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200 lines
8.5 KiB
200 lines
8.5 KiB
import torch |
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from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel |
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from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation |
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from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule |
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from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep |
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import numpy as np |
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from enum import Enum |
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from . import utils |
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class ModelType(Enum): |
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EPS = 1 |
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V_PREDICTION = 2 |
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class BaseModel(torch.nn.Module): |
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def __init__(self, model_config, model_type=ModelType.EPS): |
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super().__init__() |
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unet_config = model_config.unet_config |
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self.latent_format = model_config.latent_format |
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self.model_config = model_config |
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self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) |
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self.diffusion_model = UNetModel(**unet_config) |
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self.model_type = model_type |
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self.adm_channels = unet_config.get("adm_in_channels", None) |
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if self.adm_channels is None: |
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self.adm_channels = 0 |
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print("model_type", model_type.name) |
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print("adm", self.adm_channels) |
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def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, |
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): |
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if given_betas is not None: |
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betas = given_betas |
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else: |
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betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) |
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alphas = 1. - betas |
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alphas_cumprod = np.cumprod(alphas, axis=0) |
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alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) |
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timesteps, = betas.shape |
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self.num_timesteps = int(timesteps) |
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self.linear_start = linear_start |
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self.linear_end = linear_end |
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self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) |
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self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) |
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self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) |
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def apply_model(self, x, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}): |
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if c_concat is not None: |
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xc = torch.cat([x] + c_concat, dim=1) |
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else: |
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xc = x |
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context = torch.cat(c_crossattn, 1) |
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dtype = self.get_dtype() |
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xc = xc.to(dtype) |
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t = t.to(dtype) |
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context = context.to(dtype) |
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if c_adm is not None: |
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c_adm = c_adm.to(dtype) |
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return self.diffusion_model(xc, t, context=context, y=c_adm, control=control, transformer_options=transformer_options).float() |
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def get_dtype(self): |
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return self.diffusion_model.dtype |
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def is_adm(self): |
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return self.adm_channels > 0 |
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def encode_adm(self, **kwargs): |
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return None |
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def load_model_weights(self, sd, unet_prefix=""): |
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to_load = {} |
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keys = list(sd.keys()) |
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for k in keys: |
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if k.startswith(unet_prefix): |
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to_load[k[len(unet_prefix):]] = sd.pop(k) |
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m, u = self.diffusion_model.load_state_dict(to_load, strict=False) |
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if len(m) > 0: |
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print("unet missing:", m) |
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if len(u) > 0: |
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print("unet unexpected:", u) |
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del to_load |
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return self |
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def process_latent_in(self, latent): |
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return self.latent_format.process_in(latent) |
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def process_latent_out(self, latent): |
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return self.latent_format.process_out(latent) |
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def state_dict_for_saving(self, clip_state_dict, vae_state_dict): |
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clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict) |
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unet_state_dict = self.diffusion_model.state_dict() |
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unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) |
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vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict) |
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if self.get_dtype() == torch.float16: |
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clip_state_dict = utils.convert_sd_to(clip_state_dict, torch.float16) |
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vae_state_dict = utils.convert_sd_to(vae_state_dict, torch.float16) |
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if self.model_type == ModelType.V_PREDICTION: |
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unet_state_dict["v_pred"] = torch.tensor([]) |
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return {**unet_state_dict, **vae_state_dict, **clip_state_dict} |
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class SD21UNCLIP(BaseModel): |
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def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION): |
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super().__init__(model_config, model_type) |
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self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) |
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def encode_adm(self, **kwargs): |
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unclip_conditioning = kwargs.get("unclip_conditioning", None) |
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device = kwargs["device"] |
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if unclip_conditioning is not None: |
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adm_inputs = [] |
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weights = [] |
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noise_aug = [] |
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for unclip_cond in unclip_conditioning: |
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adm_cond = unclip_cond["clip_vision_output"].image_embeds |
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weight = unclip_cond["strength"] |
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noise_augment = unclip_cond["noise_augmentation"] |
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noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment) |
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c_adm, noise_level_emb = self.noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device)) |
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adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight |
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weights.append(weight) |
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noise_aug.append(noise_augment) |
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adm_inputs.append(adm_out) |
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if len(noise_aug) > 1: |
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adm_out = torch.stack(adm_inputs).sum(0) |
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#TODO: add a way to control this |
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noise_augment = 0.05 |
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noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment) |
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c_adm, noise_level_emb = self.noise_augmentor(adm_out[:, :self.noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) |
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adm_out = torch.cat((c_adm, noise_level_emb), 1) |
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else: |
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adm_out = torch.zeros((1, self.adm_channels)) |
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return adm_out |
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class SDInpaint(BaseModel): |
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def __init__(self, model_config, model_type=ModelType.EPS): |
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super().__init__(model_config, model_type) |
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self.concat_keys = ("mask", "masked_image") |
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class SDXLRefiner(BaseModel): |
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def __init__(self, model_config, model_type=ModelType.EPS): |
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super().__init__(model_config, model_type) |
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self.embedder = Timestep(256) |
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def encode_adm(self, **kwargs): |
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clip_pooled = kwargs["pooled_output"] |
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width = kwargs.get("width", 768) |
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height = kwargs.get("height", 768) |
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crop_w = kwargs.get("crop_w", 0) |
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crop_h = kwargs.get("crop_h", 0) |
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if kwargs.get("prompt_type", "") == "negative": |
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aesthetic_score = kwargs.get("aesthetic_score", 2.5) |
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else: |
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aesthetic_score = kwargs.get("aesthetic_score", 6) |
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print(clip_pooled.shape, width, height, crop_w, crop_h, aesthetic_score) |
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out = [] |
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out.append(self.embedder(torch.Tensor([height]))) |
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out.append(self.embedder(torch.Tensor([width]))) |
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out.append(self.embedder(torch.Tensor([crop_h]))) |
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out.append(self.embedder(torch.Tensor([crop_w]))) |
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out.append(self.embedder(torch.Tensor([aesthetic_score]))) |
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flat = torch.flatten(torch.cat(out))[None, ] |
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return torch.cat((clip_pooled.to(flat.device), flat), dim=1) |
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class SDXL(BaseModel): |
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def __init__(self, model_config, model_type=ModelType.EPS): |
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super().__init__(model_config, model_type) |
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self.embedder = Timestep(256) |
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def encode_adm(self, **kwargs): |
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clip_pooled = kwargs["pooled_output"] |
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width = kwargs.get("width", 768) |
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height = kwargs.get("height", 768) |
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crop_w = kwargs.get("crop_w", 0) |
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crop_h = kwargs.get("crop_h", 0) |
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target_width = kwargs.get("target_width", width) |
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target_height = kwargs.get("target_height", height) |
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print(clip_pooled.shape, width, height, crop_w, crop_h, target_width, target_height) |
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out = [] |
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out.append(self.embedder(torch.Tensor([height]))) |
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out.append(self.embedder(torch.Tensor([width]))) |
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out.append(self.embedder(torch.Tensor([crop_h]))) |
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out.append(self.embedder(torch.Tensor([crop_w]))) |
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out.append(self.embedder(torch.Tensor([target_height]))) |
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out.append(self.embedder(torch.Tensor([target_width]))) |
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flat = torch.flatten(torch.cat(out))[None, ] |
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return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
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