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<!DOCTYPE html>
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<title>AdaptiveDiffusion</title>
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<h1 class="title is-1 publication-title", style="font-size: 2.5rem">[NeurIPS 2024] Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy</h1>
<div class="is-size-5 publication-authors">
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<a href="https://scholar.google.com/citations?hl=en&user=I9rLoV8AAAAJ" target="_blank">Hancheng Ye</a><sup>1,*</sup>,</span>
<span class="author-block">
<a href="https://jiakangyuan.github.io" target="_blank">Jiakang Yuan</a><sup>2,*</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=E520fqQAAAAJ&hl=zh-CN" target="_blank">Renqiu Xia</a><sup>3</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=0mMk6PMAAAAJ&hl=zh-CN" target="_blank">Xiangchao Yan</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://eetchen.github.io/" target="_blank">Tao Chen</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://thinklab.sjtu.edu.cn/" target="_blank">Junchi Yan</a><sup>3</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=K0PpvLkAAAAJ&hl=en" target="_blank">Botian Shi</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://bobrown.github.io/boZhang.github.io//" target="_blank">Bo Zhang</a><sup>1,†</sup>,</span>
</span>
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<sup>1</sup>Shanghai Artificial Intelligence Laboratory<br>
<sup>2</sup>School of Information Science and Technology, Fudan University<br>
<sup>3</sup>School of Artificial Intelligence, Shanghai Jiao Tong University<br>
</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Equal Contribution, <sup>†</sup>Corresponding author</small></span>
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Different prompts may require different steps of noise prediction!!!
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For Prompt 1, we only need 20 steps out of 50 steps for noise predictions to generate an almost lossless image, while for Prompt 2, we need 26 steps out of 50 steps to achieve an almost lossless image.
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<h2 class="title is-3">Abstract</h2>
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<p>
Diffusion models have recently achieved great success in the synthesis of highquality images and videos. However, the existing denoising techniques in diffusion models are commonly based on step-by-step noise predictions, which suffers from high computation cost, resulting in a prohibitive latency for interactive applications. In this paper, we propose AdaptiveDiffusion to relieve this bottleneck by adaptively reducing the noise prediction steps during the denoising process. Our method considers the potential of skipping as many noise prediction steps as possible while keeping the final denoised results identical to the original full-step ones. Specifically, the skipping strategy is guided by the third-order latent difference that indicates the stability between timesteps during the denoising process, which benefits the reusing of previous noise prediction results. Extensive experiments on image and video diffusion models demonstrate that our method can significantly speed up the denoising process while generating identical results to the original process, achieving up to an average 2 ~ 5x speedup without quality degradation.
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Different prompts may require different steps of noise prediction!!!
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<h2 class="subtitle has-text-centered", style="font-size: 1rem">
For Prompt 1, we only need 20 steps out of 50 steps for noise predictions to generate an almost lossless image, while for Prompt 2, we need 26 steps out of 50 steps to achieve an almost lossless image.
</h2>
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Denoising process of AdaptiveDiffusion
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<h2 class="subtitle has-text-centered", style="font-size: 1rem">
We design a third-order estimator, which can find the redundancy between neighboring timesteps, and thus, the noise prediction model can be skipped or inferred according to the indicate from the estimator, achieving the adaptive diffusion process. Note that the timestep and text information embeddings are not shown for the sake of brevity.
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<h2 class="subtitle has-text-centered", style="font-size: 1.5rem; font-weight: bolder">
AdaptiveDiffusion can generate high-quality images or videos with less cost!!!
</h2>
<!-- Your image here -->
<img src="static/images/results_img.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered", style="font-size: 1rem">
Quantitative results on MS-COCO 2017.
</h2>
<img src="static/images/result_video.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered", style="font-size: 1rem">
Quantitative results on video generation tasks.
</h2>
<img src="static/images/result_imagener.png" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered", style="font-size: 1rem">
Qualitative results of text-to-image generation task using LDM-4 on ImageNet 256x256 benchmark.
</h2>
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<td><img src="static/images/i2v_ori.gif"></td>
<td><img src="static/images/i2v_ada.gif"></td>
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</table>
<h2 class="subtitle has-text-centered", style="font-size: 1rem">
Qualitative results of text-to-video generation task using I2VGen-XL.
</h2>
<table style="border-collapse: separate; border-spacing: 10pt">
<tr>
<td><img src="static/images/modelscope_ori.gif"></td>
<td><img src="static/images/modelscope_ada.gif"></td>
</tr>
</table>
<h2 class="subtitle has-text-centered", style="font-size: 1rem">
Qualitative results of text-to-video generation task using ModelScopeT2V.
</h2>
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<!-- @article{ye2024training,
title={Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy},
author={Ye, Hancheng and Yuan, Jiakang and Xia, Renqiu and Yan, Xiangchao and Chen, Tao and Yan, Junchi and Shi, Botian and Zhang, Bo},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
} -->
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