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arXiv · 2312.11826

Decoupled Textual Embeddings for Customized Image Generation

Yufei Cai·Yuxiang Wei·Zhilong Ji·Jinfeng Bai·Hu Han·Wangmeng Zuo
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Abstract

Customized text-to-image generation, which aims to learn user-specified concepts with a few images, has drawn significant attention recently. However, existing methods usually suffer from overfitting issues and entangle the subject-unrelated information (e.g., background and pose) with the learned concept, limiting the potential to compose concept into new scenes. To address these issues, we propose the DETEX, a novel approach that learns the disentangled concept embedding for flexible customized text-to-image generation. Unlike conventional methods that learn a single concept embedding from the given images, our DETEX represents each image using multiple word embeddings during training, i.e., a learnable image-shared subject embedding and several image-specific subject-unrelated embeddings. To decouple irrelevant attributes (i.e., background and pose) from the subject embedding, we further present several attribute mappers that encode each image as several image-specific subject-unrelated embeddings. To encourage these unrelated embeddings to capture the irrelevant information, we incorporate them with corresponding attribute words and propose a joint training strategy to facilitate the disentanglement. During inference, we only use the subject embedding for image generation, while selectively using image-specific embeddings to retain image-specified attributes. Extensive experiments demonstrate that the subject embedding obtained by our method can faithfully represent the target concept, while showing superior editability compared to the state-of-the-art methods. Our code will be made published available.

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Yufei Cai, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, Hu Han, Wangmeng Zuo. 2023-12-19. Decoupled Textual Embeddings for Customized Image Generation. https://arxiv.org/abs/2312.11826

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