == LiteGraph.RIGHT) { + start_x += 10; + } else { + start_y += 10; + } + if (end_dir == LiteGraph.LEFT) { + end_x -= 10; + } else { + end_y -= 10; + } + ctx.lineTo(start_x, start_y); + ctx.lineTo((start_x + end_x) * 0.5, start_y); + ctx.lineTo((start_x + end_x) * 0.5, end_y); + ctx.lineTo(end_x, end_y); + ctx.lineTo(b[0], b[1]); + } else { + return; + } //unknown + } + + //rendering the outline of the connection can be a little bit slow + if ( + this.render_connections_border && + this.ds.scale > 0.6 && + !skip_border + ) { + ctx.strokeStyle = "rgba(0,0,0,0.5)"; + ctx.stroke(); + } + + ctx.lineWidth = this.connections_width; + ctx.fillStyle = ctx.strokeStyle = color; + ctx.stroke(); + //end line shape + + var pos = this.computeConnectionPoint(a, b, 0.5, start_dir, end_dir); + if (link && link._pos) { + link._pos[0] = pos[0]; + link._pos[1] = pos[1]; + } + + //render arrow in the middle + if ( + this.ds.scale >= 0.6 && + this.highquality_render && + end_dir != LiteGraph.CENTER + ) { + //render arrow + if (this.render_connection_arrows) { + //compute two points in the connection + var posA = this.computeConnectionPoint( + a, + b, + 0.25, + start_dir, + end_dir + ); + var posB = this.computeConnectionPoint( + a, + b, + 0.26, + start_dir, + end_dir + ); + var posC = this.computeConnectionPoint( + a, + b, + 0.75, + start_dir, + end_dir + ); + var posD = this.computeConnectionPoint( + a, + b, + 0.76, + start_dir, + end_dir + ); + + //compute the angle between them so the arrow points in the right direction + var angleA = 0; + var angleB = 0; + if (this.render_curved_connections) { + angleA = -Math.atan2(posB[0] - posA[0], posB[1] - posA[1]); + angleB = -Math.atan2(posD[0] - posC[0], posD[1] - posC[1]); + } else { + angleB = angleA = b[1] > a[1] ? 0 : Math.PI; + } + + //render arrow + ctx.save(); + ctx.translate(posA[0], posA[1]); + ctx.rotate(angleA); + ctx.beginPath(); + ctx.moveTo(-5, -3); + ctx.lineTo(0, +7); + ctx.lineTo(+5, -3); + ctx.fill(); + ctx.restore(); + ctx.save(); + ctx.translate(posC[0], posC[1]); + ctx.rotate(angleB); + ctx.beginPath(); + ctx.moveTo(-5, -3); + ctx.lineTo(0, +7); + ctx.lineTo(+5, -3); + ctx.fill(); + ctx.restore(); + } + + //circle + ctx.beginPath(); + ctx.arc(pos[0], pos[1], 5, 0, Math.PI * 2); + ctx.fill(); + } + + //render flowing points + if (flow) { + ctx.fillStyle = color; + for (var i = 0; i < 5; ++i) { + var f = (LiteGraph.getTime() * 0.001 + i * 0.2) % 1; + var pos = this.computeConnectionPoint( + a, + b, + f, + start_dir, + end_dir + ); + ctx.beginPath(); + ctx.arc(pos[0], pos[1], 5, 0, 2 * Math.PI); + ctx.fill(); + } + } + }; + + //returns the link center point based on curvature + LGraphCanvas.prototype.computeConnectionPoint = function( + a, + b, + t, + start_dir, + end_dir + ) { + start_dir = start_dir || LiteGraph.RIGHT; + end_dir = end_dir || LiteGraph.LEFT; + + var dist = distance(a, b); + var p0 = a; + var p1 = [a[0], a[1]]; + var p2 = [b[0], b[1]]; + var p3 = b; + + switch (start_dir) { + case LiteGraph.LEFT: + p1[0] += dist * -0.25; + break; + case LiteGraph.RIGHT: + p1[0] += dist * 0.25; + break; + case LiteGraph.UP: + p1[1] += dist * -0.25; + break; + case LiteGraph.DOWN: + p1[1] += dist * 0.25; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + p2[0] += dist * -0.25; + break; + case LiteGraph.RIGHT: + p2[0] += dist * 0.25; + break; + case LiteGraph.UP: + p2[1] += dist * -0.25; + break; + case LiteGraph.DOWN: + p2[1] += dist * 0.25; + break; + } + + var c1 = (1 - t) * (1 - t) * (1 - t); + var c2 = 3 * ((1 - t) * (1 - t)) * t; + var c3 = 3 * (1 - t) * (t * t); + var c4 = t * t * t; + + var x = c1 * p0[0] + c2 * p1[0] + c3 * p2[0] + c4 * p3[0]; + var y = c1 * p0[1] + c2 * p1[1] + c3 * p2[1] + c4 * p3[1]; + return [x, y]; + }; + + LGraphCanvas.prototype.drawExecutionOrder = function(ctx) { + ctx.shadowColor = "transparent"; + ctx.globalAlpha = 0.25; + + ctx.textAlign = "center"; + ctx.strokeStyle = "white"; + ctx.globalAlpha = 0.75; + + var visible_nodes = this.visible_nodes; + for (var i = 0; i < visible_nodes.length; ++i) { + var node = visible_nodes[i]; + ctx.fillStyle = "black"; + ctx.fillRect( + node.pos[0] - LiteGraph.NODE_TITLE_HEIGHT, + node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT + ); + if (node.order == 0) { + ctx.strokeRect( + node.pos[0] - LiteGraph.NODE_TITLE_HEIGHT + 0.5, + node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5, + LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT + ); + } + ctx.fillStyle = "#FFF"; + ctx.fillText( + node.order, + node.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * -0.5, + node.pos[1] - 6 + ); + } + ctx.globalAlpha = 1; + }; + + /** + * draws the widgets stored inside a node + * @method drawNodeWidgets + **/ + LGraphCanvas.prototype.drawNodeWidgets = function( + node, + posY, + ctx, + active_widget + ) { + if (!node.widgets || !node.widgets.length) { + return 0; + } + var width = node.size[0]; + var widgets = node.widgets; + posY += 2; + var H = LiteGraph.NODE_WIDGET_HEIGHT; + var show_text = this.ds.scale > 0.5; + ctx.save(); + ctx.globalAlpha = this.editor_alpha; + var outline_color = LiteGraph.WIDGET_OUTLINE_COLOR; + var background_color = LiteGraph.WIDGET_BGCOLOR; + var text_color = LiteGraph.WIDGET_TEXT_COLOR; + var secondary_text_color = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + var margin = 15; + + for (var i = 0; i < widgets.length; ++i) { + var w = widgets[i]; + var y = posY; + if (w.y) { + y = w.y; + } + w.last_y = y; + ctx.strokeStyle = outline_color; + ctx.fillStyle = "#222"; + ctx.textAlign = "left"; + //ctx.lineWidth = 2; + if(w.disabled) + ctx.globalAlpha *= 0.5; + var widget_width = w.width || width; + + switch (w.type) { + case "button": + if (w.clicked) { + ctx.fillStyle = "#AAA"; + w.clicked = false; + this.dirty_canvas = true; + } + ctx.fillRect(margin, y, widget_width - margin * 2, H); + if(show_text && !w.disabled) + ctx.strokeRect( margin, y, widget_width - margin * 2, H ); + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + ctx.fillText(w.name, widget_width * 0.5, y + H * 0.7); + } + break; + case "toggle": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]); + else + ctx.rect(margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if(show_text && !w.disabled) + ctx.stroke(); + ctx.fillStyle = w.value ? "#89A" : "#333"; + ctx.beginPath(); + ctx.arc( widget_width - margin * 2, y + H * 0.5, H * 0.36, 0, Math.PI * 2 ); + ctx.fill(); + if (show_text) { + ctx.fillStyle = secondary_text_color; + if (w.name != null) { + ctx.fillText(w.name, margin * 2, y + H * 0.7); + } + ctx.fillStyle = w.value ? text_color : secondary_text_color; + ctx.textAlign = "right"; + ctx.fillText( + w.value + ? w.options.on || "true" + : w.options.off || "false", + widget_width - 40, + y + H * 0.7 + ); + } + break; + case "slider": + ctx.fillStyle = background_color; + ctx.fillRect(margin, y, widget_width - margin * 2, H); + var range = w.options.max - w.options.min; + var nvalue = (w.value - w.options.min) / range; + ctx.fillStyle = active_widget == w ? "#89A" : "#678"; + ctx.fillRect(margin, y, nvalue * (widget_width - margin * 2), H); + if(show_text && !w.disabled) + ctx.strokeRect(margin, y, widget_width - margin * 2, H); + if (w.marker) { + var marker_nvalue = (w.marker - w.options.min) / range; + ctx.fillStyle = "#AA9"; + ctx.fillRect( margin + marker_nvalue * (widget_width - margin * 2), y, 2, H ); + } + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + ctx.fillText( + w.name + " " + Number(w.value).toFixed(3), + widget_width * 0.5, + y + H * 0.7 + ); + } + break; + case "number": + case "combo": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if(show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5] ); + else + ctx.rect(margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if (show_text) { + if(!w.disabled) + ctx.stroke(); + ctx.fillStyle = text_color; + if(!w.disabled) + { + ctx.beginPath(); + ctx.moveTo(margin + 16, y + 5); + ctx.lineTo(margin + 6, y + H * 0.5); + ctx.lineTo(margin + 16, y + H - 5); + ctx.fill(); + ctx.beginPath(); + ctx.moveTo(widget_width - margin - 16, y + 5); + ctx.lineTo(widget_width - margin - 6, y + H * 0.5); + ctx.lineTo(widget_width - margin - 16, y + H - 5); + ctx.fill(); + } + ctx.fillStyle = secondary_text_color; + ctx.fillText(w.name, margin * 2 + 5, y + H * 0.7); + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + if (w.type == "number") { + ctx.fillText( + Number(w.value).toFixed( + w.options.precision !== undefined + ? w.options.precision + : 3 + ), + widget_width - margin * 2 - 20, + y + H * 0.7 + ); + } else { + var v = w.value; + if( w.options.values ) + { + var values = w.options.values; + if( values.constructor === Function ) + values = values(); + if(values && values.constructor !== Array) + v = values[ w.value ]; + } + ctx.fillText( + v, + widget_width - margin * 2 - 20, + y + H * 0.7 + ); + } + } + break; + case "string": + case "text": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]); + else + ctx.rect( margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if (show_text) { + if(!w.disabled) + ctx.stroke(); + ctx.save(); + ctx.beginPath(); + ctx.rect(margin, y, widget_width - margin * 2, H); + ctx.clip(); + + //ctx.stroke(); + ctx.fillStyle = secondary_text_color; + if (w.name != null) { + ctx.fillText(w.name, margin * 2, y + H * 0.7); + } + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + ctx.fillText(String(w.value).substr(0,30), widget_width - margin * 2, y + H * 0.7); //30 chars max + ctx.restore(); + } + break; + default: + if (w.draw) { + w.draw(ctx, node, widget_width, y, H); + } + break; + } + posY += (w.computeSize ? w.computeSize(widget_width)[1] : H) + 4; + ctx.globalAlpha = this.editor_alpha; + + } + ctx.restore(); + ctx.textAlign = "left"; + }; + + /** + * process an event on widgets + * @method processNodeWidgets + **/ + LGraphCanvas.prototype.processNodeWidgets = function( + node, + pos, + event, + active_widget + ) { + if (!node.widgets || !node.widgets.length) { + return null; + } + + var x = pos[0] - node.pos[0]; + var y = pos[1] - node.pos[1]; + var width = node.size[0]; + var that = this; + var ref_window = this.getCanvasWindow(); + + for (var i = 0; i < node.widgets.length; ++i) { + var w = node.widgets[i]; + if(!w || w.disabled) + continue; + var widget_height = w.computeSize ? w.computeSize(width)[1] : LiteGraph.NODE_WIDGET_HEIGHT; + var widget_width = w.width || width; + //outside + if ( w != active_widget && + (x < 6 || x > widget_width - 12 || y < w.last_y || y > w.last_y + widget_height || w.last_y === undefined) ) + continue; + + var old_value = w.value; + + //if ( w == active_widget || (x > 6 && x < widget_width - 12 && y > w.last_y && y < w.last_y + widget_height) ) { + //inside widget + switch (w.type) { + case "button": + if (event.type === LiteGraph.pointerevents_method+"down") { + if (w.callback) { + setTimeout(function() { + w.callback(w, that, node, pos, event); + }, 20); + } + w.clicked = true; + this.dirty_canvas = true; + } + break; + case "slider": + var range = w.options.max - w.options.min; + var nvalue = Math.clamp((x - 15) / (widget_width - 30), 0, 1); + w.value = w.options.min + (w.options.max - w.options.min) * nvalue; + if (w.callback) { + setTimeout(function() { + inner_value_change(w, w.value); + }, 20); + } + this.dirty_canvas = true; + break; + case "number": + case "combo": + var old_value = w.value; + if (event.type == LiteGraph.pointerevents_method+"move" && w.type == "number") { + w.value += event.deltaX * 0.1 * (w.options.step || 1); + if ( w.options.min != null && w.value < w.options.min ) { + w.value = w.options.min; + } + if ( w.options.max != null && w.value > w.options.max ) { + w.value = w.options.max; + } + } else if (event.type == LiteGraph.pointerevents_method+"down") { + var values = w.options.values; + if (values && values.constructor === Function) { + values = w.options.values(w, node); + } + var values_list = null; + + if( w.type != "number") + values_list = values.constructor === Array ? values : Object.keys(values); + + var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0; + if (w.type == "number") { + w.value += delta * 0.1 * (w.options.step || 1); + if ( w.options.min != null && w.value < w.options.min ) { + w.value = w.options.min; + } + if ( w.options.max != null && w.value > w.options.max ) { + w.value = w.options.max; + } + } else if (delta) { //clicked in arrow, used for combos + var index = -1; + this.last_mouseclick = 0; //avoids dobl click event + if(values.constructor === Object) + index = values_list.indexOf( String( w.value ) ) + delta; + else + index = values_list.indexOf( w.value ) + delta; + if (index >= values_list.length) { + index = values_list.length - 1; + } + if (index < 0) { + index = 0; + } + if( values.constructor === Array ) + w.value = values[index]; + else + w.value = index; + } else { //combo clicked + var text_values = values != values_list ? Object.values(values) : values; + var menu = new LiteGraph.ContextMenu(text_values, { + scale: Math.max(1, this.ds.scale), + event: event, + className: "dark", + callback: inner_clicked.bind(w) + }, + ref_window); + function inner_clicked(v, option, event) { + if(values != values_list) + v = text_values.indexOf(v); + this.value = v; + inner_value_change(this, v); + that.dirty_canvas = true; + return false; + } + } + } //end mousedown + else if(event.type == LiteGraph.pointerevents_method+"up" && w.type == "number") + { + var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0; + if (event.click_time < 200 && delta == 0) { + this.prompt("Value",w.value,function(v) { + this.value = Number(v); + inner_value_change(this, this.value); + }.bind(w), + event); + } + } + + if( old_value != w.value ) + setTimeout( + function() { + inner_value_change(this, this.value); + }.bind(w), + 20 + ); + this.dirty_canvas = true; + break; + case "toggle": + if (event.type == LiteGraph.pointerevents_method+"down") { + w.value = !w.value; + setTimeout(function() { + inner_value_change(w, w.value); + }, 20); + } + break; + case "string": + case "text": + if (event.type == LiteGraph.pointerevents_method+"down") { + this.prompt("Value",w.value,function(v) { + this.value = v; + inner_value_change(this, v); + }.bind(w), + event,w.options ? w.options.multiline : false ); + } + break; + default: + if (w.mouse) { + this.dirty_canvas = w.mouse(event, [x, y], node); + } + break; + } //end switch + + //value changed + if( old_value != w.value ) + { + if(node.onWidgetChanged) + node.onWidgetChanged( w.name,w.value,old_value,w ); + node.graph._version++; + } + + return w; + }//end for + + function inner_value_change(widget, value) { + widget.value = value; + if ( widget.options && widget.options.property && node.properties[widget.options.property] !