import{S as yn,i as vn,s as wn,e as a,k as u,w as $,t as l,M as $n,c as s,d as n,m,a as o,x as U,h as r,b as p,G as e,g as _,y as T,q as J,o as M,B as D,v as Un,L as bn}from"../../../../chunks/vendor-hf-doc-builder.js";import{T as hn}from"../../../../chunks/Tip-hf-doc-builder.js";import{D as se}from"../../../../chunks/Docstring-hf-doc-builder.js";import{C as _n}from"../../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Yt}from"../../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as gn}from"../../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Tn(C){let c,y,d,f,v,i,h,V,L,A,x,Z,j,oe,G,k,ie;return{c(){c=a("p"),y=l("Make sure to check out the Stable Diffusion "),d=a("a"),f=l("Tips"),v=l(" section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!"),i=u(),h=a("p"),V=l("If you\u2019re interested in using one of the official checkpoints for a task, explore the "),L=a("a"),A=l("CompVis"),x=l(", "),Z=a("a"),j=l("Runway"),oe=l(", and "),G=a("a"),k=l("Stability AI"),ie=l(" Hub organizations!"),this.h()},l(P){c=s(P,"P",{});var I=o(c);y=r(I,"Make sure to check out the Stable Diffusion "),d=s(I,"A",{href:!0});var Me=o(d);f=r(Me,"Tips"),Me.forEach(n),v=r(I," section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!"),I.forEach(n),i=m(P),h=s(P,"P",{});var S=o(h);V=r(S,"If you\u2019re interested in using one of the official checkpoints for a task, explore the "),L=s(S,"A",{href:!0,rel:!0});var le=o(L);A=r(le,"CompVis"),le.forEach(n),x=r(S,", "),Z=s(S,"A",{href:!0,rel:!0});var W=o(Z);j=r(W,"Runway"),W.forEach(n),oe=r(S,", and "),G=s(S,"A",{href:!0,rel:!0});var re=o(G);k=r(re,"Stability AI"),re.forEach(n),ie=r(S," Hub organizations!"),S.forEach(n),this.h()},h(){p(d,"href","overview#tips"),p(L,"href","https://huggingface.co/CompVis"),p(L,"rel","nofollow"),p(Z,"href","https://huggingface.co/runwayml"),p(Z,"rel","nofollow"),p(G,"href","https://huggingface.co/stabilityai"),p(G,"rel","nofollow")},m(P,I){_(P,c,I),e(c,y),e(c,d),e(d,f),e(c,v),_(P,i,I),_(P,h,I),e(h,V),e(h,L),e(L,A),e(h,x),e(h,Z),e(Z,j),e(h,oe),e(h,G),e(G,k),e(h,ie)},d(P){P&&n(c),P&&n(i),P&&n(h)}}}function Jn(C){let c,y,d,f,v;return f=new _n({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvbkxhdGVudFVwc2NhbGVQaXBlbGluZSUyQyUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEElMEFwaXBlbGluZSUyMCUzRCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJDb21wVmlzJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS00JTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTBBKSUwQXBpcGVsaW5lLnRvKCUyMmN1ZGElMjIpJTBBJTBBbW9kZWxfaWQlMjAlM0QlMjAlMjJzdGFiaWxpdHlhaSUyRnNkLXgyLWxhdGVudC11cHNjYWxlciUyMiUwQXVwc2NhbGVyJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uTGF0ZW50VXBzY2FsZVBpcGVsaW5lLmZyb21fcHJldHJhaW5lZChtb2RlbF9pZCUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiklMEF1cHNjYWxlci50byglMjJjdWRhJTIyKSUwQSUwQXByb21wdCUyMCUzRCUyMCUyMmElMjBwaG90byUyMG9mJTIwYW4lMjBhc3Ryb25hdXQlMjBoaWdoJTIwcmVzb2x1dGlvbiUyQyUyMHVucmVhbCUyMGVuZ2luZSUyQyUyMHVsdHJhJTIwcmVhbGlzdGljJTIyJTBBZ2VuZXJhdG9yJTIwJTNEJTIwdG9yY2gubWFudWFsX3NlZWQoMzMpJTBBJTBBbG93X3Jlc19sYXRlbnRzJTIwJTNEJTIwcGlwZWxpbmUocHJvbXB0JTJDJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yJTJDJTIwb3V0cHV0X3R5cGUlM0QlMjJsYXRlbnQlMjIpLmltYWdlcyUwQSUwQXdpdGglMjB0b3JjaC5ub19ncmFkKCklM0ElMEElMjAlMjAlMjAlMjBpbWFnZSUyMCUzRCUyMHBpcGVsaW5lLmRlY29kZV9sYXRlbnRzKGxvd19yZXNfbGF0ZW50cyklMEFpbWFnZSUyMCUzRCUyMHBpcGVsaW5lLm51bXB5X3RvX3BpbChpbWFnZSklNUIwJTVEJTBBJTBBaW1hZ2Uuc2F2ZSglMjIuLiUyRmltYWdlcyUyRmExLnBuZyUyMiklMEElMEF1cHNjYWxlZF9pbWFnZSUyMCUzRCUyMHVwc2NhbGVyKCUwQSUyMCUyMCUyMCUyMHByb21wdCUzRHByb21wdCUyQyUwQSUyMCUyMCUyMCUyMGltYWdlJTNEbG93X3Jlc19sYXRlbnRzJTJDJTBBJTIwJTIwJTIwJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDIwJTJDJTBBJTIwJTIwJTIwJTIwZ3VpZGFuY2Vfc2NhbGUlM0QwJTJDJTBBJTIwJTIwJTIwJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yJTJDJTBBKS5pbWFnZXMlNUIwJTVEJTBBJTBBdXBzY2FsZWRfaW1hZ2Uuc2F2ZSglMjIuLiUyRmltYWdlcyUyRmEyLnBuZyUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch


