基于LLM的风格与内容正则化用于个性化文本到图像生成
LLM-Enabled Style and Content Regularization for Personalized Text-to-Image Generation
AI总结：
针对个性化文本到图像生成中风格化不足和内容不准确的问题，本文提出风格细化和内容保持策略，利用视觉推理提示优化风格嵌入并保持模型泛化能力，实验证明该方法生成效果更优。
摘要：
随着Stable Diffusion的出现，个性化文本到图像生成技术得到了快速发展。现有方法通常使用嵌入标识符对模型进行微调，但由于文本可控性降低，往往面临风格化不足和图像内容不准确的问题。本文提出了风格细化和内容保持两种策略。风格细化策略利用视觉推理提示和参考图像的语义信息来优化风格嵌入，从而更精确、一致地表示风格信息。内容保持策略通过保持模型的泛化能力来解决内容偏差问题，在不影响风格化的前提下增强文本可控性。实验结果验证了我们的方法在生成一致且个性化的文本到图像输出方面取得了优越性能。
英文摘要：
The personalized text-to-image generation has rapidly advanced with the emergence of Stable Diffusion. Existing methods, which typically fine-tune models using embedded identifiers, often struggle with insufficient stylization and inaccurate image content due to reduced textual controllability. In this paper, we propose style refinement and content preservation strategies. The style refinement strategy leverages the semantic information of visual reasoning prompts and reference images to optimize style embeddings, allowing a more precise and consistent representation of style information. The content preservation strategy addresses the content bias problem by preserving the model's generalization capabilities, ensuring enhanced textual controllability without compromising stylization. Experimental results verify that our approach achieves superior performance in generating consistent and personalized text-to-image outputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
