Newest papers

SpecKCast: Spectral Kalman Forecasting for Diffusion Acceleration

Javad Rajabi, Hakkı Can Karaimer, David B. Lindell, Babak Taati, Michael S. Brown · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26837 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

传输转换器(DiTs)报告高可信度的图像和视频生成,但他们遭受重复转换器评估的重复转换器成本,因为在许多 denoising 步骤中。 功能缓存通过在 denoising 步骤中重复使用功能来降低这种成本。 最近的预测方法通过将特征扩展到跳过的步骤而不是重复使用,但现有的预测方法会再度扩展他们统一地预测的特征,尽管这些特征的空间频率组成部分在不同阶段的 denoising 变化最多。 他们也忽视了 denoising 非均匀地进展,所以平等的步骤间隔可以相当于不同数量的变化。 这些不匹配 amplify forecast 错误,因为更多的步骤是跳过的,

Machine translation (Meta M2M100); the English original follows.

Abstract

Diffusion Transformers (DiTs) achieve high-fidelity image and video generation, but they suffer from substantial inference costs due to repeated transformer evaluations across many denoising steps. Feature caching reduces this cost by reusing features across denoising steps. Recent forecasting methods extend this idea by extrapolating features to skipped steps instead of reusing them. However, existing forecasting methods extrapolate the features they predict uniformly, even though the spatial-frequency components of these features change most at different stages of denoising. They also overlook that denoising progresses nonuniformly, so equal step intervals can correspond to very different amounts of change. These mismatches amplify forecast errors as more steps are skipped, limiting the achievable acceleration. We propose SpecKCast, a training-free method that forecasts the model output in the frequency domain. SpecKCast applies a 2D discrete cosine transform (DCT) to the output and tracks each coefficient with a lightweight Kalman filter along a band-specific time coordinate derived from the noise schedule. The filter tracks each coefficient's value and rate of change, and the transformer is evaluated only when the filter's forecast uncertainty exceeds a threshold. Experiments on FLUX.1-dev, Qwen-Image, Wan2.1-14B, and HunyuanVideo show that SpecKCast achieves a better trade-off between acceleration and fidelity than existing caching and forecasting methods, reaching up to 6x speedup with less than 0.05 GiB of additional memory.

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Topics: Kalman filter, Computer science, Granularity, Inference, Algorithm, Noise reduction

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