import{S as ts,i as ss,s as ns,e as i,k as u,w as M,t as d,M as is,c as a,d as s,m as f,a as o,x as b,h as c,b as p,G as t,g as h,y as w,q as T,o as D,B as I,v as as,L as Kt}from"../../../chunks/vendor-hf-doc-builder.js";import{T as os}from"../../../chunks/Tip-hf-doc-builder.js";import{D as xe}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as es}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ye}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ft}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function rs(Z){let r,v,g,m,y,n,_,L;return{c(){r=i("p"),v=d("Make sure to check out the Schedulers "),g=i("a"),m=d("guide"),y=d(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),n=i("a"),_=d("reuse components across pipelines"),L=d(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l(B){r=a(B,"P",{});var $=o(r);v=c($,"Make sure to check out the Schedulers "),g=a($,"A",{href:!0});var S=o(g);m=c(S,"guide"),S.forEach(s),y=c($," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),n=a($,"A",{href:!0});var re=o(n);_=c(re,"reuse components across pipelines"),re.forEach(s),L=c($," section to learn how to efficiently load the same components into multiple pipelines."),$.forEach(s),this.h()},h(){p(g,"href","/using-diffusers/schedulers"),p(n,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m(B,$){h(B,r,$),t(r,v),t(r,g),t(g,m),t(r,y),t(r,n),t(n,_),t(r,L)},d(B){B&&s(r)}}}function ls(Z){let r,v,g,m,y;return m=new es({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># load model and scheduler</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>ldm = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/ldm-text2im-large-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># run pipeline in inference (sample random noise and denoise)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A painting of a squirrel eating a burger&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>images = ldm([prompt], num_inference_steps=<span class="hljs-number">50</span>, eta=<span class="hljs-number">0.3</span>, guidance_scale=<span class="hljs-number">6</span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># save images</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> idx, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
<span class="hljs-meta">... </span>    image.save(<span class="hljs-string">f&quot;squirrel-<span class="hljs-subst">{idx}</span>.png&quot;</span>)`}}),{c(){r=i("p"),v=d("Example:"),g=u(),M(m.$$.fragment)},l(n){r=a(n,"P",{});var _=o(r);v=c(_,"Example:"),_.forEach(s),g=f(n),b(m.$$.fragment,n)},m(n,_){h(n,r,_),t(r,v),h(n,g,_),w(m,n,_),y=!0},p:Kt,i(n){y||(T(m.$$.fragment,n),y=!0)},o(n){D(m.$$.fragment,n),y=!1},d(n){n&&s(r),n&&s(g),I(m,n)}}}function ps(Z){let r,v,g,m,y;return m=new es({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LDMSuperResolutionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># load model and scheduler</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = LDMSuperResolutionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/ldm-super-resolution-4x-openimages&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># let&#x27;s download an  image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = (
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>response = requests.get(url)
<span class="hljs-meta">&gt;&gt;&gt; </span>low_res_img = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>low_res_img = low_res_img.resize((<span class="hljs-number">128</span>, <span class="hljs-number">128</span>))

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># run pipeline in inference (sample random noise and denoise)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>upscaled_image = pipeline(low_res_img, num_inference_steps=<span class="hljs-number">100</span>, eta=<span class="hljs-number">1</span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># save image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>upscaled_image.save(<span class="hljs-string">&quot;ldm_generated_image.png&quot;</span>)`}}),{c(){r=i("p"),v=d("Example:"),g=u(),M(m.$$.fragment)},l(n){r=a(n,"P",{});var _=o(r);v=c(_,"Example:"),_.forEach(s),g=f(n),b(m.$$.fragment,n)},m(n,_){h(n,r,_),t(r,v),h(n,g,_),w(m,n,_),y=!0},p:Kt,i(n){y||(T(m.$$.fragment,n),y=!0)},o(n){D(m.$$.fragment,n),y=!1},d(n){n&&s(r),n&&s(g),I(m,n)}}}function ds(Z){let r,v,g,m,y,n,_,L,B,$,S,re,Y,Qe,Xe,Se,le,He,je,pe,me,Fe,Ue,V,Ke,Q,et,tt,Le,C,Be,J,N,he,X,st,ge,nt,Je,P,H,it,_e,at,ot,F,rt,de,lt,pt,dt,j,K,ct,ye,ut,ft,R,Ee,E,W,ve,ee,mt,Me,ht,ke,x,te,gt,be,_t,yt,se,vt,ce,Mt,bt,wt,U,ne,Tt,we,Dt,It,z,Ge,k,q,Te,ie,$t,De,Pt,Ze,G,ae,xt,Ie,St,Ve;return n=new Ye({}),C=new os({props:{$$slots:{default:[rs]},$$scope:{ctx:Z}}}),X=new Ye({}),H=new xe({props:{name:"class diffusers.LDMTextToImagePipeline",anchor:"diffusers.LDMTextToImagePipeline",parameters:[{name:"vqvae",val:": typing.Union[diffusers.models.vq_model.VQModel, diffusers.models.autoencoder_kl.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/v0.19.2/en/api/models/vq#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 <code>BERT</code>.`,name:"bert"},{anchor:"diffusers.LDMTextToImagePipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/bert#transformers.BertTokenizer" rel="nofollow">BertTokenizer</a>) &#x2014;
