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225              <h1 class="title is-1 publication-title" style="margin-bottom: 0.5rem;">
226                OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot
227              </h1>
228              
229              <div class="is-size-3 has-text-weight-bold" style="color: #b31b1b; margin-bottom: 1.5rem;">
230                ICLR 2026
231              </div>
232              
233              <div class="is-size-5 publication-authors">
234                <span class="author-block">
235                  <a href="https://alrightlone.github.io/" target="_blank">Junhan Zhu</a><sup>1</sup>,</span>
236                <span class="author-block">
237                  <a href="https://viridisgreen.github.io/" target="_blank">Hesong Wang</a><sup>1,2</sup>,</span>
238                <span class="author-block">
239                  <a href="https://github.com/sunshine-0903" target="_blank">Mingluo Su</a><sup>1</sup>,
240                </span>
241                <span class="author-block">
242                  <a href="https://github.com/aden9460" target="_blank">Zefang Wang</a><sup>1,2</sup>,
243                </span>
244                <span class="author-block">
245                  <a href="https://huanwang.tech/" target="_blank">Huan Wang</a><sup>1*</sup>
246                </span>
247              </div>
248  
249              <div class="is-size-5 publication-authors">
250                <span class="author-block"><sup>1</sup>Westlake University,</span>
251                <span class="author-block"><sup>2</sup>Zhejiang University</span>
252              </div>
253  
254              <div class="is-size-7" style="margin-top: 0.5rem; margin-bottom: 1.5rem;">
255                <span class="author-block"><sup>*</sup>Corresponding author: wanghuan [at] westlake [dot] edu [dot] cn</span> 
256              </div>
257  
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262                        <img src="static/images/westlake.png" alt="Westlake University" style="height: 100px;">
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266                        <img src="static/images/zju-logo.svg" alt="Zhejiang University" style="height: 100px;">
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277                <div class="publication-links">
278                  <span class="link-block">
279                    <a href="https://arxiv.org/pdf/2510.06751" target="_blank"
280                    class="external-link button is-normal is-rounded is-dark">
281                    <span class="icon">
282                      <i class="fas fa-file-pdf"></i>
283                    </span>
284                    <span>Paper</span>
285                  </a>
286                  </span>
287                  <span class="link-block">
288                    <a href="https://github.com/Alrightlone/OBS-Diff" target="_blank"
289                    class="external-link button is-normal is-rounded is-dark">
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292                    </span>
293                    <span>Code</span>
294                  </a>
295                  </span>
296                  <span class="link-block">
297                    <a href="https://arxiv.org/abs/2510.06751" target="_blank"
298                    class="external-link button is-normal is-rounded is-dark">
299                    <span class="icon">
300                      <i class="ai ai-arxiv"></i>
301                    </span>
302                    <span>arXiv</span>
303                    </a>
304                  </span>
305                  <span class="link-block">
306                    <a href="https://huggingface.co/Alrightlone/OBS-Diff-SDXL" target="_blank"
307                    class="external-link button is-normal is-rounded is-dark">
308                    <span class="icon">
309                      🤗
310                    </span>
311                    <span>SDXL</span>
312                  </a>
313                  </span>
314
315                  <span class="link-block">
316                    <a href="https://huggingface.co/Alrightlone/OBS-Diff-SD3.5-Large" target="_blank"
317                    class="external-link button is-normal is-rounded is-dark">
318                    <span class="icon">
319                      🤗
320                    </span>
321                    <span>SD3.5-Large</span>
322                  </a>
323                  </span>
324                </div>
325              </div>
326            </div>
327          </div>
328        </div>
329      </div>
330    </section>
331  </main>
332
333  <section class="hero teaser">
334    <div class="container is-max-desktop">
335      <div class="hero-body" style="padding-top: 0rem;">
336          <img src="static/images/teaser.jpg" alt="Teaser image preview">
337  
338        <h2 class="subtitle is-size-6 " style="text-align: left;">
339          Qualitative comparison of unstructured pruning methods on the SD3-Medium model. We evaluate Magnitude, DSnoT, Wanda, and our method (OBS-Diff) at various sparsity levels (20%, 30%, 40%, and 50%) using the same prompt and negative prompt. All images are generated at a resolution of 512 x 512.
340        </h2>
341      </div>
342    </div>
343  </section>
344
345<!-- Paper abstract -->
346<section class="section hero is-light">
347  <div class="container is-max-desktop">
348    <div class="columns is-centered has-text-centered">
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350        <h2 class="title is-3">Abstract</h2>
351        <div class="content has-text-justified is-size-6">
352          <!-- TODO: Replace with your paper abstract -->
353          <p>
354            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 <i>OBS-Diff</i>, a novel one-shot pruning framework that enables accurate and training-free compression of large-scale text-to-image diffusion models. Specifically, <b>(i)</b> 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; <b>(ii)</b> 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; <b>(iii)</b> 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.
