ReDiF: Resource-Efficient Few-Step Diffusion Distillation via Reinforcement Learning
ReDiF uses Reinforcement Learning to make diffusion models generate high-quality samples in very few steps. It trains a small student model to mimic a large teacher by using rewards based on how well its outputs align with the teacher's, rather than traditional loss functions. This method significantly speeds up sampling and saves computational resources.