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(sys.path[0], "comfy")) import comfy.samplers import comfy.sd import comfy.utils 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))) 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 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 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 if pixels.shape[1] != x or pixels.shape[2] != y: pixels = pixels[:,:x,:y,:] mask = mask[:x,:y] #shave off a few pixels to keep things seamless kernel_tensor = torch.ones((1, 1, 6, 6)) mask_erosion = torch.clamp(torch.nn.functional.conv2d((1.0 - mask.round())[None], kernel_tensor, padding=3), 0, 1) for i in range(3): pixels[:,:,:,i] -= 0.5 pixels[:,:,:,i] *= mask_erosion[0][:x,:y].round() pixels[:,:,:,i] += 0.5 t = vae.encode(pixels) return ({"samples":t, "noise_mask": mask}, ) 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 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 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()] n[1]['control'] = control_net.copy().set_cond_hint(control_hint, strength) c.append(n) return (c, ) 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), ), "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), }} RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" CATEGORY = "loaders" def load_clip(self, clip_name, stop_at_clip_layer): clip_path = os.path.join(self.clip_dir, clip_name) clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=CheckpointLoader.embedding_directory) clip.clip_layer(stop_at_clip_layer) return (clip,) 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" 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" 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" 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(device, 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 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 if device != "cpu": model_management.load_model_gpu(model) real_model = model.model else: #TODO: cpu support real_model = model.patch_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:]] model_management.load_controlnet_gpu(list(map(lambda a: a.control_model, control_nets))) 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: def __init__(self, device="cuda"): self.device = device @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "latent_image": ("LATENT", ), "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }} RETURN_TYPES = ("LATENT",) FUNCTION = "sample" CATEGORY = "sampling" def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): return common_ksampler(self.device, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) class KSamplerAdvanced: def __init__(self, device="cuda"): self.device = device @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(self.device, 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 for image in images: i = 255. * image.cpu().numpy() img = Image.fromarray(i.astype(np.uint8)) metadata = PngInfo() if prompt is not None: metadata.add_text("prompt", json.dumps(prompt)) if extra_pnginfo is not None: for x in extra_pnginfo: metadata.add_text(x, json.dumps(extra_pnginfo[x])) img.save(os.path.join(self.output_dir, f"{filename_prefix}_{counter:05}_.png"), pnginfo=metadata, optimize=True) counter += 1 class LoadImage: 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)), )}, } CATEGORY = "image" RETURN_TYPES = ("IMAGE",) 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,] return (image,) @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,) NODE_CLASS_MAPPINGS = { "KSampler": KSampler, "CheckpointLoader": CheckpointLoader, "CLIPTextEncode": CLIPTextEncode, "VAEDecode": VAEDecode, "VAEEncode": VAEEncode, "VAEEncodeForInpaint": VAEEncodeForInpaint, "VAELoader": VAELoader, "EmptyLatentImage": EmptyLatentImage, "LatentUpscale": LatentUpscale, "SaveImage": SaveImage, "LoadImage": LoadImage, "LoadImageMask": LoadImageMask, "ImageScale": ImageScale, "ConditioningCombine": ConditioningCombine, "ConditioningSetArea": ConditioningSetArea, "KSamplerAdvanced": KSamplerAdvanced, "SetLatentNoiseMask": SetLatentNoiseMask, "LatentComposite": LatentComposite, "LatentRotate": LatentRotate, "LatentFlip": LatentFlip, "LatentCrop": LatentCrop, "LoraLoader": LoraLoader, "CLIPLoader": CLIPLoader, "ControlNetApply": ControlNetApply, "ControlNetLoader": ControlNetLoader, } CUSTOM_NODE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "custom_nodes") def load_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 module_name = possible_module 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 {possible_module} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") except Exception as e: print(traceback.format_exc()) print(f"Cannot import {possible_module} module for custom nodes:", e) load_custom_nodes()