== undefined ) { + node.setProperty( widget.options.property, value ); + } + if (widget.callback) { + widget.callback(widget.value, that, node, pos, event); + } + } + + return null; + }; + + /** + * draws every group area in the background + * @method drawGroups + **/ + LGraphCanvas.prototype.drawGroups = function(canvas, ctx) { + if (!this.graph) { + return; + } + + var groups = this.graph._groups; + + ctx.save(); + ctx.globalAlpha = 0.5 * this.editor_alpha; + + for (var i = 0; i < groups.length; ++i) { + var group = groups[i]; + + if (!overlapBounding(this.visible_area, group._bounding)) { + continue; + } //out of the visible area + + ctx.fillStyle = group.color || "#335"; + ctx.strokeStyle = group.color || "#335"; + var pos = group._pos; + var size = group._size; + ctx.globalAlpha = 0.25 * this.editor_alpha; + ctx.beginPath(); + ctx.rect(pos[0] + 0.5, pos[1] + 0.5, size[0], size[1]); + ctx.fill(); + ctx.globalAlpha = this.editor_alpha; + ctx.stroke(); + + ctx.beginPath(); + ctx.moveTo(pos[0] + size[0], pos[1] + size[1]); + ctx.lineTo(pos[0] + size[0] - 10, pos[1] + size[1]); + ctx.lineTo(pos[0] + size[0], pos[1] + size[1] - 10); + ctx.fill(); + + var font_size = + group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE; + ctx.font = font_size + "px Arial"; + ctx.textAlign = "left"; + ctx.fillText(group.title, pos[0] + 4, pos[1] + font_size); + } + + ctx.restore(); + }; + + LGraphCanvas.prototype.adjustNodesSize = function() { + var nodes = this.graph._nodes; + for (var i = 0; i < nodes.length; ++i) { + nodes[i].size = nodes[i].computeSize(); + } + this.setDirty(true, true); + }; + + /** + * resizes the canvas to a given size, if no size is passed, then it tries to fill the parentNode + * @method resize + **/ + LGraphCanvas.prototype.resize = function(width, height) { + if (!width && !height) { + var parent = this.canvas.parentNode; + width = parent.offsetWidth; + height = parent.offsetHeight; + } + + if (this.canvas.width == width && this.canvas.height == height) { + return; + } + + this.canvas.width = width; + this.canvas.height = height; + this.bgcanvas.width = this.canvas.width; + this.bgcanvas.height = this.canvas.height; + this.setDirty(true, true); + }; + + /** + * switches to live mode (node shapes are not rendered, only the content) + * this feature was designed when graphs where meant to create user interfaces + * @method switchLiveMode + **/ + LGraphCanvas.prototype.switchLiveMode = function(transition) { + if (!transition) { + this.live_mode = !this.live_mode; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + return; + } + + var self = this; + var delta = this.live_mode ? 1.1 : 0.9; + if (this.live_mode) { + this.live_mode = false; + this.editor_alpha = 0.1; + } + + var t = setInterval(function() { + self.editor_alpha *= delta; + self.dirty_canvas = true; + self.dirty_bgcanvas = true; + + if (delta < 1 && self.editor_alpha < 0.01) { + clearInterval(t); + if (delta < 1) { + self.live_mode = true; + } + } + if (delta > 1 && self.editor_alpha > 0.99) { + clearInterval(t); + self.editor_alpha = 1; + } + }, 1); + }; + + LGraphCanvas.prototype.onNodeSelectionChange = function(node) { + return; //disabled + }; + + /* this is an implementation for touch not in production and not ready + */ + /*LGraphCanvas.prototype.touchHandler = function(event) { + //alert("foo"); + var touches = event.changedTouches, + first = touches[0], + type = ""; + + switch (event.type) { + case "touchstart": + type = "mousedown"; + break; + case "touchmove": + type = "mousemove"; + break; + case "touchend": + type = "mouseup"; + break; + default: + return; + } + + //initMouseEvent(type, canBubble, cancelable, view, clickCount, + // screenX, screenY, clientX, clientY, ctrlKey, + // altKey, shiftKey, metaKey, button, relatedTarget); + + // this is eventually a Dom object, get the LGraphCanvas back + if(typeof this.getCanvasWindow == "undefined"){ + var window = this.lgraphcanvas.getCanvasWindow(); + }else{ + var window = this.getCanvasWindow(); + } + + var document = window.document; + + var simulatedEvent = document.createEvent("MouseEvent"); + simulatedEvent.initMouseEvent( + type, + true, + true, + window, + 1, + first.screenX, + first.screenY, + first.clientX, + first.clientY, + false, + false, + false, + false, + 0, //left + null + ); + first.target.dispatchEvent(simulatedEvent); + event.preventDefault(); + };*/ + + /* CONTEXT MENU ********************/ + + LGraphCanvas.onGroupAdd = function(info, entry, mouse_event) { + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var group = new LiteGraph.LGraphGroup(); + group.pos = canvas.convertEventToCanvasOffset(mouse_event); + canvas.graph.add(group); + }; + + LGraphCanvas.onMenuAdd = function (node, options, e, prev_menu, callback) { + + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + var graph = canvas.graph; + if (!graph) + return; + + function inner_onMenuAdded(base_category ,prev_menu){ + + var categories = LiteGraph.getNodeTypesCategories(canvas.filter || graph.filter).filter(function(category){return category.startsWith(base_category)}); + var entries = []; + + categories.map(function(category){ + + if (!category) + return; + + var base_category_regex = new RegExp('^(' + base_category + ')'); + var category_name = category.replace(base_category_regex,"").split('/')[0]; + var category_path = base_category === '' ? category_name + '/' : base_category + category_name + '/'; + + var name = category_name; + if(name.indexOf("::") != -1) //in case it has a namespace like "shader::math/rand" it hides the namespace + name = name.split("::")[1]; + + var index = entries.findIndex(function(entry){return entry.value === category_path}); + if (index === -1) { + entries.push({ value: category_path, content: name, has_submenu: true, callback : function(value, event, mouseEvent, contextMenu){ + inner_onMenuAdded(value.value, contextMenu) + }}); + } + + }); + + var nodes = LiteGraph.getNodeTypesInCategory(base_category.slice(0, -1), canvas.filter || graph.filter ); + nodes.map(function(node){ + + if (node.skip_list) + return; + + var entry = { value: node.type, content: node.title, has_submenu: false , callback : function(value, event, mouseEvent, contextMenu){ + + var first_event = contextMenu.getFirstEvent(); + canvas.graph.beforeChange(); + var node = LiteGraph.createNode(value.value); + if (node) { + node.pos = canvas.convertEventToCanvasOffset(first_event); + canvas.graph.add(node); + } + if(callback) + callback(node); + canvas.graph.afterChange(); + + } + } + + entries.push(entry); + + }); + + new LiteGraph.ContextMenu( entries, { event: e, parentMenu: prev_menu }, ref_window ); + + } + + inner_onMenuAdded('',prev_menu); + return false; + + }; + + LGraphCanvas.onMenuCollapseAll = function() {}; + + LGraphCanvas.onMenuNodeEdit = function() {}; + + LGraphCanvas.showMenuNodeOptionalInputs = function( + v, + options, + e, + prev_menu, + node + ) { + if (!node) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var options = node.optional_inputs; + if (node.onGetInputs) { + options = node.onGetInputs(); + } + + var entries = []; + if (options) { + for (var i=0; i < options.length; i++) { + var entry = options[i]; + if (!entry) { + entries.push(null); + continue; + } + var label = entry[0]; + if(!entry[2]) + entry[2] = {}; + + if (entry[2].label) { + label = entry[2].label; + } + + entry[2].removable = true; + var data = { content: label, value: entry }; + if (entry[1] == LiteGraph.ACTION) { + data.className = "event"; + } + entries.push(data); + } + } + + if (node.onMenuNodeInputs) { + var retEntries = node.onMenuNodeInputs(entries); + if(retEntries) entries = retEntries; + } + + if (!entries.length) { + console.log("no input entries"); + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node + }, + ref_window + ); + + function inner_clicked(v, e, prev) { + if (!node) { + return; + } + + if (v.callback) { + v.callback.call(that, node, v, e, prev); + } + + if (v.value) { + node.graph.beforeChange(); + node.addInput(v.value[0], v.value[1], v.value[2]); + + if (node.onNodeInputAdd) { // callback to the node when adding a slot + node.onNodeInputAdd(v.value); + } + node.setDirtyCanvas(true, true); + node.graph.afterChange(); + } + } + + return false; + }; + + LGraphCanvas.showMenuNodeOptionalOutputs = function( + v, + options, + e, + prev_menu, + node + ) { + if (!node) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var options = node.optional_outputs; + if (node.onGetOutputs) { + options = node.onGetOutputs(); + } + + var entries = []; + if (options) { + for (var i=0; i < options.length; i++) { + var entry = options[i]; + if (!entry) { + //separator? + entries.push(null); + continue; + } + + if ( + node.flags && + node.flags.skip_repeated_outputs && + node.findOutputSlot(entry[0]) != -1 + ) { + continue; + } //skip the ones already on + var label = entry[0]; + if(!entry[2]) + entry[2] = {}; + if (entry[2].label) { + label = entry[2].label; + } + entry[2].removable = true; + var data = { content: label, value: entry }; + if (entry[1] == LiteGraph.EVENT) { + data.className = "event"; + } + entries.push(data); + } + } + + if (this.onMenuNodeOutputs) { + entries = this.onMenuNodeOutputs(entries); + } + if (LiteGraph.do_add_triggers_slots){ //canvas.allow_addOutSlot_onExecuted + if (node.findOutputSlot("onExecuted") == -1){ + entries.push({content: "On Executed", value: ["onExecuted", LiteGraph.EVENT, {nameLocked: true}], className: "event"}); //, opts: {} + } + } + // add callback for modifing the menu elements onMenuNodeOutputs + if (node.onMenuNodeOutputs) { + var retEntries = node.onMenuNodeOutputs(entries); + if(retEntries) entries = retEntries; + } + + if (!entries.length) { + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node + }, + ref_window + ); + + function inner_clicked(v, e, prev) { + if (!node) { + return; + } + + if (v.callback) { + v.callback.call(that, node, v, e, prev); + } + + if (!v.value) { + return; + } + + var value = v.value[1]; + + if ( + value && + (value.constructor === Object || value.constructor === Array) + ) { + //submenu why? + var entries = []; + for (var i in value) { + entries.push({ content: i, value: value[i] }); + } + new LiteGraph.ContextMenu(entries, { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node + }); + return false; + } else { + node.graph.beforeChange(); + node.addOutput(v.value[0], v.value[1], v.value[2]); + + if (node.onNodeOutputAdd) { // a callback to the node when adding a slot + node.onNodeOutputAdd(v.value); + } + node.setDirtyCanvas(true, true); + node.graph.afterChange(); + } + } + + return false; + }; + + LGraphCanvas.onShowMenuNodeProperties = function( + value, + options, + e, + prev_menu, + node + ) { + if (!node || !node.properties) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var entries = []; + for (var i in node.properties) { + var value = node.properties[i] !== undefined ? node.properties[i] : " "; + if( typeof value == "object" ) + value = JSON.stringify(value); + var info = node.getPropertyInfo(i); + if(info.type == "enum" || info.type == "combo") + value = LGraphCanvas.getPropertyPrintableValue( value, info.values ); + + //value could contain invalid html characters, clean that + value = LGraphCanvas.decodeHTML(value); + entries.push({ + content: + "" + + (info.label ? info.label : i) + + "" + + "" + + value + + "", + value: i + }); + } + if (!entries.length) { + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + allow_html: true, + node: node + }, + ref_window + ); + + function inner_clicked(v, options, e, prev) { + if (!node) { + return; + } + var rect = this.getBoundingClientRect(); + canvas.showEditPropertyValue(node, v.value, { + position: [rect.left, rect.top] + }); + } + + return false; + }; + + LGraphCanvas.decodeHTML = function(str) { + var e = document.createElement("div"); + e.innerText = str; + return e.innerHTML; + }; + + LGraphCanvas.onMenuResizeNode = function(value, options, e, menu, node) { + if (!node) { + return; + } + + var fApplyMultiNode = function(node){ + node.size = node.computeSize(); + if (node.onResize) + node.onResize(node.size); + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.prototype.showLinkMenu = function(link, e) { + var that = this; + // console.log(link); + var node_left = that.graph.getNodeById( link.origin_id ); + var node_right = that.graph.getNodeById( link.target_id ); + var fromType = false; + if (node_left && node_left.outputs && node_left.outputs[link.origin_slot]) fromType = node_left.outputs[link.origin_slot].type; + var destType = false; + if (node_right && node_right.outputs && node_right.outputs[link.target_slot]) destType = node_right.inputs[link.target_slot].type; + + var options = ["Add Node",null,"Delete",null]; + + + var menu = new LiteGraph.ContextMenu(options, { + event: e, + title: link.data != null ? link.data.constructor.name : null, + callback: inner_clicked + }); + + function inner_clicked(v,options,e) { + switch (v) { + case "Add Node": + LGraphCanvas.onMenuAdd(null, null, e, menu, function(node){ + // console.debug("node autoconnect"); + if(!node.inputs || !node.inputs.length || !node.outputs || !node.outputs.length){ + return; + } + // leave the connection type checking inside connectByType + if (node_left.connectByType( link.origin_slot, node, fromType )){ + node.connectByType( link.target_slot, node_right, destType ); + node.pos[0] -= node.size[0] * 0.5; + } + }); + break; + + case "Delete": + that.graph.removeLink(link.id); + break; + default: + /*var nodeCreated = createDefaultNodeForSlot({ nodeFrom: node_left + ,slotFrom: link.origin_slot + ,nodeTo: node + ,slotTo: link.target_slot + ,e: e + ,nodeType: "AUTO" + }); + if(nodeCreated) console.log("new node in beetween "+v+" created");*/ + } + } + + return false; + }; + + LGraphCanvas.prototype.createDefaultNodeForSlot = function(optPass) { // addNodeMenu for connection + var optPass = optPass || {}; + var opts = Object.assign({ nodeFrom: null // input + ,slotFrom: null // input + ,nodeTo: null // output + ,slotTo: null // output + ,position: [] // pass the event coords + ,nodeType: null // choose a nodetype to add, AUTO to set at first good + ,posAdd:[0,0] // adjust x,y + ,posSizeFix:[0,0] // alpha, adjust the position x,y based on the new node size w,h + } + ,optPass + ); + var that = this; + + var isFrom = opts.nodeFrom && opts.slotFrom!==null; + var isTo = !isFrom && opts.nodeTo && opts.slotTo!==null; + + if (!isFrom && !isTo){ + console.warn("No data passed to createDefaultNodeForSlot "+opts.nodeFrom+" "+opts.slotFrom+" "+opts.nodeTo+" "+opts.slotTo); + return false; + } + if (!opts.nodeType){ + console.warn("No type to createDefaultNodeForSlot"); + return false; + } + + var nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + var slotX = isFrom ? opts.slotFrom : opts.slotTo; + + var iSlotConn = false; + switch (typeof slotX){ + case "string": + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX,false) : nodeX.findInputSlot(slotX,false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "undefined": + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information "+slotX); + return false; + } + + if (slotX===false || iSlotConn===false){ + console.warn("createDefaultNodeForSlot bad slotX "+slotX+" "+iSlotConn); + } + + // check for defaults nodes for this slottype + var fromSlotType = slotX.type==LiteGraph.EVENT?"_event_":slotX.type; + var slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in; + if(slotTypesDefault && slotTypesDefault[fromSlotType]){ + if (slotX.link !