import{S as uo,i as ho,s as mo,e as o,k as d,w as P,t as s,M as go,c as r,d as n,m as c,a as i,x as v,h as l,b as a,G as e,g as f,y as I,L as _o,q as b,o as C,B as y,v as Po}from"../../../chunks/vendor-hf-doc-builder.js";import{D as ve}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as mt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function vo(wn){let L,Fe,x,D,Ie,q,gt,be,_t,We,w,M,Ce,j,Pt,ye,vt,He,F,W,It,bt,Je,le,Ct,Re,de,yt,Be,m,Lt,H,xt,wt,J,Ut,Tt,Ye,U,N,Le,R,$t,xe,kt,Ke,S,we,T,Ue,Et,Dt,Te,Mt,Nt,ce,St,Vt,B,$,$e,Y,At,Ot,ke,Ee,Gt,zt,pe,qt,jt,k,De,K,Ft,Wt,Me,Ne,Ht,Jt,fe,Rt,Qe,E,V,Se,Q,Bt,Ve,Yt,Xe,u,X,Kt,Ae,Qt,Xt,Z,Zt,ue,en,tn,nn,A,ee,on,Oe,rn,an,O,te,sn,ne,ln,Ge,dn,cn,Ze,h,oe,pn,ze,fn,un,re,hn,he,mn,gn,_n,G,ie,Pn,qe,vn,In,z,ae,bn,se,Cn,je,yn,Ln,et;return q=new mt({}),j=new mt({}),R=new mt({}),Q=new mt({}),X=new ve({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> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.UnCLIPPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.UnCLIPPipeline.prior",description:`<strong>prior</strong> (<a href="/docs/diffusers/v0.16.0/en/api/models#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonincal 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/v0.16.0/en/api/models#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/v0.16.0/en/api/models#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/v0.16.0/en/api/models#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. Just a modified DDPMScheduler.`,name:"prior_scheduler"},{anchor:"diffusers.UnCLIPPipeline.decoder_scheduler",description:`<strong>decoder_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.`,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. Just a modified DDPMScheduler.`,name:"super_res_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/unclip/pipeline_unclip.py#L34"}}),ee=new ve({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 the 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;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</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;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,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;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,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 CLIPTextModel 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 to <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
<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.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/v0.16.0/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/unclip/pipeline_unclip.py#L251"}}),te=new ve({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.UnCLIPPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/unclip/pipeline_unclip.py#L208"}}),oe=new ve({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> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.UnCLIPImageVariationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.UnCLIPImageVariationPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#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> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection" rel="nofollow">CLIP</a>,
specifically the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,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/v0.16.0/en/api/models#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/v0.16.0/en/api/models#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/v0.16.0/en/api/models#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. Just a modified DDPMScheduler.`,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. Just a modified DDPMScheduler.`,name:"super_res_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py#L38"}}),ie=new ve({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;
The image or images to guide the image generation. If you provide a tensor, it needs to comply with the
configuration of
<a href="https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json" rel="nofollow">this</a>
<code>CLIPImageProcessor</code>. Can be left to <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;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</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;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,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 the be left to <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
<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.UnCLIPImageVariationPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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