FlashClear delivers up to 122x faster object removal than prior diffusion models via adversarial step distillation and asymmetric attention caching while preserving visual quality.
From reusing to forecasting: Accelerating diffusion models with taylorseers
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.
citing papers explorer
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FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching
FlashClear delivers up to 122x faster object removal than prior diffusion models via adversarial step distillation and asymmetric attention caching while preserving visual quality.
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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.
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Swift Sampling: Selecting Temporal Surprises via Taylor Series
Swift Sampling is a training-free frame selection method that uses Taylor expansions on video latent trajectories to pick temporally surprising frames, outperforming uniform sampling on long-video QA tasks.
- PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models