diffusers/v0.27.2/en/_app/immutable/nodes/174.e86e4cb2.js

import{s as Ye,o as Pe,n as qe}from"../chunks/scheduler.182ea377.js";import{S as Ae,i as Ke,g as i,s as a,r as f,A as Oe,h as p,f as s,c as n,j as ze,u as m,x as o,k as De,y as et,a as l,v as u,d as c,t as h,w as b}from"../chunks/index.abf12888.js";import{T as tt}from"../chunks/Tip.230e2334.js";import{C as Y}from"../chunks/CodeBlock.57fe6e13.js";import{D as st}from"../chunks/DocNotebookDropdown.5fa27ace.js";import{H as $e}from"../chunks/Heading.16916d63.js";function lt(q){let r,y="Disable the FreeU mechanism by calling <code>disable_freeu()</code> on a pipeline.";return{c(){r=i("p"),r.innerHTML=y},l(d){r=p(d,"P",{"data-svelte-h":!0}),o(r)!=="svelte-vhfeba"&&(r.innerHTML=y)},m(d,D){l(d,r,D)},p:qe,d(d){d&&s(r)}}}function at(q){let r,y,d,D,g,A,J,K,w,We="The UNet is responsible for denoising during the reverse diffusion process, and there are two distinct features in its architecture:",O,T,ke="<li>Backbone features primarily contribute to the denoising process</li> <li>Skip features mainly introduce high-frequency features into the decoder module and can make the network overlook the semantics in the backbone features</li>",ee,j,Ce='However, the skip connection can sometimes introduce unnatural image details. <a href="https://hf.co/papers/2309.11497" rel="nofollow">FreeU</a> is a technique for improving image quality by rebalancing the contributions from the UNet’s skip connections and backbone feature maps.',te,U,_e="FreeU is applied during inference and it does not require any additional training. The technique works for different tasks such as text-to-image, image-to-image, and text-to-video.",se,Z,Be='In this guide, you will apply FreeU to the <a href="/docs/diffusers/v0.27.2/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline">StableDiffusionPipeline</a>, <a href="/docs/diffusers/v0.27.2/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline">StableDiffusionXLPipeline</a>, and <a href="/docs/diffusers/v0.27.2/en/api/pipelines/text_to_video#diffusers.TextToVideoSDPipeline">TextToVideoSDPipeline</a>. You need to install Diffusers from source to run the examples below.',le,v,ae,$,xe="Load the pipeline:",ne,W,ie,k,Ge="Then enable the FreeU mechanism with the FreeU-specific hyperparameters. These values are scaling factors for the backbone and skip features.",pe,C,oe,_,Se='The values above are from the official FreeU <a href="https://github.com/ChenyangSi/FreeU" rel="nofollow">code repository</a> where you can also find <a href="https://github.com/ChenyangSi/FreeU#range-for-more-parameters" rel="nofollow">reference hyperparameters</a> for different models.',re,M,fe,B,Ve="And then run inference:",me,x,ue,G,Ee="The figure below compares non-FreeU and FreeU results respectively for the same hyperparameters used above (<code>prompt</code> and <code>seed</code>):",ce,S,Ie='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/freeu/sdv1_5_freeu.jpg"/>',he,V,Re="Let’s see how Stable Diffusion 2 results are impacted:",be,E,de,I,He='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/freeu/sdv2_1_freeu.jpg"/>',Me,R,ye,H,Xe="Finally, let’s take a look at how FreeU affects Stable Diffusion XL results:",ge,X,Je,N,Ne='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/freeu/sdxl_freeu.jpg"/>',we,Q,Te,F,Qe="FreeU can also be used to improve video quality:",je,L,Ue,z,Fe='Thanks to <a href="https://github.com/kadirnar/" rel="nofollow">kadirnar</a> for helping to integrate the feature, and to <a href="https://github.com/justindujardin" rel="nofollow">justindujardin</a> for the helpful discussions.',Ze,P,ve;return g=new $e({props:{title:"Improve generation quality with FreeU",local:"improve-generation-quality-with-freeu",headingTag:"h1"}}),J=new st({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/freeu.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/freeu.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/freeu.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/freeu.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/freeu.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/freeu.ipynb"}]}}),v=new $e({props:{title:"StableDiffusionPipeline",local:"stablediffusionpipeline",headingTag:"h2"}}),W=new Y({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEFwaXBlbGluZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiUyQyUyMHNhZmV0eV9jaGVja2VyJTNETm9uZSUwQSkudG8oJTIyY3VkYSUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16, safety_checker=<span class="hljs-literal">None</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,wrap:!1}}),C=new Y({props:{code:"cGlwZWxpbmUuZW5hYmxlX2ZyZWV1KHMxJTNEMC45JTJDJTIwczIlM0QwLjIlMkMlMjBiMSUzRDEuMiUyQyUyMGIyJTNEMS40KQ==",highlighted:'pipeline.enable_freeu(s1=<span class="hljs-number">0.9</span>, s2=<span class="hljs-number">0.2</span>, b1=<span class="hljs-number">1.2</span>, b2=<span class="hljs-number">1.4</span>)',wrap:!1}}),M=new tt({props:{$$slots:{default:[lt]},$$scope:{ctx:q}}}),x=new Y({props:{code:"cHJvbXB0JTIwJTNEJTIwJTIyQSUyMHNxdWlycmVsJTIwZWF0aW5nJTIwYSUyMGJ1cmdlciUyMiUwQXNlZWQlMjAlM0QlMjAyMDIzJTBBaW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBnZW5lcmF0b3IlM0R0b3JjaC5tYW51YWxfc2VlZChzZWVkKSkuaW1hZ2VzJTVCMCU1RCUwQWltYWdl",highlighted:`prompt = <span class="hljs-string">&quot;A squirrel eating a burger&quot;</span>
seed = <span class="hljs-number">2023</span>
image = pipeline(prompt, generator=torch.manual_seed(seed)).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),E=new Y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16, safety_checker=<span class="hljs-literal">None</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A squirrel eating a burger&quot;</span>
seed = <span class="hljs-number">2023</span>

