diffusers/v0.25.0/pt/_app/immutable/nodes/179.fa8d47cd.js

import{s as Zt,n as jt,o as kt}from"../chunks/scheduler.182ea377.js";import{S as xt,i as _t,g as s,s as i,p as c,A as Gt,h as n,f as t,c as o,j as u,q as h,m,k as r,v as p,a,r as d,d as f,t as b,u as g}from"../chunks/index.008d68e4.js";import{I as ce}from"../chunks/IconCopyLink.96bbb92b.js";import{C as te}from"../chunks/CodeBlock.5ed6eb7b.js";import{D as $t}from"../chunks/DocNotebookDropdown.bb388256.js";function Wt(ze){let y,he,M,T,oe,$,Le,le,De="Stable Diffusion XL Turbo",de,W,fe,C,Ke=`SDXL Turbo is an adversarial time-distilled <a href="https://huggingface.co/papers/2307.01952" rel="nofollow">Stable Diffusion XL</a> (SDXL) model capable
of running inference in as little as 1 step.`,be,B,Oe="This guide will show you how to use SDXL-Turbo for text-to-image and image-to-image.",ge,S,et="Before you begin, make sure you have the following libraries installed:",ye,X,Me,w,Z,re,H,Qe,ae,tt="Load model checkpoints",we,I,lt='Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the <a href="/docs/diffusers/v0.25.0/pt/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained">from_pretrained()</a> method:',ve,F,Ue,N,at='You can also use the <a href="/docs/diffusers/v0.25.0/pt/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file">from_single_file()</a> method to load a model checkpoint stored in a single file format (<code>.ckpt</code> or <code>.safetensors</code>) from the Hub or locally:',Je,Y,Te,v,j,pe,R,Ae,se,st="Text-to-image",Ze,V,nt="For text-to-image, pass a text prompt. By default, SDXL Turbo generates a 512x512 image, and that resolution gives the best results. You can try setting the <code>height</code> and <code>width</code> parameters to 768x768 or 1024x1024, but you should expect quality degradations when doing so.",je,E,it=`Make sure to set <code>guidance_scale</code> to 0.0 to disable, as the model was trained without it. A single inference step is enough to generate high quality images.
Increasing the number of steps to 2, 3 or 4 should improve image quality.`,ke,L,xe,k,ot='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-text2img.png" alt="generated image of a racoon in a robe"/>',_e,U,x,me,Q,Pe,ne,rt="Image-to-image",Ge,A,pt=`For image-to-image generation, make sure that <code>num_inference_steps * strength</code> is larger or equal to 1.
The image-to-image pipeline will run for <code>int(num_inference_steps * strength)</code> steps, e.g. <code>0.5 * 2.0 = 1</code> step in
our example below.`,$e,P,We,_,mt='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-img2img.png" alt="Image-to-image generation sample using SDXL Turbo"/>',Ce,J,G,ue,q,qe,ie,ut="Speed-up SDXL Turbo even more",Be,z,ct="<li>Compile the UNet if you are using PyTorch version 2 or better. The first inference run will be very slow, but subsequent ones will be much faster.</li>",Se,D,Xe,K,ht="<li>When using the default VAE, keep it in <code>float32</code> to avoid costly <code>dtype</code> conversions before and after each generation. You only need to do this one before your first generation:</li>",He,O,Ie,ee,dt='As an alternative, you can also use a <a href="https://huggingface.co/madebyollin/sdxl-vae-fp16-fix" rel="nofollow">16-bit VAE</a> created by community member <a href="https://huggingface.co/madebyollin" rel="nofollow"><code>@madebyollin</code></a> that does not need to be upcasted to <code>float32</code>.',Fe;return $=new ce({}),W=new $t({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/pt/sdxl_turbo.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/pt/pytorch/sdxl_turbo.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/pt/tensorflow/sdxl_turbo.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/pt/sdxl_turbo.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/pt/pytorch/sdxl_turbo.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/pt/tensorflow/sdxl_turbo.ipynb"}]}}),X=new te({props:{code:"JTIzJTIwdW5jb21tZW50JTIwdG8lMjBpbnN0YWxsJTIwdGhlJTIwbmVjZXNzYXJ5JTIwbGlicmFyaWVzJTIwaW4lMjBDb2xhYiUwQSUyMyFwaXAlMjBpbnN0YWxsJTIwLXElMjBkaWZmdXNlcnMlMjB0cmFuc2Zvcm1lcnMlMjBhY2NlbGVyYXRlJTIwb21lZ2Fjb25m",highlighted:`<span class="hljs-comment"># uncomment to install the necessary libraries in Colab</span>
<span class="hljs-comment">#!pip install -q diffusers transformers accelerate omegaconf</span>`}}),H=new ce({}),F=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHQySW1hZ2UlMkMlMjBBdXRvUGlwZWxpbmVGb3JJbWFnZTJJbWFnZSUwQWltcG9ydCUyMHRvcmNoJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JUZXh0MkltYWdlLmZyb21fcHJldHJhaW5lZCglMjJzdGFiaWxpdHlhaSUyRnNkeGwtdHVyYm8lMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMkMlMjB2YXJpYW50JTNEJTIyZnAxNiUyMiklMEFwaXBlbGluZSUyMCUzRCUyMHBpcGVsaW5lLnRvKCUyMmN1ZGElMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image, AutoPipelineForImage2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(<span class="hljs-string">&quot;stabilityai/sdxl-turbo&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>)
pipeline = pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)`}}),Y=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblhMUGlwZWxpbmUlMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uWExQaXBlbGluZS5mcm9tX3NpbmdsZV9maWxlKCUwQSUyMCUyMCUyMCUyMCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZzdGFiaWxpdHlhaSUyRnNkeGwtdHVyYm8lMkZibG9iJTJGbWFpbiUyRnNkX3hsX3R1cmJvXzEuMF9mcDE2LnNhZmV0ZW5zb3JzJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2KSUwQXBpcGVsaW5lJTIwJTNEJTIwcGlwZWxpbmUudG8oJTIyY3VkYSUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionXLPipeline
<span class="hljs-keyword">import</span> torch

