MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
This addresses the problem of limited user control and slow adaptation in image generation for users and developers, offering a novel method without retraining, though it builds on existing diffusion models.
The paper tackles the challenge of user controllability and fast adaptation in text-to-image diffusion models by introducing MultiDiffusion, a unified framework that enables versatile and controlled image generation without additional training, achieving high-quality and diverse images adhering to user controls like aspect ratio and spatial signals.
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