diff --git a/nodes.py b/nodes.py new file mode 100644 index 0000000..b744b53 --- /dev/null +++ b/nodes.py @@ -0,0 +1,1985 @@ +import torch + +import os +import sys +import json +import hashlib +import traceback +import math +import time +import random +import logging + +from PIL import Image, ImageOps, ImageSequence, ImageFile +from PIL.PngImagePlugin import PngInfo + +import numpy as np +import safetensors.torch + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) + +import comfy.diffusers_load +import comfy.samplers +import comfy.sample +import comfy.sd +import comfy.utils +import comfy.controlnet + +import comfy.clip_vision + +import comfy.model_management +from comfy.cli_args import args + +import importlib + +import folder_paths +import latent_preview +import node_helpers + +def before_node_execution(): + comfy.model_management.throw_exception_if_processing_interrupted() + +def interrupt_processing(value=True): + comfy.model_management.interrupt_current_processing(value) + +MAX_RESOLUTION=16384 + +class CLIPTextEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled}]], ) + +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 ConditioningAverage : + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), + "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "addWeighted" + + CATEGORY = "conditioning" + + def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): + out = [] + + if len(conditioning_from) > 1: + logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + pooled_output_from = conditioning_from[0][1].get("pooled_output", None) + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) + t0 = cond_from[:,:t1.shape[1]] + if t0.shape[1] < t1.shape[1]: + t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) + + tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) + t_to = conditioning_to[i][1].copy() + if pooled_output_from is not None and pooled_output_to is not None: + t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) + elif pooled_output_from is not None: + t_to["pooled_output"] = pooled_output_from + + n = [tw, t_to] + out.append(n) + return (out, ) + +class ConditioningConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning_to": ("CONDITIONING",), + "conditioning_from": ("CONDITIONING",), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "concat" + + CATEGORY = "conditioning" + + def concat(self, conditioning_to, conditioning_from): + out = [] + + if len(conditioning_from) > 1: + logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from),1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + return (out, ) + +class ConditioningSetArea: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "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): + c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + +class ConditioningSetAreaPercentage: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "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): + c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + +class ConditioningSetAreaStrength: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "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, strength): + c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) + return (c, ) + + +class ConditioningSetMask: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "mask": ("MASK", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, mask, set_cond_area, strength): + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask.shape) < 3: + mask = mask.unsqueeze(0) + + c = node_helpers.conditioning_set_values(conditioning, {"mask": mask, + "set_area_to_bounds": set_area_to_bounds, + "mask_strength": strength}) + return (c, ) + +class ConditioningZeroOut: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "zero_out" + + CATEGORY = "advanced/conditioning" + + def zero_out(self, conditioning): + c = [] + for t in conditioning: + d = t[1].copy() + if "pooled_output" in d: + d["pooled_output"] = torch.zeros_like(d["pooled_output"]) + n = [torch.zeros_like(t[0]), d] + c.append(n) + return (c, ) + +class ConditioningSetTimestepRange: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "set_range" + + CATEGORY = "advanced/conditioning" + + def set_range(self, conditioning, start, end): + c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, + "end_percent": end}) + return (c, ) + +class VAEDecode: + @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: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + CATEGORY = "_for_testing" + + def decode(self, vae, samples, tile_size): + return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), ) + +class VAEEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent" + + def encode(self, vae, pixels): + t = vae.encode(pixels[:,:,:,:3]) + return ({"samples":t}, ) + +class VAEEncodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "_for_testing" + + def encode(self, vae, pixels, tile_size): + t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) + return ({"samples":t}, ) + +class VAEEncodeForInpaint: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent/inpaint" + + def encode(self, vae, pixels, mask, grow_mask_by=6): + x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio + y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + pixels = pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2 + y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + #grow mask by a few pixels to keep things seamless in latent space + if grow_mask_by == 0: + mask_erosion = mask + else: + kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) + padding = math.ceil((grow_mask_by - 1) / 2) + + mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) + + m = (1.0 - mask.round()).squeeze(1) + 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[:,:,:x,:y].round())}, ) + + +class InpaintModelConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "pixels": ("IMAGE", ), + "mask": ("MASK", ), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/inpaint" + + def encode(self, positive, negative, pixels, vae, mask): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + orig_pixels = pixels + pixels = orig_pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + concat_latent = vae.encode(pixels) + orig_latent = vae.encode(orig_pixels) + + out_latent = {} + + out_latent["samples"] = orig_latent + out_latent["noise_mask"] = mask + + out = [] + for conditioning in [positive, negative]: + c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, + "concat_mask": mask}) + out.append(c) + return (out[0], out[1], out_latent) + + +class SaveLatent: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), + "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + RETURN_TYPES = () + FUNCTION = "save" + + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + # support save metadata for latent sharing + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = None + if not args.disable_metadata: + metadata = {"prompt": prompt_info} + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + file = f"{filename}_{counter:05}_.latent" + + results = list() + results.append({ + "filename": file, + "subfolder": subfolder, + "type": "output" + }) + + file = os.path.join(full_output_folder, file) + + output = {} + output["latent_tensor"] = samples["samples"] + output["latent_format_version_0"] = torch.tensor([]) + + comfy.utils.save_torch_file(output, file, metadata=metadata) + return { "ui": { "latents": results } } + + +class LoadLatent: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] + return {"required": {"latent": [sorted(files), ]}, } + + CATEGORY = "_for_testing" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "load" + + def load(self, latent): + latent_path = folder_paths.get_annotated_filepath(latent) + latent = safetensors.torch.load_file(latent_path, device="cpu") + multiplier = 1.0 + if "latent_format_version_0" not in latent: + multiplier = 1.0 / 0.18215 + samples = {"samples": latent["latent_tensor"].float() * multiplier} + return (samples, ) + + @classmethod + def IS_CHANGED(s, latent): + image_path = folder_paths.get_annotated_filepath(latent) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, latent): + if not folder_paths.exists_annotated_filepath(latent): + return "Invalid latent file: {}".format(latent) + return True + + +class CheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ), + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders" + + def load_checkpoint(self, config_name, ckpt_name): + config_path = folder_paths.get_full_path("configs", config_name) + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + +class CheckpointLoaderSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name): + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return out[:3] + +class DiffusersLoader: + @classmethod + def INPUT_TYPES(cls): + paths = [] + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + for root, subdir, files in os.walk(search_path, followlinks=True): + if "model_index.json" in files: + paths.append(os.path.relpath(root, start=search_path)) + + return {"required": {"model_path": (paths,), }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders/deprecated" + + def load_checkpoint(self, model_path, output_vae=True, output_clip=True): + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + path = os.path.join(search_path, model_path) + if os.path.exists(path): + model_path = path + break + + return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) + + +class unCLIPCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + 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: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip": ("CLIP", ), + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "load_lora" + + CATEGORY = "loaders" + + def load_lora(self, model, clip, lora_name, strength_model, strength_clip): + if strength_model == 0 and strength_clip == 0: + return (model, clip) + + lora_path = folder_paths.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + return (model_lora, clip_lora) + +class LoraLoaderModelOnly(LoraLoader): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lora_model_only" + + def load_lora_model_only(self, model, lora_name, strength_model): + return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) + +class VAELoader: + @staticmethod + def vae_list(): + vaes = folder_paths.get_filename_list("vae") + approx_vaes = folder_paths.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = folder_paths.get_filename_list("vae_approx") + + encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) + decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) + + enc = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", encoder)) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = comfy.utils.load_torch_file(folder_paths.get_full_path("vae_approx", decoder)) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + return sd + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae_name": (s.vae_list(), )}} + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + + CATEGORY = "loaders" + + #TODO: scale factor? + def load_vae(self, vae_name): + if vae_name in ["taesd", "taesdxl"]: + sd = self.load_taesd(vae_name) + else: + vae_path = folder_paths.get_full_path("vae", vae_name) + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + return (vae,) + +class ControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, control_net_name): + controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path) + return (controlnet,) + +class DiffControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, model, control_net_name): + controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) + controlnet = comfy.controlnet.