HSA assigns variable denoising steps to spatiotemporal tokens in DiTs based on velocity dynamics, with KV-cache sync and cached Euler updates, outperforming prior caching methods on quality-runtime tradeoffs for T2V and I2V generation.
Faster diffusion through temporal attention decomposition.Transactions on Machine Learning Research, 2025
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Not All Tokens Need 40 Steps: Heterogeneous Step Allocation in Diffusion Transformers for Efficient Video Generation
HSA assigns variable denoising steps to spatiotemporal tokens in DiTs based on velocity dynamics, with KV-cache sync and cached Euler updates, outperforming prior caching methods on quality-runtime tradeoffs for T2V and I2V generation.