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	import{s as bt,o as yt,n as et}from"../chunks/scheduler.53228c21.js";import{S as vt,i as wt,e as r,s,c as _,h as xt,a as p,d as i,b as o,f as D,g as b,j as y,k as $,l as n,m,n as v,t as w,o as x,p as U}from"../chunks/index.100fac89.js";import{C as Ut}from"../chunks/CopyLLMTxtMenu.c36f1912.js";import{D as z}from"../chunks/Docstring.00e63d45.js";import{C as tt}from"../chunks/CodeBlock.d30a6509.js";import{E as Ye}from"../chunks/ExampleCodeBlock.278cf256.js";import{H as Ke,E as $t}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.c6997d0b.js";function Mt(J){let l,M="Examples:",g,c,u;return c=new tt({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> requests
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionUpscalePipeline
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># load model and scheduler</span>
	<span class="hljs-meta">>>> </span>model_id = <span class="hljs-string">"stabilityai/stable-diffusion-x4-upscaler"</span>
	<span class="hljs-meta">>>> </span>pipeline = StableDiffusionUpscalePipeline.from_pretrained(
	<span class="hljs-meta">... </span> model_id, variant=<span class="hljs-string">"fp16"</span>, torch_dtype=torch.float16
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>pipeline = pipeline.to(<span class="hljs-string">"cuda"</span>)

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># let's download an image</span>
	<span class="hljs-meta">>>> </span>url = <span class="hljs-string">"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"</span>
	<span class="hljs-meta">>>> </span>response = requests.get(url)
	<span class="hljs-meta">>>> </span>low_res_img = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">"RGB"</span>)
	<span class="hljs-meta">>>> </span>low_res_img = low_res_img.resize((<span class="hljs-number">128</span>, <span class="hljs-number">128</span>))
	<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"a white cat"</span>

	<span class="hljs-meta">>>> </span>upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[<span class="hljs-number">0</span>]
	<span class="hljs-meta">>>> </span>upscaled_image.save(<span class="hljs-string">"upsampled_cat.png"</span>)`,wrap:!1}}),{c(){l=r("p"),l.textContent=M,g=s(),_(c.$$.fragment)},l(t){l=p(t,"P",{"data-svelte-h":!0}),y(l)!=="svelte-kvfsh7"&&(l.textContent=M),g=o(t),b(c.$$.fragment,t)},m(t,h){m(t,l,h),m(t,g,h),v(c,t,h),u=!0},p:et,i(t){u\|\|(w(c.$$.fragment,t),u=!0)},o(t){x(c.$$.fragment,t),u=!1},d(t){t&&(i(l),i(g)),U(c,t)}}}function Tt(J){let l,M="Examples:",g,c,u;return c=new tt({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

	<span class="hljs-meta">>>> </span>pipe = StableDiffusionPipeline.from_pretrained(
	<span class="hljs-meta">... </span> <span class="hljs-string">"stable-diffusion-v1-5/stable-diffusion-v1-5"</span>,
	<span class="hljs-meta">... </span> torch_dtype=torch.float16,
	<span class="hljs-meta">... </span> use_safetensors=<span class="hljs-literal">True</span>,
	<span class="hljs-meta">... </span>)

	<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"a photo of an astronaut riding a horse on mars"</span>
	<span class="hljs-meta">>>> </span>pipe.enable_attention_slicing()
	<span class="hljs-meta">>>> </span>image = pipe(prompt).images[<span class="hljs-number">0</span>]`,wrap:!1}}),{c(){l=r("p"),l.textContent=M,g=s(),_(c.$$.fragment)},l(t){l=p(t,"P",{"data-svelte-h":!0}),y(l)!=="svelte-kvfsh7"&&(l.textContent=M),g=o(t),b(c.$$.fragment,t)},m(t,h){m(t,l,h),m(t,g,h),v(c,t,h),u=!0},p:et,i(t){u\|\|(w(c.$$.fragment,t),u=!0)},o(t){x(c.$$.fragment,t),u=!1},d(t){t&&(i(l),i(g)),U(c,t)}}}function St(J){let l,M="Examples:",g,c,u;return c=new tt({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

