You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1007 lines
37 KiB
1007 lines
37 KiB
import torch |
|
|
|
import os |
|
import sys |
|
import json |
|
import hashlib |
|
import copy |
|
import traceback |
|
|
|
from PIL import Image |
|
from PIL.PngImagePlugin import PngInfo |
|
import numpy as np |
|
|
|
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) |
|
|
|
|
|
import comfy.samplers |
|
import comfy.sd |
|
import comfy.utils |
|
|
|
import comfy_extras.clip_vision |
|
|
|
import model_management |
|
import importlib |
|
|
|
supported_ckpt_extensions = ['.ckpt', '.pth'] |
|
supported_pt_extensions = ['.ckpt', '.pt', '.bin', '.pth'] |
|
try: |
|
import safetensors.torch |
|
supported_ckpt_extensions += ['.safetensors'] |
|
supported_pt_extensions += ['.safetensors'] |
|
except: |
|
print("Could not import safetensors, safetensors support disabled.") |
|
|
|
def recursive_search(directory): |
|
result = [] |
|
for root, subdir, file in os.walk(directory, followlinks=True): |
|
for filepath in file: |
|
#we os.path,join directory with a blank string to generate a path separator at the end. |
|
result.append(os.path.join(root, filepath).replace(os.path.join(directory,''),'')) |
|
return result |
|
|
|
def filter_files_extensions(files, extensions): |
|
return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files))) |
|
|
|
|
|
def before_node_execution(): |
|
model_management.throw_exception_if_processing_interrupted() |
|
|
|
def interrupt_processing(value=True): |
|
model_management.interrupt_current_processing(value) |
|
|
|
class CLIPTextEncode: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}} |
|
RETURN_TYPES = ("CONDITIONING",) |
|
FUNCTION = "encode" |
|
|
|
CATEGORY = "conditioning" |
|
|
|
def encode(self, clip, text): |
|
return ([[clip.encode(text), {}]], ) |
|
|
|
class ConditioningCombine: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} |
|
RETURN_TYPES = ("CONDITIONING",) |
|
FUNCTION = "combine" |
|
|
|
CATEGORY = "conditioning" |
|
|
|
def combine(self, conditioning_1, conditioning_2): |
|
return (conditioning_1 + conditioning_2, ) |
|
|
|
class ConditioningSetArea: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": {"conditioning": ("CONDITIONING", ), |
|
"width": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}), |
|
"height": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}), |
|
"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}), |
|
"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}), |
|
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), |
|
}} |
|
RETURN_TYPES = ("CONDITIONING",) |
|
FUNCTION = "append" |
|
|
|
CATEGORY = "conditioning" |
|
|
|
def append(self, conditioning, width, height, x, y, strength, min_sigma=0.0, max_sigma=99.0): |
|
c = [] |
|
for t in conditioning: |
|
n = [t[0], t[1].copy()] |
|
n[1]['area'] = (height // 8, width // 8, y // 8, x // 8) |
|
n[1]['strength'] = strength |
|
n[1]['min_sigma'] = min_sigma |
|
n[1]['max_sigma'] = max_sigma |
|
c.append(n) |
|
return (c, ) |
|
|
|
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" |
|
|
|
CATEGORY = "latent" |
|
|
|
def decode(self, vae, samples): |
|
return (vae.decode(samples["samples"]), ) |
|
|
|
class VAEDecodeTiled: |
|
def __init__(self, device="cpu"): |
|
self.device = device |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} |
|
RETURN_TYPES = ("IMAGE",) |
|
FUNCTION = "decode" |
|
|
|
CATEGORY = "_for_testing" |
|
|
|
def decode(self, vae, samples): |
|
return (vae.decode_tiled(samples["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" |
|
|
|
CATEGORY = "latent" |
|
|
|
def encode(self, vae, pixels): |
|
x = (pixels.shape[1] // 64) * 64 |
|
y = (pixels.shape[2] // 64) * 64 |
|
if pixels.shape[1] != x or pixels.shape[2] != y: |
|
pixels = pixels[:,:x,:y,:] |
|
t = vae.encode(pixels[:,:,:,:3]) |
|
|
|
return ({"samples":t}, ) |
|
|
|
|
|
class VAEEncodeTiled: |
|
def __init__(self, device="cpu"): |
|
self.device = device |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "encode" |
|
|
|
CATEGORY = "_for_testing" |
|
|
|
def encode(self, vae, pixels): |
