FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving
Diffusion models have become the central backbone for modern image, video, and audio generation, but their efficient service remains a challenge. Unlike autoregressive decoding, diffusion inference repeatedly updates high-dimensional spatial or temporal latents over many denoising steps. This all-region execution pattern makes generation latency high and limits serving throughput. Existing multi-GPU parallelization methods can reduce per-step computation, but often introduce substantial activation exchange overhead, causing communication to offset or even outweigh the benefits of parallel execution. This paper presents FlashDiff, a diffusion serving system that improves inference efficien...
| Authors | Yaqi Qiao, Ping He, Songrun Xie et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 July 2026 |
| Last updated at source | 15 July 2026 |
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