pipeline.enable_freeu(s1=<span class="hljs-number">0.9</span>, s2=<span class="hljs-number">0.2</span>, b1=<span class="hljs-number">1.1</span>, b2=<span class="hljs-number">1.2</span>)
image = pipeline(prompt, generator=torch.manual_seed(seed)).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),R=new $e({props:{title:"Stable Diffusion XL",local:"stable-diffusion-xl",headingTag:"h2"}}),X=new Y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A squirrel eating a burger&quot;</span>
seed = <span class="hljs-number">2023</span>

<span class="hljs-comment"># Comes from</span>
<span class="hljs-comment"># https://wandb.ai/nasirk24/UNET-FreeU-SDXL/reports/FreeU-SDXL-Optimal-Parameters--Vmlldzo1NDg4NTUw</span>
pipeline.enable_freeu(s1=<span class="hljs-number">0.6</span>, s2=<span class="hljs-number">0.4</span>, b1=<span class="hljs-number">1.1</span>, b2=<span class="hljs-number">1.2</span>)
image = pipeline(prompt, generator=torch.manual_seed(seed)).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),Q=new $e({props:{title:"Text-to-video generation",local:"text-to-video-generation",headingTag:"h2"}}),L=new Y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> export_to_video
<span class="hljs-keyword">import</span> torch

model_id = <span class="hljs-string">&quot;cerspense/zeroscope_v2_576w&quot;</span>
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;an astronaut riding a horse on mars&quot;</span>
seed = <span class="hljs-number">2023</span>

<span class="hljs-comment"># The values come from</span>
<span class="hljs-comment"># https://github.com/lyn-rgb/FreeU_Diffusers#video-pipelines</span>
pipe.enable_freeu(b1=<span class="hljs-number">1.2</span>, b2=<span class="hljs-number">1.4</span>, s1=<span class="hljs-number">0.9</span>, s2=<span class="hljs-number">0.2</span>)
video_frames = pipe(prompt, height=<span class="hljs-number">320</span>, width=<span class="hljs-number">576</span>, num_frames=<span class="hljs-number">30</span>, generator=torch.manual_seed(seed)).frames[<span class="hljs-number">0</span>]
export_to_video(video_frames, <span 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