This paper explores advancements in high-fidelity personalized image
generation through the utilization of pre-trained text-to-image diffusion
models. While previous approaches have made significant strides in generating
versatile scenes based on text descriptions and a few input images, challenges
persist in maintaining the subject fidelity within the generated images. In
this work, we introduce an innovative algorithm named HiFi Tuner to enhance the
appearance preservation of objects during personalized image generation. Our
proposed method employs a parameter-efficient fine-tuning framework, comprising
a denoising process and a pivotal inversion process. Key enhancements include
the utilization of mask guidance, a novel parameter regularization technique,
and the incorporation of step-wise subject representations to elevate the
sample fidelity. Additionally, we propose a reference-guided generation
approach that leverages the pivotal inversion of a reference image to mitigate
unwanted subject variations and artifacts. We further extend our method to a
novel image editing task: substituting the subject in an image through textual
manipulations. Experimental evaluations conducted on the DreamBooth dataset
using the Stable Diffusion model showcase promising results. Fine-tuning solely
on textual embeddings improves CLIP-T score by 3.6 points and improves DINO
score by 9.6 points over Textual Inversion. When fine-tuning all parameters,
HiFi Tuner improves CLIP-T score by 1.2 points and improves DINO score by 1.2
points over DreamBooth, establishing a new state of the art.

该论文研究了利用预训练的文本到图像扩散模型实现高保真个性化图像生成的进展。通过引入一种创新算法名为 HiFi Tuner 来增强在个性化图像生成过程中对对象外观保持的能力，提出了基于参数高效的微调框架，包括去噪过程和关键性倒转过程。此外，还提出了参考引导生成方法，利用参考图像的关键性倒转来减轻不想要的主题变化和伪影。实验结果表明，通过仅在文本嵌入上进行微调，CLIP-T 得分提高了 3.6 分，DINO 得分提高了 9.6 分，当对所有参数进行微调时，HiFi Tuner 在 CLIP-T 得分上提高了 1.2 分，在 DINO 得分上提高了 1.2 分，达到了新的技术水平。