AttenCraft: Attention-guided Disentanglement of Multiple Concepts for Text-to-Image Customization

• URL: http://arxiv.org/abs/2405.17965v1
• Date: Tue, 28 May 2024 08:50:14 GMT
• Title: AttenCraft: Attention-guided Disentanglement of Multiple Concepts for Text-to-Image Customization
• Authors: Junjie Shentu, Matthew Watson, Noura Al Moubayed,
• Abstract summary: AttenCraft is an attention-guided method for multiple concept disentanglement. We introduce Uniform sampling and Reweighted sampling schemes to alleviate the non-synchronicity of feature acquisition from different concepts. Our method outperforms baseline models in terms of image-alignment, and behaves comparably on text-alignment.
• Score: 4.544788024283586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the unprecedented performance being achieved by text-to-image (T2I) diffusion models, T2I customization further empowers users to tailor the diffusion model to new concepts absent in the pre-training dataset, termed subject-driven generation. Moreover, extracting several new concepts from a single image enables the model to learn multiple concepts, and simultaneously decreases the difficulties of training data preparation, urging the disentanglement of multiple concepts to be a new challenge. However, existing models for disentanglement commonly require pre-determined masks or retain background elements. To this end, we propose an attention-guided method, AttenCraft, for multiple concept disentanglement. In particular, our method leverages self-attention and cross-attention maps to create accurate masks for each concept within a single initialization step, omitting any required mask preparation by humans or other models. The created masks are then applied to guide the cross-attention activation of each target concept during training and achieve concept disentanglement. Additionally, we introduce Uniform sampling and Reweighted sampling schemes to alleviate the non-synchronicity of feature acquisition from different concepts, and improve generation quality. Our method outperforms baseline models in terms of image-alignment, and behaves comparably on text-alignment. Finally, we showcase the applicability of AttenCraft to more complicated settings, such as an input image containing three concepts. The project is available at https://github.com/junjie-shentu/AttenCraft.

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