diffusers/pr_8376/en/_app/immutable/nodes/196.46a594af.js

import{s as ms,o as hs,n as de}from"../chunks/scheduler.8c3d61f6.js";import{S as Ms,i as ys,g,s as i,r as f,A as bs,h as w,f as l,c as r,j as fs,u as m,x as J,k as De,y as gs,a as t,v as h,d as M,t as y,w as b}from"../chunks/index.589a98e8.js";import{T as ws}from"../chunks/Tip.42aa8582.js";import{C as Z}from"../chunks/CodeBlock.36627b28.js";import{D as Us}from"../chunks/DocNotebookDropdown.108e4998.js";import{H as ue,E as Js}from"../chunks/EditOnGithub.e5a8d9cb.js";import{H as Ts,a as ze}from"../chunks/HfOption.9804ab8b.js";function $s($){let n,U='<a href="/docs/diffusers/pr_8376/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a> typically generates higher quality images than the default scheduler.',c,o,u;return o=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMExNU0Rpc2NyZXRlU2NoZWR1bGVyJTBBJTBBcGlwZWxpbmUuc2NoZWR1bGVyJTIwJTNEJTIwTE1TRGlzY3JldGVTY2hlZHVsZXIuZnJvbV9jb25maWcocGlwZWxpbmUuc2NoZWR1bGVyLmNvbmZpZyklMEFpbWFnZSUyMCUzRCUyMHBpcGVsaW5lKHByb21wdCUyQyUyMGdlbmVyYXRvciUzRGdlbmVyYXRvcikuaW1hZ2VzJTVCMCU1RCUwQWltYWdl",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LMSDiscreteScheduler

pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),{c(){n=g("p"),n.innerHTML=U,c=i(),f(o.$$.fragment)},l(a){n=w(a,"P",{"data-svelte-h":!0}),J(n)!=="svelte-14pb6u3"&&(n.innerHTML=U),c=r(a),m(o.$$.fragment,a)},m(a,d){t(a,n,d),t(a,c,d),h(o,a,d),u=!0},p:de,i(a){u||(M(o.$$.fragment,a),u=!0)},o(a){y(o.$$.fragment,a),u=!1},d(a){a&&(l(n),l(c)),b(o,a)}}}function Zs($){let n,U='<a href="/docs/diffusers/pr_8376/en/api/schedulers/euler#diffusers.EulerDiscreteScheduler">EulerDiscreteScheduler</a> can generate higher quality images in just 30 steps.',c,o,u;return o=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEV1bGVyRGlzY3JldGVTY2hlZHVsZXIlMEElMEFwaXBlbGluZS5zY2hlZHVsZXIlMjAlM0QlMjBFdWxlckRpc2NyZXRlU2NoZWR1bGVyLmZyb21fY29uZmlnKHBpcGVsaW5lLnNjaGVkdWxlci5jb25maWcpJTBBaW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IpLmltYWdlcyU1QjAlNUQlMEFpbWFnZQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> EulerDiscreteScheduler

pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),{c(){n=g("p"),n.innerHTML=U,c=i(),f(o.$$.fragment)},l(a){n=w(a,"P",{"data-svelte-h":!0}),J(n)!=="svelte-1s0qwqx"&&(n.innerHTML=U),c=r(a),m(o.$$.fragment,a)},m(a,d){t(a,n,d),t(a,c,d),h(o,a,d),u=!0},p:de,i(a){u||(M(o.$$.fragment,a),u=!0)},o(a){y(o.$$.fragment,a),u=!1},d(a){a&&(l(n),l(c)),b(o,a)}}}function js($){let n,U='<a href="/docs/diffusers/pr_8376/en/api/schedulers/euler_ancestral#diffusers.EulerAncestralDiscreteScheduler">EulerAncestralDiscreteScheduler</a> can generate higher quality images in just 30 steps.',c,o,u;return o=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEV1bGVyQW5jZXN0cmFsRGlzY3JldGVTY2hlZHVsZXIlMEElMEFwaXBlbGluZS5zY2hlZHVsZXIlMjAlM0QlMjBFdWxlckFuY2VzdHJhbERpc2NyZXRlU2NoZWR1bGVyLmZyb21fY29uZmlnKHBpcGVsaW5lLnNjaGVkdWxlci5jb25maWcpJTBBaW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IpLmltYWdlcyU1QjAlNUQlMEFpbWFnZQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> EulerAncestralDiscreteScheduler

pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),{c(){n=g("p"),n.innerHTML=U,c=i(),f(o.$$.fragment)},l(a){n=w(a,"P",{"data-svelte-h":!0}),J(n)!=="svelte-nk2o5r"&&(n.innerHTML=U),c=r(a),m(o.$$.fragment,a)},m(a,d){t(a,n,d),t(a,c,d),h(o,a,d),u=!0},p:de,i(a){u||(M(o.$$.fragment,a),u=!0)},o(a){y(o.$$.fragment,a),u=!1},d(a){a&&(l(n),l(c)),b(o,a)}}}function vs($){let n,U='<a href="/docs/diffusers/pr_8376/en/api/schedulers/multistep_dpm_solver#diffusers.DPMSolverMultistepScheduler">DPMSolverMultistepScheduler</a> provides a balance between speed and quality and can generate higher quality images in just 20 steps.',c,o,u;return o=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERQTVNvbHZlck11bHRpc3RlcFNjaGVkdWxlciUwQSUwQXBpcGVsaW5lLnNjaGVkdWxlciUyMCUzRCUyMERQTVNvbHZlck11bHRpc3RlcFNjaGVkdWxlci5mcm9tX2NvbmZpZyhwaXBlbGluZS5zY2hlZHVsZXIuY29uZmlnKSUwQWltYWdlJTIwJTNEJTIwcGlwZWxpbmUocHJvbXB0JTJDJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yKS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2U=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DPMSolverMultistepScheduler

pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
image = pipeline(prompt, generator=generator).images[<span class="hljs-number">0</span>]
image`,wrap:!1}}),{c(){n=g("p"),n.innerHTML=U,c=i(),f(o.$$.fragment)},l(a){n=w(a,"P",{"data-svelte-h":!0}),J(n)!=="svelte-18z0dgt"&&(n.innerHTML=U),c=r(a),m(o.$$.fragment,a)},m(a,d){t(a,n,d),t(a,c,d),h(o,a,d),u=!0},p:de,i(a){u||(M(o.$$.fragment,a),u=!0)},o(a){y(o.$$.fragment,a),u=!1},d(a){a&&(l(n),l(c)),b(o,a)}}}function Ws($){let n,U,c,o,u,a,d,v;return n=new ze({props:{id:"schedulers",option:"LMSDiscreteScheduler",$$slots:{default:[$s]},$$scope:{ctx:$}}}),c=new ze({props:{id:"schedulers",option:"EulerDiscreteScheduler",$$slots:{default:[Zs]},$$scope:{ctx:$}}}),u=new ze({props:{id:"schedulers",option:"EulerAncestralDiscreteScheduler",$$slots:{default:[js]},$$scope:{ctx:$}}}),d=new ze({props:{id:"schedulers",option:"DPMSolverMultistepScheduler",$$slots:{default:[vs]},$$scope:{ctx:$}}}),{c(){f(n.$$.fragment),U=i(),f(c.$$.fragment),o=i(),f(u.$$.fragment),a=i(),f(d.$$.fragment)},l(p){m(n.$$.fragment,p),U=r(p),m(c.$$.fragment,p),o=r(p),m(u.$$.fragment,p),a=r(p),m(d.$$.fragment,p)},m(p,T){h(n,p,T),t(p,U,T),h(c,p,T),t(p,o,T),h(u,p,T),t(p,a,T),h(d,p,T),v=!0},p(p,T){const V={};T&2&&(V.$$scope={dirty:T,ctx:p}),n.$set(V);const j={};T&2&&(j.$$scope={dirty:T,ctx:p}),c.$set(j);const oe={};T&2&&(oe.$$scope={dirty:T,ctx:p}),u.$set(oe);const k={};T&2&&(k.$$scope={dirty:T,ctx:p}),d.$set(k)},i(p){v||(M(n.$$.fragment,p),M(c.$$.fragment,p),M(u.$$.fragment,p),M(d.$$.fragment,p),v=!0)},o(p){y(n.$$.fragment,p),y(c.$$.fragment,p),y(u.$$.fragment,p),y(d.$$.fragment,p),v=!1},d(p){p&&(l(U),l(o),l(a)),b(n,p),b(c,p),b(u,p),b(d,p)}}}function Gs($){let n,U='The <code>FlaxLMSDiscreteScheduler</code> and <code>FlaxDDPMScheduler</code> are not compatible with the <a href="/docs/diffusers/pr_8376/en/api/pipelines/stable_diffusion/text2img#diffusers.FlaxStableDiffusionPipeline">FlaxStableDiffusionPipeline</a> yet.';return{c(){n=g("p"),n.innerHTML=U},l(c){n=w(c,"P",{"data-svelte-h":!0}),J(n)!=="svelte-1ao5spj"&&(n.innerHTML=U)},m(c,o){t(c,n,o)},p:de,d(c){c&&l(n)}}}function _s($){let n,U,c,o,u,a,d,v,p,T="Diffusion pipelines are a collection of interchangeable schedulers and models that can be mixed and matched to tailor a pipeline to a specific use case. The scheduler encapsulates the entire denoising process such as the number of denoising steps and the algorithm for finding the denoised sample. A scheduler is not parameterized or trained so they don’t take very much memory. The model is usually only concerned with the forward pass of going from a noisy input to a less noisy sample.",V,j,oe='This guide will show you how to load schedulers and models to customize a pipeline. You’ll use the <a href="https://hf.co/runwayml/stable-diffusion-v1-5" rel="nofollow">runwayml/stable-diffusion-v1-5</a> checkpoint throughout this guide, so let’s load it first.',k,C,fe,x,Le="You can see what scheduler this pipeline uses with the <code>pipeline.scheduler</code> attribute.",me,I,he,R,Me,B,Pe='Schedulers are defined by a configuration file that can be used by a variety of schedulers. Load a scheduler with the <a href="/docs/diffusers/pr_8376/en/api/schedulers/overview#diffusers.SchedulerMixin.from_pretrained">SchedulerMixin.from_pretrained()</a> method, and specify the <code>subfolder</code> parameter to load the configuration file into the correct subfolder of the pipeline repository.',ye,N,Ae='For example, to load the <a href="/docs/diffusers/pr_8376/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>:',be,X,ge,E,Ke="Then you can pass the newly loaded scheduler to the pipeline.",we,H,Ue,F,Je,Q,Oe="Schedulers have their own unique strengths and weaknesses, making it difficult to quantitatively compare which scheduler works best for a pipeline. You typically have to make a trade-off between denoising speed and denoising quality. We recommend trying out different schedulers to find one that works best for your use case. Call the <code>pipeline.scheduler.compatibles</code> attribute to see what schedulers are compatible with a pipeline.",Te,Y,es='Let’s compare the <a href="/docs/diffusers/pr_8376/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, <a href="/docs/diffusers/pr_8376/en/api/schedulers/euler#diffusers.EulerDiscreteScheduler">EulerDiscreteScheduler</a>, <a href="/docs/diffusers/pr_8376/en/api/schedulers/euler_ancestral#diffusers.EulerAncestralDiscreteScheduler">EulerAncestralDiscreteScheduler</a>, and the <a href="/docs/diffusers/pr_8376/en/api/schedulers/multistep_dpm_solver#diffusers.DPMSolverMultistepScheduler">DPMSolverMultistepScheduler</a> on the following prompt and seed.',$e,q,Ze,D,ss='To change the pipelines scheduler, use the <a href="/docs/diffusers/pr_8376/en/api/configuration#diffusers.ConfigMixin.from_config">from_config()</a> method to load a different scheduler’s <code>pipeline.scheduler.config</code> into the pipeline.',je,W,ve,G,ls='<div><img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_lms.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">LMSDiscreteScheduler</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_discrete.