import{S as Ti,i as xi,s as Di,e as n,k as l,w as S,t as p,M as Pi,c as o,d as t,m as d,a,x as N,h as c,b as r,G as i,g as f,y as q,L as $i,q as O,o as B,B as C,v as Ei}from"../../../chunks/vendor-hf-doc-builder.js";import{D as bi}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as se}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Mi(Ht){let m,Me,g,D,le,H,tt,de,it,Le,v,P,fe,z,nt,pe,ot,Ie,$,at,U,rt,st,ke,Y,lt,Ae,X,ce,dt,Se,E,ft,G,pt,ct,Ne,_,M,ue,R,ut,he,ht,qe,h,Oe,mt,Be,gt,Ce,He,w,L,me,V,vt,ge,_t,ze,I,ve,y,_e,wt,yt,we,bt,Tt,Z,xt,Dt,ye,b,be,Q,Pt,$t,Te,xe,Et,Mt,ee,Lt,Ue,T,k,De,j,It,Pe,kt,Ge,x,A,$e,W,At,Ee,St,Re,u,F,Nt,J,qt,te,Ot,Bt,Ct,ie,K,Ve;return H=new se({}),z=new se({}),R=new se({}),V=new se({}),j=new se({}),W=new se({}),F=new bi({props:{name:"class diffusers.LDMTextToImagePipeline",anchor:"diffusers.LDMTextToImagePipeline",parameters:[{name:"vqvae",val:": typing.Union[diffusers.models.vae.VQModel, diffusers.models.vae.AutoencoderKL]"},{name:"bert",val:": PreTrainedModel"},{name:"tokenizer",val:": PreTrainedTokenizer"},{name:"unet",val:": typing.Union[diffusers.models.unet_2d.UNet2DModel, diffusers.models.unet_2d_condition.UNet2DConditionModel]"},{name:"scheduler",val:": typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler]"}],parametersDescription:[{anchor:"diffusers.LDMTextToImagePipeline.vqvae",description:`<strong>vqvae</strong> (<a href="/docs/diffusers/pr_516/en/api/models#diffusers.VQModel">VQModel</a>) &#x2014;
Vector-quantized (VQ) Model to encode and decode images to and from latent representations.`,name:"vqvae"},{anchor:"diffusers.LDMTextToImagePipeline.bert",description:`<strong>bert</strong> (<code>LDMBertModel</code>) &#x2014;
Text-encoder model based on <a href="https://huggingface.co/docs/transformers/model_doc/bert" rel="nofollow">BERT</a> architecture.`,name:"bert"},{anchor:"diffusers.LDMTextToImagePipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>transformers.BertTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer" rel="nofollow">BertTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.LDMTextToImagePipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/pr_516/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.LDMTextToImagePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_516/en/api/schedulers#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latens. Can be one of
<a href="/docs/diffusers/pr_516/en/api/schedulers#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/pr_516/en/api/schedulers#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/pr_516/en/api/schedulers#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_516/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py#L20"}}),K=new bi({props:{name:"__call__",anchor:"diffusers.LDMTextToImagePipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"height",val:": typing.Optional[int] = 256"},{name:"width",val:": typing.Optional[int] = 256"},{name:"num_inference_steps",val:": typing.Optional[int] = 50"},{name:"guidance_scale",val:": typing.Optional[float] = 1.0"},{name:"eta",val:": typing.Optional[float] = 0.0"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.LDMTextToImagePipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.LDMTextToImagePipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.LDMTextToImagePipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.LDMTextToImagePipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#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.LDMTextToImagePipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#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> at
the, usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.LDMTextToImagePipeline.__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.LDMTextToImagePipeline.__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.LDMTextToImagePipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<p><code>~pipelines.utils.ImagePipelineOutput</code> if
<code>return_dict</code> is True, otherwise a \`tuple. When returning a tuple, the first element is a list with the
generated images.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/pr_516/en/api/diffusion_pipeline#diffusers.pipeline_utils.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
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