import{S as Un,i as xn,s as Pn,e as s,k as u,w as $,t as i,M as Sn,c as a,d as n,m,a as o,x as U,h as r,b as p,G as e,g as b,y as x,q as P,o as S,B as M,v as Mn,L as wn}from"../../../../chunks/vendor-hf-doc-builder.js";import{T as yn}from"../../../../chunks/Tip-hf-doc-builder.js";import{D as oe}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 vn}from"../../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Dn(A){let c,y,f,d,v,l,h,L,E,z,D,Z,J,ie,W,G,re;return{c(){c=s("p"),y=i("Make sure to check out the Stable Diffusion "),f=s("a"),d=i("Tips"),v=i(" section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!"),l=u(),h=s("p"),L=i("If you\u2019re interested in using one of the official checkpoints for a task, explore the "),E=s("a"),z=i("CompVis"),D=i(", "),Z=s("a"),J=i("Runway"),ie=i(", and "),W=s("a"),G=i("Stability AI"),re=i(" Hub organizations!"),this.h()},l(T){c=a(T,"P",{});var I=o(c);y=r(I,"Make sure to check out the Stable Diffusion "),f=a(I,"A",{href:!0});var Me=o(f);d=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),l=m(T),h=a(T,"P",{});var k=o(h);L=r(k,"If you\u2019re interested in using one of the official checkpoints for a task, explore the "),E=a(k,"A",{href:!0,rel:!0});var le=o(E);z=r(le,"CompVis"),le.forEach(n),D=r(k,", "),Z=a(k,"A",{href:!0,rel:!0});var N=o(Z);J=r(N,"Runway"),N.forEach(n),ie=r(k,", and "),W=a(k,"A",{href:!0,rel:!0});var pe=o(W);G=r(pe,"Stability AI"),pe.forEach(n),re=r(k," Hub organizations!"),k.forEach(n),this.h()},h(){p(f,"href","overview#tips"),p(E,"href","https://huggingface.co/CompVis"),p(E,"rel","nofollow"),p(Z,"href","https://huggingface.co/runwayml"),p(Z,"rel","nofollow"),p(W,"href","https://huggingface.co/stabilityai"),p(W,"rel","nofollow")},m(T,I){b(T,c,I),e(c,y),e(c,f),e(f,d),e(c,v),b(T,l,I),b(T,h,I),e(h,L),e(h,E),e(E,z),e(h,D),e(h,Z),e(Z,J),e(h,ie),e(h,W),e(W,G),e(h,re)},d(T){T&&n(c),T&&n(l),T&&n(h)}}}function In(A){let c,y,f,d,v;return d=new $n({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> StableDiffusionUpscalePipeline
<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>model_id = <span class="hljs-string">&quot;stabilityai/stable-diffusion-x4-upscaler&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionUpscalePipeline.from_pretrained(
<span class="hljs-meta">... </span>    model_id, revision=<span class="hljs-string">&quot;fp16&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </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-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png&quot;</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>prompt = <span class="hljs-string">&quot;a white cat&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>upscaled_image.save(<span class="hljs-string">&quot;upsampled_cat.png&quot;</span>)`}}),{c(){c=s("p"),y=i("Examples:"),f=u(),$(d.$$.fragment)},l(l){c=a(l,"P",{});var h=o(c);y=r(h,"Examples:"),h.forEach(n),f=m(l),U(d.$$.fragment,l)},m(l,h){b(l,c,h),e(c,y),b(l,f,h),x(d,l,h),v=!0},p:wn,i(l){v||(P(d.$$.fragment,l),v=!0)},o(l){S(d.$$.fragment,l),v=!1},d(l){l&&n(c),l&&n(f),M(d,l)}}}function Tn(A){let c,y;return{c(){c=s("p"),y=i(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(f){c=a(f,"P",{});var d=o(c);y=r(d,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),d.forEach(n)},m(f,d){b(f,c,d),e(c,y)},d(f){f&&n(c)}}}function kn(A){let c,y,f,d,v;return d=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=s("p"),y=i("Examples:"),f=u(),$(d.$$.fragment)},l(l){c=a(l,"P",{});var h=o(c);y=r(h,"Examples:"),h.forEach(n),f=m(l),U(d.$$.fragment,l)},m(l,h){b(l,c,h),e(c,y),b(l,f,h),x(d,l,h),v=!0},p:wn,i(l){v||(P(d.$$.fragment,l),v=!0)},o(l){S(d.