RT-Lynx: Putting GEMM Sparsity in the Right Place for Diffusion Models
Diffusion Transformers (DiT) achieve strong performance in image generation but incur substantial inference costs. While prior work has reduced this cost via quantization and distillation, semi-structured sparsity, which can nearly halve FLOPs, remains underexplored. A key reason is that most existing approaches focus on weight sparsification, and pruning 50% of the weights can remove critical model capacity and degrade generation quality. Our study, however, shows that DiT activations are intrinsically sparse and significantly more robust to N:M semi-structured sparsification than weights. Motivated by this observation, we advocate a paradigm shift from weight sparsification to activatio...
| Authors | Xing Cong, Hanlin Tang, Kan Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 17 August 2026 |
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