import{S as Un,i as Jn,s as Tn,e as a,k as c,w as U,t as l,M as $n,c as s,d as n,m as p,a as o,x as J,h as r,b as d,G as e,g,y as T,q as $,o as M,B as x,v as Mn,L as vn}from"../../../../chunks/vendor-hf-doc-builder.js";import{D as se}from"../../../../chunks/Docstring-hf-doc-builder.js";import{C as wn}from"../../../../chunks/CodeBlock-hf-doc-builder.js";import{I as _n}from"../../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as yn}from"../../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function xn(oe){let m,E,b,f,v;return f=new wn({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(){m=a("p"),E=l("Examples:"),b=c(),U(f.$$.fragment)},l(i){m=s(i,"P",{});var y=o(m);E=r(y,"Examples:"),y.forEach(n),b=p(i),J(f.$$.fragment,i)},m(i,y){g(i,m,y),e(m,E),g(i,b,y),T(f,i,y),v=!0},p:vn,i(i){v||($(f.$$.fragment,i),v=!0)},o(i){M(f.$$.fragment,i),v=!1},d(i){i&&n(m),i&&n(b),x(f,i)}}}function En(oe){let m,E,b,f,v;return f=new wn({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(){m=a("p"),E=l("Examples:"),b=c(),U(f.$$.fragment)},l(i){m=s(i,"P",{});var y=o(m);E=r(y,"Examples:"),y.forEach(n),b=p(i),J(f.$$.fragment,i)},m(i,y){g(i,m,y),e(m,E),g(i,b,y),T(f,i,y),v=!0},p:vn,i(i){v||($(f.$$.fragment,i),v=!0)},o(i){M(f.$$.fragment,i),v=!1},d(i){i&&n(m),i&&n(b),x(f,i)}}}function Dn(oe){let m,E,b,f,v,i,y,me,Ae,Ie,L,Z,he,V,ze,ge,Ye,Pe,D,qe,R,Qe,He,N,Oe,Ke,ie,et,tt,Se,le,nt,Le,re,be,F,at,Ze,ce,st,je,pe,X,_e,ot,it,A,lt,Ge,h,z,rt,ye,ct,pt,Y,dt,de,ft,ut,mt,I,q,ht,ve,gt,bt,j,_t,G,Q,yt,H,vt,we,wt,Ut,Jt,P,O,Tt,Ue,$t,Mt,Je,xt,Et,W,K,Dt,ee,It,Te,Pt,St,Lt,w,te,Zt,$e,jt,Gt,Me,Wt,kt,xe,Ct,Bt,k,Vt,C,ne,Rt,Ee,Nt,We;return i=new _n({}),V=new _n({}),z=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/v0.17.0/en/api/models#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> (<code>CLIPTextModel</code>) &#x2014;
Frozen text-encoder. Stable Diffusion uses the text portion of
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIP</a>, specifically
the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,name:"text_encoder"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.17.0/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.17.0/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/v0.17.0/en/api/schedulers/euler#diffusers.EulerDiscreteScheduler">EulerDiscreteScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py#L61"}}),q=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[str, typing.List[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 the 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 which will 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
will be considered a <code>latent</code> if <code>image.shape[1]</code> is <code>4</code>; otherwise, it will be 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;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,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 not to guide the image generation. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>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;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/v0.17.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</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 will ge 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 generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.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/v0.17.0/en/api/pipelines/stable_diffusion/overview#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 will be called every <code>callback_steps</code> steps during inference. The function will be
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 will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py#L295",returnDescription:`
<p><a
  href="/docs/diffusers/v0.17.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> if <code>return_dict</code> is True, otherwise a <code>tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of </code>bool<code>s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the </code>safety_checker\`.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.17.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),j=new yn({props:{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.__call__.example",$$slots:{default:[xn]},$$scope:{ctx:oe}}}),Q=new se({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.StableDiffusionLatentUpscalePipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py#L104"}}),O=new se({props:{name:"enable_attention_slicing",anchor:"diffusers.StableDiffusionLatentUpscalePipeline.enable_attention_slicing",parameters:[{name:"slice_size",val:": typing.Union[str, int, NoneType] = 'auto'"}],parametersDescription:[{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.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/v0.17.0/src/diffusers/pipelines/pipeline_utils.py#L1492"}}),K=new se({props:{name:"disable_attention_slicing",anchor:"diffusers.StableDiffusionLatentUpscalePipeline.disable_attention_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.17.0/src/diffusers/pipelines/pipeline_utils.py#L1508"}}),te=new se({props:{name:"enable_xformers_memory_efficient_attention",anchor:"diffusers.StableDiffusionLatentUpscalePipeline.enable_xformers_memory_efficient_attention",parameters:[{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionLatentUpscalePipeline.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>
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