Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models
Keywords: Text-to-Image Diffusion Models, Style Transfer, Content Leakage
TL;DR: We uncover a critical yet under-explored principle: guiding with appropriately-selected fewer conditions can efficiently avoid unwanted content flowing into the diffusion model, enhancing the style transfer performances.
Abstract: Given a style-reference image as the additional image condition, text-to-image diffusion models have demonstrated impressive capabilities in generating images that possess the content of text prompts while adopting the visual style of the reference image. However, current state-of-the-art methods often struggle to disentangle content and style from style-reference images, leading to issues such as content leakages. To address this issue, we propose a masking-based method that efficiently decouples content from style without the need of tuning any model parameters. By simply masking specific elements in the style reference's image features, we uncover a critical yet under-explored principle: guiding with appropriately-selected fewer conditions (e.g., dropping several image feature elements) can efficiently avoid unwanted content flowing into the diffusion models, enhancing the style transfer performances of text-to-image diffusion models. In this paper, we validate this finding both theoretically and experimentally. Extensive experiments across various styles demonstrate the effectiveness of our masking-based method and support our theoretical results.
Primary Area: generative models
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Submission Number: 5538
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