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Viggle/Viggle-Animate — new model trending #28 on Hugging Face

Hugging Face trending modelsby Viggle ()11 min read10 · 5 reads
AI summary · glm-5.3-flashSummaries by Vani AI

Viggle released Viggle-Animate, a 33.1B MiniMax-H3 finetune replacing video characters from one repainted frame, rendering 124 frames in 26 seconds on one GPU.

Viggle-Animate replaces the character in a video using only a driving video and one of its own repainted frames, with no pose estimator, segmentation mask, face tracker, or text encoder. It is a 33.1B full finetune of MiniMax-H3's ref2va transformer, jointly distilled with DMD across two teachers split by noise level, so rendering takes three forward passes per clip. On a B200 GPU it renders 124 frames in 26 seconds, 6.1x faster per clip than Wan2.2-Animate-14B in matched comparisons. The method assumes no person-specific representation, so it generalizes beyond humans; a demo, research write-up, and ComfyUI nodes are available.

Key facts
  • Two inputs only: driving video plus one repainted frame; no pose, segmentation, or text encoders.
  • Joint DMD distillation across two teachers cuts rendering to three forward passes per clip.
  • 6.1x faster per clip than Wan2.2-Animate-14B in matched B200 comparisons.
  • Method generalizes beyond humans since it assumes no person-specific representation.
  • Demo, research write-up, and ComfyUI nodes published alongside the weights.
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From the original article

11 min read at huggingface.co

### Character Replacement in Video from a Single Repainted Frame **[Try the demo](https://huggingface. co/spaces/Viggle/viggle-animate)**  ·  **[Research write-up](https://viggle. ai/research/viggle-animate-character-replacement-from-a-repainted-frame?

Hugging Face trending models

By Viggle · huggingface.co

Read the full article at Hugging Face trending models

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.