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Towards Memorization-Free Diffusion Models
April 2, 2024, 7:48 p.m. | Chen Chen, Daochang Liu, Chang Xu
cs.CV updates on arXiv.org arxiv.org
Abstract: Pretrained diffusion models and their outputs are widely accessible due to their exceptional capacity for synthesizing high-quality images and their open-source nature. The users, however, may face litigation risks owing to the models' tendency to memorize and regurgitate training data during inference. To address this, we introduce Anti-Memorization Guidance (AMG), a novel framework employing three targeted guidance strategies for the main causes of memorization: image and caption duplication, and highly specific user prompts. Consequently, AMG …
abstract arxiv capacity cs.cv data diffusion diffusion models face free guidance however images inference litigation nature quality risks training training data type
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