|
x = (pixels.shape[1] // 64) * 64 |
|
y = (pixels.shape[2] // 64) * 64 |
|
if pixels.shape[1] != x or pixels.shape[2] != y: |
|
pixels = pixels[:,:x,:y,:] |
|
t = vae.encode_tiled(pixels[:,:,:,:3]) |
|
|
|
return ({"samples":t}, ) |
|
class VAEEncodeForInpaint: |
|
def __init__(self, device="cpu"): |
|
self.device = device |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", )}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "encode" |
|
|
|
CATEGORY = "latent/inpaint" |
|
|
|
def encode(self, vae, pixels, mask): |
|
x = (pixels.shape[1] // 64) * 64 |
|
y = (pixels.shape[2] // 64) * 64 |
|
mask = torch.nn.functional.interpolate(mask[None,None,], size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")[0][0] |
|
|
|
if pixels.shape[1] != x or pixels.shape[2] != y: |
|
pixels = pixels[:,:x,:y,:] |
|
mask = mask[:x,:y] |
|
|
|
#grow mask by a few pixels to keep things seamless in latent space |
|
kernel_tensor = torch.ones((1, 1, 6, 6)) |
|
mask_erosion = torch.clamp(torch.nn.functional.conv2d((mask.round())[None], kernel_tensor, padding=3), 0, 1) |
|
m = (1.0 - mask.round()) |
|
for i in range(3): |
|
pixels[:,:,:,i] -= 0.5 |
|
pixels[:,:,:,i] *= m |
|
pixels[:,:,:,i] += 0.5 |
|
t = vae.encode(pixels) |
|
|
|
return ({"samples":t, "noise_mask": (mask_erosion[0][:x,:y].round())}, ) |
|
|
|
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") |
|
embedding_directory = os.path.join(models_dir, "embeddings") |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "config_name": (filter_files_extensions(recursive_search(s.config_dir), '.yaml'), ), |
|
"ckpt_name": (filter_files_extensions(recursive_search(s.ckpt_dir), supported_ckpt_extensions), )}} |
|
RETURN_TYPES = ("MODEL", "CLIP", "VAE") |
|
FUNCTION = "load_checkpoint" |
|
|
|
CATEGORY = "loaders" |
|
|
|
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, embedding_directory=self.embedding_directory) |
|
|
|
class CheckpointLoaderSimple: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
ckpt_dir = os.path.join(models_dir, "checkpoints") |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "ckpt_name": (filter_files_extensions(recursive_search(s.ckpt_dir), supported_ckpt_extensions), ), |
|
}} |
|
RETURN_TYPES = ("MODEL", "CLIP", "VAE") |
|
FUNCTION = "load_checkpoint" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): |
|
ckpt_path = os.path.join(self.ckpt_dir, ckpt_name) |
|
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=CheckpointLoader.embedding_directory) |
|
return out |
|
|
|
class CLIPSetLastLayer: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "clip": ("CLIP", ), |
|
"stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), |
|
}} |
|
RETURN_TYPES = ("CLIP",) |
|
FUNCTION = "set_last_layer" |
|
|
|
CATEGORY = "conditioning" |
|
|
|
def set_last_layer(self, clip, stop_at_clip_layer): |
|
clip = clip.clone() |
|
clip.clip_layer(stop_at_clip_layer) |
|
return (clip,) |
|
|
|
class LoraLoader: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
lora_dir = os.path.join(models_dir, "loras") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "model": ("MODEL",), |
|
"clip": ("CLIP", ), |
|
"lora_name": (filter_files_extensions(recursive_search(s.lora_dir), supported_pt_extensions), ), |
|
"strength_model": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), |
|
"strength_clip": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), |
|
}} |
|
RETURN_TYPES = ("MODEL", "CLIP") |
|
FUNCTION = "load_lora" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_lora(self, model, clip, lora_name, strength_model, strength_clip): |
|
lora_path = os.path.join(self.lora_dir, lora_name) |
|
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip) |
|
return (model_lora, clip_lora) |
|
|
|
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(recursive_search(s.vae_dir), supported_pt_extensions), )}} |
|
RETURN_TYPES = ("VAE",) |
|
FUNCTION = "load_vae" |
|
|
|
CATEGORY = "loaders" |
|
|
|
#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 ControlNetLoader: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
