标题： 针对组合图像生成的物体级视觉提示 显示英文标题

标题： Object-level Visual Prompts for Compositional Image Generation

摘要： 我们介绍了一种在文本到图像扩散模型中组合对象级视觉提示的方法。 我们的方法解决了在不同场景和风格中生成语义连贯组合的任务，类似于文本提示所提供的多功能性和表达性。 此任务中的一个关键挑战是在保留输入视觉提示中所描绘对象的身份的同时，也在不同图像中生成多样化的组合。 为了解决这个挑战，我们引入了一种新的KV混合交叉注意力机制，其中键和值是从不同的视觉表示中学习得到的。 键来自一个具有小瓶颈用于布局控制的编码器，而值则来自一个具有更大瓶颈的编码器，该编码器捕捉精细的外观细节。 通过混合来自这些互补来源的键和值，我们的模型在保留视觉提示身份的同时，支持对象排列、姿态和组合的灵活变化。 在推理过程中，我们进一步提出了对象级的组合指导，以提高方法的身份保留和布局正确性。 结果表明，我们的技术生成了保留每个视觉提示独特特征的多样化场景组合，扩展了文本到图像生成的创造力潜力。 显示英文摘要

摘要： We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. Our approach addresses the task of generating semantically coherent compositions across diverse scenes and styles, similar to the versatility and expressiveness offered by text prompts. A key challenge in this task is to preserve the identity of the objects depicted in the input visual prompts, while also generating diverse compositions across different images. To address this challenge, we introduce a new KV-mixed cross-attention mechanism, in which keys and values are learned from distinct visual representations. The keys are derived from an encoder with a small bottleneck for layout control, whereas the values come from a larger bottleneck encoder that captures fine-grained appearance details. By mixing keys and values from these complementary sources, our model preserves the identity of the visual prompts while supporting flexible variations in object arrangement, pose, and composition. During inference, we further propose object-level compositional guidance to improve the method's identity preservation and layout correctness. Results show that our technique produces diverse scene compositions that preserve the unique characteristics of each visual prompt, expanding the creative potential of text-to-image generation.