ReDiF: Reinforced Distillation for Few Step Diffusion
This work addresses the computational inefficiency of diffusion models for users needing faster inference, though it is incremental as it builds on existing distillation methods.
The paper tackles the slow sampling problem in diffusion models by proposing a reinforcement learning-based distillation framework that treats distillation as a policy optimization problem, achieving superior performance with significantly fewer inference steps and computational resources compared to existing techniques.
Distillation addresses the slow sampling problem in diffusion models by creating models with smaller size or fewer steps that approximate the behavior of high-step teachers. In this work, we propose a reinforcement learning based distillation framework for diffusion models. Instead of relying on fixed reconstruction or consistency losses, we treat the distillation process as a policy optimization problem, where the student is trained using a reward signal derived from alignment with the teacher's outputs. This RL driven approach dynamically guides the student to explore multiple denoising paths, allowing it to take longer, optimized steps toward high-probability regions of the data distribution, rather than relying on incremental refinements. Our framework utilizes the inherent ability of diffusion models to handle larger steps and effectively manage the generative process. Experimental results show that our method achieves superior performance with significantly fewer inference steps and computational resources compared to existing distillation techniques. Additionally, the framework is model agnostic, applicable to any type of diffusion models with suitable reward functions, providing a general optimization paradigm for efficient diffusion learning.