import{S as Qt,i as Xt,s as Zt,e as r,k as c,w as C,t as d,M as en,c as o,d as n,m as p,a as i,x,h as l,b as s,G as t,g as u,y as b,q as y,o as U,B as w,v as tn}from"../../../chunks/vendor-hf-doc-builder.js";import{T as nn}from"../../../chunks/Tip-hf-doc-builder.js";import{D as Ce}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as We}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function rn(xe){let f,S,m,v,$,h,H,T;return{c(){f=r("p"),S=d("Make sure to check out the Schedulers "),m=r("a"),v=d("guide"),$=d(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),h=r("a"),H=d("reuse components across pipelines"),T=d(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l(k){f=o(k,"P",{});var g=i(f);S=l(g,"Make sure to check out the Schedulers "),m=o(g,"A",{href:!0});var I=i(m);v=l(I,"guide"),I.forEach(n),$=l(g," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),h=o(g,"A",{href:!0});var L=i(h);H=l(L,"reuse components across pipelines"),L.forEach(n),T=l(g," section to learn how to efficiently load the same components into multiple pipelines."),g.forEach(n),this.h()},h(){s(m,"href","/using-diffusers/schedulers"),s(h,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m(k,g){u(k,f,g),t(f,S),t(f,m),t(m,v),t(f,$),t(f,h),t(h,H),t(f,T)},d(k){k&&n(f)}}}function on(xe){let f,S,m,v,$,h,H,T,k,g,I,L,He,Re,oe,Ye,Be,be,ie,Je,ye,se,le,Ke,Ue,O,Qe,R,Xe,Ze,we,V,$e,D,A,ce,Y,et,pe,tt,Te,_,B,nt,ue,rt,ot,J,it,ae,st,at,dt,z,K,lt,fe,ct,ke,M,j,me,Q,pt,he,ut,De,P,X,ft,ge,mt,ht,Z,gt,de,_t,Pt,vt,q,ee,It,_e,Lt,Me,N,G,Pe,te,Ct,ve,xt,Ne,E,ne,bt,Ie,yt,Ee;return h=new We({}),V=new nn({props:{$$slots:{default:[rn]},$$scope:{ctx:xe}}}),Y=new We({}),B=new Ce({props:{name:"class diffusers.UnCLIPPipeline",anchor:"diffusers.UnCLIPPipeline",parameters:[{name:"prior",val:": PriorTransformer"},{name:"decoder",val:": UNet2DConditionModel"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_proj",val:": UnCLIPTextProjModel"},{name:"super_res_first",val:": UNet2DModel"},{name:"super_res_last",val:": UNet2DModel"},{name:"prior_scheduler",val:": UnCLIPScheduler"},{name:"decoder_scheduler",val:": UnCLIPScheduler"},{name:"super_res_scheduler",val:": UnCLIPScheduler"}],parametersDescription:[{anchor:"diffusers.UnCLIPPipeline.text_encoder",description:`<strong>text_encoder</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow">CLIPTextModelWithProjection</a>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.UnCLIPPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>) &#x2014;
A <code>CLIPTokenizer</code> to tokenize text.`,name:"tokenizer"},{anchor:"diffusers.UnCLIPPipeline.prior",description:`<strong>prior</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/prior_transformer#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonical unCLIP prior to approximate the image embedding from the text embedding.`,name:"prior"},{anchor:"diffusers.UnCLIPPipeline.text_proj",description:`<strong>text_proj</strong> (<code>UnCLIPTextProjModel</code>) &#x2014;
Utility class to prepare and combine the embeddings before they are passed to the decoder.`,name:"text_proj"},{anchor:"diffusers.UnCLIPPipeline.decoder",description:`<strong>decoder</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
The decoder to invert the image embedding into an image.`,name:"decoder"},{anchor:"diffusers.UnCLIPPipeline.super_res_first",description:`<strong>super_res_first</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/unet2d#diffusers.UNet2DModel">UNet2DModel</a>) &#x2014;
Super resolution UNet. Used in all but the last step of the super resolution diffusion process.`,name:"super_res_first"},{anchor:"diffusers.UnCLIPPipeline.super_res_last",description:`<strong>super_res_last</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/unet2d#diffusers.UNet2DModel">UNet2DModel</a>) &#x2014;
Super resolution UNet. Used in the last step of the super resolution diffusion process.`,name:"super_res_last"},{anchor:"diffusers.UnCLIPPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
Scheduler used in the prior denoising process (a modified <a href="/docs/diffusers/v0.19.0/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>).`,name:"prior_scheduler"},{anchor:"diffusers.UnCLIPPipeline.decoder_scheduler",description:`<strong>decoder_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
Scheduler used in the decoder denoising process (a modified <a href="/docs/diffusers/v0.19.0/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>).`,name:"decoder_scheduler"},{anchor:"diffusers.UnCLIPPipeline.super_res_scheduler",description:`<strong>super_res_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
Scheduler used in the super resolution denoising process (a modified <a href="/docs/diffusers/v0.19.0/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>).`,name:"super_res_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/pipelines/unclip/pipeline_unclip.py#L33"}}),K=new Ce({props:{name:"__call__",anchor:"diffusers.UnCLIPPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"prior_num_inference_steps",val:": int = 25"},{name:"decoder_num_inference_steps",val:": int = 25"},{name:"super_res_num_inference_steps",val:": int = 7"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"prior_latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"decoder_latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"super_res_latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"text_model_output",val:": typing.Union[transformers.models.clip.modeling_clip.CLIPTextModelOutput, typing.Tuple, NoneType] = None"},{name:"text_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"prior_guidance_scale",val:": float = 4.0"},{name:"decoder_guidance_scale",val:": float = 8.0"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.UnCLIPPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide image generation. This can only be left undefined if <code>text_model_output</code>
