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ReDiF: Resource-Efficient Few-Step Diffusion Distillation via Reinforcement Learning
Step distillation accelerates diffusion sampling by training a few-step student to imitate a many-step teacher, but distillation itself remains expensive. Typically, this requires thousands of GPU-hours and a large pre-generated trajectory dataset. We introduce ReDiF, which casts step distillation a
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