== null) { + // is connected + }else{ + // is not not connected + } + nodeNewType = false; + if(typeof slotTypesDefault[fromSlotType] == "object" || typeof slotTypesDefault[fromSlotType] == "array"){ + for(var typeX in slotTypesDefault[fromSlotType]){ + if (opts.nodeType == slotTypesDefault[fromSlotType][typeX] || opts.nodeType == "AUTO"){ + nodeNewType = slotTypesDefault[fromSlotType][typeX]; + // console.log("opts.nodeType == slotTypesDefault[fromSlotType][typeX] :: "+opts.nodeType); + break; // -------- + } + } + }else{ + if (opts.nodeType == slotTypesDefault[fromSlotType] || opts.nodeType == "AUTO") nodeNewType = slotTypesDefault[fromSlotType]; + } + if (nodeNewType) { + var nodeNewOpts = false; + if (typeof nodeNewType == "object" && nodeNewType.node){ + nodeNewOpts = nodeNewType; + nodeNewType = nodeNewType.node; + } + + //that.graph.beforeChange(); + + var newNode = LiteGraph.createNode(nodeNewType); + if(newNode){ + // if is object pass options + if (nodeNewOpts){ + if (nodeNewOpts.properties) { + for (var i in nodeNewOpts.properties) { + newNode.addProperty( i, nodeNewOpts.properties[i] ); + } + } + if (nodeNewOpts.inputs) { + newNode.inputs = []; + for (var i in nodeNewOpts.inputs) { + newNode.addOutput( + nodeNewOpts.inputs[i][0], + nodeNewOpts.inputs[i][1] + ); + } + } + if (nodeNewOpts.outputs) { + newNode.outputs = []; + for (var i in nodeNewOpts.outputs) { + newNode.addOutput( + nodeNewOpts.outputs[i][0], + nodeNewOpts.outputs[i][1] + ); + } + } + if (nodeNewOpts.title) { + newNode.title = nodeNewOpts.title; + } + if (nodeNewOpts.json) { + newNode.configure(nodeNewOpts.json); + } + + } + + // add the node + that.graph.add(newNode); + newNode.pos = [ opts.position[0]+opts.posAdd[0]+(opts.posSizeFix[0]?opts.posSizeFix[0]*newNode.size[0]:0) + ,opts.position[1]+opts.posAdd[1]+(opts.posSizeFix[1]?opts.posSizeFix[1]*newNode.size[1]:0)]; //that.last_click_position; //[e.canvasX+30, e.canvasX+5];*/ + + //that.graph.afterChange(); + + // connect the two! + if (isFrom){ + opts.nodeFrom.connectByType( iSlotConn, newNode, fromSlotType ); + }else{ + opts.nodeTo.connectByTypeOutput( iSlotConn, newNode, fromSlotType ); + } + + // if connecting in between + if (isFrom && isTo){ + // TODO + } + + return true; + + }else{ + console.log("failed creating "+nodeNewType); + } + } + } + return false; + } + + LGraphCanvas.prototype.showConnectionMenu = function(optPass) { // addNodeMenu for connection + var optPass = optPass || {}; + var opts = Object.assign({ nodeFrom: null // input + ,slotFrom: null // input + ,nodeTo: null // output + ,slotTo: null // output + ,e: null + } + ,optPass + ); + var that = this; + + var isFrom = opts.nodeFrom && opts.slotFrom; + var isTo = !isFrom && opts.nodeTo && opts.slotTo; + + if (!isFrom && !isTo){ + console.warn("No data passed to showConnectionMenu"); + return false; + } + + var nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + var slotX = isFrom ? opts.slotFrom : opts.slotTo; + + var iSlotConn = false; + switch (typeof slotX){ + case "string": + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX,false) : nodeX.findInputSlot(slotX,false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information "+slotX); + return false; + } + + var options = ["Add Node",null]; + + if (that.allow_searchbox){ + options.push("Search"); + options.push(null); + } + + // get defaults nodes for this slottype + var fromSlotType = slotX.type==LiteGraph.EVENT?"_event_":slotX.type; + var slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in; + if(slotTypesDefault && slotTypesDefault[fromSlotType]){ + if(typeof slotTypesDefault[fromSlotType] == "object" || typeof slotTypesDefault[fromSlotType] == "array"){ + for(var typeX in slotTypesDefault[fromSlotType]){ + options.push(slotTypesDefault[fromSlotType][typeX]); + } + }else{ + options.push(slotTypesDefault[fromSlotType]); + } + } + + // build menu + var menu = new LiteGraph.ContextMenu(options, { + event: opts.e, + title: (slotX && slotX.name!="" ? (slotX.name + (fromSlotType?" | ":"")) : "")+(slotX && fromSlotType ? fromSlotType : ""), + callback: inner_clicked + }); + + // callback + function inner_clicked(v,options,e) { + //console.log("Process showConnectionMenu selection"); + switch (v) { + case "Add Node": + LGraphCanvas.onMenuAdd(null, null, e, menu, function(node){ + if (isFrom){ + opts.nodeFrom.connectByType( iSlotConn, node, fromSlotType ); + }else{ + opts.nodeTo.connectByTypeOutput( iSlotConn, node, fromSlotType ); + } + }); + break; + case "Search": + if(isFrom){ + that.showSearchBox(e,{node_from: opts.nodeFrom, slot_from: slotX, type_filter_in: fromSlotType}); + }else{ + that.showSearchBox(e,{node_to: opts.nodeTo, slot_from: slotX, type_filter_out: fromSlotType}); + } + break; + default: + // check for defaults nodes for this slottype + var nodeCreated = that.createDefaultNodeForSlot(Object.assign(opts,{ position: [opts.e.canvasX, opts.e.canvasY] + ,nodeType: v + })); + if (nodeCreated){ + // new node created + //console.log("node "+v+" created") + }else{ + // failed or v is not in defaults + } + break; + } + } + + return false; + }; + + // TODO refactor :: this is used fot title but not for properties! + LGraphCanvas.onShowPropertyEditor = function(item, options, e, menu, node) { + var input_html = ""; + var property = item.property || "title"; + var value = node[property]; + + // TODO refactor :: use createDialog ? + + var dialog = document.createElement("div"); + dialog.is_modified = false; + dialog.className = "graphdialog"; + dialog.innerHTML = + ""; + dialog.close = function() { + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + var title = dialog.querySelector(".name"); + title.innerText = property; + var input = dialog.querySelector(".value"); + if (input) { + input.value = value; + input.addEventListener("blur", function(e) { + this.focus(); + }); + input.addEventListener("keydown", function(e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + inner(); // save + } else if (e.keyCode != 13 && e.target.localName != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + } + + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + + var rect = canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + + var button = dialog.querySelector("button"); + button.addEventListener("click", inner); + canvas.parentNode.appendChild(dialog); + + if(input) input.focus(); + + var dialogCloseTimer = null; + dialog.addEventListener("mouseleave", function(e) { + if(LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay); //dialog.close(); + }); + dialog.addEventListener("mouseenter", function(e) { + if(LiteGraph.dialog_close_on_mouse_leave) + if(dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + + function inner() { + if(input) setValue(input.value); + } + + function setValue(value) { + if (item.type == "Number") { + value = Number(value); + } else if (item.type == "Boolean") { + value = Boolean(value); + } + node[property] = value; + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + node.setDirtyCanvas(true, true); + } + }; + + // refactor: there are different dialogs, some uses createDialog some dont + LGraphCanvas.prototype.prompt = function(title, value, callback, event, multiline) { + var that = this; + var input_html = ""; + title = title || ""; + + var dialog = document.createElement("div"); + dialog.is_modified = false; + dialog.className = "graphdialog rounded"; + if(multiline) + dialog.innerHTML = " "; + else + dialog.innerHTML = " "; + dialog.close = function() { + that.prompt_box = null; + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + canvas.parentNode.appendChild(dialog); + + if (this.ds.scale > 1) { + dialog.style.transform = "scale(" + this.ds.scale + ")"; + } + + var dialogCloseTimer = null; + var prevent_timeout = false; + LiteGraph.pointerListenerAdd(dialog,"leave", function(e) { + if (prevent_timeout) + return; + if(LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay); //dialog.close(); + }); + LiteGraph.pointerListenerAdd(dialog,"enter", function(e) { + if(LiteGraph.dialog_close_on_mouse_leave) + if(dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + var selInDia = dialog.querySelectorAll("select"); + if (selInDia){ + // if filtering, check focus changed to comboboxes and prevent closing + selInDia.forEach(function(selIn) { + selIn.addEventListener("click", function(e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + }); + } + + if (that.prompt_box) { + that.prompt_box.close(); + } + that.prompt_box = dialog; + + var first = null; + var timeout = null; + var selected = null; + + var name_element = dialog.querySelector(".name"); + name_element.innerText = title; + var value_element = dialog.querySelector(".value"); + value_element.value = value; + + var input = value_element; + input.addEventListener("keydown", function(e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13 && e.target.localName != "textarea") { + if (callback) { + callback(this.value); + } + dialog.close(); + } else { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + + var button = dialog.querySelector("button"); + button.addEventListener("click", function(e) { + if (callback) { + callback(input.value); + } + that.setDirty(true); + dialog.close(); + }); + + var rect = canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + + setTimeout(function() { + input.focus(); + }, 10); + + return dialog; + }; + + LGraphCanvas.search_limit = -1; + LGraphCanvas.prototype.showSearchBox = function(event, options) { + // proposed defaults + def_options = { slot_from: null + ,node_from: null + ,node_to: null + ,do_type_filter: LiteGraph.search_filter_enabled // TODO check for registered_slot_[in/out]_types not empty // this will be checked for functionality enabled : filter on slot type, in and out + ,type_filter_in: false // these are default: pass to set initially set values + ,type_filter_out: false + ,show_general_if_none_on_typefilter: true + ,show_general_after_typefiltered: true + ,hide_on_mouse_leave: LiteGraph.search_hide_on_mouse_leave + ,show_all_if_empty: true + ,show_all_on_open: LiteGraph.search_show_all_on_open + }; + options = Object.assign(def_options, options || {}); + + //console.log(options); + + var that = this; + var input_html = ""; + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + var root_document = canvas.ownerDocument || document; + + var dialog = document.createElement("div"); + dialog.className = "litegraph litesearchbox graphdialog rounded"; + dialog.innerHTML = "Search "; + if (options.do_type_filter){ + dialog.innerHTML += ""; + dialog.innerHTML += ""; + } + dialog.innerHTML += "
"; + + if( root_document.fullscreenElement ) + root_document.fullscreenElement.appendChild(dialog); + else + { + root_document.body.appendChild(dialog); + root_document.body.style.overflow = "hidden"; + } + // dialog element has been appended + + if (options.do_type_filter){ + var selIn = dialog.querySelector(".slot_in_type_filter"); + var selOut = dialog.querySelector(".slot_out_type_filter"); + } + + dialog.close = function() { + that.search_box = null; + this.blur(); + canvas.focus(); + root_document.body.style.overflow = ""; + + setTimeout(function() { + that.canvas.focus(); + }, 20); //important, if canvas loses focus keys wont be captured + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + if (this.ds.scale > 1) { + dialog.style.transform = "scale(" + this.ds.scale + ")"; + } + + // hide on mouse leave + if(options.hide_on_mouse_leave){ + var prevent_timeout = false; + var timeout_close = null; + LiteGraph.pointerListenerAdd(dialog,"enter", function(e) { + if (timeout_close) { + clearTimeout(timeout_close); + timeout_close = null; + } + }); + LiteGraph.pointerListenerAdd(dialog,"leave", function(e) { + if (prevent_timeout){ + return; + } + timeout_close = setTimeout(function() { + dialog.close(); + }, 500); + }); + // if filtering, check focus changed to comboboxes and prevent closing + if (options.do_type_filter){ + selIn.addEventListener("click", function(e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + selOut.addEventListener("click", function(e) { + prevent_timeout++; + }); + selOut.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selOut.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + } + } + + if (that.search_box) { + that.search_box.close(); + } + that.search_box = dialog; + + var helper = dialog.querySelector(".helper"); + + var first = null; + var timeout = null; + var selected = null; + + var input = dialog.querySelector("input"); + if (input) { + input.addEventListener("blur", function(e) { + this.focus(); + }); + input.addEventListener("keydown", function(e) { + if (e.keyCode == 38) { + //UP + changeSelection(false); + } else if (e.keyCode == 40) { + //DOWN + changeSelection(true); + } else if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + if (selected) { + select(selected.innerHTML); + } else if (first) { + select(first); + } else { + dialog.close(); + } + } else { + if (timeout) { + clearInterval(timeout); + } + timeout = setTimeout(refreshHelper, 250); + return; + } + e.preventDefault(); + e.stopPropagation(); + e.stopImmediatePropagation(); + return true; + }); + } + + // if should filter on type, load and fill selected and choose elements if passed + if (options.do_type_filter){ + if (selIn){ + var aSlots = LiteGraph.slot_types_in; + var nSlots = aSlots.length; // this for object :: Object.keys(aSlots).length; + + if (options.type_filter_in == LiteGraph.EVENT || options.type_filter_in == LiteGraph.ACTION) + options.type_filter_in = "_event_"; + /* this will filter on * .. but better do it manually in case + else if(options.type_filter_in === "" || options.type_filter_in === 0) + options.type_filter_in = "*";*/ + + for (var iK=0; iK (rect.height - 200)) + helper.style.maxHeight = (rect.height - event.layerY - 20) + "px"; + + /* + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + canvas.parentNode.appendChild(dialog); + */ + + input.focus(); + if (options.show_all_on_open) refreshHelper(); + + function select(name) { + if (name) { + if (that.onSearchBoxSelection) { + that.onSearchBoxSelection(name, event, graphcanvas); + } else { + var extra = LiteGraph.searchbox_extras[name.toLowerCase()]; + if (extra) { + name = extra.type; + } + + graphcanvas.graph.beforeChange(); + var node = LiteGraph.createNode(name); + if (node) { + node.pos = graphcanvas.convertEventToCanvasOffset( + event + ); + graphcanvas.graph.add(node, false); + } + + if (extra && extra.data) { + if (extra.data.properties) { + for (var i in extra.data.properties) { + node.addProperty( i, extra.data.properties[i] ); + } + } + if (extra.data.inputs) { + node.inputs = []; + for (var i in extra.data.inputs) { + node.addOutput( + extra.data.inputs[i][0], + extra.data.inputs[i][1] + ); + } + } + if (extra.data.outputs) { + node.outputs = []; + for (var i in extra.data.outputs) { + node.addOutput( + extra.data.outputs[i][0], + extra.data.outputs[i][1] + ); + } + } + if (extra.data.title) { + node.title = extra.data.title; + } + if (extra.data.json) { + node.configure(extra.data.json); + } + + } + + // join node after inserting + if (options.node_from){ + var iS = false; + switch (typeof options.slot_from){ + case "string": + iS = options.node_from.findOutputSlot(options.slot_from); + break; + case "object": + if (options.slot_from.name){ + iS = options.node_from.findOutputSlot(options.slot_from.name); + }else{ + iS = -1; + } + if (iS==-1 && typeof options.slot_from.slot_index !== "undefined") iS = options.slot_from.slot_index; + break; + case "number": + iS = options.slot_from; + break; + default: + iS = 0; // try with first if no name set + } + if (typeof options.node_from.outputs[iS] !== undefined){ + if (iS!==false && iS>-1){ + options.node_from.connectByType( iS, node, options.node_from.outputs[iS].type ); + } + }else{ + // console.warn("cant find slot " + options.slot_from); + } + } + if (options.node_to){ + var iS = false; + switch (typeof options.slot_from){ + case "string": + iS = options.node_to.findInputSlot(options.slot_from); + break; + case "object": + if (options.slot_from.name){ + iS = options.node_to.findInputSlot(options.slot_from.name); + }else{ + iS = -1; + } + if (iS==-1 && typeof options.slot_from.slot_index !