$$.fragment,l),v=!1},d(l){l&&n(c),l&&n(f),M(d,l)}}}function jn(A){let c,y,f,d,v,l,h,L,E,z,D,Z,J,ie,W,G,re,T,I,Me,k,le,N,pe,F,R,Te,ce,it,ke,rt,Ve,_,de,lt,je,pt,ct,fe,dt,De,ft,ut,mt,B,ue,ht,Ee,gt,_t,O,bt,Q,me,yt,Ze,vt,wt,q,he,$t,ge,Ut,Je,xt,Pt,St,j,_e,Mt,be,Dt,ye,It,Tt,kt,Y,jt,H,Et,K,ve,Zt,we,Jt,$e,Wt,Gt,Nt,ee,Ue,Ct,C,At,We,Bt,Lt,Ge,Ft,Xt,Ne,Vt,zt,ze,X,te,Ce,xe,Rt,Ae,Ot,Re,V,Pe,Qt,Be,qt,Oe;return l=new Yt({}),N=new yn({props:{$$slots:{default:[Dn]},$$scope:{ctx:A}}}),ce=new Yt({}),de=new oe({props:{name:"class diffusers.StableDiffusionUpscalePipeline",anchor:"diffusers.StableDiffusionUpscalePipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"low_res_scheduler",val:": DDPMScheduler"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"safety_checker",val:": typing.Optional[typing.Any] = None"},{name:"feature_extractor",val:": typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] = None"},{name:"watermarker",val:": typing.Optional[typing.Any] = None"},{name:"max_noise_level",val:": int = 350"}],parametersDescription:[{anchor:"diffusers.StableDiffusionUpscalePipeline.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.StableDiffusionUpscalePipeline.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.StableDiffusionUpscalePipeline.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.StableDiffusionUpscalePipeline.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.StableDiffusionUpscalePipeline.low_res_scheduler",description:`<strong>low_res_scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
<a href="/docs/diffusers/main/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>.`,name:"low_res_scheduler"},{anchor:"diffusers.StableDiffusionUpscalePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/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/main/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/main/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py#L68"}}),ue=new oe({props:{name:"__call__",anchor:"diffusers.StableDiffusionUpscalePipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{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:"noise_level",val:": int = 20"},{name:"negative_prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": 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:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",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"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionUpscalePipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide image generation. If not defined, you need to pass <code>prompt_embeds</code>.`,name:"prompt"},{anchor:"diffusers.StableDiffusionUpscalePipeline.__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.`,name:"image"},{anchor:"diffusers.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableDiffusionUpscalePipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, <code>negative_prompt_embeds</code> are generated from the <code>negative_prompt</code> input argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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.StableDiffusionUpscalePipeline.__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"},{anchor:"diffusers.StableDiffusionUpscalePipeline.__call__.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow"><code>self.processor</code></a>.`,name:"cross_attention_kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py#L488",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 and the
second element is a list of <code>bool</code>s indicating whether the corresponding generated image contains
\u201Cnot-safe-for-work\u201D (nsfw) content.</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>