controlnet_dir = os.path.join(models_dir, "controlnet") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "control_net_name": (filter_files_extensions(recursive_search(s.controlnet_dir), supported_pt_extensions), )}} |
|
|
|
RETURN_TYPES = ("CONTROL_NET",) |
|
FUNCTION = "load_controlnet" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_controlnet(self, control_net_name): |
|
controlnet_path = os.path.join(self.controlnet_dir, control_net_name) |
|
controlnet = comfy.sd.load_controlnet(controlnet_path) |
|
return (controlnet,) |
|
|
|
class DiffControlNetLoader: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
controlnet_dir = os.path.join(models_dir, "controlnet") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "model": ("MODEL",), |
|
"control_net_name": (filter_files_extensions(recursive_search(s.controlnet_dir), supported_pt_extensions), )}} |
|
|
|
RETURN_TYPES = ("CONTROL_NET",) |
|
FUNCTION = "load_controlnet" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_controlnet(self, model, control_net_name): |
|
controlnet_path = os.path.join(self.controlnet_dir, control_net_name) |
|
controlnet = comfy.sd.load_controlnet(controlnet_path, model) |
|
return (controlnet,) |
|
|
|
|
|
class ControlNetApply: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": {"conditioning": ("CONDITIONING", ), |
|
"control_net": ("CONTROL_NET", ), |
|
"image": ("IMAGE", ), |
|
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) |
|
}} |
|
RETURN_TYPES = ("CONDITIONING",) |
|
FUNCTION = "apply_controlnet" |
|
|
|
CATEGORY = "conditioning" |
|
|
|
def apply_controlnet(self, conditioning, control_net, image, strength): |
|
c = [] |
|
control_hint = image.movedim(-1,1) |
|
print(control_hint.shape) |
|
for t in conditioning: |
|
n = [t[0], t[1].copy()] |
|
c_net = control_net.copy().set_cond_hint(control_hint, strength) |
|
if 'control' in t[1]: |
|
c_net.set_previous_controlnet(t[1]['control']) |
|
n[1]['control'] = c_net |
|
c.append(n) |
|
return (c, ) |
|
|
|
class T2IAdapterLoader: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
t2i_adapter_dir = os.path.join(models_dir, "t2i_adapter") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "t2i_adapter_name": (filter_files_extensions(recursive_search(s.t2i_adapter_dir), supported_pt_extensions), )}} |
|
|
|
RETURN_TYPES = ("CONTROL_NET",) |
|
FUNCTION = "load_t2i_adapter" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_t2i_adapter(self, t2i_adapter_name): |
|
t2i_path = os.path.join(self.t2i_adapter_dir, t2i_adapter_name) |
|
t2i_adapter = comfy.sd.load_t2i_adapter(t2i_path) |
|
return (t2i_adapter,) |
|
|
|
class CLIPLoader: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
clip_dir = os.path.join(models_dir, "clip") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "clip_name": (filter_files_extensions(recursive_search(s.clip_dir), supported_pt_extensions), ), |
|
}} |
|
RETURN_TYPES = ("CLIP",) |
|
FUNCTION = "load_clip" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_clip(self, clip_name): |
|
clip_path = os.path.join(self.clip_dir, clip_name) |
|
clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=CheckpointLoader.embedding_directory) |
|
return (clip,) |
|
|
|
class CLIPVisionLoader: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
clip_dir = os.path.join(models_dir, "clip_vision") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "clip_name": (filter_files_extensions(recursive_search(s.clip_dir), supported_pt_extensions), ), |
|
}} |
|
RETURN_TYPES = ("CLIP_VISION",) |
|
FUNCTION = "load_clip" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_clip(self, clip_name): |
|
clip_path = os.path.join(self.clip_dir, clip_name) |
|
clip_vision = comfy_extras.clip_vision.load(clip_path) |
|
return (clip_vision,) |
|
|
|
class CLIPVisionEncode: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "clip_vision": ("CLIP_VISION",), |
|
"image": ("IMAGE",) |
|
}} |
|
RETURN_TYPES = ("CLIP_VISION_OUTPUT",) |
|
FUNCTION = "encode" |
|
|
|
CATEGORY = "conditioning/style_model" |
|
|
|
def encode(self, clip_vision, image): |
|
output = clip_vision.encode_image(image) |
