FreeU is a method that can significantly improve the sampling quality of diffusion models without any additional cost: no training, no extra parameters, no increased memory or sampling time. It achieves this by reweighting the contributions of U-Net's skip connections and main branch feature maps, leveraging the advantages of both components of the U-Net architecture to enhance generation quality. Experiments on image and video generation tasks demonstrate that FreeU can be easily integrated into existing diffusion models such as Stable Diffusion, DreamBooth, ModelScope, Rerender, and ReVersion with just a few lines of code to improve generation quality.