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LICENSE
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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
PhotoDoodle_node.py
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# !/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",
}
README.md
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# 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**
> <br>
> [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)
> <br>
> [Show Lab](https://sites.google.com/view/showlab), National University of Singapore
> <br>
# Example
![](https://github.com/smthemex/ComfyUI_PhotoDoodle/blob/main/example.png)
<a href="https://arxiv.org/abs/2502.14397"><img src="https://img.shields.io/badge/ariXv-2502.14397-A42C25.svg" alt="arXiv"></a>
<a href="https://huggingface.co/nicolaus-huang/PhotoDoodle"><img src="https://img.shields.io/badge/🤗_HuggingFace-Model-ffbd45.svg" alt="HuggingFace"></a>
<a href="https://huggingface.co/datasets/nicolaus-huang/PhotoDoodle/"><img src="https://img.shields.io/badge/🤗_HuggingFace-Dataset-ffbd45.svg" alt="HuggingFace"></a>
# Citation
<br>
<img src='./assets/teaser.png' width='100%' />
## 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
<span id="dataset_setting"></span>
#### 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},
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primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.14397},
}
``
```
__init__.py
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from .PhotoDoodle_node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
inference.py
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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()
merge.py
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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")
node_utils.py
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# !/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
pyproject.toml
+15
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[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 = ""
requirements.txt
+29
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@@ -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