import{s as Fe,n as Xe,o as Ke}from"../chunks/scheduler.53228c21.js";import{S as Ye,i as et,e as r,s as n,c as u,h as tt,a as l,d as s,b as a,f as Q,g as p,j as $,k as _,l as o,m as i,n as d,t as f,o as m,p as c}from"../chunks/index.100fac89.js";import{C as st}from"../chunks/CopyLLMTxtMenu.9360b11d.js";import{D as ee}from"../chunks/Docstring.22cc8849.js";import{C as Ve}from"../chunks/CodeBlock.d30a6509.js";import{H as te,E as it}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.c9001d7a.js";function nt(ze){let h,se,K,ie,L,ne,O,ae,I,De='All model outputs are subclasses of <a href="/docs/diffusers/main/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,w,je="For example:",le,C,ue,M,qe='The <code>outputs</code> object is a <a href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> which means it has an image attribute.',pe,H,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,k,de,B,Ne=`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>:`,fe,E,me,x,Ae="<p>To check a specific pipeline or model output, refer to its corresponding API documentation.</p>",ce,U,ge,g,V,Ce,R,Ze=`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.`,Me,z,Ge=`<p>&gt; You can’t unpack a <code>BaseOutput</code> directly. Use the <a href="/docs/diffusers/main/en/api/outputs#diffusers.utils.BaseOutput.to_tuple">to_tuple()</a> method to convert
it to a tuple &gt; first.</p>`,He,P,D,ke,S,Qe="Convert self to a tuple containing all the attributes/keys that are not <code>None</code>.",$e,j,he,b,q,Be,W,Re="Output class for image pipelines.",be,J,ye,y,N,Ee,F,Se="Output class for audio pipelines.",ve,A,_e,v,Z,Ue,X,We="Output class for joint image-text pipelines.",xe,G,Pe,Y,Te;return L=new st({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),O=new te({props:{title:"Outputs",local:"outputs",headingTag:"h1"}}),C=new Ve({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}}),k=new Ve({props:{code:"b3V0cHV0cy5pbWFnZXMlMEFvdXRwdXRzJTVCJTIyaW1hZ2VzJTIyJTVE",highlighted:`outputs.images
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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/main/src/diffusers/pipelines/pipeline_utils.py#L118"}}),J=new te({props:{title:"AudioPipelineOutput",local:"diffusers.AudioPipelineOutput",headingTag:"h2"}}),N=new ee({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/main/src/diffusers/pipelines/pipeline_utils.py#L132"}}),A=new te({props:{title:"ImageTextPipelineOutput",local:"diffusers.ImageTextPipelineOutput",headingTag:"h2"}}),Z=new ee({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/main/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py#L48"}}),G=new 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