PALP：文本到图像模型的提示词对齐个性化
PALP: Prompt Aligned Personalization of Text-to-Image Models
AI总结：
本文提出PALP方法，通过额外的分数蒸馏采样项使个性化模型与单一目标提示词对齐，在保持主体保真度的同时提升复杂提示词的文本对齐能力，并支持多主体组合与参考图像启发。
摘要：
内容创作者通常希望利用个人主体来创建个性化图像，这超出了传统文本到图像模型的能力范围。此外，他们可能还希望生成的图像包含特定的地点、风格、氛围等要素。现有的个性化方法可能会在个性化能力或与复杂文本提示词的对齐方面做出妥协。这种权衡可能会阻碍用户提示词的满足和主体保真度。我们提出了一种专注于单一提示词的个性化方法的新途径来解决这一问题。我们将这种方法称为提示词对齐个性化。虽然这看似具有限制性，但我们的方法在改善文本对齐方面表现出色，能够创建具有复杂和精细提示词的图像，而这可能对当前技术构成挑战。特别是，我们的方法通过使用额外的分数蒸馏采样项，使个性化模型与目标提示词保持对齐。我们展示了该方法在多样本和单样本设置中的多功能性，并进一步表明它可以组合多个主体，或从参考图像（如艺术品）中获取灵感。我们将我们的方法与现有基线和最先进技术进行了定量和定性比较。
英文摘要：
Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a \emph{single} prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.
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。
