import{S as _a,i as wa,s as ya,e as s,k as n,w as T,t as d,M as Ta,c as r,d as a,m as p,a as o,x as M,h as f,b as i,G as t,g as c,y as D,L as Ma,q as E,o as $,B as J,v as Da}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Zt}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Ea}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as $e}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function $a(At){let u,Je,m,I,ie,Z,Qe,le,Ke,Ie,g,P,ne,A,Oe,pe,et,Pe,S,V,tt,at,ke,O,st,xe,ee,de,rt,Ge,k,ot,F,it,lt,Ne,b,x,fe,L,nt,ce,pt,Ue,G,he,v,ue,dt,ft,me,ct,ht,te,ut,mt,ge,_,be,C,gt,bt,ve,_e,vt,_t,ae,wt,je,w,N,we,H,yt,ye,Tt,Be,X,We,y,U,Te,R,Mt,Me,Dt,Ze,h,Y,Et,q,$t,se,Jt,It,Pt,j,z,kt,De,xt,Gt,B,Q,Nt,K,Ut,Ee,jt,Bt,Ae;return Z=new $e({}),A=new $e({}),L=new $e({}),H=new $e({}),X=new Ea({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiTPipeline, DPMSolverMultistepScheduler
<span class="hljs-keyword">import</span> torch

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

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

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

class_ids = pipe.get_label_ids(words)

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

image = output.images[<span class="hljs-number">0</span>]  <span class="hljs-comment"># label &#x27;white shark&#x27;</span>`}}),R=new $e({}),Y=new Zt({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/v0.18.2/en/api/models/transformer2d#diffusers.Transformer2DModel">Transformer2DModel</a>) &#x2014;
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List of imagenet class labels for the images to be generated.`,name:"class_labels"},{anchor:"diffusers.DiTPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
Scale of the guidance signal.`,name:"guidance_scale"},{anchor:"diffusers.DiTPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
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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;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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;
The output format of the generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.DiTPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.18.2/en/api/outputs#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/pipelines/dit/pipeline_dit.py#L86"}}),Q=new Zt({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; label strings to be mapped to class ids.",name:"label"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/pipelines/dit/pipeline_dit.py#L63",returnDescription:`
<p>Class ids to be processed by pipeline.</p>
`,returnType:`
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