355          </p>
356        </div>
357      </div>
358    </div>
359  </div>
360</section>
361<!-- End paper abstract -->
362<section class="section hero is-light">
363  <div class="container is-max-desktop">
364    <div class="columns is-centered has-text-centered">
365      <div class="column is-four-fifths">
366        <h2 class="title is-3">Overview of our <i>OBS-Diff</i> method</h2>
367        <div class="content">
368            <img src="static/images/framework.jpg" alt="Overview of the OBS-Diff framework" >
369        </div>
370        <h2 class="subtitle is-size-6" style="text-align: left;">
371          Illustration of the proposed <i>OBS-Diff</i> framework applied to the MMDiT architecture. Target modules are first partitioned into a predefined number of <i>module packages</i> and processed sequentially. For each package, hooks capture layer activations during a forward pass with a calibration dataset. This data, combined with weights from a dedicated timestep weighting scheme, is used to construct Hessian matrices. These matrices guide the Optimal Brain Surgeon (OBS) algorithm to simultaneously prune all layers within the current package before proceeding to the next.
372        </h2>
373      </div>
374    </div>
375  </div>
376</section>
377
378<!-- Image carousel -->
379<section class="hero is-small" style="margin-bottom: 4rem;">
380  <div class="hero-body">
381    <div class="container is-max-desktop">
382
383      <h2 class="title is-3 has-text-centered" style="margin-bottom: 0rem;">Main Results</h2>
384      <div id="results-carousel" class="carousel results-carousel" style="margin-top: -3rem;">
385       <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
386        <!-- TODO: Replace with your research result images -->
387        <img src="static/images/main_un.png" alt="First research result visualization" loading="lazy"/>
388        <!-- TODO: Replace with description of this result -->
389        <h2 class="subtitle has-text-centered">
390          Quantitative comparison of unstructured pruning methods on text-to-image diffusion models. The best result per metric is highlighted in <b>bold</b>.
391        </h2>
392      </div>
393      <div class="item">
394        <!-- Your image here -->
395        <img src="static/images/visualization.png" alt="Second research result visualization" style="max-width: 60%; height: auto;"loading="lazy"/>
396        <h2 class="subtitle has-text-centered">
397          ImageReward vs. sparsity for various unstructured pruning methods on SD3-Medium.
398        </h2>
399      </div>
400      <div class="item">
401        <!-- Your image here -->
402        <img src="static/images/semi.png" alt="Third research result visualization" style="max-width: 60%; height: auto;"loading="lazy"/>
403        <h2 class="subtitle has-text-centered">
404          Performance of semi-structured (2:4 sparsity pattern) pruning on the Stable Diffusion 3.5-Large model. Pruning is applied to the 3rd through 25th MMDiT blocks. The best result is shown in <b>bold</b>.
405       </h2>
406     </div>
407     <div class="item">
408      <!-- Your image here -->
409      <img src="static/images/structure.png" alt="Fourth research result visualization" style="max-width: 100%; height: auto;"loading="lazy"/>
410      <h2 class="subtitle has-text-centered">
411        Performance of structured pruning on the SDXL (U-Net) model across various sparsity
412levels. Comparison includes the L1-norm baseline, EcoDiff, and our proposed OBS-Diff. The TFLOPs metric represents the theoretical computational cost for a single forward pass of the entire UNet. For each sparsity group, the best result per metric is highlighted in <b>bold</b>.
413      </h2>
414    </div>
415     <div class="item">
416      <!-- Your image here -->
417      <img src="static/images/structured2.png" alt="Fourth research result visualization" style="max-width: 100%; height: auto;"loading="lazy"/>
418      <h2 class="subtitle has-text-centered">
419        Performance of structured pruning on the Stable Diffusion 3.5-Large model across various sparsity levels. The first and last transformer blocks were excluded from the pruning process. The TFLOPs metric represents the theoretical computational cost for a single forward pass of the entire transformer. For each sparsity group, the best result per metric is highlighted in <b>bold</b>.
420      </h2>
421    </div>
422    <div class="item">
423      <!-- Your image here -->
424      <img src="static/images/wall_clock.png" alt="Fourth research result visualization" style="max-width: 60%; height: auto;"loading="lazy"/>
425      <h2 class="subtitle has-text-centered">
426        Wall-clock inference time (ms) and speedup for a single MMDiT block under various sparsity schemes.
427      </h2>
428    </div>
429    <div class="item">
430      <!-- Your image here -->
431      <img src="static/images/ddpm.png" alt="Fourth research result visualization" style="max-width: 60%; height: auto;"loading="lazy"/>
432      <h2 class="subtitle has-text-centered">
433        Performance of pruned DDPMs on CIFAR-10 (32 x 32).All pruned models are fine-tuned for 100K steps. Evaluations are conducted on samples generated via 100 DDIM steps. The best FID score is highlighted in <b>bold</b>.
