OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot

• URL: http://arxiv.org/abs/2510.06751v1
• Date: Wed, 08 Oct 2025 08:19:15 GMT
• Title: OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
• Authors: Junhan Zhu, Hesong Wang, Mingluo Su, Zefang Wang, Huan Wang,
• Abstract summary: OBS-Diff is a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models.<n>Extensive experiments show that OBS-Diff achieves state-of-the-art one-shot pruning for diffusion models, delivering inference acceleration with minimal degradation in visual quality.
• Score: 4.990334603434127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale text-to-image diffusion models, while powerful, suffer from prohibitive computational cost. Existing one-shot network pruning methods can hardly be directly applied to them due to the iterative denoising nature of diffusion models. To bridge the gap, this paper presents OBS-Diff, a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models. Specifically, (i) OBS-Diff revitalizes the classic Optimal Brain Surgeon (OBS), adapting it to the complex architectures of modern diffusion models and supporting diverse pruning granularity, including unstructured, N:M semi-structured, and structured (MHA heads and FFN neurons) sparsity; (ii) To align the pruning criteria with the iterative dynamics of the diffusion process, by examining the problem from an error-accumulation perspective, we propose a novel timestep-aware Hessian construction that incorporates a logarithmic-decrease weighting scheme, assigning greater importance to earlier timesteps to mitigate potential error accumulation; (iii) Furthermore, a computationally efficient group-wise sequential pruning strategy is proposed to amortize the expensive calibration process. Extensive experiments show that OBS-Diff achieves state-of-the-art one-shot pruning for diffusion models, delivering inference acceleration with minimal degradation in visual quality.

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