Manifold Constraint Reduces Exposure Bias in Accelerated Diffusion Sampling
Keywords: Diffusion Models, Exposure Bias
Abstract: Diffusion models have demonstrated significant potential for generating high-quality images, audio, and videos. However, their iterative inference process entails substantial computational costs, limiting practical applications. Recently, researchers have introduced accelerated sampling methods that enable diffusion models to generate samples with far fewer timesteps than those used during training. Nonetheless, as the number of sampling steps decreases, the prediction errors significantly degrade the quality of generated outputs. Additionally, the inherent exposure bias in diffusion models causes errors to propagate and amplify, further introducing non-negligible inaccuracies in inference. To address these challenges, we leverage a manifold hypothesis to explore the exposure bias problem in depth. Based on this geometric perspective, we propose a manifold constraint that effectively reduces exposure bias during accelerated sampling of diffusion models. Notably, our method involves no additional training and requires only minimal hyperparameter tuning. Extensive experiments on high-resolution datasets demonstrate the effectiveness of our approach, achieving a FID score of 15.60 with 10-step SDXL on MS-COCO, surpassing the baseline by a reduction of 2.57 in FID.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 1547
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