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): + if strength == 0: + return (conditioning, ) + + c = [] + control_hint = image.movedim(-1,1) + 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 + n[1]['control_apply_to_uncond'] = True + c.append(n) + return (c, ) + + +class ControlNetApplyAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING") + RETURN_NAMES = ("positive", "negative") + FUNCTION = "apply_controlnet" + + CATEGORY = "conditioning" + + def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent): + if strength == 0: + return (positive, negative) + + control_hint = image.movedim(-1,1) + cnets = {} + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + + prev_cnet = d.get('control', None) + if prev_cnet in cnets: + c_net = cnets[prev_cnet] + else: + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent)) + c_net.set_previous_controlnet(prev_cnet) + cnets[prev_cnet] = c_net + + d['control'] = c_net + d['control_apply_to_uncond'] = False + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1]) + + +class UNETLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + + CATEGORY = "advanced/loaders" + + def load_unet(self, unet_name): + unet_path = folder_paths.get_full_path("unet", unet_name) + model = comfy.sd.load_unet(unet_path) + return (model,) + +class CLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ), + "type": (["stable_diffusion", "stable_cascade"], ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name, type="stable_diffusion"): + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + if type == "stable_cascade": + clip_type = comfy.sd.CLIPType.STABLE_CASCADE + + clip_path = folder_paths.get_full_path("clip", clip_name) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type) + return (clip,) + +class DualCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name1, clip_name2): + clip_path1 = folder_paths.get_full_path("clip", clip_name1) + clip_path2 = folder_paths.get_full_path("clip", clip_name2) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (clip,) + +class CLIPVisionLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ), + }} + RETURN_TYPES = ("CLIP_VISION",) + FUNCTION = "load_clip" + + CATEGORY = "loaders" + + def load_clip(self, clip_name): + clip_path = folder_paths.get_full_path("clip_vision", clip_name) + clip_vision = comfy.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" + + def encode(self, clip_vision, image): + output = clip_vision.encode_image(image) + return (output,) + +class StyleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}} + + RETURN_TYPES = ("STYLE_MODEL",) + FUNCTION = "load_style_model" + + CATEGORY = "loaders" + + def load_style_model(self, style_model_name): + style_model_path = folder_paths.get_full_path("style_models", 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).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) + c = [] + for t in conditioning: + n = [torch.cat((t[0], cond), dim=1), t[1].copy()] + c.append(n) + return (c, ) + +class unCLIPConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_adm" + + CATEGORY = "conditioning" + + def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): + if strength == 0: + return (conditioning, ) + + c = [] + for t in conditioning: + o = t[1].copy() + x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} + if "unclip_conditioning" in o: + o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] + else: + o["unclip_conditioning"] = [x] + n = [t[0], o] + c.append(n) + return (c, ) + +class GLIGENLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}} + + RETURN_TYPES = ("GLIGEN",) + FUNCTION = "load_gligen" + + CATEGORY = "loaders" + + def load_gligen(self, gligen_name): + gligen_path = folder_paths.get_full_path("gligen", gligen_name) + gligen = comfy.sd.load_gligen(gligen_path) + return (gligen,) + +class GLIGENTextBoxApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), + "clip": ("CLIP", ), + "gligen_textbox_model": ("GLIGEN", ), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning/gligen" + + def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): + c = [] + cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected") + for t in conditioning_to: + n = [t[0], t[1].copy()] + position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] + prev = [] + if "gligen" in n[1]: + prev = n[1]['gligen'][2] + + n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) + c.append(n) + return (c, ) + +class EmptyLatentImage: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + 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], device=self.device) + return ({"samples":latent}, ) + + +class LatentFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), + "length": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "frombatch" + + CATEGORY = "latent/batch" + + def frombatch(self, samples, batch_index, length): + s = samples.copy() + s_in = samples["samples"] + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s["samples"] = s_in[batch_index:batch_index + length].clone() + if "noise_mask" in samples: + masks = samples["noise_mask"] + if masks.shape[0] == 1: + s["noise_mask"] = masks.clone() + else: + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = masks[batch_index:batch_index + length].clone() + if "batch_index" not in s: + s["batch_index"] = [x for x in range(batch_index, batch_index+length)] + else: + s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] + return (s,) + +class RepeatLatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "amount": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "repeat" + + CATEGORY = "latent/batch" + + def repeat(self, samples, amount): + s = samples.copy() + s_in = samples["samples"] + + s["samples"] = s_in.repeat((amount, 1,1,1)) + if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: + masks = samples["noise_mask"] + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) + if "batch_index" in s: + offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 + s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] + return (s,) + +class LatentUpscale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = samples + else: + s = samples.copy() + + if width == 0: + height = max(64, height) + width = max(64, round(samples["samples"].shape[3] * height / samples["samples"].shape[2])) + elif height == 0: + width = max(64, width) + height = max(64, round(samples["samples"].shape[2] * width / samples["samples"].shape[3])) + else: + width = max(64, width) + height = max(64, height) + + s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) + return (s,) + +class LatentUpscaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, scale_by): + s = samples.copy() + width = round(samples["samples"].shape[3] * scale_by) + height = round(samples["samples"].shape[2] * scale_by) + s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") + 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": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "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 LatentBlend: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples1": ("LATENT",), + "samples2": ("LATENT",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0, + "max": 1, + "step": 0.01 + }), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "blend" + + CATEGORY = "_for_testing" + + def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): + + samples_out = samples1.copy() + samples1 = samples1["samples"] + samples2 = samples2["samples"] + + if samples1.shape != samples2.shape: + samples2.permute(0, 3, 1, 2) + samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') + samples2.permute(0, 2, 3, 1) + + samples_blended = self.blend_mode(samples1, samples2, blend_mode) + samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) + samples_out["samples"] = samples_blended + return (samples_out,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + +class LatentCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "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 + 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.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + 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"] + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + callback = latent_preview.prepare_callback(model, steps) + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + 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, "step":0.1, "round": 0.01}), + "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, "step":0.1, "round": 0.01}), + "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 = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @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): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + for (batch_number, image) in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not args.disable_metadata: + 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])) + + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "images": results } } + +class PreviewImage(SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + +class LoadImage: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True})}, + } + + CATEGORY = "image" + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "load_image" + def load_image(self, image): + image_path = folder_paths.get_annotated_filepath(image) + + img = node_helpers.pillow(Image.open, image_path) + + output_images = [] + output_masks = [] + w, h = None, None + + excluded_formats = ['MPO'] + + for i in ImageSequence.Iterator(img): + i = node_helpers.pillow(ImageOps.exif_transpose, i) + + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + image = i.convert("RGB") + + if len(output_images) == 0: + w = image.size[0] + h = image.size[1] + + if image.size[0] != w or image.size[1] != h: + continue + + 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") + output_images.append(image) + output_masks.append(mask.unsqueeze(0)) + + if len(output_images) > 1 and img.format not in excluded_formats: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) + else: + output_image = output_images[0] + output_mask = output_masks[0] + + return (output_image, output_mask) + + @classmethod + def IS_CHANGED(s, image): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class LoadImageMask: + _color_channels = ["alpha", "red", "green", "blue"] + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True}), + "channel": (s._color_channels, ), } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "load_image" + def