import{s as hn,o as _n,n as K}from"../chunks/scheduler.8c3d61f6.js";import{S as bn,i as yn,g as l,s as i,r as b,A as vn,h as d,f as r,c as a,j as U,u as y,x as h,k as D,y as o,a as u,v,d as w,t as x,w as I}from"../chunks/index.da70eac4.js";import{T as Et}from"../chunks/Tip.1d9b8c37.js";import{D as C}from"../chunks/Docstring.91dd4639.js";import{C as ze}from"../chunks/CodeBlock.a9c4becf.js";import{E as Xe}from"../chunks/ExampleCodeBlock.d368b7c4.js";import{H as at,E as wn}from"../chunks/getInferenceSnippets.28ca3596.js";function xn($){let n,M='Make sure to check out the Stable Diffusion <a href="overview#tips">Tips</a> section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!',p,s,f='If you’re interested in using one of the official checkpoints for a task, explore the <a href="https://huggingface.co/CompVis" rel="nofollow">CompVis</a>, <a href="https://huggingface.co/runwayml" rel="nofollow">Runway</a>, and <a href="https://huggingface.co/stabilityai" rel="nofollow">Stability AI</a> Hub organizations!';return{c(){n=l("p"),n.innerHTML=M,p=i(),s=l("p"),s.innerHTML=f},l(t){n=d(t,"P",{"data-svelte-h":!0}),h(n)!=="svelte-1j961ct"&&(n.innerHTML=M),p=a(t),s=d(t,"P",{"data-svelte-h":!0}),h(s)!=="svelte-z4pn9c"&&(s.innerHTML=f)},m(t,_){u(t,n,_),u(t,p,_),u(t,s,_)},p:K,d(t){t&&(r(n),r(p),r(s))}}}function In($){let n,M="Examples:",p,s,f;return s=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> PIL
<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">import</span> torch
<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> StableDiffusionInpaintPipeline


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">download_image</span>(<span class="hljs-params">url</span>):
<span class="hljs-meta">... </span>    response = requests.get(url)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> PIL.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>img_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>init_image = download_image(img_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_image = download_image(mask_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionInpaintPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-inpainting&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<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>prompt = <span class="hljs-string">&quot;Face of a yellow cat, high resolution, sitting on a park bench&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[<span class="hljs-number">0</span>]`,wrap:!1}}),{c(){n=l("p"),n.textContent=M,p=i(),b(s.$$.fragment)},l(t){n=d(t,"P",{"data-svelte-h":!0}),h(n)!=="svelte-kvfsh7"&&(n.textContent=M),p=a(t),y(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,p,_),v(s,t,_),f=!0},p:K,i(t){f||(w(s.$$.fragment,t),f=!0)},o(t){x(s.$$.fragment,t),f=!1},d(t){t&&(r(n),r(p)),I(s,t)}}}function Mn($){let n,M=`⚠️ 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!`;return{c(){n=l("p"),n.innerHTML=M},l(p){n=d(p,"P",{"data-svelte-h":!0}),h(n)!=="svelte-ackzsn"&&(n.innerHTML=M)},m(p,s){u(p,n,s)},p:K,d(p){p&&r(n)}}}function $n($){let n,M="Examples:",p,s,f;return s=new ze({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> StableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</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">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_attention_slicing()
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt).images[<span class="hljs-number">0</span>]`,wrap:!1}}),{c(){n=l("p"),n.textContent=M,p=i(),b(s.$$.fragment)},l(t){n=d(t,"P",{"data-svelte-h":!0}),h(n)!=="svelte-kvfsh7"&&(n.textContent=M),p=a(t),y(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,p,_),v(s,t,_),f=!0},p:K,i(t){f||(w(s.$$.fragment,t),f=!0)},o(t){x(s.$$.fragment,t),f=!1},d(t){t&&(r(n),r(p)),I(s,t)}}}function Tn($){let n,M=`⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`;return{c(){n=l("p"),n.textContent=M},l(p){n=d(p,"P",{"data-svelte-h":!0}),h(n)!=="svelte-17p1lpg"&&(n.textContent=M)},m(p,s){u(p,n,s)},p:K,d(p){p&&r(n)}}}function kn($){let n,M="Examples:",p,s,f;return s=new ze({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>)`,wrap:!1}}),{c(){n=l("p"),n.textContent=M,p=i(),b(s.$$.fragment)},l(t){n=d(t,"P",{"data-svelte-h":!0}),h(n)!=="svelte-kvfsh7"&&(n.textContent=M),p=a(t),y(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,p,_),v(s,t,_),f=!0},p:K,i(t){f||(w(s.$$.fragment,t),f=!0)},o(t){x(s.$$.fragment,t),f=!1},d(t){t&&(r(n),r(p)),I(s,t)}}}function Pn($){let n,M="To load a Textual Inversion embedding vector in 🤗 Diffusers format:",p,s,f;return s=new ze({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> torch