== "undefined") iS = options.slot_from.slot_index; + break; + case "number": + iS = options.slot_from; + break; + default: + iS = 0; // try with first if no name set + } + if (typeof options.node_to.inputs[iS] !== undefined){ + if (iS!==false && iS>-1){ + // try connection + options.node_to.connectByTypeOutput(iS,node,options.node_to.inputs[iS].type); + } + }else{ + // console.warn("cant find slot_nodeTO " + options.slot_from); + } + } + + graphcanvas.graph.afterChange(); + } + } + + dialog.close(); + } + + function changeSelection(forward) { + var prev = selected; + if (selected) { + selected.classList.remove("selected"); + } + if (!selected) { + selected = forward + ? helper.childNodes[0] + : helper.childNodes[helper.childNodes.length]; + } else { + selected = forward + ? selected.nextSibling + : selected.previousSibling; + if (!selected) { + selected = prev; + } + } + if (!selected) { + return; + } + selected.classList.add("selected"); + selected.scrollIntoView({block: "end", behavior: "smooth"}); + } + + function refreshHelper() { + timeout = null; + var str = input.value; + first = null; + helper.innerHTML = ""; + if (!str && !options.show_all_if_empty) { + return; + } + + if (that.onSearchBox) { + var list = that.onSearchBox(helper, str, graphcanvas); + if (list) { + for (var i = 0; i < list.length; ++i) { + addResult(list[i]); + } + } + } else { + var c = 0; + str = str.toLowerCase(); + var filter = graphcanvas.filter || graphcanvas.graph.filter; + + // filter by type preprocess + if(options.do_type_filter && that.search_box){ + var sIn = that.search_box.querySelector(".slot_in_type_filter"); + var sOut = that.search_box.querySelector(".slot_out_type_filter"); + }else{ + var sIn = false; + var sOut = false; + } + + //extras + for (var i in LiteGraph.searchbox_extras) { + var extra = LiteGraph.searchbox_extras[i]; + if ((!options.show_all_if_empty || str) && extra.desc.toLowerCase().indexOf(str) === -1) { + continue; + } + var ctor = LiteGraph.registered_node_types[ extra.type ]; + if( ctor && ctor.filter != filter ) + continue; + if( ! inner_test_filter(extra.type) ) + continue; + addResult( extra.desc, "searchbox_extra" ); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + + var filtered = null; + if (Array.prototype.filter) { //filter supported + var keys = Object.keys( LiteGraph.registered_node_types ); //types + var filtered = keys.filter( inner_test_filter ); + } else { + filtered = []; + for (var i in LiteGraph.registered_node_types) { + if( inner_test_filter(i) ) + filtered.push(i); + } + } + + for (var i = 0; i < filtered.length; i++) { + addResult(filtered[i]); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + + // add general type if filtering + if (options.show_general_after_typefiltered + && (sIn.value || sOut.value) + ){ + filtered_extra = []; + for (var i in LiteGraph.registered_node_types) { + if( inner_test_filter(i, {inTypeOverride: sIn&&sIn.value?"*":false, outTypeOverride: sOut&&sOut.value?"*":false}) ) + filtered_extra.push(i); + } + for (var i = 0; i < filtered_extra.length; i++) { + addResult(filtered_extra[i], "generic_type"); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + } + + // check il filtering gave no results + if ((sIn.value || sOut.value) && + ( (helper.childNodes.length == 0 && options.show_general_if_none_on_typefilter) ) + ){ + filtered_extra = []; + for (var i in LiteGraph.registered_node_types) { + if( inner_test_filter(i, {skipFilter: true}) ) + filtered_extra.push(i); + } + for (var i = 0; i < filtered_extra.length; i++) { + addResult(filtered_extra[i], "not_in_filter"); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + } + + function inner_test_filter( type, optsIn ) + { + var optsIn = optsIn || {}; + var optsDef = { skipFilter: false + ,inTypeOverride: false + ,outTypeOverride: false + }; + var opts = Object.assign(optsDef,optsIn); + var ctor = LiteGraph.registered_node_types[ type ]; + if(filter && ctor.filter != filter ) + return false; + if ((!options.show_all_if_empty || str) && type.toLowerCase().indexOf(str) === -1) + return false; + + // filter by slot IN, OUT types + if(options.do_type_filter && !opts.skipFilter){ + var sType = type; + + var sV = sIn.value; + if (opts.inTypeOverride!==false) sV = opts.inTypeOverride; + //if (sV.toLowerCase() == "_event_") sV = LiteGraph.EVENT; // -1 + + if(sIn && sV){ + //console.log("will check filter against "+sV); + if (LiteGraph.registered_slot_in_types[sV] && LiteGraph.registered_slot_in_types[sV].nodes){ // type is stored + //console.debug("check "+sType+" in "+LiteGraph.registered_slot_in_types[sV].nodes); + var doesInc = LiteGraph.registered_slot_in_types[sV].nodes.includes(sType); + if (doesInc!==false){ + //console.log(sType+" HAS "+sV); + }else{ + /*console.debug(LiteGraph.registered_slot_in_types[sV]); + console.log(+" DONT includes "+type);*/ + return false; + } + } + } + + var sV = sOut.value; + if (opts.outTypeOverride!==false) sV = opts.outTypeOverride; + //if (sV.toLowerCase() == "_event_") sV = LiteGraph.EVENT; // -1 + + if(sOut && sV){ + //console.log("search will check filter against "+sV); + if (LiteGraph.registered_slot_out_types[sV] && LiteGraph.registered_slot_out_types[sV].nodes){ // type is stored + //console.debug("check "+sType+" in "+LiteGraph.registered_slot_out_types[sV].nodes); + var doesInc = LiteGraph.registered_slot_out_types[sV].nodes.includes(sType); + if (doesInc!==false){ + //console.log(sType+" HAS "+sV); + }else{ + /*console.debug(LiteGraph.registered_slot_out_types[sV]); + console.log(+" DONT includes "+type);*/ + return false; + } + } + } + } + return true; + } + } + + function addResult(type, className) { + var help = document.createElement("div"); + if (!first) { + first = type; + } + help.innerText = type; + help.dataset["type"] = escape(type); + help.className = "litegraph lite-search-item"; + if (className) { + help.className += " " + className; + } + help.addEventListener("click", function(e) { + select(unescape(this.dataset["type"])); + }); + helper.appendChild(help); + } + } + + return dialog; + }; + + LGraphCanvas.prototype.showEditPropertyValue = function( node, property, options ) { + if (!node || node.properties[property] === undefined) { + return; + } + + options = options || {}; + var that = this; + + var info = node.getPropertyInfo(property); + var type = info.type; + + var input_html = ""; + + if (type == "string" || type == "number" || type == "array" || type == "object") { + input_html = ""; + } else if ( (type == "enum" || type == "combo") && info.values) { + input_html = ""; + } else if (type == "boolean" || type == "toggle") { + input_html = + ""; + } else { + console.warn("unknown type: " + type); + return; + } + + var dialog = this.createDialog( + "" + + (info.label ? info.label : property) + + "" + + input_html + + "", + options + ); + + var input = false; + if ((type == "enum" || type == "combo") && info.values) { + input = dialog.querySelector("select"); + input.addEventListener("change", function(e) { + dialog.modified(); + setValue(e.target.value); + //var index = e.target.value; + //setValue( e.options[e.selectedIndex].value ); + }); + } else if (type == "boolean" || type == "toggle") { + input = dialog.querySelector("input"); + if (input) { + input.addEventListener("click", function(e) { + dialog.modified(); + setValue(!!input.checked); + }); + } + } else { + input = dialog.querySelector("input"); + if (input) { + input.addEventListener("blur", function(e) { + this.focus(); + }); + + var v = node.properties[property] !== undefined ? node.properties[property] : ""; + if (type !== 'string') { + v = JSON.stringify(v); + } + + input.value = v; + input.addEventListener("keydown", function(e) { + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + // ENTER + inner(); // save + } else if (e.keyCode != 13) { + dialog.modified(); + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + } + } + if (input) input.focus(); + + var button = dialog.querySelector("button"); + button.addEventListener("click", inner); + + function inner() { + setValue(input.value); + } + + function setValue(value) { + + if(info && info.values && info.values.constructor === Object && info.values[value] != undefined ) + value = info.values[value]; + + if (typeof node.properties[property] == "number") { + value = Number(value); + } + if (type == "array" || type == "object") { + value = JSON.parse(value); + } + node.properties[property] = value; + if (node.graph) { + node.graph._version++; + } + if (node.onPropertyChanged) { + node.onPropertyChanged(property, value); + } + if(options.onclose) + options.onclose(); + dialog.close(); + node.setDirtyCanvas(true, true); + } + + return dialog; + }; + + // TODO refactor, theer are different dialog, some uses createDialog, some dont + LGraphCanvas.prototype.createDialog = function(html, options) { + def_options = { checkForInput: false, closeOnLeave: true, closeOnLeave_checkModified: true }; + options = Object.assign(def_options, options || {}); + + var dialog = document.createElement("div"); + dialog.className = "graphdialog"; + dialog.innerHTML = html; + dialog.is_modified = false; + + var rect = this.canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (options.position) { + offsetx += options.position[0]; + offsety += options.position[1]; + } else if (options.event) { + offsetx += options.event.clientX; + offsety += options.event.clientY; + } //centered + else { + offsetx += this.canvas.width * 0.5; + offsety += this.canvas.height * 0.5; + } + + dialog.style.left = offsetx + "px"; + dialog.style.top = offsety + "px"; + + this.canvas.parentNode.appendChild(dialog); + + // acheck for input and use default behaviour: save on enter, close on esc + if (options.checkForInput){ + var aI = []; + var focused = false; + if (aI = dialog.querySelectorAll("input")){ + aI.forEach(function(iX) { + iX.addEventListener("keydown",function(e){ + dialog.modified(); + if (e.keyCode == 27) { + dialog.close(); + } else if (e.keyCode != 13) { + return; + } + // set value ? + e.preventDefault(); + e.stopPropagation(); + }); + if (!focused) iX.focus(); + }); + } + } + + dialog.modified = function(){ + dialog.is_modified = true; + } + dialog.close = function() { + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + var dialogCloseTimer = null; + var prevent_timeout = false; + dialog.addEventListener("mouseleave", function(e) { + if (prevent_timeout) + return; + if(options.closeOnLeave || LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay); //dialog.close(); + }); + dialog.addEventListener("mouseenter", function(e) { + if(options.closeOnLeave || LiteGraph.dialog_close_on_mouse_leave) + if(dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + var selInDia = dialog.querySelectorAll("select"); + if (selInDia){ + // if filtering, check focus changed to comboboxes and prevent closing + selInDia.forEach(function(selIn) { + selIn.addEventListener("click", function(e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + }); + } + + return dialog; + }; + + LGraphCanvas.prototype.createPanel = function(title, options) { + options = options || {}; + + var ref_window = options.window || window; + var root = document.createElement("div"); + root.className = "litegraph dialog"; + root.innerHTML = "
"; + root.header = root.querySelector(".dialog-header"); + + if(options.width) + root.style.width = options.width + (options.width.constructor === Number ? "px" : ""); + if(options.height) + root.style.height = options.height + (options.height.constructor === Number ? "px" : ""); + if(options.closable) + { + var close = document.createElement("span"); + close.innerHTML = "✕"; + close.classList.add("close"); + close.addEventListener("click",function(){ + root.close(); + }); + root.header.appendChild(close); + } + root.title_element = root.querySelector(".dialog-title"); + root.title_element.innerText = title; + root.content = root.querySelector(".dialog-content"); + root.alt_content = root.querySelector(".dialog-alt-content"); + root.footer = root.querySelector(".dialog-footer"); + + root.close = function() + { + if (root.onClose && typeof root.onClose == "function"){ + root.onClose(); + } + root.parentNode.removeChild(root); + /* XXX CHECK THIS */ + if(this.parentNode){ + this.parentNode.removeChild(this); + } + /* XXX this was not working, was fixed with an IF, check this */ + } + + // function to swap panel content + root.toggleAltContent = function(force){ + if (typeof force != "undefined"){ + var vTo = force ? "block" : "none"; + var vAlt = force ? "none" : "block"; + }else{ + var vTo = root.alt_content.style.display != "block" ? "block" : "none"; + var vAlt = root.alt_content.style.display != "block" ? "none" : "block"; + } + root.alt_content.style.display = vTo; + root.content.style.display = vAlt; + } + + root.toggleFooterVisibility = function(force){ + if (typeof force != "undefined"){ + var vTo = force ? "block" : "none"; + }else{ + var vTo = root.footer.style.display != "block" ? "block" : "none"; + } + root.footer.style.display = vTo; + } + + root.clear = function() + { + this.content.innerHTML = ""; + } + + root.addHTML = function(code, classname, on_footer) + { + var elem = document.createElement("div"); + if(classname) + elem.className = classname; + elem.innerHTML = code; + if(on_footer) + root.footer.appendChild(elem); + else + root.content.appendChild(elem); + return elem; + } + + root.addButton = function( name, callback, options ) + { + var elem = document.createElement("button"); + elem.innerText = name; + elem.options = options; + elem.classList.add("btn"); + elem.addEventListener("click",callback); + root.footer.appendChild(elem); + return elem; + } + + root.addSeparator = function() + { + var elem = document.createElement("div"); + elem.className = "separator"; + root.content.appendChild(elem); + } + + root.addWidget = function( type, name, value, options, callback ) + { + options = options || {}; + var str_value = String(value); + type = type.toLowerCase(); + if(type == "number") + str_value = value.toFixed(3); + + var elem = document.createElement("div"); + elem.className = "property"; + elem.innerHTML = ""; + elem.querySelector(".property_name").innerText = options.label || name; + var value_element = elem.querySelector(".property_value"); + value_element.innerText = str_value; + elem.dataset["property"] = name; + elem.dataset["type"] = options.type || type; + elem.options = options; + elem.value = value; + + if( type == "code" ) + elem.addEventListener("click", function(e){ root.inner_showCodePad( this.dataset["property"] ); }); + else if (type == "boolean") + { + elem.classList.add("boolean"); + if(value) + elem.classList.add("bool-on"); + elem.addEventListener("click", function(){ + //var v = node.properties[this.dataset["property"]]; + //node.setProperty(this.dataset["property"],!v); this.innerText = v ? "true" : "false"; + var propname = this.dataset["property"]; + this.value = !this.value; + this.classList.toggle("bool-on"); + this.querySelector(".property_value").innerText = this.value ? "true" : "false"; + innerChange(propname, this.value ); + }); + } + else if (type == "string" || type == "number") + { + value_element.setAttribute("contenteditable",true); + value_element.addEventListener("keydown", function(e){ + if(e.code == "Enter" && (type != "string" || !e.shiftKey)) // allow for multiline + { + e.preventDefault(); + this.blur(); + } + }); + value_element.addEventListener("blur", function(){ + var v = this.innerText; + var propname = this.parentNode.dataset["property"]; + var proptype = this.parentNode.dataset["type"]; + if( proptype == "number") + v = Number(v); + innerChange(propname, v); + }); + } + else if (type == "enum" || type == "combo") { + var str_value = LGraphCanvas.getPropertyPrintableValue( value, options.values ); + value_element.innerText = str_value; + + value_element.addEventListener("click", function(event){ + var values = options.values || []; + var propname = this.parentNode.dataset["property"]; + var elem_that = this; + var menu = new LiteGraph.ContextMenu(values,{ + event: event, + className: "dark", + callback: inner_clicked + }, + ref_window); + function inner_clicked(v, option, event) { + //node.setProperty(propname,v); + //graphcanvas.dirty_canvas = true; + elem_that.innerText = v; + innerChange(propname,v); + return false; + } + }); + } + + root.content.appendChild(elem); + + function innerChange(name, value) + { + //console.log("change",name,value); + //that.dirty_canvas = true; + if(options.callback) + options.callback(name,value,options); + if(callback) + callback(name,value,options); + } + + return elem; + } + + if (root.onOpen && typeof root.onOpen == "function") root.onOpen(); + + return root; + }; + + LGraphCanvas.getPropertyPrintableValue = function(value, values) + { + if(!values) + return String(value); + + if(values.constructor === Array) + { + return String(value); + } + + if(values.constructor === Object) + { + var desc_value = ""; + for(var k in values) + { + if(values[k] != value) + continue; + desc_value = k; + break; + } + return String(value) + " ("+desc_value+")"; + } + } + + LGraphCanvas.prototype.closePanels = function(){ + var panel = document.querySelector("#node-panel"); + if(panel) + panel.close(); + var panel = document.querySelector("#option-panel"); + if(panel) + panel.close(); + } + + LGraphCanvas.prototype.showShowGraphOptionsPanel = function(refOpts, obEv, refMenu, refMenu2){ + if(this.constructor && this.constructor.name == "HTMLDivElement"){ + // assume coming from the menu event click + if (!obEv || !obEv.event || !obEv.event.target || !obEv.event.target.lgraphcanvas){ + console.warn("Canvas not found"); // need a ref to canvas obj + /*console.debug(event); + console.debug(event.target);*/ + return; + } + var graphcanvas = obEv.event.target.lgraphcanvas; + }else{ + // assume called internally + var graphcanvas = this; + } + graphcanvas.closePanels(); + var ref_window = graphcanvas.getCanvasWindow(); + panel = graphcanvas.createPanel("Options",{ + closable: true + ,window: ref_window + ,onOpen: function(){ + graphcanvas.OPTIONPANEL_IS_OPEN = true; + } + ,onClose: function(){ + graphcanvas.OPTIONPANEL_IS_OPEN = false; + graphcanvas.options_panel = null; + } + }); + graphcanvas.options_panel = panel; + panel.id = "option-panel"; + panel.classList.add("settings"); + + function inner_refresh(){ + + panel.content.innerHTML = ""; //clear + + var fUpdate = function(name, value, options){ + switch(name){ + /*case "Render mode": + // Case "".. + if (options.values && options.key){ + var kV = Object.values(options.values).indexOf(value); + if (kV>=0 && options.values[kV]){ + console.debug("update graph options: "+options.key+": "+kV); + graphcanvas[options.key] = kV; + //console.debug(graphcanvas); + break; + } + } + console.warn("unexpected options"); + console.debug(options); + break;*/ + default: + //console.debug("want to update graph options: "+name+": "+value); + if (options && options.key){ + name = options.key; + } + if (options.values){ + value = Object.values(options.values).indexOf(value); + } + //console.debug("update graph option: "+name+": "+value); + graphcanvas[name] = value; + break; + } + }; + + // panel.addWidget( "string", "Graph name", "", {}, fUpdate); // implement + + var aProps = LiteGraph.availableCanvasOptions; + aProps.sort(); + for(pI in aProps){ + var pX = aProps[pI]; + panel.addWidget( "boolean", pX, graphcanvas[pX], {key: pX, on: "True", off: "False"}, fUpdate); + } + + var aLinks = [ graphcanvas.links_render_mode ]; + panel.addWidget( "combo", "Render mode", LiteGraph.LINK_RENDER_MODES[graphcanvas.links_render_mode], {key: "links_render_mode", values: LiteGraph.LINK_RENDER_MODES}, fUpdate); + + panel.addSeparator(); + + panel.footer.innerHTML = ""; // clear + + } + inner_refresh(); + + graphcanvas.canvas.parentNode.appendChild( panel ); + } + + LGraphCanvas.prototype.showShowNodePanel = function( node ) + { + this.SELECTED_NODE = node; + this.closePanels(); + var ref_window = this.getCanvasWindow(); + var that = this; + var graphcanvas = this; + panel = this.createPanel(node.title || "",{ + closable: true + ,window: ref_window + ,onOpen: function(){ + graphcanvas.NODEPANEL_IS_OPEN = true; + } + ,onClose: function(){ + graphcanvas.NODEPANEL_IS_OPEN = false; + graphcanvas.node_panel = null; + } + }); + graphcanvas.node_panel = panel; + panel.id = "node-panel"; + panel.node = node; + panel.classList.add("settings"); + + function inner_refresh() + { + panel.content.innerHTML = ""; //clear + panel.addHTML(""+node.type+""+(node.constructor.desc || "")+""); + + panel.addHTML("

Properties

"); + + var fUpdate = function(name,value){ + graphcanvas.graph.beforeChange(node); + switch(name){ + case "Title": + node.title = value; + break; + case "Mode": + var kV = Object.values(LiteGraph.NODE_MODES).indexOf(value); + if (kV>=0 && LiteGraph.NODE_MODES[kV]){ + node.changeMode(kV); + }else{ + console.warn("unexpected mode: "+value); + } + break; + case "Color": + if (LGraphCanvas.node_colors[value]){ + node.color = LGraphCanvas.node_colors[value].color; + node.bgcolor = LGraphCanvas.node_colors[value].bgcolor; + }else{ + console.warn("unexpected color: "+value); + } + break; + default: + node.setProperty(name,value); + break; + } + graphcanvas.graph.afterChange(); + graphcanvas.dirty_canvas = true; + }; + + panel.addWidget( "string", "Title", node.title, {}, fUpdate); + + panel.addWidget( "combo", "Mode", LiteGraph.NODE_MODES[node.mode], {values: LiteGraph.NODE_MODES}, fUpdate); + + var nodeCol = ""; + if (node.color !== undefined){ + nodeCol = Object.keys(LGraphCanvas.node_colors).filter(function(nK){ return LGraphCanvas.node_colors[nK].color == node.color; }); + } + + panel.addWidget( "combo", "Color", nodeCol, {values: Object.keys(LGraphCanvas.node_colors)}, fUpdate); + + for(var pName in node.properties) + { + var value = node.properties[pName]; + var info = node.getPropertyInfo(pName); + var type = info.type || "string"; + + //in case the user wants control over the side panel widget + if( node.onAddPropertyToPanel && node.onAddPropertyToPanel(pName,panel) ) + continue; + + panel.addWidget( info.widget || info.type, pName, value, info, fUpdate); + } + + panel.addSeparator(); + + if(node.onShowCustomPanelInfo) + node.onShowCustomPanelInfo(panel); + + panel.footer.innerHTML = ""; // clear + panel.addButton("Delete",function(){ + if(node.block_delete) + return; + node.graph.remove(node); + panel.close(); + }).classList.add("delete"); + } + + panel.inner_showCodePad = function( propname ) + { + panel.classList.remove("settings"); + panel.classList.add("centered"); + + + /*if(window.CodeFlask) //disabled for now + { + panel.content.innerHTML = "
"; + var flask = new CodeFlask( "div.code", { language: 'js' }); + flask.updateCode(node.properties[propname]); + flask.onUpdate( function(code) { + node.setProperty(propname, code); + }); + } + else + {*/ + panel.alt_content.innerHTML = ""; + var textarea = panel.alt_content.querySelector("textarea"); + var fDoneWith = function(){ + panel.toggleAltContent(false); //if(node_prop_div) node_prop_div.style.display = "block"; // panel.close(); + panel.toggleFooterVisibility(true); + textarea.parentNode.removeChild(textarea); + panel.classList.add("settings"); + panel.classList.remove("centered"); + inner_refresh(); + } + textarea.value = node.properties[propname]; + textarea.addEventListener("keydown", function(e){ + if(e.code == "Enter" && e.ctrlKey ) + { + node.setProperty(propname, textarea.value); + fDoneWith(); + } + }); + panel.toggleAltContent(true); + panel.toggleFooterVisibility(false); + textarea.style.height = "calc(100% - 40px)"; + /*}*/ + var assign = panel.addButton( "Assign", function(){ + node.setProperty(propname, textarea.value); + fDoneWith(); + }); + panel.alt_content.appendChild(assign); //panel.content.appendChild(assign); + var button = panel.addButton( "Close", fDoneWith); + button.style.float = "right"; + panel.alt_content.appendChild(button); // panel.content.appendChild(button); + } + + inner_refresh(); + + this.canvas.parentNode.appendChild( panel ); + } + + LGraphCanvas.prototype.showSubgraphPropertiesDialog = function(node) + { + console.log("showing subgraph properties dialog"); + + var old_panel = this.canvas.parentNode.querySelector(".subgraph_dialog"); + if(old_panel) + old_panel.close(); + + var panel = this.createPanel("Subgraph Inputs",{closable:true, width: 500}); + panel.node = node; + panel.classList.add("subgraph_dialog"); + + function inner_refresh() + { + panel.clear(); + + //show currents + if(node.inputs) + for(var i = 0; i < node.inputs.length; ++i) + { + var input = node.inputs[i]; + if(input.not_subgraph_input) + continue; + var html = " "; + var elem = panel.addHTML(html,"subgraph_property"); + elem.dataset["name"] = input.name; + elem.dataset["slot"] = i; + elem.querySelector(".name").innerText = input.name; + elem.querySelector(".type").innerText = input.type; + elem.querySelector("button").addEventListener("click",function(e){ + node.removeInput( Number( this.parentNode.dataset["slot"] ) ); + inner_refresh(); + }); + } + } + + //add extra + var html = " + NameType"; + var elem = panel.addHTML(html,"subgraph_property extra", true); + elem.querySelector("button").addEventListener("click", function(e){ + var elem = this.parentNode; + var name = elem.querySelector(".name").value; + var type = elem.querySelector(".type").value; + if(!name || node.findInputSlot(name) != -1) + return; + node.addInput(name,type); + elem.querySelector(".name").value = ""; + elem.querySelector(".type").value = ""; + inner_refresh(); + }); + + inner_refresh(); + this.canvas.parentNode.appendChild(panel); + return panel; + } + LGraphCanvas.prototype.showSubgraphPropertiesDialogRight = function (node) { + + // console.log("showing subgraph properties dialog"); + var that = this; + // old_panel if old_panel is exist close it + var old_panel = this.canvas.parentNode.querySelector(".subgraph_dialog"); + if (old_panel) + old_panel.close(); + // new panel + var panel = this.createPanel("Subgraph Outputs", { closable: true, width: 500 }); + panel.node = node; + panel.classList.add("subgraph_dialog"); + + function inner_refresh() { + panel.clear(); + //show currents + if (node.outputs) + for (var i = 0; i < node.outputs.length; ++i) { + var input = node.outputs[i]; + if (input.not_subgraph_output) + continue; + var html = " "; + var elem = panel.addHTML(html, "subgraph_property"); + elem.dataset["name"] = input.name; + elem.dataset["slot"] = i; + elem.querySelector(".name").innerText = input.name; + elem.querySelector(".type").innerText = input.type; + elem.querySelector("button").addEventListener("click", function (e) { + node.removeOutput(Number(this.parentNode.dataset["slot"])); + inner_refresh(); + }); + } + } + + //add extra + var html = " + NameType"; + var elem = panel.addHTML(html, "subgraph_property extra", true); + elem.querySelector(".name").addEventListener("keydown", function (e) { + if (e.keyCode == 13) { + addOutput.apply(this) + } + }) + elem.querySelector("button").addEventListener("click", function (e) { + addOutput.apply(this) + }); + function addOutput() { + var elem = this.parentNode; + var name = elem.querySelector(".name").value; + var type = elem.querySelector(".type").value; + if (!name || node.findOutputSlot(name) != -1) + return; + node.addOutput(name, type); + elem.querySelector(".name").value = ""; + elem.querySelector(".type").value = ""; + inner_refresh(); + } + + inner_refresh(); + this.canvas.parentNode.appendChild(panel); + return panel; + } + LGraphCanvas.prototype.checkPanels = function() + { + if(!this.canvas) + return; + var panels = this.canvas.parentNode.querySelectorAll(".litegraph.dialog"); + for(var i = 0; i < panels.length; ++i) + { + var panel = panels[i]; + if( !panel.node ) + continue; + if( !panel.node.graph || panel.graph != this.graph ) + panel.close(); + } + } + + LGraphCanvas.onMenuNodeCollapse = function(value, options, e, menu, node) { + node.graph.beforeChange(/*?*/); + + var fApplyMultiNode = function(node){ + node.collapse(); + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.graph.afterChange(/*?*/); + }; + + LGraphCanvas.onMenuNodePin = function(value, options, e, menu, node) { + node.pin(); + }; + + LGraphCanvas.onMenuNodeMode = function(value, options, e, menu, node) { + new LiteGraph.ContextMenu( + LiteGraph.NODE_MODES, + { event: e, callback: inner_clicked, parentMenu: menu, node: node } + ); + + function inner_clicked(v) { + if (!node) { + return; + } + var kV = Object.values(LiteGraph.NODE_MODES).indexOf(v); + var fApplyMultiNode = function(node){ + if (kV>=0 && LiteGraph.NODE_MODES[kV]) + node.changeMode(kV); + else{ + console.warn("unexpected mode: "+v); + node.changeMode(LiteGraph.ALWAYS); + } + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + } + + return false; + }; + + LGraphCanvas.onMenuNodeColors = function(value, options, e, menu, node) { + if (!node) { + throw "no node for color"; + } + + var values = []; + values.push({ + value: null, + content: + "No color" + }); + + for (var i in LGraphCanvas.node_colors) { + var color = LGraphCanvas.node_colors[i]; + var value = { + value: i, + content: + "" + + i + + "" + }; + values.push(value); + } + new LiteGraph.ContextMenu(values, { + event: e, + callback: inner_clicked, + parentMenu: menu, + node: node + }); + + function inner_clicked(v) { + if (!node) { + return; + } + + var color = v.value ? LGraphCanvas.node_colors[v.value] : null; + + var fApplyColor = function(node){ + if (color) { + if (node.constructor === LiteGraph.LGraphGroup) { + node.color = color.groupcolor; + } else { + node.color = color.color; + node.bgcolor = color.bgcolor; + } + } else { + delete node.color; + delete node.bgcolor; + } + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyColor(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyColor(graphcanvas.selected_nodes[i]); + } + } + node.setDirtyCanvas(true, true); + } + + return false; + }; + + LGraphCanvas.onMenuNodeShapes = function(value, options, e, menu, node) { + if (!node) { + throw "no node passed"; + } + + new LiteGraph.ContextMenu(LiteGraph.VALID_SHAPES, { + event: e, + callback: inner_clicked, + parentMenu: menu, + node: node + }); + + function inner_clicked(v) { + if (!node) { + return; + } + node.graph.beforeChange(/*?*/); //node + + var fApplyMultiNode = function(node){ + node.shape = v; + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.graph.afterChange(/*?