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MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
Omer Bar-Tal · Lior Yariv · Yaron Lipman · Tali Dekel

Tue Jul 25 02:00 PM -- 04:30 PM (PDT) @ Exhibit Hall 1 #635
Event URL: https://multidiffusion.github.io/ »

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.

Author Information

Omer Bar-Tal (Weizmann Institute of Science)
Lior Yariv (Weizmann Institute of Science)
Yaron Lipman (Meta AI, WIS)
Tali Dekel (Weizmann Institute of Science)

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