load_image(self, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + i = node_helpers.pillow(Image.open, image_path) + i = node_helpers.pillow(ImageOps.exif_transpose, i) + if i.getbands() != ("R", "G", "B", "A"): + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + i = i.convert("RGBA") + 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.unsqueeze(0),) + + @classmethod + def IS_CHANGED(s, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class ImageScale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = image + else: + samples = image.movedim(-1,1) + + if width == 0: + width = max(1, round(samples.shape[3] * height / samples.shape[2])) + elif height == 0: + height = max(1, round(samples.shape[2] * width / samples.shape[3])) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop) + s = s.movedim(1,-1) + return (s,) + +class ImageScaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, scale_by): + samples = image.movedim(-1,1) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + 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,) + +class ImageBatch: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "batch" + + CATEGORY = "image" + + def batch(self, image1, image2): + if image1.shape[1:] != image2.shape[1:]: + image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) + s = torch.cat((image1, image2), dim=0) + return (s,) + +class EmptyImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate" + + CATEGORY = "image" + + def generate(self, width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return (torch.cat((r, g, b), dim=-1), ) + +class ImagePadForOutpaint: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, image, left, top, right, bottom, feathering): + d1, d2, d3, d4 = image.size() + + new_image = torch.ones( + (d1, d2 + top + bottom, d3 + left + right, d4), + dtype=torch.float32, + ) * 0.5 + + new_image[:, top:top + d2, left:left + d3, :] = image + + mask = torch.ones( + (d2 + top + bottom, d3 + left + right), + dtype=torch.float32, + ) + + t = torch.zeros( + (d2, d3), + dtype=torch.float32 + ) + + if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: + + for i in range(d2): + for j in range(d3): + dt = i if top != 0 else d2 + db = d2 - i if bottom != 0 else d2 + + dl = j if left != 0 else d3 + dr = d3 - j if right != 0 else d3 + + d = min(dt, db, dl, dr) + + if d >= feathering: + continue + + v = (feathering - d) / feathering + + t[i, j] = v * v + + mask[top:top + d2, left:left + d3] = t + + return (new_image, mask) + + +NODE_CLASS_MAPPINGS = { + "KSampler": KSampler, + "CheckpointLoaderSimple": CheckpointLoaderSimple, + "CLIPTextEncode": CLIPTextEncode, + "CLIPSetLastLayer": CLIPSetLastLayer, + "VAEDecode": VAEDecode, + "VAEEncode": VAEEncode, + "VAEEncodeForInpaint": VAEEncodeForInpaint, + "VAELoader": VAELoader, + "EmptyLatentImage": EmptyLatentImage, + "LatentUpscale": LatentUpscale, + "LatentUpscaleBy": LatentUpscaleBy, + "LatentFromBatch": LatentFromBatch, + "RepeatLatentBatch": RepeatLatentBatch, + "SaveImage": SaveImage, + "PreviewImage": PreviewImage, + "LoadImage": LoadImage, + "LoadImageMask": LoadImageMask, + "ImageScale": ImageScale, + "ImageScaleBy": ImageScaleBy, + "ImageInvert": ImageInvert, + "ImageBatch": ImageBatch, + "ImagePadForOutpaint": ImagePadForOutpaint, + "EmptyImage": EmptyImage, + "ConditioningAverage": ConditioningAverage , + "ConditioningCombine": ConditioningCombine, + "ConditioningConcat": ConditioningConcat, + "ConditioningSetArea": ConditioningSetArea, + "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, + "ConditioningSetAreaStrength": ConditioningSetAreaStrength, + "ConditioningSetMask": ConditioningSetMask, + "KSamplerAdvanced": KSamplerAdvanced, + "SetLatentNoiseMask": SetLatentNoiseMask, + "LatentComposite": LatentComposite, + "LatentBlend": LatentBlend, + "LatentRotate": LatentRotate, + "LatentFlip": LatentFlip, + "LatentCrop": LatentCrop, + "LoraLoader": LoraLoader, + "CLIPLoader": CLIPLoader, + "UNETLoader": UNETLoader, + "DualCLIPLoader": DualCLIPLoader, + "CLIPVisionEncode": CLIPVisionEncode, + "StyleModelApply": StyleModelApply, + "unCLIPConditioning": unCLIPConditioning, + "ControlNetApply": ControlNetApply, + "ControlNetApplyAdvanced": ControlNetApplyAdvanced, + "ControlNetLoader": ControlNetLoader, + "DiffControlNetLoader": DiffControlNetLoader, + "StyleModelLoader": StyleModelLoader, + "CLIPVisionLoader": CLIPVisionLoader, + "VAEDecodeTiled": VAEDecodeTiled, + "VAEEncodeTiled": VAEEncodeTiled, + "unCLIPCheckpointLoader": unCLIPCheckpointLoader, + "GLIGENLoader": GLIGENLoader, + "GLIGENTextBoxApply": GLIGENTextBoxApply, + "InpaintModelConditioning": InpaintModelConditioning, + + "CheckpointLoader": CheckpointLoader, + "DiffusersLoader": DiffusersLoader, + + "LoadLatent": LoadLatent, + "SaveLatent": SaveLatent, + + "ConditioningZeroOut": ConditioningZeroOut, + "ConditioningSetTimestepRange": ConditioningSetTimestepRange, + "LoraLoaderModelOnly": LoraLoaderModelOnly, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "KSampler": "KSampler", + "KSamplerAdvanced": "KSampler (Advanced)", + # Loaders + "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", + "CheckpointLoaderSimple": "Load Checkpoint", + "VAELoader": "Load VAE", + "LoraLoader": "Load LoRA", + "CLIPLoader": "Load CLIP", + "ControlNetLoader": "Load ControlNet Model", + "DiffControlNetLoader": "Load ControlNet Model (diff)", + "StyleModelLoader": "Load Style Model", + "CLIPVisionLoader": "Load CLIP Vision", + "UpscaleModelLoader": "Load