TMElyralab/MuseV

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2846 stars · 303 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MuseV is a diffusion-based framework for generating high-fidelity virtual human videos of infinite length using a Visual Conditioned Parallel Denoising scheme. It supports Image2Video, Text2Image2Video, and Video2Video generation and is compatible with the Stable Diffusion ecosystem including LoRA, ControlNet, and IP-Adapter.

Use cases

• generate videos of virtual humans from a single reference image
• create infinite-length talking avatar videos
• convert text prompts into human video clips
• restyle existing videos with reference images (video2video)
• build virtual human pipelines with MuseTalk and MusePose
• run a gradio demo to generate videos in a browser

When to choose

• you need diffusion-based image-to-video generation of human characters
• you want long or infinite-length video generation beyond typical clip limits
• you want compatibility with Stable Diffusion checkpoints, LoRA, and ControlNet
• you are building a virtual human / digital avatar generation stack

When to avoid

• you need real-time lip sync alone - use MuseTalk instead
• you need pose-controlled animation - use MusePose instead
• you lack a GPU or cannot run large diffusion models locally
• you need a production-ready, fully documented training pipeline - training code was not yet released

Facets

library · maturity active

video-processing image-processing machine-learning deep-learning stable-diffusion artificial-intelligence deep-learning image-processing computer-vision python diffusion-models image-to-video text-to-video video-generation virtual-human parallel-denoising infinite-length generative-ai video linux gpu docker

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markdown · JSON · MCP: product_card(name="TMElyralab/MuseV")