Elastic Diffusion Transformer
Diffusion Transformers (DiT) have demonstrated remarkable generative capabilities but remain highly computationally expensive. Previous acceleration methods, such as pruning and distillation, typically rely on a fixed computational capacity, leading to insufficient acceleration and degraded generation quality. To address this limitation, we propose \textbf{Elastic Diffusion Transformer (E-DiT)}, an adaptive acceleration framework for DiT that effectively improves efficiency while maintaining generation quality. Specifically, we observe that the generative process of DiT exhibits substantial sparsity (i.e., some computations can be skipped with minimal impact on quality), and this sparsity...
| Authors | Jiangshan Wang, Zeqiang Lai, Jiarui Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 February 2026 |
| Last updated at source | 15 February 2026 |
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