*/); //node + node.setDirtyCanvas(true); + } + + return false; + }; + + LGraphCanvas.onMenuNodeRemove = function(value, options, e, menu, node) { + if (!node) { + throw "no node passed"; + } + + var graph = node.graph; + graph.beforeChange(); + + + var fApplyMultiNode = function(node){ + if (node.removable === false) { + return; + } + graph.remove(node); + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + graph.afterChange(); + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.onMenuNodeToSubgraph = function(value, options, e, menu, node) { + var graph = node.graph; + var graphcanvas = LGraphCanvas.active_canvas; + if(!graphcanvas) //?? + return; + + var nodes_list = Object.values( graphcanvas.selected_nodes || {} ); + if( !nodes_list.length ) + nodes_list = [ node ]; + + var subgraph_node = LiteGraph.createNode("graph/subgraph"); + subgraph_node.pos = node.pos.concat(); + graph.add(subgraph_node); + + subgraph_node.buildFromNodes( nodes_list ); + + graphcanvas.deselectAllNodes(); + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.onMenuNodeClone = function(value, options, e, menu, node) { + + node.graph.beforeChange(); + + var newSelected = {}; + + var fApplyMultiNode = function(node){ + if (node.clonable == false) { + return; + } + var newnode = node.clone(); + if (!newnode) { + return; + } + newnode.pos = [node.pos[0] + 5, node.pos[1] + 5]; + node.graph.add(newnode); + newSelected[newnode.id] = newnode; + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + if(Object.keys(newSelected).length){ + graphcanvas.selectNodes(newSelected); + } + + node.graph.afterChange(); + + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.node_colors = { + red: { color: "#322", bgcolor: "#533", groupcolor: "#A88" }, + brown: { color: "#332922", bgcolor: "#593930", groupcolor: "#b06634" }, + green: { color: "#232", bgcolor: "#353", groupcolor: "#8A8" }, + blue: { color: "#223", bgcolor: "#335", groupcolor: "#88A" }, + pale_blue: { + color: "#2a363b", + bgcolor: "#3f5159", + groupcolor: "#3f789e" + }, + cyan: { color: "#233", bgcolor: "#355", groupcolor: "#8AA" }, + purple: { color: "#323", bgcolor: "#535", groupcolor: "#a1309b" }, + yellow: { color: "#432", bgcolor: "#653", groupcolor: "#b58b2a" }, + black: { color: "#222", bgcolor: "#000", groupcolor: "#444" } + }; + + LGraphCanvas.prototype.getCanvasMenuOptions = function() { + var options = null; + var that = this; + if (this.getMenuOptions) { + options = this.getMenuOptions(); + } else { + options = [ + { + content: "Add Node", + has_submenu: true, + callback: LGraphCanvas.onMenuAdd + }, + { content: "Add Group", callback: LGraphCanvas.onGroupAdd }, + //{ content: "Arrange", callback: that.graph.arrange }, + //{content:"Collapse All", callback: LGraphCanvas.onMenuCollapseAll } + ]; + /*if (LiteGraph.showCanvasOptions){ + options.push({ content: "Options", callback: that.showShowGraphOptionsPanel }); + }*/ + + if (this._graph_stack && this._graph_stack.length > 0) { + options.push(null, { + content: "Close subgraph", + callback: this.closeSubgraph.bind(this) + }); + } + } + + if (this.getExtraMenuOptions) { + var extra = this.getExtraMenuOptions(this, options); + if (extra) { + options = options.concat(extra); + } + } + + return options; + }; + + //called by processContextMenu to extract the menu list + LGraphCanvas.prototype.getNodeMenuOptions = function(node) { + var options = null; + + if (node.getMenuOptions) { + options = node.getMenuOptions(this); + } else { + options = [ + { + content: "Inputs", + has_submenu: true, + disabled: true, + callback: LGraphCanvas.showMenuNodeOptionalInputs + }, + { + content: "Outputs", + has_submenu: true, + disabled: true, + callback: LGraphCanvas.showMenuNodeOptionalOutputs + }, + null, + { + content: "Properties", + has_submenu: true, + callback: LGraphCanvas.onShowMenuNodeProperties + }, + null, + { + content: "Title", + callback: LGraphCanvas.onShowPropertyEditor + }, + { + content: "Mode", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeMode + }]; + if(node.resizable !== false){ + options.push({ + content: "Resize", callback: LGraphCanvas.onMenuResizeNode + }); + } + options.push( + { + content: "Collapse", + callback: LGraphCanvas.onMenuNodeCollapse + }, + { content: "Pin", callback: LGraphCanvas.onMenuNodePin }, + { + content: "Colors", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeColors + }, + { + content: "Shapes", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeShapes + }, + null + ); + } + + if (node.onGetInputs) { + var inputs = node.onGetInputs(); + if (inputs && inputs.length) { + options[0].disabled = false; + } + } + + if (node.onGetOutputs) { + var outputs = node.onGetOutputs(); + if (outputs && outputs.length) { + options[1].disabled = false; + } + } + + if (node.getExtraMenuOptions) { + var extra = node.getExtraMenuOptions(this, options); + if (extra) { + extra.push(null); + options = extra.concat(options); + } + } + + if (node.clonable !== false) { + options.push({ + content: "Clone", + callback: LGraphCanvas.onMenuNodeClone + }); + } + + if(0) //TODO + options.push({ + content: "To Subgraph", + callback: LGraphCanvas.onMenuNodeToSubgraph + }); + + options.push(null, { + content: "Remove", + disabled: !(node.removable !== false && !node.block_delete ), + callback: LGraphCanvas.onMenuNodeRemove + }); + + if (node.graph && node.graph.onGetNodeMenuOptions) { + node.graph.onGetNodeMenuOptions(options, node); + } + + return options; + }; + + LGraphCanvas.prototype.getGroupMenuOptions = function(node) { + var o = [ + { content: "Title", callback: LGraphCanvas.onShowPropertyEditor }, + { + content: "Color", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeColors + }, + { + content: "Font size", + property: "font_size", + type: "Number", + callback: LGraphCanvas.onShowPropertyEditor + }, + null, + { content: "Remove", callback: LGraphCanvas.onMenuNodeRemove } + ]; + + return o; + }; + + LGraphCanvas.prototype.processContextMenu = function(node, event) { + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var menu_info = null; + var options = { + event: event, + callback: inner_option_clicked, + extra: node + }; + + if(node) + options.title = node.type; + + //check if mouse is in input + var slot = null; + if (node) { + slot = node.getSlotInPosition(event.canvasX, event.canvasY); + LGraphCanvas.active_node = node; + } + + if (slot) { + //on slot + menu_info = []; + if (node.getSlotMenuOptions) { + menu_info = node.getSlotMenuOptions(slot); + } else { + if ( + slot && + slot.output && + slot.output.links && + slot.output.links.length + ) { + menu_info.push({ content: "Disconnect Links", slot: slot }); + } + var _slot = slot.input || slot.output; + if (_slot.removable){ + menu_info.push( + _slot.locked + ? "Cannot remove" + : { content: "Remove Slot", slot: slot } + ); + } + if (!_slot.nameLocked){ + menu_info.push({ content: "Rename Slot", slot: slot }); + } + + } + options.title = + (slot.input ? slot.input.type : slot.output.type) || "*"; + if (slot.input && slot.input.type == LiteGraph.ACTION) { + options.title = "Action"; + } + if (slot.output && slot.output.type == LiteGraph.EVENT) { + options.title = "Event"; + } + } else { + if (node) { + //on node + menu_info = this.getNodeMenuOptions(node); + } else { + menu_info = this.getCanvasMenuOptions(); + var group = this.graph.getGroupOnPos( + event.canvasX, + event.canvasY + ); + if (group) { + //on group + menu_info.push(null, { + content: "Edit Group", + has_submenu: true, + submenu: { + title: "Group", + extra: group, + options: this.getGroupMenuOptions(group) + } + }); + } + } + } + + //show menu + if (!menu_info) { + return; + } + + var menu = new LiteGraph.ContextMenu(menu_info, options, ref_window); + + function inner_option_clicked(v, options, e) { + if (!v) { + return; + } + + if (v.content == "Remove Slot") { + var info = v.slot; + node.graph.beforeChange(); + if (info.input) { + node.removeInput(info.slot); + } else if (info.output) { + node.removeOutput(info.slot); + } + node.graph.afterChange(); + return; + } else if (v.content == "Disconnect Links") { + var info = v.slot; + node.graph.beforeChange(); + if (info.output) { + node.disconnectOutput(info.slot); + } else if (info.input) { + node.disconnectInput(info.slot); + } + node.graph.afterChange(); + return; + } else if (v.content == "Rename Slot") { + var info = v.slot; + var slot_info = info.input + ? node.getInputInfo(info.slot) + : node.getOutputInfo(info.slot); + var dialog = that.createDialog( + "Name", + options + ); + var input = dialog.querySelector("input"); + if (input && slot_info) { + input.value = slot_info.label || ""; + } + var inner = function(){ + node.graph.beforeChange(); + if (input.value) { + if (slot_info) { + slot_info.label = input.value; + } + that.setDirty(true); + } + dialog.close(); + node.graph.afterChange(); + } + dialog.querySelector("button").addEventListener("click", inner); + input.addEventListener("keydown", function(e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + inner(); // save + } else if (e.keyCode != 13 && e.target.localName != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + input.focus(); + } + + //if(v.callback) + // return v.callback.call(that, node, options, e, menu, that, event ); + } + }; + + //API ************************************************* + //like rect but rounded corners + if (typeof(window) != "undefined" && window.CanvasRenderingContext2D && !window.CanvasRenderingContext2D.prototype.roundRect) { + window.CanvasRenderingContext2D.prototype.roundRect = function( + x, + y, + w, + h, + radius, + radius_low + ) { + var top_left_radius = 0; + var top_right_radius = 0; + var bottom_left_radius = 0; + var bottom_right_radius = 0; + + if ( radius === 0 ) + { + this.rect(x,y,w,h); + return; + } + + if(radius_low === undefined) + radius_low = radius; + + //make it compatible with official one + if(radius != null && radius.constructor === Array) + { + if(radius.length == 1) + top_left_radius = top_right_radius = bottom_left_radius = bottom_right_radius = radius[0]; + else if(radius.length == 2) + { + top_left_radius = bottom_right_radius = radius[0]; + top_right_radius = bottom_left_radius = radius[1]; + } + else if(radius.length == 4) + { + top_left_radius = radius[0]; + top_right_radius = radius[1]; + bottom_left_radius = radius[2]; + bottom_right_radius = radius[3]; + } + else + return; + } + else //old using numbers + { + top_left_radius = radius || 0; + top_right_radius = radius || 0; + bottom_left_radius = radius_low || 0; + bottom_right_radius = radius_low || 0; + } + + //top right + this.moveTo(x + top_left_radius, y); + this.lineTo(x + w - top_right_radius, y); + this.quadraticCurveTo(x + w, y, x + w, y + top_right_radius); + + //bottom right + this.lineTo(x + w, y + h - bottom_right_radius); + this.quadraticCurveTo( + x + w, + y + h, + x + w - bottom_right_radius, + y + h + ); + + //bottom left + this.lineTo(x + bottom_right_radius, y + h); + this.quadraticCurveTo(x, y + h, x, y + h - bottom_left_radius); + + //top left + this.lineTo(x, y + bottom_left_radius); + this.quadraticCurveTo(x, y, x + top_left_radius, y); + }; + }//if + + function compareObjects(a, b) { + for (var i in a) { + if (a[i] != b[i]) { + return false; + } + } + return true; + } + LiteGraph.compareObjects = compareObjects; + + function distance(a, b) { + return Math.sqrt( + (b[0] - a[0]) * (b[0] - a[0]) + (b[1] - a[1]) * (b[1] - a[1]) + ); + } + LiteGraph.distance = distance; + + function colorToString(c) { + return ( + "rgba(" + + Math.round(c[0] * 255).toFixed() + + "," + + Math.round(c[1] * 255).toFixed() + + "," + + Math.round(c[2] * 255).toFixed() + + "," + + (c.length == 4 ? c[3].toFixed(2) : "1.0") + + ")" + ); + } + LiteGraph.colorToString = colorToString; + + function isInsideRectangle(x, y, left, top, width, height) { + if (left < x && left + width > x && top < y && top + height > y) { + return true; + } + return false; + } + LiteGraph.isInsideRectangle = isInsideRectangle; + + //[minx,miny,maxx,maxy] + function growBounding(bounding, x, y) { + if (x < bounding[0]) { + bounding[0] = x; + } else if (x > bounding[2]) { + bounding[2] = x; + } + + if (y < bounding[1]) { + bounding[1] = y; + } else if (y > bounding[3]) { + bounding[3] = y; + } + } + LiteGraph.growBounding = growBounding; + + //point inside bounding box + function isInsideBounding(p, bb) { + if ( + p[0] < bb[0][0] || + p[1] < bb[0][1] || + p[0] > bb[1][0] || + p[1] > bb[1][1] + ) { + return false; + } + return true; + } + LiteGraph.isInsideBounding = isInsideBounding; + + //bounding overlap, format: [ startx, starty, width, height ] + function overlapBounding(a, b) { + var A_end_x = a[0] + a[2]; + var A_end_y = a[1] + a[3]; + var B_end_x = b[0] + b[2]; + var B_end_y = b[1] + b[3]; + + if ( + a[0] > B_end_x || + a[1] > B_end_y || + A_end_x < b[0] || + A_end_y < b[1] + ) { + return false; + } + return true; + } + LiteGraph.overlapBounding = overlapBounding; + + //Convert a hex value to its decimal value - the inputted hex must be in the + // format of a hex triplet - the kind we use for HTML colours. The function + // will return an array with three values. + function hex2num(hex) { + if (hex.charAt(0) == "#") { + hex = hex.slice(1); + } //Remove the '#' char - if there is one. + hex = hex.toUpperCase(); + var hex_alphabets = "0123456789ABCDEF"; + var value = new Array(3); + var k = 0; + var int1, int2; + for (var i = 0; i < 6; i += 2) { + int1 = hex_alphabets.indexOf(hex.charAt(i)); + int2 = hex_alphabets.indexOf(hex.charAt(i + 1)); + value[k] = int1 * 16 + int2; + k++; + } + return value; + } + + LiteGraph.hex2num = hex2num; + + //Give a array with three values as the argument and the function will return + // the corresponding hex triplet. + function num2hex(triplet) { + var hex_alphabets = "0123456789ABCDEF"; + var hex = "#"; + var int1, int2; + for (var i = 0; i < 3; i++) { + int1 = triplet[i] / 16; + int2 = triplet[i] % 16; + + hex += hex_alphabets.charAt(int1) + hex_alphabets.charAt(int2); + } + return hex; + } + + LiteGraph.num2hex = num2hex; + + /* LiteGraph GUI elements used for canvas editing *************************************/ + + /** + * ContextMenu from LiteGUI + * + * @class ContextMenu + * @constructor + * @param {Array} values (allows object { title: "Nice text", callback: function ... }) + * @param {Object} options [optional] Some options:\ + * - title: title to show on top of the menu + * - callback: function to call when an option is clicked, it receives the item information + * - ignore_item_callbacks: ignores the callback inside the item, it just calls the options.callback + * - event: you can pass a MouseEvent, this way the ContextMenu appears in that position + */ + function ContextMenu(values, options) { + options = options || {}; + this.options = options; + var that = this; + + //to link a menu with its parent + if (options.parentMenu) { + if (options.parentMenu.constructor !== this.constructor) { + console.error( + "parentMenu must be of class ContextMenu, ignoring it" + ); + options.parentMenu = null; + } else { + this.parentMenu = options.parentMenu; + this.parentMenu.lock = true; + this.parentMenu.current_submenu = this; + } + } + + var eventClass = null; + if(options.event) //use strings because comparing classes between windows doesnt work + eventClass = options.event.constructor.name; + if ( eventClass !== "MouseEvent" && + eventClass !== "CustomEvent" && + eventClass !