# Improve generation quality with FreeU [[open-in-colab]] The UNet is responsible for denoising during the reverse diffusion process, and there are two distinct features in its architecture: 1. Backbone features primarily contribute to the denoising process 2. 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](https://hf.co/papers/2309.11497) 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: ```py from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None ).to("cuda") ``` Then enable the FreeU mechanism with the FreeU-specific hyperparameters. These values are scaling factors for the backbone and skip features. ```py pipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4) ``` The values above are from the official FreeU [code repository](https://github.com/ChenyangSi/FreeU) where you can also find [reference hyperparameters](https://github.com/ChenyangSi/FreeU#range-for-more-parameters) for different models. Disable the FreeU mechanism by calling `disable_freeu()` on a pipeline. And then run inference: ```py prompt = "A squirrel eating a burger" seed = 2023 image = pipeline(prompt, generator=torch.manual_seed(seed)).images[0] image ``` The figure below compares non-FreeU and FreeU results respectively for the same hyperparameters used above (`prompt` and `seed`):  Let's see how Stable Diffusion 2 results are impacted: ```py from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, safety_checker=None ).to("cuda") prompt = "A squirrel eating a burger" seed = 2023 pipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.1, b2=1.2) image = pipeline(prompt, generator=torch.manual_seed(seed)).images[0] image ```  ## Stable Diffusion XL Finally, let's take a look at how FreeU affects Stable Diffusion XL results: ```py from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, ).to("cuda") prompt = "A squirrel eating a burger" seed = 2023 # Comes from # https://wandb.ai/nasirk24/UNET-FreeU-SDXL/reports/FreeU-SDXL-Optimal-Parameters--Vmlldzo1NDg4NTUw pipeline.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2) image = pipeline(prompt, generator=torch.manual_seed(seed)).images[0] image ```  ## Text-to-video generation FreeU can also be used to improve video quality: ```python from diffusers import DiffusionPipeline from diffusers.utils import export_to_video import torch model_id = "cerspense/zeroscope_v2_576w" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") prompt = "an astronaut riding a horse on mars" seed = 2023 # The values come from # https://github.com/lyn-rgb/FreeU_Diffusers#video-pipelines pipe.enable_freeu(b1=1.2, b2=1.4, s1=0.9, s2=0.2) video_frames = pipe(prompt, height=320, width=576, num_frames=30, generator=torch.manual_seed(seed)).frames export_to_video(video_frames, "astronaut_rides_horse.mp4") ``` Thanks to [kadirnar](https://github.com/kadirnar/) for helping to integrate the feature, and to [justindujardin](https://github.com/justindujardin) for the helpful discussions.