Do We Need to Design Specific Diffusion Models for Different Tasks? Try ONE-PIC

• URL: http://arxiv.org/abs/2412.05619v1
• Date: Sat, 07 Dec 2024 11:19:32 GMT
• Title: Do We Need to Design Specific Diffusion Models for Different Tasks? Try ONE-PIC
• Authors: Ming Tao, Bing-Kun Bao, Yaowei Wang, Changsheng Xu,
• Abstract summary: We propose a simple, efficient, and general approach to fine-tune diffusion models. ONE-PIC enhances the inherited generative ability in the pretrained diffusion models without introducing additional modules. Our method is simple and efficient which streamlines the adaptation process and achieves excellent performance with lower costs.
• Score: 77.8851460746251
• License:
• Abstract: Large pretrained diffusion models have demonstrated impressive generation capabilities and have been adapted to various downstream tasks. However, unlike Large Language Models (LLMs) that can learn multiple tasks in a single model based on instructed data, diffusion models always require additional branches, task-specific training strategies, and losses for effective adaptation to different downstream tasks. This task-specific fine-tuning approach brings two drawbacks. 1) The task-specific additional networks create gaps between pretraining and fine-tuning which hinders the transfer of pretrained knowledge. 2) It necessitates careful additional network design, raising the barrier to learning and implementation, and making it less user-friendly. Thus, a question arises: Can we achieve a simple, efficient, and general approach to fine-tune diffusion models? To this end, we propose ONE-PIC. It enhances the inherited generative ability in the pretrained diffusion models without introducing additional modules. Specifically, we propose In-Visual-Context Tuning, which constructs task-specific training data by arranging source images and target images into a single image. This approach makes downstream fine-tuning closer to the pertaining, allowing our model to adapt more quickly to various downstream tasks. Moreover, we propose a Masking Strategy to unify different generative tasks. This strategy transforms various downstream fine-tuning tasks into predictions of the masked portions. The extensive experimental results demonstrate that our method is simple and efficient which streamlines the adaptation process and achieves excellent performance with lower costs. Code is available at https://github.com/tobran/ONE-PIC.

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