|
return (output,) |
|
|
|
class StyleModelLoader: |
|
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
|
style_model_dir = os.path.join(models_dir, "style_models") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "style_model_name": (filter_files_extensions(recursive_search(s.style_model_dir), supported_pt_extensions), )}} |
|
|
|
RETURN_TYPES = ("STYLE_MODEL",) |
|
FUNCTION = "load_style_model" |
|
|
|
CATEGORY = "loaders" |
|
|
|
def load_style_model(self, style_model_name): |
|
style_model_path = os.path.join(self.style_model_dir, style_model_name) |
|
style_model = comfy.sd.load_style_model(style_model_path) |
|
return (style_model,) |
|
|
|
|
|
class StyleModelApply: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": {"conditioning": ("CONDITIONING", ), |
|
"style_model": ("STYLE_MODEL", ), |
|
"clip_vision_output": ("CLIP_VISION_OUTPUT", ), |
|
}} |
|
RETURN_TYPES = ("CONDITIONING",) |
|
FUNCTION = "apply_stylemodel" |
|
|
|
CATEGORY = "conditioning/style_model" |
|
|
|
def apply_stylemodel(self, clip_vision_output, style_model, conditioning): |
|
cond = style_model.get_cond(clip_vision_output) |
|
c = [] |
|
for t in conditioning: |
|
n = [torch.cat((t[0], cond), dim=1), t[1].copy()] |
|
c.append(n) |
|
return (c, ) |
|
|
|
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" |
|
|
|
CATEGORY = "latent" |
|
|
|
def generate(self, width, height, batch_size=1): |
|
latent = torch.zeros([batch_size, 4, height // 8, width // 8]) |
|
return ({"samples":latent}, ) |
|
|
|
|
|
|
|
class LatentUpscale: |
|
upscale_methods = ["nearest-exact", "bilinear", "area"] |
|
crop_methods = ["disabled", "center"] |
|
|
|
@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}), |
|
"crop": (s.crop_methods,)}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "upscale" |
|
|
|
CATEGORY = "latent" |
|
|
|
def upscale(self, samples, upscale_method, width, height, crop): |
|
s = samples.copy() |
|
s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) |
|
return (s,) |
|
|
|
class LatentRotate: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "samples": ("LATENT",), |
|
"rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), |
|
}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "rotate" |
|
|
|
CATEGORY = "latent/transform" |
|
|
|
def rotate(self, samples, rotation): |
|
s = samples.copy() |
|
rotate_by = 0 |
|
if rotation.startswith("90"): |
|
rotate_by = 1 |
|
elif rotation.startswith("180"): |
|
rotate_by = 2 |
|
elif rotation.startswith("270"): |
|
rotate_by = 3 |
|
|
|
s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) |
|
return (s,) |
|
|
|
class LatentFlip: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "samples": ("LATENT",), |
|
"flip_method": (["x-axis: vertically", "y-axis: horizontally"],), |
|
}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "flip" |
|
|
|
CATEGORY = "latent/transform" |
|
|
|
def flip(self, samples, flip_method): |
|
s = samples.copy() |
|
if flip_method.startswith("x"): |
|
s["samples"] = torch.flip(samples["samples"], dims=[2]) |
|
elif flip_method.startswith("y"): |
|
s["samples"] = torch.flip(samples["samples"], dims=[3]) |
|
|
|
return (s,) |
|
|
|
class LatentComposite: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "samples_to": ("LATENT",), |
|
"samples_from": ("LATENT",), |
|
"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}), |
|
"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}), |
|
"feather": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}), |
|
}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "composite" |
|
|
|
CATEGORY = "latent" |
|
|
|
def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): |
|
x = x // 8 |
|
y = y // 8 |
|
feather = feather // 8 |
|
samples_out = samples_to.copy() |
|
s = samples_to["samples"].clone() |
|
samples_to = samples_to["samples"] |
|
samples_from = samples_from["samples"] |
|
if feather == 0: |
|
s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] |
|
else: |
|
samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] |
|
mask = torch.ones_like(samples_from) |
|
