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arXiv · 2607.20048

Importance-Aware OBS Pruning for Diffusion Models

Ba-Thinh Lam·Srijan Das·Hieu Le
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Abstract

We propose importance-aware pruning for diffusion models, a training-free framework that prioritizes preserving parameters critical to semantically salient image regions. To do so, we incorporate spatial importance maps -- derived from conditioning signals or model attention -- into the pruning objective. This produces parameter rankings aligned with perceptual relevance rather than uniform reconstruction error. On MS-COCO dataset, our proposed approach consistently retains subject fidelity and structural correctness at high compression ratios where conventional pruning causes visible degradation. These results demonstrate that content-aware objectives are key to perceptually faithful compression of generative models.

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Ba-Thinh Lam, Srijan Das, Hieu Le. 2026-07-22. Importance-Aware OBS Pruning for Diffusion Models. https://arxiv.org/abs/2607.20048

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