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GRACE: generation-aware latent compression cuts Wan2.1 video diffusion tokens 8x

AI summary

An arXiv paper introduces GRACE (Generation-Aware Latent Compression for Efficient Video Generation), a two-stage framework that compresses a pretrained video autoencoder while keeping it compatible with a pretrained DiT: it keeps a frozen base latent, learns a residual latent for information lost under stronger compression, and aligns the compressed latent in the frozen DiT's feature space so the autoencoder is optimized for generation. The DiT is then adapted with lightweight fine-tuning and asymmetric denoising in which the base is denoised ahead of the residual. The authors report an 8x token reduction and 11.1x latency reduction for Wan2.1-I2V-14B at 480x832x81 while matching the pre-compression pipeline's quality on VBench.

In-depth insight

If reproducible, this latent-compression approach could lower inference costs for video diffusion models, though it is so far validated on a single model and benchmark.

Computer Science > Computer Vision and Pattern Recognition

arXiv:2610.10524 (cs)

[Submitted on 7 Oct 2026]

Title:GRACE: Generation-aware latent compression for efficient video generation

Authors:Jiyoung Kim, Paul Hyunbin Cho, Jisu Nam, Donghoon Lee, Hyunsung Go, Yeonkyeong Lee, Hansaem Kim, Seungryong Kim

View a PDF of the paper titled GRACE: Generation-aware latent compression for efficient video generation, by Jiyoung Kim and 7 other authors

#GRACE#Wan2.1

Original · 3 min readRead the original
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