for t in range(feather): |
|
if y != 0: |
|
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) |
|
|
|
if y + samples_from.shape[2] < samples_to.shape[2]: |
|
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) |
|
if x != 0: |
|
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) |
|
if x + samples_from.shape[3] < samples_to.shape[3]: |
|
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) |
|
rev_mask = torch.ones_like(mask) - mask |
|
s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask |
|
samples_out["samples"] = s |
|
return (samples_out,) |
|
|
|
class LatentCrop: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "samples": ("LATENT",), |
|
"width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), |
|
"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), |
|
"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}), |
|
"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 8}), |
|
}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "crop" |
|
|
|
CATEGORY = "latent/transform" |
|
|
|
def crop(self, samples, width, height, x, y): |
|
s = samples.copy() |
|
samples = samples['samples'] |
|
x = x // 8 |
|
y = y // 8 |
|
|
|
#enfonce minimum size of 64 |
|
if x > (samples.shape[3] - 8): |
|
x = samples.shape[3] - 8 |
|
if y > (samples.shape[2] - 8): |
|
y = samples.shape[2] - 8 |
|
|
|
new_height = height // 8 |
|
new_width = width // 8 |
|
to_x = new_width + x |
|
to_y = new_height + y |
|
def enforce_image_dim(d, to_d, max_d): |
|
if to_d > max_d: |
|
leftover = (to_d - max_d) % 8 |
|
to_d = max_d |
|
d -= leftover |
|
return (d, to_d) |
|
|
|
#make sure size is always multiple of 64 |
|
x, to_x = enforce_image_dim(x, to_x, samples.shape[3]) |
|
y, to_y = enforce_image_dim(y, to_y, samples.shape[2]) |
|
s['samples'] = samples[:,:,y:to_y, x:to_x] |
|
return (s,) |
|
|
|
class SetLatentNoiseMask: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "samples": ("LATENT",), |
|
"mask": ("MASK",), |
|
}} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "set_mask" |
|
|
|
CATEGORY = "latent/inpaint" |
|
|
|
def set_mask(self, samples, mask): |
|
s = samples.copy() |
|
s["noise_mask"] = mask |
|
return (s,) |
|
|
|
|
|
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): |
|
latent_image = latent["samples"] |
|
noise_mask = None |
|
device = model_management.get_torch_device() |
|
|
|
if disable_noise: |
|
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") |
|
else: |
|
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu") |
|
|
|
if "noise_mask" in latent: |
|
noise_mask = latent['noise_mask'] |
|
noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear") |
|
noise_mask = noise_mask.round() |
|
noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1) |
|
noise_mask = torch.cat([noise_mask] * noise.shape[0]) |
|
noise_mask = noise_mask.to(device) |
|
|
|
real_model = None |
|
model_management.load_model_gpu(model) |
|
real_model = model.model |
|
|
|
noise = noise.to(device) |
|
latent_image = latent_image.to(device) |
|
|
|
positive_copy = [] |
|
negative_copy = [] |
|
|
|
control_nets = [] |
|
for p in positive: |
|
t = p[0] |
|
if t.shape[0] < noise.shape[0]: |
|
t = torch.cat([t] * noise.shape[0]) |
|
t = t.to(device) |
|
if 'control' in p[1]: |
|
control_nets += [p[1]['control']] |
|
positive_copy += [[t] + p[1:]] |
|
for n in negative: |
|
t = n[0] |
|
if t.shape[0] < noise.shape[0]: |
|
t = torch.cat([t] * noise.shape[0]) |
|
t = t.to(device) |
|
if 'control' in p[1]: |
|
control_nets += [p[1]['control']] |
|
negative_copy += [[t] + n[1:]] |
|
|
|
control_net_models = [] |
|
for x in control_nets: |
|
control_net_models += x.get_control_models() |
|
model_management.load_controlnet_gpu(control_net_models) |
|
|
|
if sampler_name in comfy.samplers.KSampler.SAMPLERS: |
|
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise) |
|
else: |
|
#other samplers |
|
pass |
|
|
|
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask) |
|
samples = samples.cpu() |
|
for c in control_nets: |
|
c.cleanup() |
|
|
|
out = latent.copy() |
|
out["samples"] = samples |
|
return (out, ) |