pipeline = StableDiffusionXLPipeline.from_single_file(
    <span class="hljs-string">&quot;https://huggingface.co/stabilityai/sdxl-turbo/blob/main/sd_xl_turbo_1.0_fp16.safetensors&quot;</span>, torch_dtype=torch.float16)
pipeline = pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)`}}),R=new ce({}),L=new te({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline_text2image = AutoPipelineForText2Image.from_pretrained(<span class="hljs-string">&quot;stabilityai/sdxl-turbo&quot;</span>, torch_dtype=torch.float16, variant=<span class="hljs-string">&quot;fp16&quot;</span>)
pipeline_text2image = pipeline_text2image.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A cinematic shot of a baby racoon wearing an intricate italian priest robe.&quot;</span>

image = pipeline_text2image(prompt=prompt, guidance_scale=<span class="hljs-number">0.0</span>, num_inference_steps=<span class="hljs-number">1</span>).images[<span class="hljs-number">0</span>]
image`}}),Q=new ce({}),P=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvckltYWdlMkltYWdlJTBBZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGxvYWRfaW1hZ2UlMkMlMjBtYWtlX2ltYWdlX2dyaWQlMEElMEElMjMlMjB1c2UlMjBmcm9tX3BpcGUlMjB0byUyMGF2b2lkJTIwY29uc3VtaW5nJTIwYWRkaXRpb25hbCUyMG1lbW9yeSUyMHdoZW4lMjBsb2FkaW5nJTIwYSUyMGNoZWNrcG9pbnQlMEFwaXBlbGluZSUyMCUzRCUyMEF1dG9QaXBlbGluZUZvckltYWdlMkltYWdlLmZyb21fcGlwZShwaXBlbGluZV90ZXh0MmltYWdlKS50byglMjJjdWRhJTIyKSUwQSUwQWluaXRfaW1hZ2UlMjAlM0QlMjBsb2FkX2ltYWdlKCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZkYXRhc2V0cyUyRmh1Z2dpbmdmYWNlJTJGZG9jdW1lbnRhdGlvbi1pbWFnZXMlMkZyZXNvbHZlJTJGbWFpbiUyRmRpZmZ1c2VycyUyRmNhdC5wbmclMjIpJTBBaW5pdF9pbWFnZSUyMCUzRCUyMGluaXRfaW1hZ2UucmVzaXplKCg1MTIlMkMlMjA1MTIpKSUwQSUwQXByb21wdCUyMCUzRCUyMCUyMmNhdCUyMHdpemFyZCUyQyUyMGdhbmRhbGYlMkMlMjBsb3JkJTIwb2YlMjB0aGUlMjByaW5ncyUyQyUyMGRldGFpbGVkJTJDJTIwZmFudGFzeSUyQyUyMGN1dGUlMkMlMjBhZG9yYWJsZSUyQyUyMFBpeGFyJTJDJTIwRGlzbmV5JTJDJTIwOGslMjIlMEElMEFpbWFnZSUyMCUzRCUyMHBpcGVsaW5lKHByb21wdCUyQyUyMGltYWdlJTNEaW5pdF9pbWFnZSUyQyUyMHN0cmVuZ3RoJTNEMC41JTJDJTIwZ3VpZGFuY2Vfc2NhbGUlM0QwLjAlMkMlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNEMikuaW1hZ2VzJTVCMCU1RCUwQW1ha2VfaW1hZ2VfZ3JpZCglNUJpbml0X2ltYWdlJTJDJTIwaW1hZ2UlNUQlMkMlMjByb3dzJTNEMSUyQyUyMGNvbHMlM0QyKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForImage2Image
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

