Mansour et al.
Training-free wrapper accelerates causal video diffusion to 50 FPS without quality loss
UnStep reduces inference steps and attention window size, then recovers quality with renoising and truncated SVD.
Industry
Youssef Mansour · Enis Simsar · Fadime Sener · Markos Georgopoulos · Albert Pumarola · Ali Thabet · +1 more
Meta Superintelligence Labs
Research Digest··3 min read
The authors present UnStep, a training-free wrapper that accelerates already-distilled causal video diffusion models by running them with fewer denoising steps and a limited temporal KV cache.
Why this paper
From Meta Superintelligence Labs · Released code
In one line
UnStep accelerates causal video diffusion models to 50 FPS on an H100 without quality loss and without retraining.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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