== "PointerEvent" + ) { + console.error( + "Event passed to ContextMenu is not of type MouseEvent or CustomEvent. Ignoring it. ("+eventClass+")" + ); + options.event = null; + } + + var root = document.createElement("div"); + root.className = "litegraph litecontextmenu litemenubar-panel"; + if (options.className) { + root.className += " " + options.className; + } + root.style.minWidth = 100; + root.style.minHeight = 100; + root.style.pointerEvents = "none"; + setTimeout(function() { + root.style.pointerEvents = "auto"; + }, 100); //delay so the mouse up event is not caught by this element + + //this prevents the default context browser menu to open in case this menu was created when pressing right button + LiteGraph.pointerListenerAdd(root,"up", + function(e) { + //console.log("pointerevents: ContextMenu up root prevent"); + e.preventDefault(); + return true; + }, + true + ); + root.addEventListener( + "contextmenu", + function(e) { + if (e.button != 2) { + //right button + return false; + } + e.preventDefault(); + return false; + }, + true + ); + + LiteGraph.pointerListenerAdd(root,"down", + function(e) { + //console.log("pointerevents: ContextMenu down"); + if (e.button == 2) { + that.close(); + e.preventDefault(); + return true; + } + }, + true + ); + + function on_mouse_wheel(e) { + var pos = parseInt(root.style.top); + root.style.top = + (pos + e.deltaY * options.scroll_speed).toFixed() + "px"; + e.preventDefault(); + return true; + } + + if (!options.scroll_speed) { + options.scroll_speed = 0.1; + } + + root.addEventListener("wheel", on_mouse_wheel, true); + root.addEventListener("mousewheel", on_mouse_wheel, true); + + this.root = root; + + //title + if (options.title) { + var element = document.createElement("div"); + element.className = "litemenu-title"; + element.innerHTML = options.title; + root.appendChild(element); + } + + //entries + var num = 0; + for (var i=0; i < values.length; i++) { + var name = values.constructor == Array ? values[i] : i; + if (name != null && name.constructor !== String) { + name = name.content === undefined ? String(name) : name.content; + } + var value = values[i]; + this.addItem(name, value, options); + num++; + } + + //close on leave? touch enabled devices won't work TODO use a global device detector and condition on that + /*LiteGraph.pointerListenerAdd(root,"leave", function(e) { + console.log("pointerevents: ContextMenu leave"); + if (that.lock) { + return; + } + if (root.closing_timer) { + clearTimeout(root.closing_timer); + } + root.closing_timer = setTimeout(that.close.bind(that, e), 500); + //that.close(e); + });*/ + + LiteGraph.pointerListenerAdd(root,"enter", function(e) { + //console.log("pointerevents: ContextMenu enter"); + if (root.closing_timer) { + clearTimeout(root.closing_timer); + } + }); + + //insert before checking position + var root_document = document; + if (options.event) { + root_document = options.event.target.ownerDocument; + } + + if (!root_document) { + root_document = document; + } + + if( root_document.fullscreenElement ) + root_document.fullscreenElement.appendChild(root); + else + root_document.body.appendChild(root); + + //compute best position + var left = options.left || 0; + var top = options.top || 0; + if (options.event) { + left = options.event.clientX - 10; + top = options.event.clientY - 10; + if (options.title) { + top -= 20; + } + + if (options.parentMenu) { + var rect = options.parentMenu.root.getBoundingClientRect(); + left = rect.left + rect.width; + } + + var body_rect = document.body.getBoundingClientRect(); + var root_rect = root.getBoundingClientRect(); + if(body_rect.height == 0) + console.error("document.body height is 0. That is dangerous, set html,body { height: 100%; }"); + + if (body_rect.width && left > body_rect.width - root_rect.width - 10) { + left = body_rect.width - root_rect.width - 10; + } + if (body_rect.height && top > body_rect.height - root_rect.height - 10) { + top = body_rect.height - root_rect.height - 10; + } + } + + root.style.left = left + "px"; + root.style.top = top + "px"; + + if (options.scale) { + root.style.transform = "scale(" + options.scale + ")"; + } + } + + ContextMenu.prototype.addItem = function(name, value, options) { + var that = this; + options = options || {}; + + var element = document.createElement("div"); + element.className = "litemenu-entry submenu"; + + var disabled = false; + + if (value === null) { + element.classList.add("separator"); + //element.innerHTML = "
" + //continue; + } else { + element.innerHTML = value && value.title ? value.title : name; + element.value = value; + + if (value) { + if (value.disabled) { + disabled = true; + element.classList.add("disabled"); + } + if (value.submenu || value.has_submenu) { + element.classList.add("has_submenu"); + } + } + + if (typeof value == "function") { + element.dataset["value"] = name; + element.onclick_callback = value; + } else { + element.dataset["value"] = value; + } + + if (value.className) { + element.className += " " + value.className; + } + } + + this.root.appendChild(element); + if (!disabled) { + element.addEventListener("click", inner_onclick); + } + if (options.autoopen) { + LiteGraph.pointerListenerAdd(element,"enter",inner_over); + } + + function inner_over(e) { + var value = this.value; + if (!value || !value.has_submenu) { + return; + } + //if it is a submenu, autoopen like the item was clicked + inner_onclick.call(this, e); + } + + //menu option clicked + function inner_onclick(e) { + var value = this.value; + var close_parent = true; + + if (that.current_submenu) { + that.current_submenu.close(e); + } + + //global callback + if (options.callback) { + var r = options.callback.call( + this, + value, + options, + e, + that, + options.node + ); + if (r === true) { + close_parent = false; + } + } + + //special cases + if (value) { + if ( + value.callback && + !options.ignore_item_callbacks && + value.disabled !== true + ) { + //item callback + var r = value.callback.call( + this, + value, + options, + e, + that, + options.extra + ); + if (r === true) { + close_parent = false; + } + } + if (value.submenu) { + if (!value.submenu.options) { + throw "ContextMenu submenu needs options"; + } + var submenu = new that.constructor(value.submenu.options, { + callback: value.submenu.callback, + event: e, + parentMenu: that, + ignore_item_callbacks: + value.submenu.ignore_item_callbacks, + title: value.submenu.title, + extra: value.submenu.extra, + autoopen: options.autoopen + }); + close_parent = false; + } + } + + if (close_parent && !that.lock) { + that.close(); + } + } + + return element; + }; + + ContextMenu.prototype.close = function(e, ignore_parent_menu) { + if (this.root.parentNode) { + this.root.parentNode.removeChild(this.root); + } + if (this.parentMenu && !ignore_parent_menu) { + this.parentMenu.lock = false; + this.parentMenu.current_submenu = null; + if (e === undefined) { + this.parentMenu.close(); + } else if ( + e && + !ContextMenu.isCursorOverElement(e, this.parentMenu.root) + ) { + ContextMenu.trigger(this.parentMenu.root, LiteGraph.pointerevents_method+"leave", e); + } + } + if (this.current_submenu) { + this.current_submenu.close(e, true); + } + + if (this.root.closing_timer) { + clearTimeout(this.root.closing_timer); + } + + // TODO implement : LiteGraph.contextMenuClosed(); :: keep track of opened / closed / current ContextMenu + // on key press, allow filtering/selecting the context menu elements + }; + + //this code is used to trigger events easily (used in the context menu mouseleave + ContextMenu.trigger = function(element, event_name, params, origin) { + var evt = document.createEvent("CustomEvent"); + evt.initCustomEvent(event_name, true, true, params); //canBubble, cancelable, detail + evt.srcElement = origin; + if (element.dispatchEvent) { + element.dispatchEvent(evt); + } else if (element.__events) { + element.__events.dispatchEvent(evt); + } + //else nothing seems binded here so nothing to do + return evt; + }; + + //returns the top most menu + ContextMenu.prototype.getTopMenu = function() { + if (this.options.parentMenu) { + return this.options.parentMenu.getTopMenu(); + } + return this; + }; + + ContextMenu.prototype.getFirstEvent = function() { + if (this.options.parentMenu) { + return this.options.parentMenu.getFirstEvent(); + } + return this.options.event; + }; + + ContextMenu.isCursorOverElement = function(event, element) { + var left = event.clientX; + var top = event.clientY; + var rect = element.getBoundingClientRect(); + if (!rect) { + return false; + } + if ( + top > rect.top && + top < rect.top + rect.height && + left > rect.left && + left < rect.left + rect.width + ) { + return true; + } + return false; + }; + + LiteGraph.ContextMenu = ContextMenu; + + LiteGraph.closeAllContextMenus = function(ref_window) { + ref_window = ref_window || window; + + var elements = ref_window.document.querySelectorAll(".litecontextmenu"); + if (!elements.length) { + return; + } + + var result = []; + for (var i = 0; i < elements.length; i++) { + result.push(elements[i]); + } + + for (var i=0; i < result.length; i++) { + if (result[i].close) { + result[i].close(); + } else if (result[i].parentNode) { + result[i].parentNode.removeChild(result[i]); + } + } + }; + + LiteGraph.extendClass = function(target, origin) { + for (var i in origin) { + //copy class properties + if (target.hasOwnProperty(i)) { + continue; + } + target[i] = origin[i]; + } + + if (origin.prototype) { + //copy prototype properties + for (var i in origin.prototype) { + //only enumerable + if (!origin.prototype.hasOwnProperty(i)) { + continue; + } + + if (target.prototype.hasOwnProperty(i)) { + //avoid overwriting existing ones + continue; + } + + //copy getters + if (origin.prototype.__lookupGetter__(i)) { + target.prototype.__defineGetter__( + i, + origin.prototype.__lookupGetter__(i) + ); + } else { + target.prototype[i] = origin.prototype[i]; + } + + //and setters + if (origin.prototype.__lookupSetter__(i)) { + target.prototype.__defineSetter__( + i, + origin.prototype.__lookupSetter__(i) + ); + } + } + } + }; + + //used by some widgets to render a curve editor + function CurveEditor( points ) + { + this.points = points; + this.selected = -1; + this.nearest = -1; + this.size = null; //stores last size used + this.must_update = true; + this.margin = 5; + } + + CurveEditor.sampleCurve = function(f,points) + { + if(!points) + return; + for(var i = 0; i < points.length - 1; ++i) + { + var p = points[i]; + var pn = points[i+1]; + if(pn[0] < f) + continue; + var r = (pn[0] - p[0]); + if( Math.abs(r) < 0.00001 ) + return p[1]; + var local_f = (f - p[0]) / r; + return p[1] * (1.0 - local_f) + pn[1] * local_f; + } + return 0; + } + + CurveEditor.prototype.draw = function( ctx, size, graphcanvas, background_color, line_color, inactive ) + { + var points = this.points; + if(!points) + return; + this.size = size; + var w = size[0] - this.margin * 2; + var h = size[1] - this.margin * 2; + + line_color = line_color || "#666"; + + ctx.save(); + ctx.translate(this.margin,this.margin); + + if(background_color) + { + ctx.fillStyle = "#111"; + ctx.fillRect(0,0,w,h); + ctx.fillStyle = "#222"; + ctx.fillRect(w*0.5,0,1,h); + ctx.strokeStyle = "#333"; + ctx.strokeRect(0,0,w,h); + } + ctx.strokeStyle = line_color; + if(inactive) + ctx.globalAlpha = 0.5; + ctx.beginPath(); + for(var i = 0; i < points.length; ++i) + { + var p = points[i]; + ctx.lineTo( p[0] * w, (1.0 - p[1]) * h ); + } + ctx.stroke(); + ctx.globalAlpha = 1; + if(!inactive) + for(var i = 0; i < points.length; ++i) + { + var p = points[i]; + ctx.fillStyle = this.selected == i ? "#FFF" : (this.nearest == i ? "#DDD" : "#AAA"); + ctx.beginPath(); + ctx.arc( p[0] * w, (1.0 - p[1]) * h, 2, 0, Math.PI * 2 ); + ctx.fill(); + } + ctx.restore(); + } + + //localpos is mouse in curve editor space + CurveEditor.prototype.onMouseDown = function( localpos, graphcanvas ) + { + var points = this.points; + if(!points) + return; + if( localpos[1] < 0 ) + return; + + //this.captureInput(true); + var w = this.size[0] - this.margin * 2; + var h = this.size[1] - this.margin * 2; + var x = localpos[0] - this.margin; + var y = localpos[1] - this.margin; + var pos = [x,y]; + var max_dist = 30 / graphcanvas.ds.scale; + //search closer one + this.selected = this.getCloserPoint(pos, max_dist); + //create one + if(this.selected == -1) + { + var point = [x / w, 1 - y / h]; + points.push(point); + points.sort(function(a,b){ return a[0] - b[0]; }); + this.selected = points.indexOf(point); + this.must_update = true; + } + if(this.selected != -1) + return true; + } + + CurveEditor.prototype.onMouseMove = function( localpos, graphcanvas ) + { + var points = this.points; + if(!points) + return; + var s = this.selected; + if(s < 0) + return; + var x = (localpos[0] - this.margin) / (this.size[0] - this.margin * 2 ); + var y = (localpos[1] - this.margin) / (this.size[1] - this.margin * 2 ); + var curvepos = [(localpos[0] - this.margin),(localpos[1] - this.margin)]; + var max_dist = 30 / graphcanvas.ds.scale; + this._nearest = this.getCloserPoint(curvepos, max_dist); + var point = points[s]; + if(point) + { + var is_edge_point = s == 0 || s == points.length - 1; + if( !is_edge_point && (localpos[0] < -10 || localpos[0] > this.size[0] + 10 || localpos[1] < -10 || localpos[1] > this.size[1] + 10) ) + { + points.splice(s,1); + this.selected = -1; + return; + } + if( !is_edge_point ) //not edges + point[0] = Math.clamp(x,0,1); + else + point[0] = s == 0 ? 0 : 1; + point[1] = 1.0 - Math.clamp(y,0,1); + points.sort(function(a,b){ return a[0] - b[0]; }); + this.selected = points.indexOf(point); + this.must_update = true; + } + } + + CurveEditor.prototype.onMouseUp = function( localpos, graphcanvas ) + { + this.selected = -1; + return false; + } + + CurveEditor.prototype.getCloserPoint = function(pos, max_dist) + { + var points = this.points; + if(!points) + return -1; + max_dist = max_dist || 30; + var w = (this.size[0] - this.margin * 2); + var h = (this.size[1] - this.margin * 2); + var num = points.length; + var p2 = [0,0]; + var min_dist = 1000000; + var closest = -1; + var last_valid = -1; + for(var i = 0; i < num; ++i) + { + var p = points[i]; + p2[0] = p[0] * w; + p2[1] = (1.0 - p[1]) * h; + if(p2[0] < pos[0]) + last_valid = i; + var dist = vec2.distance(pos,p2); + if(dist > min_dist || dist > max_dist) + continue; + closest = i; + min_dist = dist; + } + return closest; + } + + LiteGraph.CurveEditor = CurveEditor; + + //used to create nodes from wrapping functions + LiteGraph.getParameterNames = function(func) { + return (func + "") + .replace(/[/][/].*$/gm, "") // strip single-line comments + .replace(/\s+/g, "") // strip white space + .replace(/[/][*][^/*]*[*][/]/g, "") // strip multi-line comments /**/ + .split("){", 1)[0] + .replace(/^[^(]*[(]/, "") // extract the parameters + .replace(/=[^,]+/g, "") // strip any ES6 defaults + .split(",") + .filter(Boolean); // split & filter [""] + }; + + /* helper for interaction: pointer, touch, mouse Listeners + used by LGraphCanvas DragAndScale ContextMenu*/ + LiteGraph.pointerListenerAdd = function(oDOM, sEvIn, fCall, capture=false) { + if (!oDOM || !oDOM.addEventListener || !sEvIn || typeof fCall!