本repo是基于Comfyui以及facechain源git改造的，想详细了解的请移步

Nvidia users should install stable pytorch using this command:

pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121

If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:

pip uninstall torch

And install it again with the command above.

Install the dependencies by opening your terminal inside the ComfyUI folder and:

pip install -r requirements.txt

After this you should have everything installed and can proceed to running ComfyUI.

Download needed facechain models in folder facechain File directory structure is as follows .
├── base_models
│   ├── cv_portrait_model
│   └── put_base_model_here
├── post_process_mdels
│   ├── damo
│   ├── put_post_model_here
│   └── temp
├── styles
│   ├── Barbie_Doll.json
│   ├── Casual_Lifestyle.json
│   ├── Chinese_traditional_gorgeous_suit.json
│   ├── PinkBarbie.safetensors
│   ├── PortraitMasterV1.safetensors
│   └── xiapei.safetensors
└── trained_models
└── 50000

1. Download base models in modelscope to folder base_models

{'name': 'leosamsMoonfilm_filmGrain20',
    'model_id': 'ly261666/cv_portrait_model',
    'revision': 'v2.0',
    'sub_path': "film/film"}

2. Download post models in modelscope to post_process_models first ,otherwise facechain_node will download online

def init_post_model(self):
        post_models_dir=folder_paths.facechain_post_model_dir
        #face_quality
        if os.path.exists(os.path.join(post_models_dir,'damo/cv_manual_face-quality-assessment_fqa')):
            self.face_quality_func = pipeline(Tasks.face_quality_assessment, os.path.join(post_models_dir,
                                                                                            'damo/cv_manual_face-quality-assessment_fqa'))                  
        else:
            model_path=snapshot_download(model_id='damo/cv_manual_face-quality-assessment_fqa',revision='v2.0',cache_dir=post_models_dir)
            self.face_quality_func = pipeline(Tasks.face_quality_assessment,model=model_path)
        #face_swap
        if os.path.exists(os.path.join(post_models_dir,'damo/cv_unet_face_fusion_torch')):
            self.image_face_fusion_func = pipeline('face_fusion_torch',
                                     model=os.path.join(post_models_dir,'damo/cv_unet_face_fusion_torch'))
        else:
            model_path=snapshot_download(model_id='damo/cv_unet_face_fusion_torch',revision='v1.0.3',cache_dir=post_models_dir)
            self.image_face_fusion_func = pipeline('face_fusion_torch',
                                     model=model_path)
        if os.path.exists(os.path.join(post_models_dir,'damo/cv_ir_face-recognition-ood_rts')):
            self.face_recognition_func=pipeline(Tasks.face_recognition, os.path.join(post_models_dir,'damo/cv_ir_face-recognition-ood_rts'))
        else:
            model_path=snapshot_download(model_id='damo/cv_ir_face-recognition-ood_rts',revision='v2.5',cache_dir=post_models_dir)
            self.face_recognition_func = pipeline(Tasks.face_recognition, model=model_path)
        if os.path.exists(os.path.join(post_models_dir,'damo/cv_ddsar_face-detection_iclr23-damofd')):
            self.face_det_func=pipeline(task=Tasks.face_detection, model=os.path.join(post_models_dir,'damo/cv_ddsar_face-detection_iclr23-damofd'))
        else:
            model_path=snapshot_download(model_id='damo/cv_ddsar_face-detection_iclr23-damofd',revision='v1.1',cache_dir=post_models_dir)
            self.face_det_func = pipeline(task=Tasks.face_detection, model=model_path)

3. Download style lora to folder styles from FaceChain including json and lora safetensor I have downloaded some style in this folder and you are free to add more

4. cp the facechain trained files to folder trained_models ,the file name is user id like 50000 .
   └── 50000
   ├── 0.png
   ├── 1.png
   ├── metadata.jsonl
   └── pytorch_lora_weights.bin
   

this file is generated by facechain human training processing,the image is cropped face from images uploaded by users after facechain training ,it will genererate three folders,processed ,processed_labeled,output cropped images like 0.png,1.png and tag file metadata.jsonl are in processed_labeled the lora model file is in output file if you follows instruction by official FaceChain repo

python main.py --port=8100 --listen=0.0.0.0

surely ,you can use another port note that ,it's recommended that you run this app in Ubuntu 22.04,because the modescope post model need system dynamic library

1. add automatic fresh in file web\scripts\ui.js
2. customize facechain inference node in custom_nodes\facechain_node.py,which support only load newly human lora file and if base_model and post_process_model are not changed ,it only load once in app initialization,which will accelerate inference