model_id = <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.load_textual_inversion(<span class="hljs-string">&quot;sd-concepts-library/cat-toy&quot;</span>)

prompt = <span class="hljs-string">&quot;A &lt;cat-toy&gt; backpack&quot;</span>

image = pipe(prompt, num_inference_steps=<span class="hljs-number">50</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;cat-backpack.png&quot;</span>)`,wrap:!1}}),{c(){n=l("p"),n.textContent=M,p=i(),b(s.$$.fragment)},l(t){n=d(t,"P",{"data-svelte-h":!0}),h(n)!=="svelte-1gc783q"&&(n.textContent=M),p=a(t),y(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,p,_),v(s,t,_),f=!0},p:K,i(t){f||(w(s.$$.fragment,t),f=!0)},o(t){x(s.$$.fragment,t),f=!1},d(t){t&&(r(n),r(p)),I(s,t)}}}function Sn($){let n,M="locally:",p,s,f;return s=new ze({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> torch

model_id = <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.load_textual_inversion(<span class="hljs-string">&quot;./charturnerv2.pt&quot;</span>, token=<span class="hljs-string">&quot;charturnerv2&quot;</span>)

prompt = <span class="hljs-string">&quot;charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details.&quot;</span>

image = pipe(prompt, num_inference_steps=<span class="hljs-number">50</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;character.png&quot;</span>)`,wrap:!1}}),{c(){n=l("p"),n.textContent=M,p=i(),b(s.$$.fragment)},l(t){n=d(t,"P",{"data-svelte-h":!0}),h(n)!=="svelte-4c75kq"&&(n.textContent=M),p=a(t),y(s.$$.fragment,t)},m(t,_){u(t,n,_),u(t,p,_),v(s,t,_),f=!0},p:K,i(t){f||(w(s.$$.fragment,t),f=!0)},o(t){x(s.$$.fragment,t),f=!1},d(t){t&&(r(n),r(p)),I(s,t)}}}function Dn($){let n,M,p,s,f,t,_,Ht='<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>',Ee,ee,Ft="The Stable Diffusion model can also be applied to inpainting which lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion.",He,te,Fe,ne,Vt=`It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
as <a href="https://huggingface.co/runwayml/stable-diffusion-inpainting" rel="nofollow">runwayml/stable-diffusion-inpainting</a>. Default
text-to-image Stable Diffusion checkpoints, such as
<a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow">stable-diffusion-v1-5/stable-diffusion-v1-5</a> are also compatible but they might be less performant.`,Ve,N,Ye,oe,Ae,m,ie,st,ve,Yt="Pipeline for text-guided image inpainting using Stable Diffusion.",rt,we,At=`This model inherits from <a href="/docs/diffusers/main/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.).`,lt,xe,qt="The pipeline also inherits the following loading methods:",dt,Ie,Qt='<li><a href="/docs/diffusers/main/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/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for loading LoRA weights</li> <li><a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for saving LoRA weights</li> <li><a href="/docs/diffusers/main/en/api/loaders/ip_adapter#diffusers.loaders.IPAdapterMixin.load_ip_adapter">load_ip_adapter()</a> for loading IP Adapters</li> <li><a href="/docs/diffusers/main/en/api/loaders/single_file#diffusers.loaders.FromSingleFileMixin.from_single_file">from_single_file()</a> for loading <code>.ckpt</code> files</li>',pt,W,ae,ct,Me,Ot="The call function to the pipeline for generation.",ft,R,mt,J,se,ut,$e,Kt=`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.`,gt,X,ht,z,_t,E,re,bt,Te,en=`Disable sliced attention computation. If <code>enable_attention_slicing</code> was previously called, attention is
computed in one step.`,yt,j,le,vt,ke,tn=`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.`,wt,H,xt,F,It,V,de,Mt,Pe,nn='Disable memory efficient attention from <a href="https://facebookresearch.github.io/xformers/" rel="nofollow">xFormers</a>.',$t,T,pe,Tt,Se,on=`Load Textual Inversion embeddings into the text encoder of <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline">StableDiffusionPipeline</a> (both 🤗 Diffusers and
Automatic1111 formats are supported).`,kt,De,an="Example:",Pt,Y,St,Ue,sn=`To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first
