ComFusion: Personalized Subject Generation in Multiple Specific Scenes From Single Image

Yan Hong; Jianfu Zhang

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arXiv · 2402.11849

ComFusion: Personalized Subject Generation in Multiple Specific Scenes From Single Image

Yan Hong·Jianfu Zhang

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Abstract

Recent advancements in personalizing text-to-image (T2I) diffusion models have shown the capability to generate images based on personalized visual concepts using a limited number of user-provided examples. However, these models often struggle with maintaining high visual fidelity, particularly in manipulating scenes as defined by textual inputs. Addressing this, we introduce ComFusion, a novel approach that leverages pretrained models generating composition of a few user-provided subject images and predefined-text scenes, effectively fusing visual-subject instances with textual-specific scenes, resulting in the generation of high-fidelity instances within diverse scenes. ComFusion integrates a class-scene prior preservation regularization, which leverages composites the subject class and scene-specific knowledge from pretrained models to enhance generation fidelity. Additionally, ComFusion uses coarse generated images, ensuring they align effectively with both the instance image and scene texts. Consequently, ComFusion maintains a delicate balance between capturing the essence of the subject and maintaining scene fidelity.Extensive evaluations of ComFusion against various baselines in T2I personalization have demonstrated its qualitative and quantitative superiority.

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Yan Hong, Jianfu Zhang. 2024-02-19. ComFusion: Personalized Subject Generation in Multiple Specific Scenes From Single Image. https://arxiv.org/abs/2402.11849

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