From 9852178282b09e3e80ff6a648bb44c2ed025f8e4 Mon Sep 17 00:00:00 2001 From: smthemex <138738845+smthemex@users.noreply.github.com> Date: Thu, 27 Feb 2025 19:17:18 +0800 Subject: [PATCH] init --- LICENSE | 2 +- PhotoDoodle_node.py | 191 +++++++++++++++++++++++++++++++++++++++ README.md | 134 ++++++++++++++++++++++++++-- __init__.py | 4 + inference.py | 69 ++++++++++++++ merge.py | 13 +++ node_utils.py | 213 ++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 15 ++++ requirements.txt | 29 ++++++ 9 files changed, 660 insertions(+), 10 deletions(-) create mode 100644 PhotoDoodle_node.py create mode 100644 __init__.py create mode 100644 inference.py create mode 100644 merge.py create mode 100644 node_utils.py create mode 100644 pyproject.toml create mode 100644 requirements.txt diff --git a/LICENSE b/LICENSE index a68732e..dd76e79 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2025 smthemex +Copyright (c) 2025 Show Lab Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/PhotoDoodle_node.py b/PhotoDoodle_node.py new file mode 100644 index 0000000..5be8285 --- /dev/null +++ b/PhotoDoodle_node.py @@ -0,0 +1,191 @@ +# !/usr/bin/env python +# -*- coding: UTF-8 -*- +import os +import torch +import numpy as np +from diffusers import AutoencoderKL,FluxTransformer2DModel + +from .node_utils import tensor2pil_list, load_images,cleanup +from .src.pipeline_pe_clone import FluxPipeline +from .src.pipeline_pe_clone_orgin import FluxPipeline as FluxPipeline_orgin + +import folder_paths +from comfy.utils import ProgressBar +MAX_SEED = np.iinfo(np.int32).max +current_node_path = os.path.dirname(os.path.abspath(__file__)) + +device = torch.device( + "cuda:0") if torch.cuda.is_available() else torch.device( + "mps") if torch.backends.mps.is_available() else torch.device( + "cpu") + + +class PhotoDoodle_Loader: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "flux_unet": (["none"] + folder_paths.get_filename_list("diffusion_models"),), + "vae": (["none"] + folder_paths.get_filename_list("vae"),), + "pre_lora": (["none"] + [i for i in folder_paths.get_filename_list("loras") if "pre" in i],), + "loras": (["none"] + folder_paths.get_filename_list("loras"),), + "flux_repo":("STRING", {"default": "", "multiline": False}), + "use_mmgp":("BOOLEAN",{"default":False}), + "profile_number":([0,1,2,3,4,5],) + }, + } + + RETURN_TYPES = ("MODEL_PhotoDoodle",) + RETURN_NAMES = ("model",) + FUNCTION = "loader_main" + CATEGORY = "PhotoDoodle" + + + def loader_main(self,flux_unet,vae,pre_lora,loras,flux_repo,use_mmgp,profile_number): + + flux_repo_local=os.path.join(current_node_path, 'src/FLUX.1-dev') + print("***********Load model ***********") + #load model + + if pre_lora != "none": + pre_lora_path = folder_paths.get_full_path("loras", pre_lora) + else: + raise ValueError("No model selected") + + if flux_unet != "none": + flux_transformer_path = folder_paths.get_full_path("diffusion_models", flux_unet) + else: + raise ValueError("No model selected") + + if loras != "none": + lora_path = folder_paths.get_full_path("loras", loras) + else: + raise ValueError("No model selected") + need_clip=False + if not flux_repo: + if vae != "none": + vae_path = folder_paths.get_full_path("vae", vae) + vae_config=os.path.join(flux_repo_local, 'vae') + ae = AutoencoderKL.from_single_file(vae_path,config=vae_config, torch_dtype=torch.bfloat16) + transformer = FluxTransformer2DModel.from_single_file( + flux_transformer_path, + config=os.path.join(flux_repo_local, "transformer"), + torch_dtype=torch.bfloat16, + ) + pipeline = FluxPipeline.from_pretrained( + flux_repo_local, + vae=ae, + transformer=transformer, + torch_dtype=torch.bfloat16, + ) + need_clip=True + else: + pipeline = FluxPipeline_orgin.from_single_file( + flux_transformer_path, + config=flux_repo_local, + torch_dtype=torch.bfloat16, + ) + + else: + # flux_repo='F:/test/ComfyUI/models/diffusers/black-forest-labs/FLUX.1-dev' + pipeline =FluxPipeline_orgin.from_pretrained(flux_repo,torch_dtype=torch.bfloat16,) + + # Load