In-Context Learning Unlocked for Diffusion Models

• URL: http://arxiv.org/abs/2305.01115v2
• Date: Wed, 18 Oct 2023 21:56:31 GMT
• Title: In-Context Learning Unlocked for Diffusion Models
• Authors: Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang Wang, Mingyuan Zhou
• Abstract summary: We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. We propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The resulting Prompt Diffusion model is the first diffusion-based vision-language foundation model capable of in-context learning.
• Score: 163.54453915874402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly over six different tasks using these prompts. The resulting Prompt Diffusion model is the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation on the trained tasks and generalizes effectively to new, unseen vision tasks with their respective prompts. Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision. We share our code and pre-trained models at https://github.com/Zhendong-Wang/Prompt-Diffusion.

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