EDCache: Endpoint-Driven Timestep Caching for Diffusion Transformers

Diffusion transformers excel at image and video generation, but iterative denoising incurs high inference costs. Existing caching methods often use local proxy signals to decide what to reuse. However, under matched compute, we find that importance-based token selection does not consistently outperform random selection and incurs additional overhead. We therefore shift our focus from token-level to timestep-level caching: reusing whole-timestep outputs can cause trajectory drift, while the position of reuse strongly affects final generation quality. To address trajectory drift and refresh timing in whole-timestep reuse, we propose EDCache, a two-stage method combining trajectory correction and timestep scheduling. Stage 1 uses recent full-computation outputs to correct drift during reuse without offline calibration. Stage 2 constructs schedule costs from model-specific single-position endpoint-damage profiles and uses dynamic programming to select full-computation timesteps under a fixed budget. Experiments on DiT-XL/2, FLUX.1-dev, and Wan2.1 show that EDCache improves generation fidelity at high denoising speedups. On DiT-XL/2, EDCache achieves more than 5× denoising speedup with FID 3.64, outperforming all evaluated competing methods in the 2.5–5× speedup range across all five reported distribution metrics.