diffusers/pr_12262/en/_app/immutable/nodes/86.ea3cb6bc.js

import{s as Se,o as Qe,n as We}from"../chunks/scheduler.8c3d61f6.js";import{S as Fe,i as Xe,g as u,s as n,r as o,A as Ye,h as p,f as s,c as a,j as S,u as f,x as b,k as Q,y as $,a as i,v as d,d as m,t as c,w as g}from"../chunks/index.da70eac4.js";import{T as Re}from"../chunks/Tip.1d9b8c37.js";import{D as ie}from"../chunks/Docstring.2187c15d.js";import{C as Ee}from"../chunks/CodeBlock.a9c4becf.js";import{H as ne,E as Ke}from"../chunks/getInferenceSnippets.676f6ee5.js";function et(F){let r,y="To check a specific pipeline or model output, refer to its corresponding API documentation.";return{c(){r=u("p"),r.textContent=y},l(l){r=p(l,"P",{"data-svelte-h":!0}),b(r)!=="svelte-60gd7d"&&(r.textContent=y)},m(l,w){i(l,r,w)},p:We,d(l){l&&s(r)}}}function tt(F){let r,y=`You can’t unpack a <code>BaseOutput</code> directly. Use the <a href="/docs/diffusers/pr_12262/en/api/outputs#diffusers.utils.BaseOutput.to_tuple">to_tuple()</a> method to convert it to a tuple
first.`;return{c(){r=u("p"),r.innerHTML=y},l(l){r=p(l,"P",{"data-svelte-h":!0}),b(r)!=="svelte-8us4eu"&&(r.innerHTML=y)},m(l,w){i(l,r,w)},p:We,d(l){l&&s(r)}}}function st(F){let r,y,l,w,O,ae,C,Ue='All model outputs are subclasses of <a href="/docs/diffusers/pr_12262/en/api/outputs#diffusers.utils.BaseOutput">BaseOutput</a>, data structures containing all the information returned by the model. The outputs can also be used as tuples or dictionaries.',re,M,ze="For example:",ue,H,pe,B,De='The <code>outputs</code> object is a <a href="/docs/diffusers/pr_12262/en/api/pipelines/dit#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> which means it has an image attribute.',le,k,Je="You can access each attribute as you normally would or with a keyword lookup, and if that attribute is not returned by the model, you will get <code>None</code>:",oe,V,fe,E,Ae=`When considering the <code>outputs</code> object as a tuple, it only considers the attributes that don’t have <code>None</code> values.
For instance, retrieving an image by indexing into it returns the tuple <code>(outputs.images)</code>:`,de,U,me,T,ce,z,ge,h,D,Oe,X,Ne=`Base class for all model outputs as dataclass. Has a <code>__getitem__</code> that allows indexing by integer or slice (like a
tuple) or strings (like a dictionary) that will ignore the <code>None</code> attributes. Otherwise behaves like a regular
Python dictionary.`,Ce,I,Me,L,J,He,Y,je="Convert self to a tuple containing all the attributes/keys that are not <code>None</code>.",$e,A,he,v,N,Be,K,Ze="Output class for image pipelines.",be,j,_e,P,Z,ke,ee,qe="Output class for audio pipelines.",ye,q,ve,x,G,Ve,te,Ge="Output class for joint image-text pipelines.",Pe,R,xe,se,we;return O=new ne({props:{title:"Outputs",local:"outputs",headingTag:"h1"}}),H=new Ee({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERESU1QaXBlbGluZSUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwRERJTVBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZkZHBtLWNpZmFyMTAtMzIlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMHBpcGVsaW5lKCk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMPipeline

pipeline = DDIMPipeline.from_pretrained(<span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>)
outputs = pipeline()`,wrap:!1}}),V=new Ee({props:{code:"b3V0cHV0cy5pbWFnZXMlMEFvdXRwdXRzJTVCJTIyaW1hZ2VzJTIyJTVE",highlighted:`outputs.images
outputs[<span class="hljs-string">&quot;images&quot;</span>]`,wrap:!1}}),U=new Ee({props:{code:"b3V0cHV0cyU1QiUzQTElNUQ=",highlighted:'outputs[:<span class="hljs-number">1</span>]',wrap:!1}}),T=new Re({props:{$$slots:{default:[et]},$$scope:{ctx:F}}}),z=new ne({props:{title:"BaseOutput",local:"diffusers.utils.BaseOutput",headingTag:"h2"}}),D=new ie({props:{name:"class diffusers.utils.BaseOutput",anchor:"diffusers.utils.BaseOutput",parameters:"",source:"https://github.com/huggingface/diffusers/blob/vr_12262/src/diffusers/utils/outputs.py#L40"}}),I=new Re({props:{warning:!0,$$slots:{default:[tt]},$$scope:{ctx:F}}}),J=new ie({props:{name:"to_tuple",anchor:"diffusers.utils.BaseOutput.to_tuple",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12262/src/diffusers/utils/outputs.py#L134"}}),A=new ne({props:{title:"ImagePipelineOutput",local:"diffusers.ImagePipelineOutput",headingTag:"h2"}}),N=new ie({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;
List of denoised PIL images of length <code>batch_size</code> or NumPy array of shape <code>(batch_size, height, width, num_channels)</code>.`,name:"images"}],source:"https://github.com/huggingface/diffusers/blob/vr_12262/src/diffusers/pipelines/pipeline_utils.py#L117"}}),j=new ne({props:{title:"AudioPipelineOutput",local:"diffusers.AudioPipelineOutput",headingTag:"h2"}}),Z=new ie({props:{name:"class diffusers.AudioPipelineOutput",anchor:"diffusers.AudioPipelineOutput",parameters:[{name:"audios",val:": ndarray"}],parametersDescription:[{anchor:"diffusers.AudioPipelineOutput.audios",description:`<strong>audios</strong> (<code>np.ndarray</code>) &#x2014;
List of denoised audio samples of a NumPy array of shape <code>(batch_size, num_channels, sample_rate)</code>.`,name:"audios"}],source:"https://github.com/huggingface/diffusers/blob/vr_12262/src/diffusers/pipelines/pipeline_utils.py#L131"}}),q=new ne({props:{title:"ImageTextPipelineOutput",local:"diffusers.ImageTextPipelineOutput",headingTag:"h2"}}),G=new ie({props:{name:"class diffusers.ImageTextPipelineOutput",anchor:"diffusers.ImageTextPipelineOutput",parameters:[{name:"images",val:": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray, NoneType]"},{name:"text",val:": typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType]"}],parametersDescription:[{anchor:"diffusers.ImageTextPipelineOutput.images",description:`<strong>images</strong> (<code>List[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
List of denoised PIL images of length <code>batch_size</code> or NumPy array of shape <code>(batch_size, height, width, num_channels)</code>.`,name:"images"},{anchor:"diffusers.ImageTextPipelineOutput.text",description:`<strong>text</strong> (<code>List[str]</code> or <code>List[List[str]]</code>) &#x2014;
List of generated text strings of length <code>batch_size</code> or a list of list of strings whose outer list has
length <code>batch_size</code>.`,name:"text"}],source:"https://github.com/huggingface/diffusers/blob/vr_12262/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py#L48"}}),R=new 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