Composer：具有合成条件的创造性可控图像合成
Composer: Creative and Controllable Image Synthesis with Composable Conditions
摘要：
最近在大数据上学习的大规模生成模型能够合成令人难以置信的图像，但可控性有限。这项工作提供了一个新一代的范例，允许灵活的控制输出图像，如空间布局和调色板，同时保持合成质量和模型的创造性。以组合性为核心思想，首先将图像分解为具有代表性的因素，然后以这些因素为条件训练一个扩散模型，对输入进行重组。在推理阶段，丰富的中间表示作为可组合元素工作，导致巨大的设计空间（即，与分解因子的数量成指数比例），用于可定制的内容创建。值得注意的是，我们的方法，我们称之为作曲家，支持各种级别的条件，如文本描述的全球信息，深度图和草图的本地指导，颜色直方图的低级别的细节，等等，除了提高可控性，我们确认作曲家作为一个通用的框架，方便了广泛的经典生成任务，而无需再培训。将提供代码和模型。
英文摘要：
Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability. This work offers a new generation paradigm that allows flexible control of the output image, such as spatial layout and palette, while maintaining the synthesis quality and model creativity. With compositionality as the core idea, we first decompose an image into representative factors, and then train a diffusion model with all these factors as the conditions to recompose the input. At the inference stage, the rich intermediate representations work as composable elements, leading to a huge design space (i.e., exponentially proportional to the number of decomposed factors) for customizable content creation. It is noteworthy that our approach, which we call Composer, supports various levels of conditions, such as text description as the global information, depth map and sketch as the local guidance, color histogram for low-level details, etc. Besides improving controllability, we confirm that Composer serves as a general framework and facilitates a wide range of classical generative tasks without retraining. Code and models will be made available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