=="function"){ + //console.log("cant pointerListenerAdd "+oDOM+", "+sEvent+", "+fCall); + return; // -- break -- + } + + var sMethod = LiteGraph.pointerevents_method; + var sEvent = sEvIn; + + // UNDER CONSTRUCTION + // convert pointerevents to touch event when not available + if (sMethod=="pointer" && !window.PointerEvent){ + console.warn("sMethod=='pointer' && !window.PointerEvent"); + console.log("Converting pointer["+sEvent+"] : down move up cancel enter TO touchstart touchmove touchend, etc .."); + switch(sEvent){ + case "down":{ + sMethod = "touch"; + sEvent = "start"; + break; + } + case "move":{ + sMethod = "touch"; + //sEvent = "move"; + break; + } + case "up":{ + sMethod = "touch"; + sEvent = "end"; + break; + } + case "cancel":{ + sMethod = "touch"; + //sEvent = "cancel"; + break; + } + case "enter":{ + console.log("debug: Should I send a move event?"); // ??? + break; + } + // case "over": case "out": not used at now + default:{ + console.warn("PointerEvent not available in this browser ? The event "+sEvent+" would not be called"); + } + } + } + + switch(sEvent){ + //both pointer and move events + case "down": case "up": case "move": case "over": case "out": case "enter": + { + oDOM.addEventListener(sMethod+sEvent, fCall, capture); + } + // only pointerevents + case "leave": case "cancel": case "gotpointercapture": case "lostpointercapture": + { + if (sMethod!="mouse"){ + return oDOM.addEventListener(sMethod+sEvent, fCall, capture); + } + } + // not "pointer" || "mouse" + default: + return oDOM.addEventListener(sEvent, fCall, capture); + } + } + LiteGraph.pointerListenerRemove = function(oDOM, sEvent, fCall, capture=false) { + if (!oDOM || !oDOM.removeEventListener || !sEvent || typeof fCall!=="function"){ + //console.log("cant pointerListenerRemove "+oDOM+", "+sEvent+", "+fCall); + return; // -- break -- + } + switch(sEvent){ + //both pointer and move events + case "down": case "up": case "move": case "over": case "out": case "enter": + { + if (LiteGraph.pointerevents_method=="pointer" || LiteGraph.pointerevents_method=="mouse"){ + oDOM.removeEventListener(LiteGraph.pointerevents_method+sEvent, fCall, capture); + } + } + // only pointerevents + case "leave": case "cancel": case "gotpointercapture": case "lostpointercapture": + { + if (LiteGraph.pointerevents_method=="pointer"){ + return oDOM.removeEventListener(LiteGraph.pointerevents_method+sEvent, fCall, capture); + } + } + // not "pointer" || "mouse" + default: + return oDOM.removeEventListener(sEvent, fCall, capture); + } + } + + Math.clamp = function(v, a, b) { + return a > v ? a : b < v ? b : v; + }; + + if (typeof window != "undefined" && !window["requestAnimationFrame"]) { + window.requestAnimationFrame = + window.webkitRequestAnimationFrame || + window.mozRequestAnimationFrame || + function(callback) { + window.setTimeout(callback, 1000 / 60); + }; + } +})(this); + +if (typeof exports != "undefined") { + exports.LiteGraph = this.LiteGraph; +} + + diff --git a/webshit/litegraph.css b/webshit/litegraph.css new file mode 100644 index 00000000..918858f4 --- /dev/null +++ b/webshit/litegraph.css @@ -0,0 +1,680 @@ +/* this CSS contains only the basic CSS needed to run the app and use it */ + +.lgraphcanvas { + /*cursor: crosshair;*/ + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + outline: none; + font-family: Tahoma, sans-serif; +} + +.lgraphcanvas * { + box-sizing: border-box; +} + +.litegraph.litecontextmenu { + font-family: Tahoma, sans-serif; + position: fixed; + top: 100px; + left: 100px; + min-width: 100px; + color: #aaf; + padding: 0; + box-shadow: 0 0 10px black !important; + background-color: #2e2e2e !important; + z-index: 10; +} + +.litegraph.litecontextmenu.dark { + background-color: #000 !important; +} + +.litegraph.litecontextmenu .litemenu-title img { + margin-top: 2px; + margin-left: 2px; + margin-right: 4px; +} + +.litegraph.litecontextmenu .litemenu-entry { + margin: 2px; + padding: 2px; +} + +.litegraph.litecontextmenu .litemenu-entry.submenu { + background-color: #2e2e2e !important; +} + +.litegraph.litecontextmenu.dark .litemenu-entry.submenu { + background-color: #000 !important; +} + +.litegraph .litemenubar ul { + font-family: Tahoma, sans-serif; + margin: 0; + padding: 0; +} + +.litegraph .litemenubar li { + font-size: 14px; + color: #999; + display: inline-block; + min-width: 50px; + padding-left: 10px; + padding-right: 10px; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + cursor: pointer; +} + +.litegraph .litemenubar li:hover { + background-color: #777; + color: #eee; +} + +.litegraph .litegraph .litemenubar-panel { + position: absolute; + top: 5px; + left: 5px; + min-width: 100px; + background-color: #444; + box-shadow: 0 0 3px black; + padding: 4px; + border-bottom: 2px solid #aaf; + z-index: 10; +} + +.litegraph .litemenu-entry, +.litemenu-title { + font-size: 12px; + color: #aaa; + padding: 0 0 0 4px; + margin: 2px; + padding-left: 2px; + -moz-user-select: none; + -webkit-user-select: none; + user-select: none; + cursor: pointer; +} + +.litegraph .litemenu-entry .icon { + display: inline-block; + width: 12px; + height: 12px; + margin: 2px; + vertical-align: top; +} + +.litegraph .litemenu-entry.checked .icon { + background-color: #aaf; +} + +.litegraph .litemenu-entry .more { + float: right; + padding-right: 5px; +} + +.litegraph .litemenu-entry.disabled { + opacity: 0.5; + cursor: default; +} + +.litegraph .litemenu-entry.separator { + display: block; + border-top: 1px solid #333; + border-bottom: 1px solid #666; + width: 100%; + height: 0px; + margin: 3px 0 2px 0; + background-color: transparent; + padding: 0 !important; + cursor: default !important; +} + +.litegraph .litemenu-entry.has_submenu { + border-right: 2px solid cyan; +} + +.litegraph .litemenu-title { + color: #dde; + background-color: #111; + margin: 0; + padding: 2px; + cursor: default; +} + +.litegraph .litemenu-entry:hover:not(.disabled):not(.separator) { + background-color: #444 !important; + color: #eee; + transition: all 0.2s; +} + +.litegraph .litemenu-entry .property_name { + display: inline-block; + text-align: left; + min-width: 80px; + min-height: 1.2em; +} + +.litegraph .litemenu-entry .property_value { + display: inline-block; + background-color: rgba(0, 0, 0, 0.5); + text-align: right; + min-width: 80px; + min-height: 1.2em; + vertical-align: middle; + padding-right: 10px; +} + +.litegraph.litesearchbox { + font-family: Tahoma, sans-serif; + position: absolute; + background-color: rgba(0, 0, 0, 0.5); + padding-top: 4px; +} + +.litegraph.litesearchbox input, +.litegraph.litesearchbox select { + margin-top: 3px; + min-width: 60px; + min-height: 1.5em; + background-color: black; + border: 0; + color: white; + padding-left: 10px; + margin-right: 5px; +} + +.litegraph.litesearchbox .name { + display: inline-block; + min-width: 60px; + min-height: 1.5em; + padding-left: 10px; +} + +.litegraph.litesearchbox .helper { + overflow: auto; + max-height: 200px; + margin-top: 2px; +} + +.litegraph.lite-search-item { + font-family: Tahoma, sans-serif; + background-color: rgba(0, 0, 0, 0.5); + color: white; + padding-top: 2px; +} + +.litegraph.lite-search-item.not_in_filter{ + /*background-color: rgba(50, 50, 50, 0.5);*/ + /*color: #999;*/ + color: #B99; + font-style: italic; +} + +.litegraph.lite-search-item.generic_type{ + /*background-color: rgba(50, 50, 50, 0.5);*/ + /*color: #DD9;*/ + color: #999; + font-style: italic; +} + +.litegraph.lite-search-item:hover, +.litegraph.lite-search-item.selected { + cursor: pointer; + background-color: white; + color: black; +} + +/* DIALOGs ******/ + +.litegraph .dialog { + position: absolute; + top: 50%; + left: 50%; + margin-top: -150px; + margin-left: -200px; + + background-color: #2A2A2A; + + min-width: 400px; + min-height: 200px; + box-shadow: 0 0 4px #111; + border-radius: 6px; +} + +.litegraph .dialog.settings { + left: 10px; + top: 10px; + height: calc( 100% - 20px ); + margin: auto; + max-width: 50%; +} + +.litegraph .dialog.centered { + top: 50px; + left: 50%; + position: absolute; + transform: translateX(-50%); + min-width: 600px; + min-height: 300px; + height: calc( 100% - 100px ); + margin: auto; +} + +.litegraph .dialog .close { + float: right; + margin: 4px; + margin-right: 10px; + cursor: pointer; + font-size: 1.4em; +} + +.litegraph .dialog .close:hover { + color: white; +} + +.litegraph .dialog .dialog-header { + color: #AAA; + border-bottom: 1px solid #161616; +} + +.litegraph .dialog .dialog-header { height: 40px; } +.litegraph .dialog .dialog-footer { height: 50px; padding: 10px; border-top: 1px solid #1a1a1a;} + +.litegraph .dialog .dialog-header .dialog-title { + font: 20px "Arial"; + margin: 4px; + padding: 4px 10px; + display: inline-block; +} + +.litegraph .dialog .dialog-content, .litegraph .dialog .dialog-alt-content { + height: calc(100% - 90px); + width: 100%; + min-height: 100px; + display: inline-block; + color: #AAA; + /*background-color: black;*/ + overflow: auto; +} + +.litegraph .dialog .dialog-content h3 { + margin: 10px; +} + +.litegraph .dialog .dialog-content .connections { + flex-direction: row; +} + +.litegraph .dialog .dialog-content .connections .connections_side { + width: calc(50% - 5px); + min-height: 100px; + background-color: black; + display: flex; +} + +.litegraph .dialog .node_type { + font-size: 1.2em; + display: block; + margin: 10px; +} + +.litegraph .dialog .node_desc { + opacity: 0.5; + display: block; + margin: 10px; +} + +.litegraph .dialog .separator { + display: block; + width: calc( 100% - 4px ); + height: 1px; + border-top: 1px solid #000; + border-bottom: 1px solid #333; + margin: 10px 2px; + padding: 0; +} + +.litegraph .dialog .property { + margin-bottom: 2px; + padding: 4px; +} + +.litegraph .dialog .property:hover { + background: #545454; +} + +.litegraph .dialog .property_name { + color: #737373; + display: inline-block; + text-align: left; + vertical-align: top; + width: 160px; + padding-left: 4px; + overflow: hidden; + margin-right: 6px; +} + +.litegraph .dialog .property:hover .property_name { + color: white; +} + +.litegraph .dialog .property_value { + display: inline-block; + text-align: right; + color: #AAA; + background-color: #1A1A1A; + /*width: calc( 100% - 122px );*/ + max-width: calc( 100% - 162px ); + min-width: 200px; + max-height: 300px; + min-height: 20px; + padding: 4px; + padding-right: 12px; + overflow: hidden; + cursor: pointer; + border-radius: 3px; +} + +.litegraph .dialog .property_value:hover { + color: white; +} + +.litegraph .dialog .property.boolean .property_value { + padding-right: 30px; + color: #A88; + /*width: auto; + float: right;*/ +} + +.litegraph .dialog .property.boolean.bool-on .property_name{ + color: #8A8; +} +.litegraph .dialog .property.boolean.bool-on .property_value{ + color: #8A8; +} + +.litegraph .dialog .btn { + border: 0; + border-radius: 4px; + padding: 4px 20px; + margin-left: 0px; + background-color: #060606; + color: #8e8e8e; +} + +.litegraph .dialog .btn:hover { + background-color: #111; + color: #FFF; +} + +.litegraph .dialog .btn.delete:hover { + background-color: #F33; + color: black; +} + +.litegraph .subgraph_property { + padding: 4px; +} + +.litegraph .subgraph_property:hover { + background-color: #333; +} + +.litegraph .subgraph_property.extra { + margin-top: 8px; +} + +.litegraph .subgraph_property span.name { + font-size: 1.3em; + padding-left: 4px; +} + +.litegraph .subgraph_property span.type { + opacity: 0.5; + margin-right: 20px; + padding-left: 4px; +} + +.litegraph .subgraph_property span.label { + display: inline-block; + width: 60px; + padding: 0px 10px; +} + +.litegraph .subgraph_property input { + width: 140px; + color: #999; + background-color: #1A1A1A; + border-radius: 4px; + border: 0; + margin-right: 10px; + padding: 4px; + padding-left: 10px; +} + +.litegraph .subgraph_property button { + background-color: #1c1c1c; + color: #aaa; + border: 0; + border-radius: 2px; + padding: 4px 10px; + cursor: pointer; +} + +.litegraph .subgraph_property.extra { + color: #ccc; +} + +.litegraph .subgraph_property.extra input { + background-color: #111; +} + +.litegraph .bullet_icon { + margin-left: 10px; + border-radius: 10px; + width: 12px; + height: 12px; + background-color: #666; + display: inline-block; + margin-top: 2px; + margin-right: 4px; + transition: background-color 0.1s ease 0s; + -moz-transition: background-color 0.1s ease 0s; +} + +.litegraph .bullet_icon:hover { + background-color: #698; + cursor: pointer; +} + +/* OLD */ + +.graphcontextmenu { + padding: 4px; + min-width: 100px; +} + +.graphcontextmenu-title { + color: #dde; + background-color: #222; + margin: 0; + padding: 2px; + cursor: default; +} + +.graphmenu-entry { + box-sizing: border-box; + margin: 2px; + padding-left: 20px; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + transition: all linear 0.3s; +} + +.graphmenu-entry.event, +.litemenu-entry.event { + border-left: 8px solid orange; + padding-left: 12px; +} + +.graphmenu-entry.disabled { + opacity: 0.3; +} + +.graphmenu-entry.submenu { + border-right: 2px solid #eee; +} + +.graphmenu-entry:hover { + background-color: #555; +} + +.graphmenu-entry.separator { + background-color: #111; + border-bottom: 1px solid #666; + height: 1px; + width: calc(100% - 20px); + -moz-width: calc(100% - 20px); + -webkit-width: calc(100% - 20px); +} + +.graphmenu-entry .property_name { + display: inline-block; + text-align: left; + min-width: 80px; + min-height: 1.2em; +} + +.graphmenu-entry .property_value, +.litemenu-entry .property_value { + display: inline-block; + background-color: rgba(0, 0, 0, 0.5); + text-align: right; + min-width: 80px; + min-height: 1.2em; + vertical-align: middle; + padding-right: 10px; +} + +.graphdialog { + position: absolute; + top: 10px; + left: 10px; + min-height: 2em; + background-color: #333; + font-size: 1.2em; + box-shadow: 0 0 10px black !important; + z-index: 10; +} + +.graphdialog.rounded { + border-radius: 12px; + padding-right: 2px; +} + +.graphdialog .name { + display: inline-block; + min-width: 60px; + min-height: 1.5em; + padding-left: 10px; +} + +.graphdialog input, +.graphdialog textarea, +.graphdialog select { + margin: 3px; + min-width: 60px; + min-height: 1.5em; + background-color: black; + border: 0; + color: white; + padding-left: 10px; + outline: none; +} + +.graphdialog textarea { + min-height: 150px; +} + +.graphdialog button { + margin-top: 3px; + vertical-align: top; + background-color: #999; + border: 0; +} + +.graphdialog button.rounded, +.graphdialog input.rounded { + border-radius: 0 12px 12px 0; +} + +.graphdialog .helper { + overflow: auto; + max-height: 200px; +} + +.graphdialog .help-item { + padding-left: 10px; +} + +.graphdialog .help-item:hover, +.graphdialog .help-item.selected { + cursor: pointer; + background-color: white; + color: black; +} + +.litegraph .dialog { + min-height: 0; +} +.litegraph .dialog .dialog-content { +display: block; +} +.litegraph .dialog .dialog-content .subgraph_property { +padding: 5px; +} +.litegraph .dialog .dialog-footer { +margin: 0; +} +.litegraph .dialog .dialog-footer .subgraph_property { +margin-top: 0; +display: flex; +align-items: center; +padding: 5px; +} +.litegraph .dialog .dialog-footer .subgraph_property .name { +flex: 1; +} +.litegraph .graphdialog { +display: flex; +align-items: center; +border-radius: 20px; +padding: 4px 10px; +position: fixed; +} +.litegraph .graphdialog .name { +padding: 0; +min-height: 0; +font-size: 16px; +vertical-align: middle; +} +.litegraph .graphdialog .value { +font-size: 16px; +min-height: 0; +margin: 0 10px; +padding: 2px 5px; +} +.litegraph .graphdialog input[type="checkbox"] { +width: 16px; +height: 16px; +} +.litegraph .graphdialog button { +padding: 4px 18px; +border-radius: 20px; +cursor: pointer; +} +