`}}),O=new vn({props:{anchor:"diffusers.StableDiffusionUpscalePipeline.__call__.example",$$slots:{default:[In]},$$scope:{ctx:A}}}),me=new oe({props:{name:"enable_attention_slicing",anchor:"diffusers.StableDiffusionUpscalePipeline.enable_attention_slicing",parameters:[{name:"slice_size",val:": typing.Union[str, int, NoneType] = 'auto'"}],parametersDescription:[{anchor:"diffusers.StableDiffusionUpscalePipeline.enable_attention_slicing.slice_size",description:`<strong>slice_size</strong> (<code>str</code> or <code>int</code>, <em>optional</em>, defaults to <code>&quot;auto&quot;</code>) &#x2014;
When <code>&quot;auto&quot;</code>, halves the input to the attention heads, so attention will be computed in two steps. If
<code>&quot;max&quot;</code>, maximum amount of memory will be saved by running only one slice at a time. If a number is
provided, uses as many slices as <code>attention_head_dim // slice_size</code>. In this case, <code>attention_head_dim</code>
must be a multiple of <code>slice_size</code>.`,name:"slice_size"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1669"}}),he=new oe({props:{name:"disable_attention_slicing",anchor:"diffusers.StableDiffusionUpscalePipeline.disable_attention_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1684"}}),_e=new oe({props:{name:"enable_xformers_memory_efficient_attention",anchor:"diffusers.StableDiffusionUpscalePipeline.enable_xformers_memory_efficient_attention",parameters:[{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionUpscalePipeline.enable_xformers_memory_efficient_attention.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
Override the default <code>None</code> operator for use as <code>op</code> argument to the
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention" rel="nofollow"><code>memory_efficient_attention()</code></a>
function of xFormers.`,name:"attention_op"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1608"}}),Y=new yn({props:{warning:!0,$$slots:{default:[Tn]},$$scope:{ctx:A}}}),H=new vn({props:{anchor:"diffusers.StableDiffusionUpscalePipeline.enable_xformers_memory_efficient_attention.example",$$slots:{default:[kn]},$$scope:{ctx:A}}}),ve=new oe({props:{name:"disable_xformers_memory_efficient_attention",anchor:"diffusers.StableDiffusionUpscalePipeline.disable_xformers_memory_efficient_attention",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1643"}}),Ue=new oe({props:{name:"enable_model_cpu_offload",anchor:"diffusers.StableDiffusionUpscalePipeline.enable_model_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py#L141"}}),xe=new Yt({}),Pe=new oe({props:{name:"class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",parameters:[{name:"images",val:": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"},{name:"nsfw_content_detected",val:": typing.Optional[typing.List[bool]]"}],parametersDescription:[{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.images",description:`<strong>images</strong> (<code>List[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
List of denoised PIL images of length <code>batch_size</code> or NumPy array of shape <code>(batch_size, height, width, num_channels)</code>.`,name:"images"},{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.nsfw_content_detected",description:`<strong>nsfw_content_detected</strong> (<code>List[bool]</code>) &#x2014;
List indicating whether the corresponding generated image contains &#x201C;not-safe-for-work&#x201D; (nsfw) content or
