Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation
This addresses the challenge of performance plateaus in fine-tuning diffusion models for generative AI, offering a more efficient alternative to existing methods.
The paper tackles the problem of fine-tuning diffusion models for text-to-image generation by introducing SPIN-Diffusion, a self-play technique that enables iterative self-improvement without requiring human preference data, resulting in outperforming supervised fine-tuning and RLHF-based methods in human preference alignment and visual appeal with less data.
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI), especially when compared with the remarkable progress made in fine-tuning Large Language Models (LLMs). While cutting-edge diffusion models such as Stable Diffusion (SD) and SDXL rely on supervised fine-tuning, their performance inevitably plateaus after seeing a certain volume of data. Recently, reinforcement learning (RL) has been employed to fine-tune diffusion models with human preference data, but it requires at least two images ("winner" and "loser" images) for each text prompt. In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion), where the diffusion model engages in competition with its earlier versions, facilitating an iterative self-improvement process. Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment. Our experiments on the Pick-a-Pic dataset reveal that SPIN-Diffusion outperforms the existing supervised fine-tuning method in aspects of human preference alignment and visual appeal right from its first iteration. By the second iteration, it exceeds the performance of RLHF-based methods across all metrics, achieving these results with less data.