Diff-ES: Stage-wise Structural Diffusion Pruning via Evolutionary Search
Diffusion models have achieved remarkable success in high-fidelity image generation but remain computationally demanding due to their multi-step denoising process and large model sizes. Although prior work improves efficiency either by reducing sampling steps or by compressing model parameters, existing structured pruning approaches still struggle to balance real acceleration and image quality preservation. In particular, prior methods such as MosaicDiff rely on heuristic, manually tuned stage-wise sparsity schedules and stitch multiple independently pruned models during inference, which increases memory overhead. However, the importance of diffusion steps is highly non-uniform and model-...
| Authors | Zongfang Liu, Shengkun Tang, Zongliang Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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