and fuse base LoRA weights + pipeline.load_lora_weights(lora_path) + pipeline.fuse_lora() + pipeline.unload_lora_weights() + pipeline.load_lora_weights(pre_lora_path) + pipeline.enable_model_cpu_offload() + + print("***********Load model done ***********") + + cleanup() + + if use_mmgp: + from mmgp import offload as offloadobj + pipe = {"transformer": pipeline, } + # offloadobj.profile(pipe, quantizeTransformer = False, profile_no = 1 ) # uncomment this line and comment the previous one if you have 24 GB of VRAM and wants faster generation + offloadobj.profile(pipe, quantizeTransformer = False, extraModelsToQuantize = [], profile_no = profile_number, ) + return ({"pipeline":pipeline,"need_clip":need_clip},) + + + +class PhotoDoodle_Sampler: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL_PhotoDoodle",), + "images": ("IMAGE",), + "prompt": ("STRING", {"default": "add a halo and wings for the cat by sksmagiceffects", "multiline": True}), + "width": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 64, "display": "number"}), + "height": ("INT", {"default": 768, "min": 256, "max": 4096, "step": 64, "display": "number"}), + "steps": ("INT", {"default": 20, "min": 1, "max": 1024, "step": 1, "display": "number"}), + "guidance_scale": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 10.0, "step": 0.1}), + "max_sequence_length": ("INT", {"default": 512, "min": 128, "max": 512, "step": 1, "display": "number"}), + }, + "optional": { "clip":("CLIP",),}, + } + + RETURN_TYPES = ("IMAGE", ) + RETURN_NAMES = ("image",) + FUNCTION = "sampler_main" + CATEGORY = "PhotoDoodle" + + def sampler_main(self, model,images,prompt, width, height,steps,guidance_scale, max_sequence_length,**kwargs): + + need_clip=model.get("need_clip") + pipeline=model.get("pipeline") + clip=kwargs.get("clip") + # load model + #model.to(device) + if need_clip: + if clip is None: + raise ValueError("No clip selected") + prompt_embeds,pooled_prompt_embeds=clip.encode_from_tokens( clip.tokenize(prompt,return_word_ids=True),return_pooled=True) + prompt_embeds=prompt_embeds.to(device,torch.bfloat16) + pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.bfloat16) + prompt=None + else: + prompt_embeds,pooled_prompt_embeds=None,None + + cleanup() + condition_image_list = tensor2pil_list(images, width, height) + total_images=len(condition_image_list) + pbar = ProgressBar(total_images) + img_list=[] + + for i, condition_image in enumerate(condition_image_list): + result = pipeline( + prompt=prompt, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + condition_image=condition_image, + height=height, + width=width, + guidance_scale=guidance_scale, + num_inference_steps=steps, + max_sequence_length=max_sequence_length, + ).images[0] + pbar.update_absolute(i, total_images) + img_list.append(result) + + cleanup() + return (load_images(img_list),) + + +NODE_CLASS_MAPPINGS = { + "PhotoDoodle_Loader": PhotoDoodle_Loader, + "PhotoDoodle_Sampler": PhotoDoodle_Sampler, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PhotoDoodle_Loader": "PhotoDoodle_Loader", + "PhotoDoodle_Sampler": "PhotoDoodle_Sampler", +} diff --git a/README.md b/README.md index af5f4cd..8247024 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,130 @@ -# ComfyUI_PhotoDoodle -[PhotoDoodle](https://github.com/showlab/PhotoDoodle): Learning Artistic Image Editing from Few-Shot Pairwise Data，you can use it in comfyUI +# PhotoDoodle -# Tips -* Need Vram>=12G(origin repositories need 40G Vram),coming soon... -* 12G显存起步，8G未测试，毕竟官方要求40G起步，实在不好优化，晚上上线，白天忙。 +> **PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data** +>
+> [Huang Shijie](https://scholar.google.com/citations?user=HmqYYosAAAAJ), +> [Yiren Song](https://scholar.google.com.hk/citations?user=L2YS0jgAAAAJ), +> [Yuxuan Zhang](https://xiaojiu-z.github.io/YuxuanZhang.github.io/), +> [Hailong Guo](https://github.com/logn-2024), +> Xueyin Wang, +> and +> [Mike Zheng Shou](https://sites.google.com/view/showlab), +> [Liu Jiaming](https://scholar.google.com/citations?user=SmL7oMQAAAAJ&hl=en) +>
