Frequency-Aware Error-Bounded Caching for Accelerating Diffusion Transformers
This work provides a significant acceleration for DiT inference, which is crucial for researchers and practitioners working on high-quality image and video generation, making these models more practical for real-world applications.
The paper addresses the high computational cost of Diffusion Transformers (DiTs) during inference by proposing SpectralCache, a caching framework that accounts for non-uniformities in the denoising process across time, depth, and feature dimensions. SpectralCache achieves a 2.46x speedup on FLUX.1-schnell at 512x512 resolution, outperforming TeaCache by 16% in speed while maintaining comparable image quality (LPIPS difference < 1%).
Diffusion Transformers (DiTs) have emerged as the dominant architecture for high-quality image and video generation, yet their iterative denoising process incurs substantial computational cost during inference. Existing caching methods accelerate DiTs by reusing intermediate computations across timesteps, but they share a common limitation: treating the denoising process as uniform across time,depth, and feature dimensions. In this work, we identify three orthogonal axes of non-uniformity in DiT denoising: (1) temporal -- sensitivity to caching errors varies dramatically across the denoising trajectory; (2) depth -- consecutive caching decisions lead to cascading approximation errors; and (3) feature -- different components of the hidden state exhibit heterogeneous temporal dynamics. Based on these observations, we propose SpectralCache, a unified caching framework comprising Timestep-Aware Dynamic Scheduling (TADS), Cumulative Error Budgets (CEB), and Frequency-Decomposed Caching (FDC). On FLUX.1-schnell at 512x512 resolution, SpectralCache achieves 2.46x speedup with LPIPS 0.217 and SSIM 0.727, outperforming TeaCache (2.12x, LPIPS 0.215, SSIM 0.734) by 16% in speed while maintaining comparable quality (LPIPS difference < 1%). Our approach is training-free, plug-and-play, and compatible with existing DiT architectures.