ComfyUI Extension: ComfyUI-MuseV

ComfyUI-MuseV Introduction

ComfyUI-MuseV is an advanced extension designed to generate high-fidelity virtual human videos using diffusion models. This extension leverages innovative visual condition parallel denoising techniques to create videos of unlimited length without cumulative errors, making it particularly suitable for scenarios with fixed camera positions. ComfyUI-MuseV is a powerful tool for AI artists, enabling the creation of realistic and dynamic virtual human videos from images, text, or other videos.

How ComfyUI-MuseV Works

ComfyUI-MuseV operates on the principles of diffusion models, which are a type of generative model that iteratively refines an image or video from noise. The extension uses a novel parallel denoising algorithm that processes visual conditions in parallel, allowing for the generation of long videos without the typical accumulation of errors seen in sequential processing. This method ensures that each frame is consistent with the previous ones, maintaining high visual fidelity throughout the video.

ComfyUI-MuseV Features

1. Unlimited Length Generation: Generate videos of any length without cumulative errors, ideal for fixed camera scenarios.
2. Pre-trained Models: Utilize pre-trained models based on various human datasets to generate realistic virtual human videos.
3. Multi-Modal Input: Supports image-to-video, text-to-image-to-video, and video-to-video generation.
4. Stable Diffusion Compatibility: Compatible with the Stable Diffusion ecosystem, including base models, LoRA, and ControlNet.
5. Multi-Reference Image Techniques: Incorporates techniques like IPAdapter, ReferenceOnly, ReferenceNet, and IPAdapterFaceID for enhanced video generation.
6. Training Code Availability: Training code will be released for users to train their own models.

ComfyUI-MuseV Models

ComfyUI-MuseV includes several models tailored for different tasks:

1. musev/unet: Trains only the UNet motion module, suitable for generating videos with lower GPU memory consumption (~8GB).
2. musev_referencenet: Trains the UNet motion module, ReferenceNet, and IPAdapter, requiring more GPU memory (~12GB).
3. musev_referencenet_pose: Based on musev_referencenet, this model fixes the ReferenceNet and ControlNet pose, training the UNet motion and IPAdapter.
4. t2i/sd1.5: A text-to-image model used as a base for training motion modules.

What's New with ComfyUI-MuseV

Recent Updates

1. March 27, 2024: Released the MuseV project and trained models (musev, muse_referencenet, muse_referencenet_pose).
2. March 30, 2024: Added a GUI on Hugging Face Space for interactive video generation.

Important Fixes

• Corrected model name specifications for musev_referencenet_pose in the main branch.

Troubleshooting ComfyUI-MuseV

Common Issues and Solutions

1. Model Loading Errors:

• Ensure you are using the correct model names and paths as specified in the configuration files.
• Verify that all required models are downloaded and placed in the correct directories.

2. Video Generation Quality:

• Adjust the video_guidance_scale and context_frames parameters to improve video quality.
• Ensure the reference images and videos are properly aligned with the initial frames.

3. Performance Issues:

• Use Docker for a consistent environment setup.
• Ensure your GPU meets the memory requirements for the selected models.

Frequently Asked Questions

1. How do I generate long videos?

• Use the visual condition parallel denoising method by setting n_batch=1 and time_size to the desired number of frames.

2. Can I use my own models?

• Yes, you can train your own models using the provided training code and integrate them into ComfyUI-MuseV.

Learn More about ComfyUI-MuseV

For additional resources, tutorials, and community support, visit the following links:

• GitHub Repository
• Hugging Face Model Page
• Hugging Face Space for Interactive Demos Explore these resources to get the most out of ComfyUI-MuseV and join the community of AI artists creating stunning virtual human videos.