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.

以几张图片为输入，定制化文本到图像生成技术DETEX的创新方法通过学习多个单词嵌入来表示每个图像，包括可以共享的主题嵌入和多个图像特定的非主题嵌入，以便解耦不相关的属性，并利用联合训练策略鼓励这些不相关的嵌入捕捉无关信息。在推理过程中，仅使用主题嵌入进行图像生成，同时选择性地使用图像特定的嵌入以保留图像特定的属性。经过广泛实验证明，我们的方法获得的主题嵌入可以忠实地表示目标概念，并显示出与最先进方法相比的优越编辑性能。

定制图像生成的解耦文本嵌入