and <code>text_attention_mask</code> is passed.`,name:"prompt"},{anchor:"diffusers.UnCLIPPipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.UnCLIPPipeline.__call__.prior_num_inference_steps",description:`<strong>prior_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 25) &#x2014;
The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
image at the expense of slower inference.`,name:"prior_num_inference_steps"},{anchor:"diffusers.UnCLIPPipeline.__call__.decoder_num_inference_steps",description:`<strong>decoder_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 25) &#x2014;
The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
image at the expense of slower inference.`,name:"decoder_num_inference_steps"},{anchor:"diffusers.UnCLIPPipeline.__call__.super_res_num_inference_steps",description:`<strong>super_res_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 7) &#x2014;
The number of denoising steps for super resolution. More denoising steps usually lead to a higher
quality image at the expense of slower inference.`,name:"super_res_num_inference_steps"},{anchor:"diffusers.UnCLIPPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.UnCLIPPipeline.__call__.prior_latents",description:`<strong>prior_latents</strong> (<code>torch.FloatTensor</code> of shape (batch size, embeddings dimension), <em>optional</em>) &#x2014;
Pre-generated noisy latents to be used as inputs for the prior.`,name:"prior_latents"},{anchor:"diffusers.UnCLIPPipeline.__call__.decoder_latents",description:`<strong>decoder_latents</strong> (<code>torch.FloatTensor</code> of shape (batch size, channels, height, width), <em>optional</em>) &#x2014;
Pre-generated noisy latents to be used as inputs for the decoder.`,name:"decoder_latents"},{anchor:"diffusers.UnCLIPPipeline.__call__.super_res_latents",description:`<strong>super_res_latents</strong> (<code>torch.FloatTensor</code> of shape (batch size, channels, super res height, super res width), <em>optional</em>) &#x2014;
Pre-generated noisy latents to be used as inputs for the decoder.`,name:"super_res_latents"},{anchor:"diffusers.UnCLIPPipeline.__call__.prior_guidance_scale",description:`<strong>prior_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
A higher guidance scale value encourages the model to generate images closely linked to the text
<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,name:"prior_guidance_scale"},{anchor:"diffusers.UnCLIPPipeline.__call__.decoder_guidance_scale",description:`<strong>decoder_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
A higher guidance scale value encourages the model to generate images closely linked to the text
<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,name:"decoder_guidance_scale"},{anchor:"diffusers.UnCLIPPipeline.__call__.text_model_output",description:`<strong>text_model_output</strong> (<code>CLIPTextModelOutput</code>, <em>optional</em>) &#x2014;
Pre-defined <code>CLIPTextModel</code> outputs that can be derived from the text encoder. Pre-defined text
outputs can be passed for tasks like text embedding interpolations. Make sure to also pass
<code>text_attention_mask</code> in this case. <code>prompt</code> can the be left <code>None</code>.`,name:"text_model_output"},{anchor:"diffusers.UnCLIPPipeline.__call__.text_attention_mask",description:`<strong>text_attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-defined CLIP text attention mask that can be derived from the tokenizer. Pre-defined text attention
masks are necessary when passing <code>text_model_output</code>.`,name:"text_attention_mask"},{anchor:"diffusers.UnCLIPPipeline.__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 generated image. Choose between <code>PIL.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.UnCLIPPipeline.__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/v0.19.0/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/pipelines/unclip/pipeline_unclip.py#L208",returnDescription:`
<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/v0.19.0/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> is returned, otherwise a <code>tuple</code> is
returned where the first element is a list with the generated images.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.19.0/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),Q=new We({}),X=new Ce({props:{name:"class diffusers.UnCLIPImageVariationPipeline",anchor:"diffusers.UnCLIPImageVariationPipeline",parameters:[{name:"decoder",val:": UNet2DConditionModel"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_proj",val:": UnCLIPTextProjModel"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"image_encoder",val:": CLIPVisionModelWithProjection"},{name:"super_res_first",val:": UNet2DModel"},{name:"super_res_last",val:": UNet2DModel"},{name:"decoder_scheduler",val:": UnCLIPScheduler"},{name:"super_res_scheduler",val:": UnCLIPScheduler"}],parametersDescription:[{anchor:"diffusers.UnCLIPImageVariationPipeline.text_encoder",description:`<strong>text_encoder</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow">CLIPTextModelWithProjection</a>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.UnCLIPImageVariationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>) &#x2014;
A <code>CLIPTokenizer</code> to tokenize text.`,name:"tokenizer"},{anchor:"diffusers.UnCLIPImageVariationPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPImageProcessor" rel="nofollow">CLIPImageProcessor</a>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>image_encoder</code>.`,name:"feature_extractor"},{anchor:"diffusers.UnCLIPImageVariationPipeline.image_encoder",description:`<strong>image_encoder</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPVisionModelWithProjection" rel="nofollow">CLIPVisionModelWithProjection</a>) &#x2014;
Frozen CLIP image-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"image_encoder"},{anchor:"diffusers.UnCLIPImageVariationPipeline.text_proj",description:`<strong>text_proj</strong> (<code>UnCLIPTextProjModel</code>) &#x2014;
