import{S as Tt,i as wt,s as $t,e as a,k as f,w as D,t as c,M as Mt,c as r,d as t,m as u,a as n,x as P,h as d,b as p,G as s,g as m,y as J,q as E,o as j,B as x,v as It,L as Dt}from"../../../chunks/vendor-hf-doc-builder.js";import{T as Pt}from"../../../chunks/Tip-hf-doc-builder.js";import{D as Je}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Jt}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as st}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Et}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function jt(Q){let l,T,h,g,_,o,y,k;return{c(){l=a("p"),T=c("Make sure to check out the Schedulers "),h=a("a"),g=c("guide"),_=c(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),o=a("a"),y=c("reuse components across pipelines"),k=c(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l(G){l=r(G,"P",{});var v=n(l);T=d(v,"Make sure to check out the Schedulers "),h=r(v,"A",{href:!0});var w=n(h);g=d(w,"guide"),w.forEach(t),_=d(v," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),o=r(v,"A",{href:!0});var $=n(o);y=d($,"reuse components across pipelines"),$.forEach(t),k=d(v," section to learn how to efficiently load the same components into multiple pipelines."),v.forEach(t),this.h()},h(){p(h,"href","/using-diffusers/schedulers"),p(o,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m(G,v){m(G,l,v),s(l,T),s(l,h),s(h,g),s(l,_),s(l,o),s(o,y),s(l,k)},d(G){G&&t(l)}}}function xt(Q){let l,T,h,g,_;return g=new Jt({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpVFBpcGVsaW5lJTJDJTIwRFBNU29sdmVyTXVsdGlzdGVwU2NoZWR1bGVyJTBBaW1wb3J0JTIwdG9yY2glMEElMEFwaXBlJTIwJTNEJTIwRGlUUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMmZhY2Vib29rJTJGRGlULVhMLTItMjU2JTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2KSUwQXBpcGUuc2NoZWR1bGVyJTIwJTNEJTIwRFBNU29sdmVyTXVsdGlzdGVwU2NoZWR1bGVyLmZyb21fY29uZmlnKHBpcGUuc2NoZWR1bGVyLmNvbmZpZyklMEFwaXBlJTIwJTNEJTIwcGlwZS50byglMjJjdWRhJTIyKSUwQSUwQSUyMyUyMHBpY2slMjB3b3JkcyUyMGZyb20lMjBJbWFnZW5ldCUyMGNsYXNzJTIwbGFiZWxzJTBBcGlwZS5sYWJlbHMlMjAlMjAlMjMlMjB0byUyMHByaW50JTIwYWxsJTIwYXZhaWxhYmxlJTIwd29yZHMlMEElMEElMjMlMjBwaWNrJTIwd29yZHMlMjB0aGF0JTIwZXhpc3QlMjBpbiUyMEltYWdlTmV0JTBBd29yZHMlMjAlM0QlMjAlNUIlMjJ3aGl0ZSUyMHNoYXJrJTIyJTJDJTIwJTIydW1icmVsbGElMjIlNUQlMEElMEFjbGFzc19pZHMlMjAlM0QlMjBwaXBlLmdldF9sYWJlbF9pZHMod29yZHMpJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwdG9yY2gubWFudWFsX3NlZWQoMzMpJTBBb3V0cHV0JTIwJTNEJTIwcGlwZShjbGFzc19sYWJlbHMlM0RjbGFzc19pZHMlMkMlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNEMjUlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IpJTBBJTBBaW1hZ2UlMjAlM0QlMjBvdXRwdXQuaW1hZ2VzJTVCMCU1RCUyMCUyMCUyMyUyMGxhYmVsJTIwJ3doaXRlJTIwc2hhcmsn",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiTPipeline, DPMSolverMultistepScheduler
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiTPipeline.from_pretrained(<span class="hljs-string">&quot;facebook/DiT-XL-2-256&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># pick words from Imagenet class labels</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.labels  <span class="hljs-comment"># to print all available words</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># pick words that exist in ImageNet</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>words = [<span class="hljs-string">&quot;white shark&quot;</span>, <span class="hljs-string">&quot;umbrella&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>class_ids = pipe.get_label_ids(words)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.manual_seed(<span class="hljs-number">33</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>output = pipe(class_labels=class_ids, num_inference_steps=<span class="hljs-number">25</span>, generator=generator)

<span class="hljs-meta">&gt;&gt;&gt; </span>image = output.images[<span class="hljs-number">0</span>]  <span class="hljs-comment"># label &#x27;white shark&#x27;</span>`}}),{c(){l=a("p"),T=c("Examples:"),h=f(),D(g.$$.fragment)},l(o){l=r(o,"P",{});var y=n(l);T=d(y,"Examples:"),y.forEach(t),h=u(o),P(g.$$.fragment,o)},m(o,y){m(o,l,y),s(l,T),m(o,h,y),J(g,o,y),_=!0},p:Dt,i(o){_||(E(g.$$.fragment,o),_=!0)},o(o){j(g.$$.fragment,o),_=!1},d(o){o&&t(l),o&&t(h),x(g,o)}}}function kt(Q){let l,T,h,g,_,o,y,k,G,v,w,$,Ee,je,ue,K,xe,me,ee,ae,ke,he,W,Ge,F,Ne,Ue,ge,Z,be,N,S,re,X,Be,ne,We,_e,b,C,Ze,oe,Se,Ve,O,Le,te,Ae,Fe,Xe,M,R,Ce,ie,Oe,Re,V,He,L,H,qe,le,Ye,ye,U,A,pe,q,ze,ce,Qe,ve,B,Y,Ke,de,et,Te;return o=new st({}),Z=new Pt({props:{$$slots:{default:[jt]},$$scope:{ctx:Q}}}),X=new st({}),C=new Je({props:{name:"class diffusers.DiTPipeline",anchor:"diffusers.DiTPipeline",parameters:[{name:"transformer",val:": Transformer2DModel"},{name:"vae",val:": AutoencoderKL"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"id2label",val:": typing.Union[typing.Dict[int, str], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.DiTPipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/v0.19.2/en/api/models/transformer2d#diffusers.Transformer2DModel">Transformer2DModel</a>) &#x2014;
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generation deterministic.`,name:"generator"},{anchor:"diffusers.DiTPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 250) &#x2014;
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expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.DiTPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
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<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/v0.19.2/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
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`,returnType:`
<p><a
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`}}),V=new Et({props:{anchor:"diffusers.DiTPipeline.__call__.example",$$slots:{default:[xt]},$$scope:{ctx:Q}}}),H=new Je({props:{name:"get_label_ids",anchor:"diffusers.DiTPipeline.get_label_ids",parameters:[{name:"label",val:": typing.Union[str, typing.List[str]]"}],parametersDescription:[{anchor:"diffusers.DiTPipeline.get_label_ids.label",description:`<strong>label</strong> (<code>str</code> or <code>dict</code> of <code>str</code>) &#x2014;
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<p>Class ids to be processed by pipeline.</p>
`,returnType:`
<p><code>list</code> of <code>int</code></p>
`}}),q=new st({}),Y=new Je({props:{name:"class diffusers.ImagePipelineOutput",anchor:"diffusers.ImagePipelineOutput",parameters:[{name:"images",val:": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"}],parametersDescription:[{anchor:"diffusers.ImagePipelineOutput.images",description:`<strong>images</strong> (<code>List[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
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