|
|
|
class KSampler: |
|
@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" |
|
|
|
CATEGORY = "sampling" |
|
|
|
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): |
|
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) |
|
|
|
class KSamplerAdvanced: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": |
|
{"model": ("MODEL",), |
|
"add_noise": (["enable", "disable"], ), |
|
"noise_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", ), |
|
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), |
|
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), |
|
"return_with_leftover_noise": (["disable", "enable"], ), |
|
}} |
|
|
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "sample" |
|
|
|
CATEGORY = "sampling" |
|
|
|
def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): |
|
force_full_denoise = True |
|
if return_with_leftover_noise == "enable": |
|
force_full_denoise = False |
|
disable_noise = False |
|
if add_noise == "disable": |
|
disable_noise = True |
|
return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) |
|
|
|
class SaveImage: |
|
def __init__(self): |
|
self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": |
|
{"images": ("IMAGE", ), |
|
"filename_prefix": ("STRING", {"default": "ComfyUI"})}, |
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, |
|
} |
|
|
|
RETURN_TYPES = () |
|
FUNCTION = "save_images" |
|
|
|
OUTPUT_NODE = True |
|
|
|
CATEGORY = "image" |
|
|
|
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): |
|
def map_filename(filename): |
|
prefix_len = len(filename_prefix) |
|
prefix = filename[:prefix_len + 1] |
|
try: |
|
digits = int(filename[prefix_len + 1:].split('_')[0]) |
|
except: |
|
digits = 0 |
|
return (digits, prefix) |
|
try: |
|
counter = max(filter(lambda a: a[1][:-1] == filename_prefix and a[1][-1] == "_", map(map_filename, os.listdir(self.output_dir))))[0] + 1 |
|
except ValueError: |
|
counter = 1 |
|
except FileNotFoundError: |
|
os.mkdir(self.output_dir) |
|
counter = 1 |
|
|
|
paths = list() |
|
for image in images: |
|
i = 255. * image.cpu().numpy() |
|
img = Image.fromarray(np.clip(i, 0, 255).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])) |
|
file = f"{filename_prefix}_{counter:05}_.png" |
|
img.save(os.path.join(self.output_dir, file), pnginfo=metadata, optimize=True) |
|
paths.append(file) |
|
counter += 1 |
|
return { "ui": { "images": paths } } |
|
|
|
class LoadImage: |
|
input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
if not os.path.exists(s.input_dir): |
|
os.makedirs(s.input_dir) |
|
return {"required": |
|
{"image": (sorted(os.listdir(s.input_dir)), )}, |
|
} |
|
|
|
CATEGORY = "image" |
|
|
|
RETURN_TYPES = ("IMAGE", "MASK") |
|
FUNCTION = "load_image" |
|
def load_image(self, image): |
|
image_path = os.path.join(self.input_dir, image) |
|
i = Image.open(image_path) |
|
image = i.convert("RGB") |
|
image = np.array(image).astype(np.float32) / 255.0 |
|
image = torch.from_numpy(image)[None,] |
|
if 'A' in i.getbands(): |
|
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 |
|
mask = 1. - torch.from_numpy(mask) |
|
else: |
|
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") |
|
return (image, mask) |
|
|
|
@classmethod |
|
def IS_CHANGED(s, image): |
|
image_path = os.path.join(s.input_dir, image) |
|
m = hashlib.sha256() |
|
with open(image_path, 'rb') as f: |
|
m.update(f.read()) |
|
return m.digest().hex() |
|
|
|
class LoadImageMask: |
|
input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input") |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": |
|
{"image": (sorted(os.listdir(s.input_dir)), ), |
|
"channel": (["alpha", "red", "green", "blue"], ),} |
|
} |
|
|
|
CATEGORY = "image" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
FUNCTION = "load_image" |
|
def load_image(self, image, channel): |
|
image_path = os.path.join(self.input_dir, image) |
|
i = Image.open(image_path) |
|
mask = None |
|
c = channel[0].upper() |
|
if c in i.getbands(): |
|
mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 |
|
mask = torch.from_numpy(mask) |
|
if c == 'A': |