	<span class="hljs-meta">>>> </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">"stabilityai/stable-diffusion-2-1"</span>, torch_dtype=torch.float16)
	<span class="hljs-meta">>>> </span>pipe = pipe.to(<span class="hljs-string">"cuda"</span>)
	<span class="hljs-meta">>>> </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
	<span class="hljs-meta">>>> </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
	<span class="hljs-meta">>>> </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`,wrap:!1}}),{c(){l=r("p"),l.textContent=M,g=s(),_(c.$$.fragment)},l(t){l=p(t,"P",{"data-svelte-h":!0}),y(l)!=="svelte-kvfsh7"&&(l.textContent=M),g=o(t),b(c.$$.fragment,t)},m(t,h){m(t,l,h),m(t,g,h),v(c,t,h),u=!0},p:et,i(t){u\|\|(w(c.$$.fragment,t),u=!0)},o(t){x(c.$$.fragment,t),u=!1},d(t){t&&(i(l),i(g)),U(c,t)}}}function It(J){let l,M,g,c,u,t,h,he,j,nt='<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>',_e,E,st='The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from <a href="https://github.com/CompVis" rel="nofollow">CompVis</a>, <a href="https://stability.ai/" rel="nofollow">Stability AI</a>, and <a href="https://laion.ai/" rel="nofollow">LAION</a>. It is used to enhance the resolution of input images by a factor of 4.',be,Z,ot='<p>Make sure to check out the Stable Diffusion <a href="overview#tips">Tips</a> section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!</p> <p>If you’re interested in using one of the official checkpoints for a task, explore the <a href="https://huggingface.co/CompVis" rel="nofollow">CompVis</a> and <a href="https://huggingface.co/stabilityai" rel="nofollow">Stability AI</a> Hub organizations!</p>',ye,R,ve,d,q,De,oe,it="Pipeline for text-guided image super-resolution using Stable Diffusion 2.",Ce,ie,at=`This model inherits from <a href="/docs/diffusers/pr_12509/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods
	implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,ke,ae,lt="The pipeline also inherits the following loading methods:",Je,le,rt='<li><a href="/docs/diffusers/pr_12509/en/api/loaders/textual_inversion#diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion">load_textual_inversion()</a> for loading textual inversion embeddings</li> <li><a href="/docs/diffusers/pr_12509/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for loading LoRA weights</li> <li><a href="/docs/diffusers/pr_12509/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for saving LoRA weights</li> <li><a href="/docs/diffusers/pr_12509/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file">from_single_file()</a> for loading <code>.ckpt</code> files</li>',je,P,A,Ze,re,pt="The call function to the pipeline for generation.",Le,L,Ge,T,F,We,pe,ct=`Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor
	in slices to compute attention in several steps. For more than one attention head, the computation is performed
	sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.`,Ne,O,dt=`<p>> ⚠️ Don’t enable attention slicing if you’re already using <code>scaled_dot_product_attention</code> (SDPA)
	from PyTorch > 2.0 or xFormers. These attention computations are already very memory efficient so you won’t
	need to enable > this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious
	slow downs!</p>`,Be,G,Ve,W,H,Xe,ce,ft=`Disable sliced attention computation. If <code>enable_attention_slicing</code> was previously called, attention is
	computed in one step.`,ze,S,Q,Ee,de,mt=`Enable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>. 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.`,Re,Y,ut=`<p>> ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient
	attention takes > precedent.</p>`,qe,N,Ae,B,K,Fe,fe,gt='Disable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>.',Oe,V,ee,He,me,ht="Encodes the prompt into text encoder hidden states.",we,te,xe,C,ne,Qe,ue,_t="Output class for Stable Diffusion pipelines.",Ue,se,$e,ge,Me;return u=new Ut({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),h=new Ke({props:{title:"Super-resolution",local:"super-resolution",headingTag:"h1"}}),R=new Ke({props:{title:"StableDiffusionUpscalePipeline",local:"diffusers.StableDiffusionUpscalePipeline",headingTag:"h2"}}),q=new z({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/pr_12509/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) —
	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>) —
	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>) —
	A <code>CLIPTokenizer</code> to tokenize text.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionUpscalePipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_12509/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) —
	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/pr_12509/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) —
	A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of
	<a href="/docs/diffusers/pr_12509/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a>.`,name:"low_res_scheduler"},{anchor:"diffusers.StableDiffusionUpscalePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_12509/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) —
	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/pr_12509/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/pr_12509/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/pr_12509/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_12509/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py#L82"}}),A=new z({props:{name:"__call__",anchor:"diffusers.StableDiffusionUpscalePipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"image",val:": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = 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[str, typing.List[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.Tensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"},{name:"callback_steps",val:": int = 1"},{name:"cross_attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"clip_skip",val:": int = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionUpscalePipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) —
	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.Tensor</code>, <code>PIL.Image.Image</code>, <code>np.ndarray</code>, <code>List[torch.Tensor]</code>, <code>List[PIL.Image.Image]</code>, or <code>List[np.ndarray]</code>) —
	<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) —
	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) —
	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 > 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>) —
	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 < 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) —
	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) —
	Corresponds to parameter eta (η) from the <a href="https://huggingface.co/papers/2010.02502" rel="nofollow">DDIM</a> paper. Only
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	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.Tensor</code>, <em>optional</em>) —
	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.Tensor</code>, <em>optional</em>) —
	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.Tensor</code>, <em>optional</em>) —
	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>"pil"</code>) —
	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>) —
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	plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableDiffusionUpscalePipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) —
	A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
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	The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
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	A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined in
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	Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
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	<p>If <code>return_dict</code> is <code>True</code>, <a
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	“not-safe-for-work” (nsfw) content.</p>
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	<p><a
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