<code>None</code> if safety checking could not be performed.`,name:"nsfw_content_detected"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/__init__.py#L22"}}),{c(){c=s("meta"),y=u(),f=s("h1"),d=s("a"),v=s("span"),$(l.$$.fragment),h=u(),L=s("span"),E=i("Super-resolution"),z=u(),D=s("p"),Z=i("The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from "),J=s("a"),ie=i("CompVis"),W=i(", "),G=s("a"),re=i("Stability AI"),T=i(", and "),I=s("a"),Me=i("LAION"),k=i(". It is used to enhance the resolution of input images by a factor of 4."),le=u(),$(N.$$.fragment),pe=u(),F=s("h2"),R=s("a"),Te=s("span"),$(ce.$$.fragment),it=u(),ke=s("span"),rt=i("StableDiffusionUpscalePipeline"),Ve=u(),_=s("div"),$(de.$$.fragment),lt=u(),je=s("p"),pt=i("Pipeline for text-guided image super-resolution using Stable Diffusion 2."),ct=u(),fe=s("p"),dt=i("This model inherits from "),De=s("a"),ft=i("DiffusionPipeline"),ut=i(`. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`),mt=u(),B=s("div"),$(ue.$$.fragment),ht=u(),Ee=s("p"),gt=i("The call function to the pipeline for generation."),_t=u(),$(O.$$.fragment),bt=u(),Q=s("div"),$(me.$$.fragment),yt=u(),Ze=s("p"),vt=i(`Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
in slices to compute attention in several steps. This is useful to save some memory in exchange for a small
speed decrease.`),wt=u(),q=s("div"),$(he.$$.fragment),$t=u(),ge=s("p"),Ut=i("Disable sliced attention computation. If "),Je=s("code"),xt=i("enable_attention_slicing"),Pt=i(` was previously called, attention is
computed in one step.`),St=u(),j=s("div"),$(_e.$$.fragment),Mt=u(),be=s("p"),Dt=i("Enable memory efficient attention from "),ye=s("a"),It=i("xFormers"),Tt=i(`. When this
option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
up during training is not guaranteed.`),kt=u(),$(Y.$$.fragment),jt=u(),$(H.$$.fragment),Et=u(),K=s("div"),$(ve.$$.fragment),Zt=u(),we=s("p"),Jt=i("Disable memory efficient attention from "),$e=s("a"),Wt=i("xFormers"),Gt=i("."),Nt=u(),ee=s("div"),$(Ue.$$.fragment),Ct=u(),C=s("p"),At=i(`Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `),We=s("code"),Bt=i("forward"),Lt=i(` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `),Ge=s("code"),Ft=i("enable_sequential_cpu_offload"),Xt=i(`, but performance is much better due to the
iterative execution of the `),Ne=s("code"),Vt=i("unet"),zt=i("."),ze=u(),X=s("h2"),te=s("a"),Ce=s("span"),$(xe.$$.fragment),Rt=u(),Ae=s("span"),Ot=i("StableDiffusionPipelineOutput"),Re=u(),V=s("div"),$(Pe.$$.fragment),Qt=u(),Be=s("p"),qt=i("Output class for Stable Diffusion pipelines."),this.h()},l(t){const g=Sn('[data-svelte="svelte-1phssyn"]',document.head);c=a(g,"META",{name:!0,content:!0}),g.forEach(n),y=m(t),f=a(t,"H1",{class:!0});var Se=o(f);d=a(Se,"A",{id:!0,class:!0,href:!0});var Le=o(d);v=a(Le,"SPAN",{});var Fe=o(v);U(l.$$.fragment,Fe),Fe.forEach(n),Le.forEach(n),h=m(Se),L=a(Se,"SPAN",{});var Xe=o(L);E=r(Xe,"Super-resolution"),Xe.forEach(n),Se.forEach(n),z=m(t),D=a(t,"P",{});var ne=o(D);Z=r(ne,"The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from "),J=a(ne,"A",{href:!0,rel:!0});var Ht=o(J);ie=r(Ht,"CompVis"),Ht.forEach(n),W=r(ne,", "),G=a(ne,"A",{href:!0,rel:!0});var Kt=o(G);re=r(Kt,"Stability AI"),Kt.forEach(n),T=r(ne,", and "),I=a(ne,"A",{href:!0,rel:!0});var en=o(I);Me=r(en,"LAION"),en.forEach(n),k=r(ne,". It is used to enhance the resolution of input images by a factor of 4."),ne.forEach(n),le=m(t),U(N.$$.fragment,t),pe=m(t),F=a(t,"H2",{class:!0});var Qe=o(F);R=a(Qe,"A",{id:!0,class:!0,href:!0});var tn=o(R);Te=a(tn,"SPAN",{});var nn=o(Te);U(ce.$$.fragment,nn),nn.forEach(n),tn.forEach(n),it=m(Qe),ke=a(Qe,"SPAN",{});var sn=o(ke);rt=r(sn,"StableDiffusionUpscalePipeline"),sn.forEach(n),Qe.forEach(n),Ve=m(t),_=a(t,"DIV",{class:!0});var w=o(_);U(de.