|
mask = 1. - mask |
|
else: |
|
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") |
|
return (mask,) |
|
|
|
@classmethod |
|
def IS_CHANGED(s, image, channel): |
|
image_path = os.path.join(s.input_dir, image) |
|
m = hashlib.sha256() |
|
with open(image_path, 'rb') as f: |
|
m.update(f.read()) |
|
return m.digest().hex() |
|
|
|
class ImageScale: |
|
upscale_methods = ["nearest-exact", "bilinear", "area"] |
|
crop_methods = ["disabled", "center"] |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), |
|
"width": ("INT", {"default": 512, "min": 1, "max": 4096, "step": 1}), |
|
"height": ("INT", {"default": 512, "min": 1, "max": 4096, "step": 1}), |
|
"crop": (s.crop_methods,)}} |
|
RETURN_TYPES = ("IMAGE",) |
|
FUNCTION = "upscale" |
|
|
|
CATEGORY = "image" |
|
|
|
def upscale(self, image, upscale_method, width, height, crop): |
|
samples = image.movedim(-1,1) |
|
s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop) |
|
s = s.movedim(1,-1) |
|
return (s,) |
|
|
|
class ImageInvert: |
|
|
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return {"required": { "image": ("IMAGE",)}} |
|
|
|
RETURN_TYPES = ("IMAGE",) |
|
FUNCTION = "invert" |
|
|
|
CATEGORY = "image" |
|
|
|
def invert(self, image): |
|
s = 1.0 - image |
|
return (s,) |
|
|
|
|
|
NODE_CLASS_MAPPINGS = { |
|
"KSampler": KSampler, |
|
"CheckpointLoader": CheckpointLoader, |
|
"CheckpointLoaderSimple": CheckpointLoaderSimple, |
|
"CLIPTextEncode": CLIPTextEncode, |
|
"CLIPSetLastLayer": CLIPSetLastLayer, |
|
"VAEDecode": VAEDecode, |
|
"VAEEncode": VAEEncode, |
|
"VAEEncodeForInpaint": VAEEncodeForInpaint, |
|
"VAELoader": VAELoader, |
|
"EmptyLatentImage": EmptyLatentImage, |
|
"LatentUpscale": LatentUpscale, |
|
"SaveImage": SaveImage, |
|
"LoadImage": LoadImage, |
|
"LoadImageMask": LoadImageMask, |
|
"ImageScale": ImageScale, |
|
"ImageInvert": ImageInvert, |
|
"ConditioningCombine": ConditioningCombine, |
|
"ConditioningSetArea": ConditioningSetArea, |
|
"KSamplerAdvanced": KSamplerAdvanced, |
|
"SetLatentNoiseMask": SetLatentNoiseMask, |
|
"LatentComposite": LatentComposite, |
|
"LatentRotate": LatentRotate, |
|
"LatentFlip": LatentFlip, |
|
"LatentCrop": LatentCrop, |
|
"LoraLoader": LoraLoader, |
|
"CLIPLoader": CLIPLoader, |
|
"CLIPVisionEncode": CLIPVisionEncode, |
|
"StyleModelApply": StyleModelApply, |
|
"ControlNetApply": ControlNetApply, |
|
"ControlNetLoader": ControlNetLoader, |
|
"DiffControlNetLoader": DiffControlNetLoader, |
|
"T2IAdapterLoader": T2IAdapterLoader, |
|
"StyleModelLoader": StyleModelLoader, |
|
"CLIPVisionLoader": CLIPVisionLoader, |
|
"VAEDecodeTiled": VAEDecodeTiled, |
|
"VAEEncodeTiled": VAEEncodeTiled, |
|
} |
|
|
|
def load_custom_node(module_path): |
|
module_name = os.path.basename(module_path) |
|
if os.path.isfile(module_path): |
|
sp = os.path.splitext(module_path) |
|
module_name = sp[0] |
|
try: |
|
if os.path.isfile(module_path): |
|
module_spec = importlib.util.spec_from_file_location(module_name, module_path) |
|
else: |
|
module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) |
|
module = importlib.util.module_from_spec(module_spec) |
|
sys.modules[module_name] = module |
|
module_spec.loader.exec_module(module) |
|
if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: |
|
NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS) |
|
else: |
|
print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") |
|
except Exception as e: |
|
print(traceback.format_exc()) |
|
print(f"Cannot import {module_path} module for custom nodes:", e) |
|
|
|
def load_custom_nodes(): |
|
CUSTOM_NODE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "custom_nodes") |
|
possible_modules = os.listdir(CUSTOM_NODE_PATH) |
|
if "__pycache__" in possible_modules: |
|
possible_modules.remove("__pycache__") |
|
|
|
for possible_module in possible_modules: |
|
module_path = os.path.join(CUSTOM_NODE_PATH, possible_module) |
|
if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue |
|
load_custom_node(module_path) |
|
|
|
load_custom_nodes() |
|
|
|
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
|
|
|