smthemex / ComfyUI_Pops

using pops-paper method in ComfyUI
MIT License
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ComfyUI_Pops

using pOpsPaper method in ComfyUI

pOps: Photo-Inspired Diffusion Operators pOpsPaper method From: pOpsPaper

Update

-- 暂时去掉SDXL板块/Temporarily remove the SDXL sector
-- 改了代码结构,运行很快,但是抽卡的几率太大了。 --后期直接连入其他工作流,待完成。

1.Installation

In the ./ComfyUI /custom_node directory, run the following:

  git clone https://github.com/smthemex/ComfyUI_Pops.git

2.requirements

pip install -r requirements.txt

3 Need models

3.1 online

The default repo node will automatically connect to the network to download all necessary models !!

kandinsky-community/kandinsky-2-2-prior: link

kandinsky-community/kandinsky-2-2-decoder: link

pOpsPaper/operators (four models) link

3.2 离线模式 offline
在插件的weights目录下,如图放置ip-adapter_sdxl.bin,然后diffusers目录,放置其余3组模型。

├── ComfyUI/models/diffusers/
|      ├──kandinsky-community/
|             ├── kandinsky-2-2-decoder/
|                    ├── model_index.json 
|                    ├──unet/
|                        ├── config.json
|                        ├── diffusion_pytorch_model.safetensors
|                    ├──scheduler/
|                        ├── scheduler_config.json
|                    ├──movq/
|                        ├── config.json
|                        ├── diffusion_pytorch_model.safetensors
├── ComfyUI/models/diffusers/
|      ├──kandinsky-community/
|            ├── kandinsky-2-2-prior/       
|                    ├── model_index.json 
|                    ├──tokenizer/
|                        ├── merges.txt
|                        ├── special_tokens_map.json
|                        ├── tokenizer_config.json
|                        ├── vocab.json
|                    ├──text_encoder/
|                        ├── config.json
|                        ├── model.safetensors
|                    ├──scheduler/
|                        ├── scheduler_config.json
|                    ├──prior/
|                        ├── config.json
|                        ├── diffusion_pytorch_model.safetensors  
|                    ├──image_processor/
|                        ├── preprocessor_config.json  
|                    ├──image_encoder/
|                        ├── config.json
|                        ├── model.safetensors
│
├──ComfyUI/models/checkpoints
|             ├── instruct_learned_prior.pth  (rename from instruct/learned_prior.pth )
|             ├── scene_learned_prior.pth  (rename from scene/learned_prior.pth )
|             ├── texturing_learned_prior.pth  (rename from texturing/learned_prior.pth )
|             ├── union_learned_prior.pth  (rename from union/learned_prior.pth )

4 Example

object2texture 物体材质模型
)

object scene 物体场景模型,

drop cond 删掉某个条件 物体场景模型,

5 My ComfyUI node list:

1、ParlerTTS node:ComfyUI_ParlerTTS
2、Llama3_8B node:ComfyUI_Llama3_8B
3、HiDiffusion node:ComfyUI_HiDiffusion_Pro
4、ID_Animator node: ComfyUI_ID_Animator
5、StoryDiffusion node:ComfyUI_StoryDiffusion
6、Pops node:ComfyUI_Pops
7、stable-audio-open-1.0 node :ComfyUI_StableAudio_Open
8、GLM4 node:ComfyUI_ChatGLM_API
9、CustomNet node:ComfyUI_CustomNet
10、Pipeline_Tool node :ComfyUI_Pipeline_Tool
11、Pic2Story node :ComfyUI_Pic2Story
12、PBR_Maker node:ComfyUI_PBR_Maker
13、ComfyUI_Streamv2v_Plus node:ComfyUI_Streamv2v_Plus
14、ComfyUI_MS_Diffusion node:ComfyUI_MS_Diffusion
15、ComfyUI_AnyDoor node: ComfyUI_AnyDoor
16、ComfyUI_Stable_Makeup node: ComfyUI_Stable_Makeup
17、ComfyUI_EchoMimic node: ComfyUI_EchoMimic
18、ComfyUI_FollowYourEmoji node: ComfyUI_FollowYourEmoji
19、ComfyUI_Diffree node: ComfyUI_Diffree
20、ComfyUI_FoleyCrafter node: ComfyUI_FoleyCrafter

6 Citation

pOps

@article{richardson2024pops,
  title={pOps: Photo-Inspired Diffusion Operators},
  author={Richardson, Elad and Alaluf, Yuval and Mahdavi-Amiri, Ali and Cohen-Or, Daniel},
  journal={arXiv preprint arXiv:2406.01300},
  year={2024}
}

IP-Adapter


python  
@article{ye2023ip-adapter,
  title={IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models},
  author={Ye, Hu and Zhang, Jun and Liu, Sibo and Han, Xiao and Yang, Wei},
  booktitle={arXiv preprint arxiv:2308.06721},
  year={2023}