Utility class to prepare and combine the embeddings before they are passed to the decoder.`,name:"text_proj"},{anchor:"diffusers.UnCLIPImageVariationPipeline.decoder",description:`<strong>decoder</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
The decoder to invert the image embedding into an image.`,name:"decoder"},{anchor:"diffusers.UnCLIPImageVariationPipeline.super_res_first",description:`<strong>super_res_first</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/unet2d#diffusers.UNet2DModel">UNet2DModel</a>) &#x2014;
Super resolution UNet. Used in all but the last step of the super resolution diffusion process.`,name:"super_res_first"},{anchor:"diffusers.UnCLIPImageVariationPipeline.super_res_last",description:`<strong>super_res_last</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/unet2d#diffusers.UNet2DModel">UNet2DModel</a>) &#x2014;
Super resolution UNet. Used in the last step of the super resolution diffusion process.`,name:"super_res_last"},{anchor:"diffusers.UnCLIPImageVariationPipeline.decoder_scheduler",description:`<strong>decoder_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
Scheduler used in the decoder denoising process (a modified <a href="/docs/diffusers/v0.19.0/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>).`,name:"decoder_scheduler"},{anchor:"diffusers.UnCLIPImageVariationPipeline.super_res_scheduler",description:`<strong>super_res_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
Scheduler used in the super resolution denoising process (a modified <a href="/docs/diffusers/v0.19.0/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>).`,name:"super_res_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py#L38"}}),ee=new Ce({props:{name:"__call__",anchor:"diffusers.UnCLIPImageVariationPipeline.__call__",parameters:[{name:"image",val:": typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image], torch.FloatTensor, NoneType] = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"decoder_num_inference_steps",val:": int = 25"},{name:"super_res_num_inference_steps",val:": int = 7"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"decoder_latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"super_res_latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_embeddings",val:": typing.Optional[torch.Tensor] = None"},{name:"decoder_guidance_scale",val:": float = 8.0"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>List[PIL.Image.Image]</code> or <code>torch.FloatTensor</code>) &#x2014;
<code>Image</code> or tensor representing an image batch to be used as the starting point. If you provide a
tensor, it needs to be compatible with the <code>CLIPImageProcessor</code>
<a href="https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json" rel="nofollow">configuration</a>.
Can be left as <code>None</code> only when <code>image_embeddings</code> are passed.`,name:"image"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.decoder_num_inference_steps",description:`<strong>decoder_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 25) &#x2014;
The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
image at the expense of slower inference.`,name:"decoder_num_inference_steps"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.super_res_num_inference_steps",description:`<strong>super_res_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 7) &#x2014;
The number of denoising steps for super resolution. More denoising steps usually lead to a higher
quality image at the expense of slower inference.`,name:"super_res_num_inference_steps"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__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"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.decoder_latents",description:`<strong>decoder_latents</strong> (<code>torch.FloatTensor</code> of shape (batch size, channels, height, width), <em>optional</em>) &#x2014;
Pre-generated noisy latents to be used as inputs for the decoder.`,name:"decoder_latents"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.super_res_latents",description:`<strong>super_res_latents</strong> (<code>torch.FloatTensor</code> of shape (batch size, channels, super res height, super res width), <em>optional</em>) &#x2014;
Pre-generated noisy latents to be used as inputs for the decoder.`,name:"super_res_latents"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.decoder_guidance_scale",description:`<strong>decoder_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
A higher guidance scale value encourages the model to generate images closely linked to the text
<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,name:"decoder_guidance_scale"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__call__.image_embeddings",description:`<strong>image_embeddings</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings
can be passed for tasks like image interpolations. <code>image</code> can be left as <code>None</code>.`,name:"image_embeddings"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__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 generated image. Choose between <code>PIL.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.UnCLIPImageVariationPipeline.__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/v0.19.0/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py#L197",returnDescription:`
<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/v0.19.0/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> is returned, otherwise a <code>tuple</code> is
returned where the first element is a list with the generated images.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.19.0/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),te=new We({}),ne=new Ce({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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