<span class="hljs-comment"># use from_pipe to avoid consuming additional memory when loading a checkpoint</span>
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to(<span class="hljs-string">&quot;cuda&quot;</span>)

init_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png&quot;</span>)
init_image = init_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

prompt = <span class="hljs-string">&quot;cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k&quot;</span>

image = pipeline(prompt, image=init_image, strength=<span class="hljs-number">0.5</span>, guidance_scale=<span class="hljs-number">0.0</span>, num_inference_steps=<span class="hljs-number">2</span>).images[<span class="hljs-number">0</span>]
make_image_grid([init_image, image], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`}}),q=new ce({}),D=new te({props:{code:"cGlwZS51bmV0JTIwJTNEJTIwdG9yY2guY29tcGlsZShwaXBlLnVuZXQlMkMlMjBtb2RlJTNEJTIycmVkdWNlLW92ZXJoZWFkJTIyJTJDJTIwZnVsbGdyYXBoJTNEVHJ1ZSk=",highlighted:'pipe.unet = torch.<span class="hljs-built_in">compile</span>(pipe.unet, mode=<span class="hljs-string">&quot;reduce-overhead&quot;</span>, fullgraph=<span class="hljs-literal">True</span>)'}}),O=new te({props:{code:"cGlwZS51cGNhc3RfdmFlKCk=",highlighted:"pipe.upcast_vae()"}}),{c(){y=s("meta"),he=i(),M=s("h1"),T=s("a"),oe=s("span"),c($.$$.fragment),Le=i(),le=s("span"),le.textContent=De,de=i(),c(W.$$.fragment),fe=i(),C=s("p"),C.innerHTML=Ke,be=i(),B=s("p"),B.textContent=Oe,ge=i(),S=s("p"),S.textContent=et,ye=i(),c(X.$$.fragment),Me=i(),w=s("h2"),Z=s("a"),re=s("span"),c(H.$$.fragment),Qe=i(),ae=s("span"),ae.textContent=tt,we=i(),I=s("p"),I.innerHTML=lt,ve=i(),c(F.$$.fragment),Ue=i(),N=s("p"),N.innerHTML=at,Je=i(),c(Y.$$.fragment),Te=i(),v=s("h2"),j=s("a"),pe=s("span"),c(R.$$.fragment),Ae=i(),se=s("span"),se.textContent=st,Ze=i(),V=s("p"),V.innerHTML=nt,je=i(),E=s("p"),E.innerHTML=it,ke=i(),c(L.$$.fragment),xe=i(),k=s("div"),k.innerHTML=ot,_e=i(),U=s("h2"),x=s("a"),me=s("span"),c(Q.$$.fragment),Pe=i(),ne=s("span"),ne.textContent=rt,Ge=i(),A=s("p"),A.innerHTML=pt,$e=i(),c(P.$$.fragment),We=i(),_=s("div"),_.innerHTML=mt,Ce=i(),J=s("h2"),G=s("a"),ue=s("span"),c(q.$$.fragment),qe=i(),ie=s("span"),ie.textContent=ut,Be=i(),z=s("ul"),z.innerHTML=ct,Se=i(),c(D.$$.fragment),Xe=i(),K=s("ul"),K.innerHTML=ht,He=i(),c(O.$$.fragment),Ie=i(),ee=s("p"),ee.innerHTML=dt,this.h()},l(e){const 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