<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>model_id = <span class="hljs-string">&quot;stabilityai/sd-x2-latent-upscaler&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>upscaler.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut high resolution, unreal engine, ultra realistic&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.manual_seed(<span class="hljs-number">33</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>low_res_latents = pipeline(prompt, generator=generator, output_type=<span class="hljs-string">&quot;latent&quot;</span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    image = pipeline.decode_latents(low_res_latents)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipeline.numpy_to_pil(image)[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;../images/a1.png&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>upscaled_image = upscaler(
<span class="hljs-meta">... </span>    prompt=prompt,
<span class="hljs-meta">... </span>    image=low_res_latents,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">20</span>,
<span class="hljs-meta">... </span>    guidance_scale=<span class="hljs-number">0</span>,
<span class="hljs-meta">... </span>    generator=generator,
<span class="hljs-meta">... </span>).images[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>upscaled_image.save(<span class="hljs-string">&quot;../images/a2.png&quot;</span>)`}}),{c(){c=a("p"),y=l("Examples:"),d=u(),$(f.$$.fragment)},l(i){c=s(i,"P",{});var h=o(c);y=r(h,"Examples:"),h.forEach(n),d=m(i),U(f.$$.fragment,i)},m(i,h){_(i,c,h),e(c,y),_(i,d,h),T(f,i,h),v=!0},p:bn,i(i){v||(J(f.$$.fragment,i),v=!0)},o(i){M(f.$$.fragment,i),v=!1},d(i){i&&n(c),i&&n(d),D(f,i)}}}function Mn(C){let c,y;return{c(){c=a("p"),y=l(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(d){c=s(d,"P",{});var f=o(c);y=r(f,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),f.forEach(n)},m(d,f){_(d,c,f),e(c,y)},d(d){d&&n(c)}}}function Dn(C){let c,y,d,f,v;return f=new _n({props:{code:"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",highlighted:`<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-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`}}),{c(){c=a("p"),y=l("Examples:"),d=u(),$(f.$$.fragment)},l(i){c=s(i,"P",{});var h=o(c);y=r(h,"Examples:"),h.forEach(n),d=m(i),U(f.$$.fragment,i)},m(i,h){_(i,c,h),e(c,y),_(i,d,h),T(f,i,h),v=!0},p:bn,i(i){v||(J(f.$$.fragment,i),v=!0)},o(i){M(f.$$.fragment,i),v=!1},d(i){i&&n(c),i&&n(d),D(f,i)}}}function xn(C){let c,y,d,f,v,i,h,V,L,A,x,Z,j,oe,G,k,ie,P,I,Me,S,le,W,re,N,z,Pe,pe,ot,Se,it,Fe,b,ce,lt,Ee,rt,pt,fe,ct,De,ft,dt,ut,B,de,mt,Le,ht,gt,Y,bt,q,ue,_t,R,yt,Ze,vt,wt,je,$t,Ut,Tt,Q,me,Jt,Ge,Mt,Dt,O,he,xt,ge,It,ke,Pt,St,Et,E,be,Lt,_e,Zt,ye,jt,Gt,kt,H,Wt,K,Ct,ee,ve,Bt,we,Vt,$e,Nt,Rt,Xe,F,te,We,Ue,Ft,Ce,Xt,Ae,X,Te,At,Be,zt,ze;return i=new Yt({}),W=new hn({props:{$$slots:{default:[Tn]},$$scope:{ctx:C}}}),pe=new Yt({}),ce=new se({props:{name:"class diffusers.StableDiffusionLatentUpscalePipeline",anchor:"diffusers.StableDiffusionLatentUpscalePipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": EulerDiscreteScheduler"}],parametersDescription:[{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.text_encoder",description:`<strong>text_encoder</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIPTextModel</a>) &#x2014;
