Non-confusing Generation of Customized Concepts in Diffusion Models

Wang Lin; Jingyuan Chen; Jiaxin Shi; Yichen Zhu; Chen Liang; Junzhong Miao; Tao Jin; Zhou Zhao; Fei Wu; Shuicheng Yan; Hanwang Zhang

arXiv · 2405.06914

Non-confusing Generation of Customized Concepts in Diffusion Models

Wang Lin·Jingyuan Chen·Jiaxin Shi·Yichen Zhu·Chen Liang·Junzhong Miao·Tao Jin·Zhou Zhao·Fei Wu·Shuicheng Yan·Hanwang Zhang

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Abstract

We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs). It becomes even more pronounced in the generation of customized concepts, due to the scarcity of user-provided concept visual examples. By revisiting the two major stages leading to the success of TGDMs -- 1) contrastive image-language pre-training (CLIP) for text encoder that encodes visual semantics, and 2) training TGDM that decodes the textual embeddings into pixels -- we point that existing customized generation methods only focus on fine-tuning the second stage while overlooking the first one. To this end, we propose a simple yet effective solution called CLIF: contrastive image-language fine-tuning. Specifically, given a few samples of customized concepts, we obtain non-confusing textual embeddings of a concept by fine-tuning CLIP via contrasting a concept and the over-segmented visual regions of other concepts. Experimental results demonstrate the effectiveness of CLIF in preventing the confusion of multi-customized concept generation.

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Wang Lin, Jingyuan Chen, Jiaxin Shi, Yichen Zhu, Chen Liang, Junzhong Miao, Tao Jin, Zhou Zhao, Fei Wu, Shuicheng Yan, Hanwang Zhang. 2024-05-11. Non-confusing Generation of Customized Concepts in Diffusion Models. https://arxiv.org/abs/2405.06914

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