(for example from <a href="https://civitai.com/models/3036?modelVersionId=9857" rel="nofollow">civitAI</a>) and then load the vector`,Dt,A,Ut,k,ce,Jt,Je,rn=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.unet</code> and
<code>self.text_encoder</code>.`,jt,je,ln="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Ct,Ce,dn=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Lt,Le,pn=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is
loaded into <code>self.unet</code>.`,Zt,Ze,cn=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder">load_lora_into_text_encoder()</a> for more details on how the state
dict is loaded into <code>self.text_encoder</code>.`,Wt,q,fe,Gt,We,fn="Save the LoRA parameters corresponding to the UNet and text encoder.",Bt,Q,me,Nt,Ge,mn="Encodes the prompt into text encoder hidden states.",Rt,O,ue,Xt,Be,un='See <a href="https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298" rel="nofollow">https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298</a>',qe,ge,Qe,G,he,zt,Ne,gn="Output class for Stable Diffusion pipelines.",Oe,_e,Ke,Re,et;return f=new at({props:{title:"Inpainting",local:"inpainting",headingTag:"h1"}}),te=new at({props:{title:"Tips",local:"tips",headingTag:"h2"}}),N=new Et({props:{$$slots:{default:[xn]},$$scope:{ctx:$}}}),oe=new at({props:{title:"StableDiffusionInpaintPipeline",local:"diffusers.StableDiffusionInpaintPipeline",headingTag:"h2"}}),ie=new C({props:{name:"class diffusers.StableDiffusionInpaintPipeline",anchor:"diffusers.StableDiffusionInpaintPipeline",parameters:[{name:"vae",val:": typing.Union[diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL, diffusers.models.autoencoders.autoencoder_asym_kl.AsymmetricAutoencoderKL]"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"image_encoder",val:": CLIPVisionModelWithProjection = None"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.vae",description:`<strong>vae</strong> ([<code>AutoencoderKL</code>, <code>AsymmetricAutoencoderKL</code>]) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.StableDiffusionInpaintPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#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.StableDiffusionInpaintPipeline.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.StableDiffusionInpaintPipeline.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.StableDiffusionInpaintPipeline.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"},{anchor:"diffusers.StableDiffusionInpaintPipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>StableDiffusionSafetyChecker</code>) &#x2014;
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the <a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow">model card</a> for
more details about a model&#x2019;s potential harms.`,name:"safety_checker"},{anchor:"diffusers.StableDiffusionInpaintPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor" rel="nofollow">CLIPImageProcessor</a>) &#x2014;
A <code>CLIPImageProcessor</code> to extract features from generated images; used as inputs to the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L128"}}),ae=new C({props:{name:"__call__",anchor:"diffusers.StableDiffusionInpaintPipeline.__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:"mask_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:"masked_image_latents",val:": Tensor = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"padding_mask_crop",val:": typing.Optional[int] = None"},{name:"strength",val:": float = 1.0"},{name:"num_inference_steps",val:": int = 50"},{name:"timesteps",val:": typing.List[int] = None"},{name:"sigmas",val:": typing.List[float] = None"},{name:"guidance_scale",val:": float = 7.5"},{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:"ip_adapter_image",val:": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None"},{name:"ip_adapter_image_embeds",val:": typing.Optional[typing.List[torch.Tensor]] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"cross_attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"clip_skip",val:": int = None"},{name:"callback_on_step_end",val:": typing.Union[typing.Callable[[int, int, typing.Dict], NoneType], diffusers.callbacks.PipelineCallback, diffusers.callbacks.MultiPipelineCallbacks, NoneType] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.__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.StableDiffusionInpaintPipeline.__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>) &#x2014;
