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 o,d as n,m,a,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 ae}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=o(T,"P",{});var I=a(c);y=r(I,"Make sure to check out the Stable Diffusion "),f=o(I,"A",{href:!0});var Me=a(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=o(T,"P",{});var k=a(h);L=r(k,"If you\u2019re interested in using one of the official checkpoints for a task, explore the "),E=o(k,"A",{href:!0,rel:!0});var le=a(E);z=r(le,"CompVis"),le.forEach(n),D=r(k,", "),Z=o(k,"A",{href:!0,rel:!0});var N=a(Z);J=r(N,"Runway"),N.forEach(n),ie=r(k,", and "),W=o(k,"A",{href:!0,rel:!0});var pe=a(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.$$.fragment)},l(l){c=o(l,"P",{});var h=a(c);y=r(h,"Examples:"),h.forEach(n),f=m(l),U(d.$$.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=o(f,"P",{});var d=a(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.$$.fragment)},l(l){c=o(l,"P",{});var h=a(c);y=r(h,"Examples:"),h.forEach(n),f=m(l),U(d.$$.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 ae({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/v0.19.2/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/v4.31.0/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/v4.31.0/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/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.StableDiffusionUpscalePipeline.low_res_scheduler",description:`<strong>low_res_scheduler</strong> (<a href="/docs/diffusers/v0.19.2/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/v0.19.2/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>.`,name:"low_res_scheduler"},{anchor:"diffusers.StableDiffusionUpscalePipeline.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/stable_diffusion/pipeline_stable_diffusion_upscale.py#L68"}}),ue=new ae({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/v0.19.2/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/v0.19.2/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
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