A <code>BertTokenizer</code> to tokenize text.`,name:"tokenizer"},{anchor:"diffusers.LDMTextToImagePipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/v0.19.2/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <code>UNet2DConditionModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.LDMTextToImagePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.19.2/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents. Can be one of
<a href="/docs/diffusers/v0.19.2/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.19.2/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.19.2/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py#L32"}}),K=new xe({props:{name:"__call__",anchor:"diffusers.LDMTextToImagePipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{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.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = 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 <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#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 <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#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;
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:"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"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.LDMTextToImagePipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{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 generated image. Choose between <code>PIL.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>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.19.2/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py#L65",returnDescription:`
<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/v0.19.2/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/v0.19.2/en/api/pipelines/latent_diffusion#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),R=new Ft({props:{anchor:"diffusers.LDMTextToImagePipeline.__call__.example",$$slots:{default:[ls]},$$scope:{ctx:Z}}}),ee=new Ye({}),te=new xe({props:{name:"class diffusers.LDMSuperResolutionPipeline",anchor:"diffusers.LDMSuperResolutionPipeline",parameters:[{name:"vqvae",val:": VQModel"},{name:"unet",val:": UNet2DModel"},{name:"scheduler",val:": typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler]"}],parametersDescription:[{anchor:"diffusers.LDMSuperResolutionPipeline.vqvae",description:`<strong>vqvae</strong> (<a href="/docs/diffusers/v0.19.2/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
Vector-quantized (VQ) model to encode and decode images to and from latent representations.`,name:"vqvae"},{anchor:"diffusers.LDMSuperResolutionPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/v0.19.2/en/api/models/unet2d#diffusers.UNet2DModel">UNet2DModel</a>) &#x2014;
A <code>UNet2DModel</code> to denoise the encoded image.`,name:"unet"},{anchor:"diffusers.LDMSuperResolutionPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.19.2/en/api/schedulers/overview#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/v0.19.2/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.19.2/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, <a href="/docs/diffusers/v0.19.2/en/api/schedulers/euler#diffusers.EulerDiscreteScheduler">EulerDiscreteScheduler</a>,
<a href="/docs/diffusers/v0.19.2/en/api/schedulers/euler_ancestral#diffusers.EulerAncestralDiscreteScheduler">EulerAncestralDiscreteScheduler</a>, <a href="/docs/diffusers/v0.19.2/en/api/schedulers/multistep_dpm_solver#diffusers.DPMSolverMultistepScheduler">DPMSolverMultistepScheduler</a>, or <a href="/docs/diffusers/v0.19.2/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py#L32"}}),ne=new xe({props:{name:"__call__",anchor:"diffusers.LDMSuperResolutionPipeline.__call__",parameters:[{name:"image",val:": typing.Union[torch.Tensor, PIL.Image.Image] = None"},{name:"batch_size",val:": typing.Optional[int] = 1"},{name:"num_inference_steps",val:": typing.Optional[int] = 100"},{name:"eta",val:": typing.Optional[float] = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.LDMSuperResolutionPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.Tensor</code> or <code>PIL.Image.Image</code>) &#x2014;
<code>Image</code> or tensor representing an image batch to be used as the starting point for the process.`,name:"image"},{anchor:"diffusers.LDMSuperResolutionPipeline.__call__.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of images to generate.`,name:"batch_size"},{anchor:"diffusers.LDMSuperResolutionPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#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.LDMSuperResolutionPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) from the <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">DDIM</a> paper. Only applies
to the <a href="/docs/diffusers/v0.19.2/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,name:"eta"},{anchor:"diffusers.LDMSuperResolutionPipeline.__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
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