+> [Show Lab](https://sites.google.com/view/showlab), National University of Singapore +>
-# Example -![](https://github.com/smthemex/ComfyUI_PhotoDoodle/blob/main/example.png) +arXiv +HuggingFace +HuggingFace -# Citation +
+ + + + +## Quick Start +### Configuration +#### 1. **Environment setup** +```bash +git clone git@github.com:showlab/PhotoDoodle.git +cd PhotoDoodle + +conda create -n doodle python=3.11.10 +conda activate doodle +``` +#### 2. **Requirements installation** +```bash +pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124 +pip install --upgrade -r requirements.txt +``` + + +### 2. Inference +We provided the intergration of diffusers pipeline with our model and uploaded the model weights to huggingface, it's easy to use the our model as example below: + +```bash +from src.pipeline_pe_clone import FluxPipeline +import torch +from PIL import Image + +pretrained_model_name_or_path = "black-forest-labs/FLUX.1-dev" +pipeline = FluxPipeline.from_pretrained( + pretrained_model_name_or_path, + torch_dtype=torch.bfloat16, +).to('cuda') + +pipeline.load_lora_weights("nicolaus-huang/PhotoDoodle", weight_name="pretrain.safetensors") +pipeline.fuse_lora() +pipeline.unload_lora_weights() + +pipeline.load_lora_weights("nicolaus-huang/PhotoDoodle", weight_name="sksmagiceffects.safetensors") + +height=768 +width=512 + +validation_image = "assets/1.png" +validation_prompt = "add a halo and wings for the cat by sksmagiceffects" +condition_image = Image.open(validation_image).resize((height, width)).convert("RGB") + +result = pipeline(prompt=validation_prompt, + condition_image=condition_image, + height=height, + width=width, + guidance_scale=3.5, + num_inference_steps=20, + max_sequence_length=512).images[0] + +result.save("output.png") +``` + +or simply run the inference script: +``` +python inference.py +``` + + + +### 3. Weights +You can download the trained checkpoints of PhotoDoodle for inference. Below are the details of available models, checkpoint name are also trigger words. + +You would need to load and fuse the `pretrained ` checkpoints model in order to load the other models. + +| **Model** | **Description** | **Resolution** | +| :----------------------------------------------------------: | :---------------------------------------------------------: | :------------: | +| [pretrained](https://huggingface.co/nicolaus-huang/PhotoDoodle/blob/main/pretrain.safetensors) | PhotoDoodle model trained on `SeedEdit` dataset | 768, 768 | +| [sksmonstercalledlulu](https://huggingface.co/nicolaus-huang/PhotoDoodle/blob/main/sksmonstercalledlulu.safetensors) | PhotoDoodle model trained on `Cartoon monster` dataset | 768, 512 | +| [sksmagiceffects](https://huggingface.co/nicolaus-huang/PhotoDoodle/blob/main/sksmagiceffects.safetensors) | PhotoDoodle model trained on `3D effects` dataset | 768, 512 | +| [skspaintingeffects ](https://huggingface.co/nicolaus-huang/PhotoDoodle/blob/main/skspaintingeffects.safetensors) | PhotoDoodle model trained on `Flowing color blocks` dataset | 768, 512 | +| [sksedgeeffect ](https://huggingface.co/nicolaus-huang/PhotoDoodle/blob/main/sksedgeeffect.safetensors) | PhotoDoodle model trained on `Hand-drawn outline` dataset | 768, 512 | + + +### 4. Dataset + +#### 2.1 Settings for dataset +The training process uses a paired dataset stored in a .jsonl file, where each entry contains image file paths and corresponding text descriptions. Each entry includes the source image path, the target (modified) image path, and a caption describing the modification. + +Example format: + +```json +{"source": "path/to/source.jpg", "target": "path/to/modified.jpg", "caption": "Instruction of modifications"} +{"source": "path/to/source2.jpg", "target": "path/to/modified2.jpg", "caption": "Another instruction"} +``` + +We have uploaded our datasets to [Hugging Face](https://huggingface.co/datasets/nicolaus-huang/PhotoDoodle). + + +### 5. Results + +![R-F](./assets/R-F.jpg) + + +### 6. Acknowledgments + +1. Thanks to **[Yuxuan Zhang](https://xiaojiu-z.github.io/YuxuanZhang.github.io/)** and **[Hailong Guo](mailto:guohailong@bupt.edu.cn)** for providing the code base. +2. Thanks to **[Diffusers](https://github.com/huggingface/diffusers)** for the open-source project. + +## Citation ``` @misc{huang2025photodoodlelearningartisticimage, title={PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data}, @@ -19,4 +135,4 @@ primaryClass={cs.CV}, url={https://arxiv.org/abs/2502.14397}, } -`` +``` diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..2264f1e --- /dev/null +++ b/__init__.py @@ -0,0 +1,4 @@ + +from .PhotoDoodle_node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/inference.py b/inference.py new file mode 100644 index 0000000..95313d6 --- /dev/null +++ b/inference.py @@ -0,0 +1,69 @@ +import argparse +from .src.pipeline_pe_clone import FluxPipeline +import torch +from PIL import Image + +def parse_args(): + parser = argparse.ArgumentParser(description='FLUX image generation with LoRA') + parser.add_argument('--model_path', type=str, + default="black-forest-labs/FLUX.1-dev", + help='Path to pretrained model') + parser.add_argument('--image_path', type=str, + default="assets/1.png", + help='Input image path') + parser.add_argument('--output_path', type=str, + default="output.png", + help='Output image path') + parser.add_argument('--height', type=int, default=768) + parser.add_argument('--width', type=int, default=512) + parser.add_argument('--prompt', type=str, + default="add a halo and wings for the cat by sksmagiceffects", + help="""Different LoRA effects and their example prompts: + - sksmagiceffects: "add a halo and wings for the cat by sksmagiceffects" + - sksmonstercalledlulu: "add a red sksmonstercalledlulu hugging the cat" + - skspaintingeffects: "add a yellow flower on the cat's head and psychedelic colors and dynamic flows by skspaintingeffects" + - sksedgeeffect: "add yellow flames to the cat by sksedgeeffect" + """) + parser.add_argument('--guidance_scale', type=float, default=3.5) + parser.add_argument('--num_steps', type=int, default=20, + help='Number of inference steps') + parser.add_argument('--lora_name', type=str, + choices=['pretrained', 'sksmagiceffects', 'sksmonstercalledlulu', + 'skspaintingeffects', 'sksedgeeffect'], + default="sksmagiceffects", + help='Name of LoRA weights to use. Use "pretrained" for base model only') + return parser.parse_args() + +def main(): + args = parse_args() + + pipeline = FluxPipeline.from_pretrained( + args.model_path, + torch_dtype=torch.bfloat16, + ).to('cuda') + + # Load and fuse base LoRA weights + pipeline.load_lora_weights("nicolaus-huang/PhotoDoodle", weight_name="pretrain.safetensors") + pipeline.fuse_lora() + pipeline.unload_lora_weights() + + # Load selected LoRA effect only if not using pretrained + if args.lora_name != 'pretrained': + pipeline.load_lora_weights("nicolaus-huang/PhotoDoodle", weight_name=f"{args.lora_name}.safetensors") + + condition_image = Image.open(args.image_path).resize((args.height, args.width)).convert("RGB") + + result = pipeline( + prompt=args.prompt, + condition_image=condition_image, + height=args.height, + width=args.width, + guidance_scale=args.guidance_scale, + num_inference_steps=args.num_steps, + max_sequence_length=512 + ).images[0] + + result.save(args.output_path) + +if __name__ == "__main__": + main() diff --git a/merge.py b/merge.py new file mode 100644 index 0000000..e875d78 --- /dev/null +++ b/merge.py @@ -0,0 +1,13 @@ + +from .src.pipeline_pe_clone import FluxPipeline +import torch +from PIL import Image +pretrained_model_name_or_path = "black-forest-labs/FLUX.1-dev" +pipeline = FluxPipeline.from_pretrained( + pretrained_model_name_or_path, + torch_dtype=torch.bfloat16, +) +pipeline.load_lora_weights("outputs/doodle_pretrain_4508000/pytorch_lora_weights.safetensors") +pipeline.fuse_lora() +pipeline.unload_lora_weights() +pipeline.save_pretrained("edit_pretrain") \ No newline at end of file diff --git a/node_utils.py b/node_utils.py new file mode 100644 index 0000000..78a80af --- /dev/null +++ b/node_utils.py @@ -0,0 +1,213 @@ +# !