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">EulerDiscreteScheduler</figcaption></div>',We,_,ts='<div><img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_euler_ancestral.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">EulerAncestralDiscreteScheduler</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/diffusers_docs/astronaut_dpm.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">DPMSolverMultistepScheduler</figcaption></div>',Ge,z,as="Most images look very similar and are comparable in quality. Again, it often comes down to your specific use case so a good approach is to run multiple different schedulers and compare the results.",_e,L,Se,P,ns='To compare Flax schedulers, you need to additionally load the scheduler state into the model parameters. For example, let’s change the default scheduler in <a href="/docs/diffusers/pr_8376/en/api/pipelines/stable_diffusion/text2img#diffusers.FlaxStableDiffusionPipeline">FlaxStableDiffusionPipeline</a> to use the super fast <code>FlaxDPMSolverMultistepScheduler</code>.',Ve,S,ke,A,Ce,K,is="Then you can take advantage of Flax’s compatibility with TPUs to generate a number of images in parallel. You’ll need to make a copy of the model parameters for each available device and then split the inputs across them to generate your desired number of images.",xe,O,Ie,ee,Re,se,rs='Models are loaded from the <a href="/docs/diffusers/pr_8376/en/api/models/overview#diffusers.ModelMixin.from_pretrained">ModelMixin.from_pretrained()</a> method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, <a href="/docs/diffusers/pr_8376/en/api/models/overview#diffusers.ModelMixin.from_pretrained">from_pretrained()</a> reuses files in the cache instead of re-downloading them.',Be,le,ps='Models can be loaded from a subfolder with the <code>subfolder</code> argument. For example, the model weights for <a href="https://hf.co/runwayml/stable-diffusion-v1-5" rel="nofollow">runwayml/stable-diffusion-v1-5</a> are stored in the <a href="https://hf.co/runwayml/stable-diffusion-v1-5/tree/main/unet" rel="nofollow">unet</a> subfolder.',Ne,te,Xe,ae,os='They can also be directly loaded from a <a href="https://huggingface.co/google/ddpm-cifar10-32/tree/main" rel="nofollow">repository</a>.',Ee,ne,He,ie,cs='To load and save model variants, specify the <code>variant</code> argument in <a href="/docs/diffusers/pr_8376/en/api/models/overview#diffusers.ModelMixin.from_pretrained">ModelMixin.from_pretrained()</a> and <a href="/docs/diffusers/pr_8376/en/api/models/overview#diffusers.ModelMixin.save_pretrained">ModelMixin.save_pretrained()</a>.',Fe,re,Qe,pe,Ye,ce,qe;return u=new ue({props:{title:"Load schedulers and models",local:"load-schedulers-and-models",headingTag:"h1"}}),d=new Us({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/schedulers.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/schedulers.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/schedulers.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/schedulers.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/schedulers.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/schedulers.ipynb"}]}}),C=new Z({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwRGlmZnVzaW9uUGlwZWxpbmUlMEElMEFwaXBlbGluZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiUyQyUyMHVzZV9zYWZldGVuc29ycyUzRFRydWUlMEEpLnRvKCUyMmN1ZGElMjIp",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,wrap:!1}}),I=new Z({props:{code:"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",highlighted:`pipeline.scheduler
PNDMScheduler {
  <span class="hljs-string">&quot;_class_name&quot;</span>: <span class="hljs-string">&quot;PNDMScheduler&quot;</span>,
  <span class="hljs-string">&quot;_diffusers_version&quot;</span>: <span class="hljs-string">&quot;0.21.4&quot;</span>,
  <span class="hljs-string">&quot;beta_end&quot;</span>: <span class="hljs-number">0.012</span>,
  <span class="hljs-string">&quot;beta_schedule&quot;</span>: <span class="hljs-string">&quot;scaled_linear&quot;</span>,
  <span class="hljs-string">&quot;beta_start&quot;</span>: <span class="hljs-number">0.00085</span>,
  <span class="hljs-string">&quot;clip_sample&quot;</span>: false,
  <span class="hljs-string">&quot;num_train_timesteps&quot;</span>: <span class="hljs-number">1000</span>,
  <span class="hljs-string">&quot;set_alpha_to_one&quot;</span>: false,
  <span class="hljs-string">&quot;skip_prk_steps&quot;</span>: true,
  <span class="hljs-string">&quot;steps_offset&quot;</span>: <span class="hljs-number">1</span>,
  <span class="hljs-string">&quot;timestep_spacing&quot;</span>: <span class="hljs-string">&quot;leading&quot;</span>,
  <span class="hljs-string">&quot;trained_betas&quot;</span>: null
}`,wrap:!1}}),R=new ue({props:{title:"Load a scheduler",local:"load-a-scheduler",headingTag:"h2"}}),X=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERESU1TY2hlZHVsZXIlMkMlMjBEaWZmdXNpb25QaXBlbGluZSUwQSUwQWRkaW0lMjAlM0QlMjBERElNU2NoZWR1bGVyLmZyb21fcHJldHJhaW5lZCglMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUyQyUyMHN1YmZvbGRlciUzRCUyMnNjaGVkdWxlciUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMScheduler, DiffusionPipeline