$$.fragment,w),lt=m(w),je=a(w,"P",{});var an=o(je);pt=r(an,"Pipeline for text-guided image super-resolution using Stable Diffusion 2."),an.forEach(n),ct=m(w),fe=a(w,"P",{});var qe=o(fe);dt=r(qe,"This model inherits from "),De=a(qe,"A",{href:!0});var on=o(De);ft=r(on,"DiffusionPipeline"),on.forEach(n),ut=r(qe,`. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`),qe.forEach(n),mt=m(w),B=a(w,"DIV",{class:!0});var Ie=o(B);U(ue.$$.fragment,Ie),ht=m(Ie),Ee=a(Ie,"P",{});var rn=o(Ee);gt=r(rn,"The call function to the pipeline for generation."),rn.forEach(n),_t=m(Ie),U(O.$$.fragment,Ie),Ie.forEach(n),bt=m(w),Q=a(w,"DIV",{class:!0});var Ye=o(Q);U(me.$$.fragment,Ye),yt=m(Ye),Ze=a(Ye,"P",{});var ln=o(Ze);vt=r(ln,`Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
in slices to compute attention in several steps. This is useful to save some memory in exchange for a small
speed decrease.`),ln.forEach(n),Ye.forEach(n),wt=m(w),q=a(w,"DIV",{class:!0});var He=o(q);U(he.$$.fragment,He),$t=m(He),ge=a(He,"P",{});var Ke=o(ge);Ut=r(Ke,"Disable sliced attention computation. If "),Je=a(Ke,"CODE",{});var pn=o(Je);xt=r(pn,"enable_attention_slicing"),pn.forEach(n),Pt=r(Ke,` was previously called, attention is
computed in one step.`),Ke.forEach(n),He.forEach(n),St=m(w),j=a(w,"DIV",{class:!0});var se=o(j);U(_e.$$.fragment,se),Mt=m(se),be=a(se,"P",{});var et=o(be);Dt=r(et,"Enable memory efficient attention from "),ye=a(et,"A",{href:!0,rel:!0});var cn=o(ye);It=r(cn,"xFormers"),cn.forEach(n),Tt=r(et,`. When this
option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed
up during training is not guaranteed.`),et.forEach(n),kt=m(se),U(Y.$$.fragment,se),jt=m(se),U(H.$$.fragment,se),se.forEach(n),Et=m(w),K=a(w,"DIV",{class:!0});var tt=o(K);U(ve.$$.fragment,tt),Zt=m(tt),we=a(tt,"P",{});var nt=o(we);Jt=r(nt,"Disable memory efficient attention from "),$e=a(nt,"A",{href:!0,rel:!0});var dn=o($e);Wt=r(dn,"xFormers"),dn.forEach(n),Gt=r(nt,"."),nt.forEach(n),tt.forEach(n),Nt=m(w),ee=a(w,"DIV",{class:!0});var st=o(ee);U(Ue.$$.fragment,st),Ct=m(st),C=a(st,"P",{});var ae=o(C);At=r(ae,`Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
time to the GPU when its `),We=a(ae,"CODE",{});var fn=o(We);Bt=r(fn,"forward"),fn.forEach(n),Lt=r(ae,` method is called, and the model remains in GPU until the next model runs.
Memory savings are lower than using `),Ge=a(ae,"CODE",{});var un=o(Ge);Ft=r(un,"enable_sequential_cpu_offload"),un.forEach(n),Xt=r(ae,`, but performance is much better due to the
iterative execution of the `),Ne=a(ae,"CODE",{});var mn=o(Ne);Vt=r(mn,"unet"),mn.forEach(n),zt=r(ae,"."),ae.forEach(n),st.forEach(n),w.forEach(n),ze=m(t),X=a(t,"H2",{class:!0});var at=o(X);te=a(at,"A",{id:!0,class:!0,href:!0});var hn=o(te);Ce=a(hn,"SPAN",{});var gn=o(Ce);U(xe.$$.fragment,gn),gn.forEach(n),hn.forEach(n),Rt=m(at),Ae=a(at,"SPAN",{});var _n=o(Ae);Ot=r(_n,"StableDiffusionPipelineOutput"),_n.forEach(n),at.forEach(n),Re=m(t),V=a(t,"DIV",{class:!0});var ot=o(V);U(Pe.$$.fragment,ot),Qt=m(ot),Be=a(ot,"P",{});var bn=o(Be);qt=r(bn,"Output class for Stable Diffusion pipelines."),bn.forEach(n),ot.forEach(n),this.h()},h(){p(c,"name","hf:doc:metadata"),p(c,"content",JSON.stringify(En)),p(d,"id","superresolution"),p(d,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),p(d,"href","#superresolution"),p(f,"class","relative group"),p(J,"href","https://github.com/CompVis"),p(J,"rel","nofollow"),p(G,"href","https://stability.ai/"),p(G,"rel","nofollow"),p(I,"href","https://laion.ai/"),p(I,"rel","nofollow"),p(R,"id","diffusers.StableDiffusionUpscalePipeline"),p(R,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),p(R,"href","#diffusers.StableDiffusionUpscalePipeline"),p(F,"class","relative