OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
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.
68e722757ae125f9582e69d9
Junhan Zhu
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2025-10-09T06:50:12.218Z
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/avatars/158168a753271b6e024e1fbdf52c9e73.svg
Junhan ZHU
user
Alrightlone
68e722757ae125f9582e69da
Hesong Wang
68e722757ae125f9582e69db
Mingluo Su
68e722757ae125f9582e69dc
Zefang Wang
68e722757ae125f9582e69dd
Huan Wang
claimed_verified
2025-10-09T06:50:08.795Z
62b624f3b52bef716e248fd7
https://cdn-avatars.huggingface.co/v1/production/uploads/62b624f3b52bef716e248fd7/AllcccKH-eBWduA8KVnOQ.png
Huan Wang
user
Huan-WhoRegisteredMyName
OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
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.
OBS-Diff is a novel one-shot pruning framework that compresses large-scale text-to-image diffusion models with minimal quality loss and significant inference acceleration.
diffusion models one-shot pruning OBS-Diff Optimal Brain Surgeon unstructured N:M semi-structured structured MHA heads FFN neurons timestep-aware Hessian logarithmic-decrease weighting scheme group-wise sequential pruning