AccelAes: Accelerating Diffusion Transformers for Training-Free Aesthetic-Enhanced Image Generation

Diffusion Transformers (DiTs) are a dominant backbone forhigh-fidelity text-to-image generation due to strong scalability and align-ment at high resolutions. However, quadratic self-attention over densespatial tokens leads to high inference latency and limits deployment. Weobserve that denoising is spatially non-uniform with respect to aestheticdescriptors in the prompt. Regions associated with aesthetic tokens re-ceive concentrated cross-attention and show larger temporal variation,while low-affinity regions evolve smoothly with redundant computation.Based on this insight, we propose AccelAes, a training-free frameworkthat accelerates DiTs through aesthetics-aware spatio-temporal reduc-tion while improving perceptual aesthetics. AccelAes builds AesMask, aone-shot aesthetic focus mask derived from prompt semantics and cross-attention signals. When localized computation is feasible, SkipSparsereallocates computation and guidance to masked regions. We further re-duce temporal redundancy using a lightweight step-level prediction cachethat periodically replaces full Transformer evaluations. Experiments onrepresentative DiT families show consistent acceleration and improvedaesthetics-oriented quality. On Lumina-Next, AccelAes achieves a 2.11×speedup and improves ImageReward by +11.9% over the dense baseline.Code is available at https://github.com/xuanhuayin/AccelAes.