ddim = DDIMScheduler.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)`,wrap:!1}}),H=new Z({props:{code:"cGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMkMlMjBzY2hlZHVsZXIlM0RkZGltJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSUwQSkudG8oJTIyY3VkYSUyMik=",highlighted:`pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, scheduler=ddim, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,wrap:!1}}),F=new ue({props:{title:"Compare schedulers",local:"compare-schedulers",headingTag:"h2"}}),q=new Z({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A photograph of an astronaut riding a horse on Mars, high resolution, high definition.&quot;</span>
generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">8</span>)`,wrap:!1}}),W=new Ts({props:{id:"schedulers",options:["LMSDiscreteScheduler","EulerDiscreteScheduler","EulerAncestralDiscreteScheduler","DPMSolverMultistepScheduler"],$$slots:{default:[Ws]},$$scope:{ctx:$}}}),L=new ue({props:{title:"Flax schedulers",local:"flax-schedulers",headingTag:"h3"}}),S=new ws({props:{warning:!0,$$slots:{default:[Gs]},$$scope:{ctx:$}}}),A=new Z({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> jax
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> flax.jax_utils <span class="hljs-keyword">import</span> replicate
<span class="hljs-keyword">from</span> flax.training.common_utils <span class="hljs-keyword">import</span> shard
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler

scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>,
    subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>
)
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>,
    scheduler=scheduler,
    revision=<span class="hljs-string">&quot;bf16&quot;</span>,
    dtype=jax.numpy.bfloat16,
)
params[<span class="hljs-string">&quot;scheduler&quot;</span>] = scheduler_state`,wrap:!1}}),O=new Z({props:{code:"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",highlighted:`<span class="hljs-comment"># Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)</span>
prompt = <span class="hljs-string">&quot;A photograph of an astronaut riding a horse on Mars, high resolution, high definition.&quot;</span>
num_samples = jax.device_count()
prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)

prng_seed = jax.random.PRNGKey(<span class="hljs-number">0</span>)
num_inference_steps = <span class="hljs-number">25</span>

<span class="hljs-comment"># shard inputs and rng</span>
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)

images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=<span class="hljs-literal">True</span>).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-<span class="hljs-number">3</span>:])))`,wrap:!1}}),ee=new ue({props:{title:"Models",local:"models",headingTag:"h2"}}),te=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVOZXQyRENvbmRpdGlvbk1vZGVsJTBBJTBBdW5ldCUyMCUzRCUyMFVOZXQyRENvbmRpdGlvbk1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUyQyUyMHN1YmZvbGRlciUzRCUyMnVuZXQlMjIlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DConditionModel

unet = UNet2DConditionModel.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, subfolder=<span class="hljs-string">&quot;unet&quot;</span>, use_safetensors=<span class="hljs-literal">True</span>)`,wrap:!1}}),ne=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVOZXQyRE1vZGVsJTBBJTBBdW5ldCUyMCUzRCUyMFVOZXQyRE1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZkZHBtLWNpZmFyMTAtMzIlMjIlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DModel

unet = UNet2DModel.from_pretrained(<span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>, use_safetensors=<span class="hljs-literal">True</span>)`,wrap:!1}}),re=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVOZXQyRENvbmRpdGlvbk1vZGVsJTBBJTBBdW5ldCUyMCUzRCUyMFVOZXQyRENvbmRpdGlvbk1vZGVsLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUyQyUyMHN1YmZvbGRlciUzRCUyMnVuZXQlMjIlMkMlMjB2YXJpYW50JTNEJTIybm9uX2VtYSUyMiUyQyUyMHVzZV9zYWZldGVuc29ycyUzRFRydWUlMEEpJTBBdW5ldC5zYXZlX3ByZXRyYWluZWQoJTIyLiUyRmxvY2FsLXVuZXQlMjIlMkMlMjB2YXJpYW50JTNEJTIybm9uX2VtYSUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DConditionModel

unet = UNet2DConditionModel.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, subfolder=<span class="hljs-string">&quot;unet&quot;</span>, variant=<span class="hljs-string">&quot;non_ema&quot;</span>, use_safetensors=<span class="hljs-literal">True</span>
)
unet.save_pretrained(<span class="hljs-string">&quot;./local-unet&quot;</span>, variant=<span class="hljs-string">&quot;non_ema&quot;</span>)`,wrap:!1}}),pe=new Js({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/using-diffusers/schedulers.md"}}),{c(){n=g("meta"),U=i(),c=g("p"),o=i(),f(u.$$.fragment),a=i(),f(d.$$.fragment),v=i(),p=g("p"),p.textContent=T,V=i(),j=g("p"),j.innerHTML=oe,k=i(),f(C.$$.fragment),fe=i(),x=g("p"),x.innerHTML=Le,me=i(),f(I.$$.fragment),he=i(),f(R.$$.fragment),Me=i(),B=g("p"),B.innerHTML=Pe,ye=i(),N=g("p"),N.innerHTML=Ae,be=i(),f(X.$$.fragment),ge=i(),E=g("p"),E.textContent=Ke,we=i(),f(H.$$.fragment),Ue=i(),f(F.$$.fragment),Je=i(),Q=g("p"),Q.innerHTML=Oe,Te=i(),Y=g("p"),Y.innerHTML=es,$e=i(),f(q.$$.fragment),Ze=i(),D=g("p"),D.innerHTML=ss,je=i(),f(W.$$.fragment),ve=i(),G=g("div"),G.innerHTML=ls,We=i(),_=g("div"),_.innerHTML=ts,Ge=i(),z=g("p"),z.textContent=as,_e=i(),f(L.$$.fragment),Se=i(),P=g("p"),P.innerHTML=ns,Ve=i(),f(S.$$.fragment),ke=i(),f(A.$$.fragment),Ce=i(),K=g("p"),K.textContent=is,xe=i(),f(O.$$.fragment),Ie=i(),f(ee.$$.fragment),Re=i(),se=g("p"),se.innerHTML=rs,Be=i(),le=g("p"),le.innerHTML=ps,Ne=i(),f(te.$$.fragment),Xe=i(),ae=g("p"),ae.innerHTML=os,Ee=i(),f(ne.$$.fragment),He=i(),ie=g("p"),ie.innerHTML=cs,Fe=i(),f(re.$$.fragment),Qe=i(),f(pe.$$.fragment),Ye=i(),ce=g("p"),this.h()},l(e){const 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