Upscale Model", + # Conditioning + "CLIPVisionEncode": "CLIP Vision Encode", + "StyleModelApply": "Apply Style Model", + "CLIPTextEncode": "CLIP Text Encode (Prompt)", + "CLIPSetLastLayer": "CLIP Set Last Layer", + "ConditioningCombine": "Conditioning (Combine)", + "ConditioningAverage ": "Conditioning (Average)", + "ConditioningConcat": "Conditioning (Concat)", + "ConditioningSetArea": "Conditioning (Set Area)", + "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", + "ConditioningSetMask": "Conditioning (Set Mask)", + "ControlNetApply": "Apply ControlNet", + "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)", + # Latent + "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", + "SetLatentNoiseMask": "Set Latent Noise Mask", + "VAEDecode": "VAE Decode", + "VAEEncode": "VAE Encode", + "LatentRotate": "Rotate Latent", + "LatentFlip": "Flip Latent", + "LatentCrop": "Crop Latent", + "EmptyLatentImage": "Empty Latent Image", + "LatentUpscale": "Upscale Latent", + "LatentUpscaleBy": "Upscale Latent By", + "LatentComposite": "Latent Composite", + "LatentBlend": "Latent Blend", + "LatentFromBatch" : "Latent From Batch", + "RepeatLatentBatch": "Repeat Latent Batch", + # Image + "SaveImage": "Save Image", + "PreviewImage": "Preview Image", + "LoadImage": "Load Image", + "LoadImageMask": "Load Image (as Mask)", + "ImageScale": "Upscale Image", + "ImageScaleBy": "Upscale Image By", + "ImageUpscaleWithModel": "Upscale Image (using Model)", + "ImageInvert": "Invert Image", + "ImagePadForOutpaint": "Pad Image for Outpainting", + "ImageBatch": "Batch Images", + # _for_testing + "VAEDecodeTiled": "VAE Decode (Tiled)", + "VAEEncodeTiled": "VAE Encode (Tiled)", +} + +EXTENSION_WEB_DIRS = {} + +def load_custom_node(module_path, ignore=set()): + module_name = os.path.basename(module_path) + if os.path.isfile(module_path): + sp = os.path.splitext(module_path) + module_name = sp[0] + try: + logging.debug("Trying to load custom node {}".format(module_path)) + if os.path.isfile(module_path): + module_spec = importlib.util.spec_from_file_location(module_name, module_path) + module_dir = os.path.split(module_path)[0] + else: + module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) + module_dir = module_path + + module = importlib.util.module_from_spec(module_spec) + sys.modules[module_name] = module + module_spec.loader.exec_module(module) + + if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: + web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) + if os.path.isdir(web_dir): + EXTENSION_WEB_DIRS[module_name] = web_dir + + if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: + for name in module.NODE_CLASS_MAPPINGS: + if name not in ignore: + NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name] + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: + NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) + return True + else: + logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") + return False + except Exception as e: + logging.warning(traceback.format_exc()) + logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") + return False + +def load_custom_nodes(): + base_node_names = set(NODE_CLASS_MAPPINGS.keys()) + node_paths = folder_paths.get_folder_paths("custom_nodes") + node_import_times = [] + for custom_node_path in node_paths: + possible_modules = os.listdir(os.path.realpath(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 + if module_path.endswith(".disabled"): continue + time_before = time.perf_counter() + success = load_custom_node(module_path, base_node_names) + node_import_times.append((time.perf_counter() - time_before, module_path, success)) + + if len(node_import_times) > 0: + logging.info("\nImport times for custom nodes:") + for n in sorted(node_import_times): + if n[2]: + import_message = "" + else: + import_message = " (IMPORT FAILED)" + logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) + logging.info("") + +def init_custom_nodes(): + extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") + extras_files = [ + "nodes_latent.py", + "nodes_hypernetwork.py", + "nodes_upscale_model.py", + "nodes_post_processing.py", + "nodes_mask.py", + "nodes_compositing.py", + "nodes_rebatch.py", + "nodes_model_merging.py", + "nodes_tomesd.py", + "nodes_clip_sdxl.py", + "nodes_canny.py", + "nodes_freelunch.py", + "nodes_custom_sampler.py", + "nodes_hypertile.py", + "nodes_model_advanced.py", + "nodes_model_downscale.py", + "nodes_images.py", + "nodes_video_model.py", + "nodes_sag.py", + "nodes_perpneg.py", + "nodes_stable3d.py", + "nodes_sdupscale.py", + "nodes_photomaker.py", + "nodes_cond.py", + "nodes_morphology.py", + "nodes_stable_cascade.py", + "nodes_differential_diffusion.py", + "nodes_ip2p.py", + "nodes_model_merging_model_specific.py", + "nodes_pag.py", + "nodes_align_your_steps.py", + "nodes_attention_multiply.py", + "nodes_advanced_samplers.py", + "nodes_webcam.py", + ] + + import_failed = [] + for node_file in extras_files: + if not load_custom_node(os.path.join(extras_dir, node_file)): + import_failed.append(node_file) + + load_custom_nodes() + + if len(import_failed) > 0: + logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + for node in import_failed: + logging.warning("IMPORT FAILED: {}".format(node)) + logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") + if args.windows_standalone_build: + logging.warning("Please run the update script: update/update_comfyui.bat") + else: + logging.warning("Please do a: pip install -r requirements.txt") + logging.warning("")