Non-confusing Generation of Customized Concepts in Diffusion Models

• URL: http://arxiv.org/abs/2405.06914v1
• Date: Sat, 11 May 2024 05:01:53 GMT
• Title: Non-confusing Generation of Customized Concepts in Diffusion Models
• Authors: Wang Lin, Jingyuan Chen, Jiaxin Shi, Yichen Zhu, Chen Liang, Junzhong Miao, Tao Jin, Zhou Zhao, Fei Wu, Shuicheng Yan, Hanwang Zhang,
• Abstract summary: We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs) Existing customized generation methods only focus on fine-tuning the second stage while overlooking the first one. We propose a simple yet effective solution called CLIF: contrastive image-language fine-tuning.
• Score: 135.4385383284657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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