Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes. Project webpage: https://multidiffusion.github.io

翻译：近期，基于扩散模型的文本到图像生成技术在图像质量方面取得了突破性进展。然而，生成图像的可控性以及对新任务的快速适应仍是待解决的挑战，目前主要通过昂贵且耗时长的重新训练、微调或针对特定图像生成任务的临时性适配来处理。本文提出MultiDiffusion——一个统一框架，利用预训练的文本到图像扩散模型实现灵活可控的图像生成，无需任何额外训练或微调。该框架的核心是一种基于优化任务的新生成过程，该过程通过共享参数或约束将多个扩散生成过程绑定。实验表明，MultiDiffusion可轻松应用于生成高质量多样化的图像，同时满足用户提供的控制条件，例如预期的宽高比（如全景图）以及从紧致分割掩码到边界框的空间引导信号。项目页面：https://multidiffusion.github.io