场景分解：从单个图像中提取多个概念

Break-A-Scene: Extracting Multiple Concepts from a Single Image

May 25, 2023

作者: Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, Dani Lischinski

cs.AI

摘要

文本到图像模型个性化旨在向模型引入用户提供的概念，使其能够在不同背景下进行综合合成。然而，当前方法主要集中在从多个图像中学习单个概念的情况，这些图像具有不同的背景和姿势变化，但在适应不同场景时存在困难。在这项工作中，我们引入了文本场景分解任务：给定可能包含多个概念的场景的单个图像，我们旨在为每个概念提取一个不同的文本标记，从而实现对生成场景的精细控制。为此，我们提出了通过指示目标概念存在的蒙版来增强输入图像的方法。这些蒙版可以由用户提供，也可以由预训练分割模型自动生成。然后，我们提出了一种新颖的两阶段定制过程，优化一组专用文本嵌入（句柄）以及模型权重，找到准确捕捉概念并避免过拟合之间的微妙平衡。我们采用掩蔽扩散损失来使句柄能够生成其分配的概念，同时结合一种新颖的交叉注意力图损失以防止纠缠。我们还引入了联合采样，这是一种旨在改善在生成图像中组合多个概念能力的训练策略。我们使用多个自动度量标准定量比较我们的方法与几种基线方法，并通过用户研究进一步确认结果。最后，我们展示了我们方法的几个应用。项目页面链接：https://omriavrahami.com/break-a-scene/

English

Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a different scenario. In this work, we introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes. To this end, we propose augmenting the input image with masks that indicate the presence of target concepts. These masks can be provided by the user or generated automatically by a pre-trained segmentation model. We then present a novel two-phase customization process that optimizes a set of dedicated textual embeddings (handles), as well as the model weights, striking a delicate balance between accurately capturing the concepts and avoiding overfitting. We employ a masked diffusion loss to enable handles to generate their assigned concepts, complemented by a novel loss on cross-attention maps to prevent entanglement. We also introduce union-sampling, a training strategy aimed to improve the ability of combining multiple concepts in generated images. We use several automatic metrics to quantitatively compare our method against several baselines, and further affirm the results using a user study. Finally, we showcase several applications of our method. Project page is available at: https://omriavrahami.com/break-a-scene/