ComfyUI-AnimateAnyone-Evolved

Improved AnimateAnyone implementation that allows you to use the opse image sequence and reference image to generate stylized video.<br> The current goal of this project is to achieve desired pose2video result with 1+FPS on GPUs that are equal to or better than RTX 3080!🚀

<video controls autoplay loop src="https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved/assets/62230687/572eaa8d-6011-42dc-9ac5-9bbd86e4ac9d" muted="false"></video>

Currently Support

• Please check example workflows for usage. You can use Test Inputs to generate the exactly same results that I showed here. (I got Chun-Li image from civitai)
• Support different sampler & scheduler:
  • DDIM
    • 24 frames pose image sequences, steps=20, context_frames=24; Takes 835.67 seconds to generate on a RTX3080 GPU <br><video controls autoplay loop src="https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved/assets/62230687/4e5f6b80-88a7-4bf8-9c81-7a00b5a02c76" muted="false" width="320"></video>
    • 24 frames pose image sequences, steps=20, context_frames=12; Takes 425.65 seconds to generate on a RTX3080 GPU
      <br><video controls autoplay loop src="https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved/assets/62230687/5691ac83-1400-43e2-a930-531d7695e506" muted="false" width="320"></video>
  • DPM++ 2M Karras
    • 24 frames pose image sequences, steps=20, context_frames=12; Takes 407.48 seconds to generate on a RTX3080 GPU <br><video controls autoplay loop src="https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved/assets/62230687/45c6aaeb-b750-4d44-8c31-edbdcf1068d8" muted="false" width="320"></video>
  • LCM
    • 24 frames pose image sequences, steps=20, context_frames=24; Takes 606.56 seconds to generate on a RTX3080 GPU <br><video controls autoplay loop src="https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved/assets/62230687/e8c712ec-fc7f-4679-ae41-99449f4f76aa" muted="false" width="320"></video>
    • Note:<br>Pre-trained LCM Lora for SD1.5 does not working well here, since model is retrained for quite a long time steps from SD1.5 checkpoint, however retain a new lcm lora is feasible
  • Euler
    • 24 frames pose image sequences, steps=20, context_frames=12; Takes 450.66 seconds to generate on a RTX3080 GPU <br><video controls autoplay loop src="https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved/assets/62230687/6a5b7c28-943d-4ff2-83de-3460ab1a6b61" muted="false" width="320"></video>
  • Euler Ancestral
  • LMS
  • PNDM
• Support add Lora
  • I did this for insert lcm lora
• Support quite long pose image sequences
  • Tested on my RTX3080 GPU, can handle 120+ frames pose image sequences with context_frames=24
  • As long as system can fit all the pose image sequences inside a single tensor without GPU memory leak, then the main parameters will determine the GPU usage is context_frames, which does not correlate to the length of pose image sequences.
• Current implementation is adopted from Moore-AnimateAnyone,
  • I tried to break it down into as many modules as possible, so the workflow in ComfyUI would closely resemble the original pipeline from AnimateAnyone paper: <br>

Roadmap

• ✅ Implement the compoents (Residual CFG) proposed in StreamDiffusion (Estimated speed up: 2X)
  • Result:
    Generated result is not good enough when using DDIM Scheduler togather with RCFG, even though it speed up the generating process by about 4X.<br> In StreamDiffusion, RCFG works with LCM, could also be the case here, so keep it in another branch for now.
• ⬜ Incorporate the implementation & Pre-trained Models from Open-AnimateAnyone & AnimateAnyone once they released
• ⬜ Convert Model using stable-fast (Estimated speed up: 2X)
• ⬜ Train a LCM Lora for denoise unet (Estimated speed up: 5X)
• ⬜ Training a new Model using better dataset to improve results quality (Optional, we'll see if there is any need for me to do it ;)
• Continuous research, always moving towards something better & faster🚀

Install (You can also use ComfyUI Manager)

1. Clone this repo into the Your ComfyUI root directory\ComfyUI\custom_nodes\ and install dependent Python packages:

   cd Your_ComfyUI_root_directory\ComfyUI\custom_nodes\
   
   git clone https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved.git
   
   pip install -r requirements.txt
   
   # If you got error regards diffusers then run:
   pip install --force-reinstall diffusers>=0.26.1
   

2. Download pre-trained models:
   • stable-diffusion-v1-5_unet
   • Moore-AnimateAnyone Pre-trained Models
   • Above models need to be put under folder pretrained_weights as follow:

   ./pretrained_weights/
   |-- denoising_unet.pth
   |-- motion_module.pth
   |-- pose_guider.pth
   |-- reference_unet.pth
   `-- stable-diffusion-v1-5
       |-- feature_extractor
       |   `-- preprocessor_config.json
       |-- model_index.json
       |-- unet
       |   |-- config.json
       |   `-- diffusion_pytorch_model.bin
       `-- v1-inference.yaml
   

   • Download clip image encoder (e.g. sd-image-variations-diffusers ) and put it under Your_ComfyUI_root_directory\ComfyUI\models\clip_vision
   • Download vae (e.g. sd-vae-ft-mse) and put it under Your_ComfyUI_root_directory\ComfyUI\models\vae