DisEnvisioner：用于定制化图像生成的解耦与丰富视觉提示
DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation
AI总结：
本文提出免微调的 DisEnvisioner，仅用单图将主体本质特征与无关信息解耦为视觉 token 并加以细粒度丰富，从而提升定制生成的可编辑性、ID一致性、速度与画质。
摘要：
在图像生成领域，根据视觉提示并结合额外文本指令创建定制化图像，是一项颇具前景的研究方向。然而，现有方法无论基于微调还是无需微调，都难以从视觉提示中理解主体本质属性。这导致与主体无关的属性渗入生成过程，最终在可编辑性和ID保持两方面损害个性化质量。本文提出 DisEnvisioner，这是一种新颖方法，可有效提取并丰富主体本质特征，同时过滤无关信息，仅使用单张图像、以免微调方式实现出色的定制性能。具体而言，主体特征与其他无关成分被有效分离为不同的视觉 token，从而实现更准确的定制。为进一步提升ID一致性，我们丰富了解耦后的特征，将其塑造为更细粒度的表示。实验表明，本方法在指令响应（即可编辑性）、ID一致性、推理速度和整体图像质量方面均优于现有方法，凸显了 DisEnvisioner 的有效性与效率。项目主页：https://disenvisioner.github.io/。
英文摘要：
In the realm of image generation, creating customized images from visual prompt with additional textual instruction emerges as a promising endeavor. However, existing methods, both tuning-based and tuning-free, struggle with interpreting the subject-essential attributes from the visual prompt. This leads to subject-irrelevant attributes infiltrating the generation process, ultimately compromising the personalization quality in both editability and ID preservation. In this paper, we present DisEnvisioner, a novel approach for effectively extracting and enriching the subject-essential features while filtering out -irrelevant information, enabling exceptional customization performance, in a tuning-free manner and using only a single image. Specifically, the feature of the subject and other irrelevant components are effectively separated into distinctive visual tokens, enabling a much more accurate customization. Aiming to further improving the ID consistency, we enrich the disentangled features, sculpting them into more granular representations. Experiments demonstrate the superiority of our approach over existing methods in instruction response (editability), ID consistency, inference speed, and the overall image quality, highlighting the effectiveness and efficiency of DisEnvisioner. Project page: https://disenvisioner.github.io/.
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。