group"),p(De,"href","/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline"),p(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),p(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),p(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),p(ye,"href","https://facebookresearch.github.io/xformers/"),p(ye,"rel","nofollow"),p(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),p($e,"href","https://facebookresearch.github.io/xformers/"),p($e,"rel","nofollow"),p(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),p(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),p(_,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),p(te,"id","diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"),p(te,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),p(te,"href","#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"),p(X,"class","relative group"),p(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(t,g){e(document.head,c),b(t,y,g),b(t,f,g),e(f,d),e(d,v),x(l,v,null),e(f,h),e(f,L),e(L,E),b(t,z,g),b(t,D,g),e(D,Z),e(D,J),e(J,ie),e(D,W),e(D,G),e(G,re),e(D,T),e(D,I),e(I,Me),e(D,k),b(t,le,g),x(N,t,g),b(t,pe,g),b(t,F,g),e(F,R),e(R,Te),x(ce,Te,null),e(F,it),e(F,ke),e(ke,rt),b(t,Ve,g),b(t,_,g),x(de,_,null),e(_,lt),e(_,je),e(je,pt),e(_,ct),e(_,fe),e(fe,dt),e(fe,De),e(De,ft),e(fe,ut),e(_,mt),e(_,B),x(ue,B,null),e(B,ht),e(B,Ee),e(Ee,gt),e(B,_t),x(O,B,null),e(_,bt),e(_,Q),x(me,Q,null),e(Q,yt),e(Q,Ze),e(Ze,vt),e(_,wt),e(_,q),x(he,q,null),e(q,$t),e(q,ge),e(ge,Ut),e(ge,Je),e(Je,xt),e(ge,Pt),e(_,St),e(_,j),x(_e,j,null),e(j,Mt),e(j,be),e(be,Dt),e(be,ye),e(ye,It),e(be,Tt),e(j,kt),x(Y,j,null),e(j,jt),x(H,j,null),e(_,Et),e(_,K),x(ve,K,null),e(K,Zt),e(K,we),e(we,Jt),e(we,$e),e($e,Wt),e(we,Gt),e(_,Nt),e(_,ee),x(Ue,ee,null),e(ee,Ct),e(ee,C),e(C,At),e(C,We),e(We,Bt),e(C,Lt),e(C,Ge),e(Ge,Ft),e(C,Xt),e(C,Ne),e(Ne,Vt),e(C,zt),b(t,ze,g),b(t,X,g),e(X,te),e(te,Ce),x(xe,Ce,null),e(X,Rt),e(X,Ae),e(Ae,Ot),b(t,Re,g),b(t,V,g),x(Pe,V,null),e(V,Qt),e(V,Be),e(Be,qt),Oe=!0},p(t,[g]){const Se={};g&2&&(Se.$$scope={dirty:g,ctx:t}),N.$set(Se);const Le={};g&2&&(Le.$$scope={dirty:g,ctx:t}),O.$set(Le);const Fe={};g&2&&(Fe.$$scope={dirty:g,ctx:t}),Y.$set(Fe);const Xe={};g&2&&(Xe.$$scope={dirty:g,ctx:t}),H.$set(Xe)},i(t){Oe||(P(l.$$.fragment,t),P(N.$$.fragment,t),P(ce.$$.fragment,t),P(de.$$.fragment,t),P(ue.$$.fragment,t),P(O.$$.fragment,t),P(me.$$.fragment,t),P(he.$$.fragment,t),P(_e.$$.fragment,t),P(Y.$$.fragment,t),P(H.$$.fragment,t),P(ve.$$.fragment,t),P(Ue.$$.fragment,t),P(xe.$$.fragment,t),P(Pe.$$.fragment,t),Oe=!0)},o(t){S(l.$$.fragment,t),S(N.$$.fragment,t),S(ce.$$.fragment,t),S(de.$$.fragment,t),S(ue.$$.fragment,t),S(O.$$.fragment,t),S(me.$$.fragment,t),S(he.$$.fragment,t),S(_e.$$.fragment,t),S(Y.$$.fragment,t),S(H.$$.fragment,t),S(ve.$$.fragment,t),S(Ue.$$.fragment,t),S(xe.$$.fragment,t),S(Pe.$$.fragment,t),Oe=!1},d(t){n(c),t&&n(y),t&&n(f),M(l),t&&n(z),t&&n(D),t&&n(le),M(N,t),t&&n(pe),t&&n(F),M(ce),t&&n(Ve),t&&n(_),M(de),M(ue),M(O),M(me),M(he),M(_e),M(Y),M(H),M(ve),M(Ue),t&&n(ze),t&&n(X),M(xe),t&&n(Re),t&&n(V),M(Pe)}}}const En={local:"superresolution",sections:[{local:"diffusers.StableDiffusionUpscalePipeline",title:"StableDiffusionUpscalePipeline"},{local:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",title:"StableDiffusionPipelineOutput"}],title:"Super-resolution"};function Zn(A){return Mn(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Bn extends Un{constructor(c){super();xn(this,c,Zn,jn,Pn,{})}}export{Bn as default,En as metadata};