/usr/bin/env python +# -*- coding: UTF-8 -*- +import os +import torch +from PIL import Image +import numpy as np +import cv2 +import gc + +from comfy.utils import common_upscale,ProgressBar +from huggingface_hub import hf_hub_download + +cur_path = os.path.dirname(os.path.abspath(__file__)) +device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" + + +def cleanup(): + gc.collect() + torch.cuda.empty_cache() + +def cv2pil(cv_image): + """ + 将OpenCV图像转换为PIL图像 + :param cv_image: OpenCV图像 + :return: PIL图像 + """ + # 将图像从BGR转换为RGB + rgb_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB) + # 使用PIL的Image.fromarray方法将NumPy数组转换为PIL图像 + pil_image = Image.fromarray(rgb_image) + return pil_image + + +def tensor_to_pil(tensor): + image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy() + image = Image.fromarray(image_np, mode='RGB') + return image + +def tensor2pil_list(image,width,height): + B,_,_,_=image.size() + if B==1: + ref_image_list=[tensor2pil_upscale(image,width,height)] + else: + img_list = list(torch.chunk(image, chunks=B)) + ref_image_list = [tensor2pil_upscale(img,width,height) for img in img_list] + return ref_image_list + + +def tensor_upscale(img_tensor, width, height): + samples = img_tensor.movedim(-1, 1) + img = common_upscale(samples, width, height, "nearest-exact", "center") + samples = img.movedim(1, -1) + return samples + +def tensor2pil_upscale(img_tensor, width, height): + samples = img_tensor.movedim(-1, 1) + img = common_upscale(samples, width, height, "nearest-exact", "center") + samples = img.movedim(1, -1) + img_pil = tensor_to_pil(samples) + return img_pil + + +def tensor2cv(tensor_image,RGB2BGR=True): + if len(tensor_image.shape)==4:#bhwc to hwc + tensor_image=tensor_image.squeeze(0) + if tensor_image.is_cuda: + tensor_image = tensor_image.cpu().detach() + tensor_image=tensor_image.numpy() + #反归一化 + maxValue=tensor_image.max() + tensor_image=tensor_image*255/maxValue + img_cv2=np.uint8(tensor_image)#32 to uint8 + if RGB2BGR: + img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR) + return img_cv2 + +def cvargb2tensor(img): + assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img)) + img = torch.from_numpy(img.transpose((2, 0, 1))) + return img.float().div(255).unsqueeze(0) # 255也可以改为256 + +def cv2tensor(img): + assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img)) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = torch.from_numpy(img.transpose((2, 0, 1))) + return img.float().div(255).unsqueeze(0) # 255也可以改为256 + +def images_generator(img_list: list,): + #get img size + sizes = {} + for image_ in img_list: + if isinstance(image_,Image.Image): + count = sizes.get(image_.size, 0) + sizes[image_.size] = count + 1 + elif isinstance(image_,np.ndarray): + count = sizes.get(image_.shape[:2][::-1], 0) + sizes[image_.shape[:2][::-1]] = count + 1 + else: + raise "unsupport image list,must be pil or cv2!!!" + size = max(sizes.items(), key=lambda x: x[1])[0] + yield size[0], size[1] + + # any to tensor + def load_image(img_in): + if isinstance(img_in, Image.Image): + img_in=img_in.convert("RGB") + i = np.array(img_in, dtype=np.float32) + i = torch.from_numpy(i).div_(255) + if i.shape[0] != size[1] or i.shape[1] != size[0]: + i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0) + i = common_upscale(i, size[0], size[1], "lanczos", "center") + i = i.squeeze(0).movedim(0, -1).numpy() + return i + elif isinstance(img_in,np.ndarray): + i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32) + i = torch.from_numpy(i).div_(255) + #print(i.shape) + return i + else: + raise "unsupport image list,must be pil,cv2 or tensor!!!" + + total_images = len(img_list) + processed_images = 0 + pbar = ProgressBar(total_images) + images = map(load_image, img_list) + try: + prev_image = next(images) + while True: + next_image = next(images) + yield prev_image + processed_images += 1 + pbar.update_absolute(processed_images, total_images) + prev_image = next_image + except StopIteration: + pass + if prev_image is not None: + yield prev_image + +def load_images(img_list: list,): + gen = images_generator(img_list) + (width, height) = next(gen) + images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))) + if len(images) == 0: + raise FileNotFoundError(f"No images could be loaded .") + return images + +def tensor2pil(tensor): + image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy() + image = Image.fromarray(image_np, mode='RGB') + return image + +def pil2narry(img): + narry = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0) + return narry + +def equalize_lists(list1, list2): + """ + 比较两个列表的长度，如果不一致，则将较短的列表复制以匹配较长列表的长度。 + + 参数: + list1 (list): 第一个列表 + list2 (list): 第二个列表 + + 返回: + tuple: 包含两个长度相等的列表的元组 + """ + len1 = len(list1) + len2 = len(list2) + + if len1 == len2: + pass + elif len1 < len2: + print("list1 is shorter than list2, copying list1 to match list2's length.") + list1.extend(list1 * ((len2 // len1) + 1)) # 复制list1以匹配list2的长度 + list1 = list1[:len2] # 确保长度一致 + else: + print("list2 is shorter than list1, copying list2 to match list1's length.") + list2.extend(list2 * ((len1 // len2) + 1)) # 复制list2以匹配list1的长度 + list2 = list2[:len1] # 确保长度一致 + + return list1, list2 + +def file_exists(directory, filename): + # 构建文件的完整路径 + file_path = os.path.join(directory, filename) + # 检查文件是否存在 + return os.path.isfile(file_path) + +def download_weights(file_dir,repo_id,subfolder="",pt_name=""): + if subfolder: + file_path = os.path.join(file_dir,subfolder, pt_name) + sub_dir=os.path.join(file_dir,subfolder) + if not os.path.exists(sub_dir): + os.makedirs(sub_dir) + if not os.path.exists(file_path): + file_path = hf_hub_download( + repo_id=repo_id, + subfolder=subfolder, + filename=pt_name, + local_dir = file_dir, + ) + return file_path + else: + file_path = os.path.join(file_dir, pt_name) + if not os.path.exists(file_dir): + os.makedirs(file_dir) + if not os.path.exists(file_path): + file_path = hf_hub_download( + repo_id=repo_id, + filename=pt_name, + local_dir=file_dir, + ) + return file_path diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..e1df07c --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui_photodoodel" +description = "PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data，you can use it in comfyUI" +version = "1.0.0" +license = {file = "LICENSE"} +dependencies = ["accelerate>=0.33.0", "transformers>=4.44.0", "diffusers>=0.31.0", "#diffusers[torch]==0.25.0", "#ftfy==6.1.1", "# albumentations==1.3.0", "#opencv-python==4.8.1.78", "#einops==0.7.0", "#pytorch-lightning==1.9.0", "bitsandbytes>=0.44.0", "#prodigyopt==1.0", "#lion-pytorch==0.0.6", "#came_pytorch==0.1.3", "#schedulefree==1.4", "#tensorboard", "#safetensors==0.4.4", "# for gradio", "#gradio==3.6", "#altair==4.2.2", "#easygui==0.98.3", "#toml==0.10.2", "#voluptuous==0.13.1", "#huggingface-hub==0.24.5", "# for Image utils", "#imagesize==1.4.1", "#numpy<=2.0", "#rich==13.7.0", "# for T5XXL tokenizer (SD3/FLUX)", "#sentencepiece==0.2.0"] + +[project.urls] +Repository = "https://github.com/smthemex/ComfyUI_PhotoDoodle" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "smthemex" +DisplayName = "ComfyUI_PhotoDoodle" +Icon = "" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4229050 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,29 @@ +accelerate>=0.33.0 +transformers>=4.44.0 +diffusers>=0.31.0 +#diffusers[torch]==0.25.0 +#ftfy==6.1.1 +# albumentations==1.3.0 +#opencv-python==4.8.1.78 +#einops==0.7.0 +#pytorch-lightning==1.9.0 +bitsandbytes>=0.44.0 +#prodigyopt==1.0 +#lion-pytorch==0.0.6 +#came_pytorch==0.1.3 +#schedulefree==1.4 +#tensorboard +#safetensors==0.4.4 +# for gradio +#gradio==3.6 +#altair==4.2.2 +#easygui==0.98.3 +#toml==0.10.2 +#voluptuous==0.13.1 +#huggingface-hub==0.24.5 +# for Image utils +#imagesize==1.4.1 +#numpy<=2.0 +#rich==13.7.0 +# for T5XXL tokenizer (SD3/FLUX) +#sentencepiece==0.2.0 \ No newline at end of file