import{S as br,i as wr,s as Dr,e as a,k as l,w as y,t as f,M as yr,c as s,d as r,m as p,a as i,x as T,h as d,b as o,G as t,g as c,y as $,L as Tr,q as P,o as E,B as k,v as $r}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Gt}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Pr}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ee}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Er(Wt){let u,ke,m,x,oe,G,Ye,ne,Qe,xe,g,S,le,W,Ze,pe,et,Se,H,U,tt,rt,Ae,Z,at,Ie,ee,fe,st,Me,A,it,O,ot,nt,Ne,v,I,de,F,lt,ce,pt,Le,M,he,_,ue,ft,dt,me,ct,ht,te,ut,mt,ge,b,ve,X,gt,vt,_e,be,_t,bt,re,wt,Ce,w,N,we,V,Dt,De,yt,je,B,qe,D,L,ye,K,Tt,Te,$t,Ge,h,R,Pt,z,Et,ae,kt,xt,St,C,J,At,$e,It,Mt,j,Y,Nt,Q,Lt,Pe,Ct,jt,We;return G=new Ee({}),W=new Ee({}),F=new Ee({}),V=new Ee({}),B=new Pr({props:{code:`from diffusers import DiTPipeline, DPMSolverMultistepScheduler
import torch

pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

# pick words from Imagenet class labels
pipe.labels  # to print all available words

# pick words that exist in ImageNet
words = ["white shark", "umbrella"]

class_ids = pipe.get_label_ids(words)

generator = torch.manual_seed(33)
output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator)

image = output.images[0]  # label 'white shark'`,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>`}}),K=new Ee({}),R=new Gt({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/v0.16.0/en/api/models#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;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator</a> to make 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;
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/v0.16.0/en/api/diffusion_pipeline#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/dit/pipeline_dit.py#L86"}}),Y=new Gt({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/v0.16.0/src/diffusers/pipelines/dit/pipeline_dit.py#L63",returnDescription:`
<p>Class ids to be processed by pipeline.</p>
`,returnType:`
<p><code>list</code> of <code>int</code></p>
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