Improve generation quality with FreeU            The UNet is responsible for denoising during the reverse diffusion process, and there are two distinct features in its architecture: Backbone features primarily contribute to the denoising process Skip features mainly introduce high-frequency features into the decoder module and can make the network overlook the semantics in the backbone features However, the skip connection can sometimes introduce unnatural image details. FreeU is a technique for improving image quality by rebalancing the contributions from the UNet’s skip connections and backbone feature maps. FreeU is applied during inference and it does not require any additional training. The technique works for different tasks such as text-to-image, image-to-image, and text-to-video. In this guide, you will apply FreeU to the StableDiffusionPipeline, StableDiffusionXLPipeline, and TextToVideoSDPipeline. You need to install Diffusers from source to run the examples below.  StableDiffusionPipeline Load the pipeline:   Copied from diffusers import DiffusionPipeline