AccelAes: Accelerating Diffusion Transformers for Training-Free Aesthetic-Enhanced Image Generation
This addresses efficiency and aesthetic quality issues in image generation for users of DiT-based models, representing an incremental improvement through optimization techniques.
The paper tackles the high inference latency of Diffusion Transformers (DiTs) in text-to-image generation by proposing AccelAes, a training-free framework that accelerates DiTs through aesthetics-aware spatio-temporal reduction, achieving a 2.11× speedup and improving ImageReward by +11.9% over the baseline.
Diffusion Transformers (DiTs) are a dominant backbone for high-fidelity text-to-image generation due to strong scalability and alignment at high resolutions. However, quadratic self-attention over dense spatial tokens leads to high inference latency and limits deployment. We observe that denoising is spatially non-uniform with respect to aesthetic descriptors in the prompt. Regions associated with aesthetic tokens receive 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 framework that accelerates DiTs through aesthetics-aware spatio-temporal reduction while improving perceptual aesthetics. AccelAes builds AesMask, a one-shot aesthetic focus mask derived from prompt semantics and cross-attention signals. When localized computation is feasible, SkipSparse reallocates computation and guidance to masked regions. We further reduce temporal redundancy using a lightweight step-level prediction cache that periodically replaces full Transformer evaluations. Experiments on representative DiT families show consistent acceleration and improved aesthetics-oriented quality. On Lumina-Next, AccelAes achieves a 2.11$\times$ speedup and improves ImageReward by +11.9% over the dense baseline. Code is available at https://github.com/xuanhuayin/AccelAes.