Frozen text-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).`,name:"text_encoder"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>) &#x2014;
A <code>CLIPTokenizer</code> to tokenize text.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
A <code>UNet2DConditionModel</code> to denoise the encoded image latents.`,name:"unet"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A <a href="/docs/diffusers/main/en/api/schedulers/euler#diffusers.EulerDiscreteScheduler">EulerDiscreteScheduler</a> to be used in combination with <code>unet</code> to denoise the encoded image latents.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py#L61"}}),de=new se({props:{name:"__call__",anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"image",val:": typing.Union[torch.FloatTensor, PIL.Image.Image, numpy.ndarray, typing.List[torch.FloatTensor], typing.List[PIL.Image.Image], typing.List[numpy.ndarray]] = None"},{name:"num_inference_steps",val:": int = 75"},{name:"guidance_scale",val:": float = 9.0"},{name:"negative_prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{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:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"}],parametersDescription:[{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide image upscaling.`,name:"prompt"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__call__.image",description:`<strong>image</strong> (<code>torch.FloatTensor</code>, <code>PIL.Image.Image</code>, <code>np.ndarray</code>, <code>List[torch.FloatTensor]</code>, <code>List[PIL.Image.Image]</code>, or <code>List[np.ndarray]</code>) &#x2014;
<code>Image</code> or tensor representing an image batch to be upscaled. If it&#x2019;s a tensor, it can be either a
latent output from a Stable Diffusion model or an image tensor in the range <code>[-1, 1]</code>. It is considered
a <code>latent</code> if <code>image.shape[1]</code> is <code>4</code>; otherwise, it is considered to be an image representation and
encoded using this pipeline&#x2019;s <code>vae</code> encoder.`,name:"image"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__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.StableDiffusionLatentUpscalePipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#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.StableDiffusionLatentUpscalePipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass <code>negative_prompt_embeds</code> instead. Ignored when not using guidance (<code>guidance_scale &lt; 1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__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/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, and is ignored in other schedulers.`,name:"eta"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__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.StableDiffusionLatentUpscalePipeline.__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.StableDiffusionLatentUpscalePipeline.__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.StableDiffusionLatentUpscalePipeline.__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/main/en/api/pipelines/stable_diffusion/inpaint#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py#L256",returnDescription:`
<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/inpaint#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</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/main/en/api/pipelines/stable_diffusion/inpaint#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
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