Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning
This addresses the issue for users needing accurate and consistent image generation from text and reference images, representing a novel method for a known bottleneck.
The paper tackles the problem of maintaining structural consistency in personalized text-to-image generation, where diffusion models often alter object details, by proposing a reinforcement learning framework that improves visual fidelity and outperforms state-of-the-art methods by a large margin.
Personalized text-to-image models allow users to generate varied styles of images (specified with a sentence) for an object (specified with a set of reference images). While remarkable results have been achieved using diffusion-based generation models, the visual structure and details of the object are often unexpectedly changed during the diffusion process. One major reason is that these diffusion-based approaches typically adopt a simple reconstruction objective during training, which can hardly enforce appropriate structural consistency between the generated and the reference images. To this end, in this paper, we design a novel reinforcement learning framework by utilizing the deterministic policy gradient method for personalized text-to-image generation, with which various objectives, differential or even non-differential, can be easily incorporated to supervise the diffusion models to improve the quality of the generated images. Experimental results on personalized text-to-image generation benchmark datasets demonstrate that our proposed approach outperforms existing state-of-the-art methods by a large margin on visual fidelity while maintaining text-alignment. Our code is available at: \url{https://github.com/wfanyue/DPG-T2I-Personalization}.