434      </h2>
435    </div>
436  </div>
437</div>
438</div>
439</section>
440<!-- End image carousel -->
441
442
443<!-- Image carousel -->
444<section class="hero is-small">
445  <div class="hero-body">
446    <div class="container is-max-desktop">
447
448      <h2 class="title is-3 has-text-centered" style="margin-bottom: 0rem;">More Qualitative Results</h2>
449      <div id="results-carousel" class="carousel results-carousel" style="margin-top: -3rem;">
450       <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
451        <!-- TODO: Replace with your research result images -->
452        <img src="static/images/un_sd3_2.jpg" alt="First research result visualization" loading="lazy" />
453        <!-- TODO: Replace with description of this result -->
454        <h2 class="subtitle has-text-centered" >
455          Qualitative comparison of unstructured pruning methods on the SD3-Medium model. We evaluate Magnitude, DSnoT, Wanda, and our method (OBS-Diff) at various sparsity levels (20%, 30%, 40%, and 50%) using the same prompt and negative prompt. All images are generated at a resolution of 512 x 512.
456        </h2>
457      </div>
458      <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
459        <!-- Your image here -->
460        <img src="static/images/un_sd3_1.jpg" alt="Second research result visualization" loading="lazy" />
461        <h2 class="subtitle has-text-centered" >
462          Qualitative comparison of unstructured pruning methods on the SD3-Medium model. We evaluate Magnitude, DSnoT, Wanda, and our method (OBS-Diff) at various sparsity levels (20%, 30%, 40%, and 50%) using the same prompt and negative prompt. All images are generated at a resolution of 512 x 512.
463        </h2>
464      </div>
465      <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
466        <!-- Your image here -->
467        <img src="static/images/un_sd3_3.jpg" alt="Third research result visualization" loading="lazy" />
468        <h2 class="subtitle has-text-centered" >
469          Qualitative comparison of unstructured pruning methods on the SD3-Medium model. We evaluate Magnitude, DSnoT, Wanda, and our method (OBS-Diff) at various sparsity levels (20%, 30%, 40%, and 50%) using the same prompt and negative prompt. All images are generated at a resolution of 512 x 512.
470       </h2>
471     </div>
472     <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
473      <!-- Your image here -->
474      <img src="static/images/flux_un.jpg" alt="Fourth research result visualization" loading="lazy"/>
475      <h2 class="subtitle has-text-centered" >
476        Qualitative comparison of unstructured pruning methods on Flux 1.dev at 70% sparsity. Results from Magnitude, DSnoT, Wanda, and our proposed OBS-Diff are shown.
477      </h2>
478    </div>
479    <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
480      <!-- Your image here -->
481      <img src="static/images/struct_1.jpg" alt="Fourth research result visualization" loading="lazy" />
482      <h2 class="subtitle has-text-centered" >
483        Qualitative comparison of structured pruning methods on the SD3.5-Large model at various sparsity levels (15%, 20%, 25%, and 30%). Results from the L1-norm baseline and our proposed OBS-Diff are shown.
484      </h2>
485    </div>
486    <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
487      <!-- Your image here -->
488      <img src="static/images/struct_2.jpg" alt="Fourth research result visualization" loading="lazy" />
489      <h2 class="subtitle has-text-centered" >
490        Qualitative comparison of structured pruning methods on the SD3.5-Large model at various sparsity levels (15%, 20%, 25%, and 30%). Results from the L1-norm baseline and our proposed OBS-Diff are shown.
491      </h2>
492    </div>
493    <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
494      <!-- Your image here -->
495      <img src="static/images/struct_3.jpg" alt="Fourth research result visualization" loading="lazy" />
496      <h2 class="subtitle has-text-centered" >
497        Qualitative comparison of structured pruning methods on the SD3.5-Large model at various sparsity levels (15%, 20%, 25%, and 30%). Results from the L1-norm baseline and our proposed OBS-Diff are shown.
498      </h2>
499    </div>
500    <div class="item" style="min-height: 100%; display: flex; flex-direction: column; justify-content: center; align-items: center;">
501      <!-- Your image here -->
502      <img src="static/images/struct_4.jpg" alt="Fourth research result visualization" loading="lazy" />
503      <h2 class="subtitle has-text-centered" >
504        Qualitative comparison of structured pruning methods on the SD3.5-Large model at various sparsity levels (15%, 20%, 25%, and 30%). Results from the L1-norm baseline and our proposed OBS-Diff are shown.
505      </h2>
506    </div>
507  </div>
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528      <pre id="bibtex-code"><code>@article{zhu2025obs,
529        title={OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot},
530        author={Zhu, Junhan and Wang, Hesong and Su, Mingluo and Wang, Zefang and Wang, Huan},
531        journal={arXiv preprint arXiv:2510.06751},
532        year={2025}
533      }
534  </code></pre>
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