<code>Image</code>, numpy array or tensor representing an image batch to be inpainted (which parts of the image to
be masked out with <code>mask_image</code> and repainted according to <code>prompt</code>). For both numpy array and pytorch
tensor, the expected value range is between <code>[0, 1]</code> If it&#x2019;s a tensor or a list or tensors, the
expected shape should be <code>(B, C, H, W)</code> or <code>(C, H, W)</code>. If it is a numpy array or a list of arrays, the
expected shape should be <code>(B, H, W, C)</code> or <code>(H, W, C)</code> It can also accept image latents as <code>image</code>, but
if passing latents directly it is not encoded again.`,name:"image"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.mask_image",description:`<strong>mask_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>) &#x2014;
<code>Image</code>, numpy array or tensor representing an image batch to mask <code>image</code>. White pixels in the mask
are repainted while black pixels are preserved. If <code>mask_image</code> is a PIL image, it is converted to a
single channel (luminance) before use. If it&#x2019;s a numpy array or pytorch tensor, it should contain one
color channel (L) instead of 3, so the expected shape for pytorch tensor would be <code>(B, 1, H, W)</code>, <code>(B, H, W)</code>, <code>(1, H, W)</code>, <code>(H, W)</code>. And for numpy array would be for <code>(B, H, W, 1)</code>, <code>(B, H, W)</code>, <code>(H, W, 1)</code>, or <code>(H, W)</code>.`,name:"mask_image"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.unet.config.sample_size * self.vae_scale_factor</code>) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.padding_mask_crop",description:`<strong>padding_mask_crop</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The size of margin in the crop to be applied to the image and masking. If <code>None</code>, no crop is applied to
image and mask_image. If <code>padding_mask_crop</code> is not <code>None</code>, it will first find a rectangular region
with the same aspect ration of the image and contains all masked area, and then expand that area based
on <code>padding_mask_crop</code>. The image and mask_image will then be cropped based on the expanded area before
resizing to the original image size for inpainting. This is useful when the masked area is small while
the image is large and contain information irrelevant for inpainting, such as background.`,name:"padding_mask_crop"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Indicates extent to transform the reference <code>image</code>. Must be between 0 and 1. <code>image</code> is used as a
starting point and more noise is added the higher the <code>strength</code>. The number of denoising steps depends
on the amount of noise initially added. When <code>strength</code> is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in <code>num_inference_steps</code>. A value of 1
essentially ignores <code>image</code>.`,name:"strength"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__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. This parameter is modulated by <code>strength</code>.`,name:"num_inference_steps"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.timesteps",description:`<strong>timesteps</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Custom timesteps to use for the denoising process with schedulers which support a <code>timesteps</code> argument
in their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is
passed will be used. Must be in descending order.`,name:"timesteps"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.sigmas",description:`<strong>sigmas</strong> (<code>List[float]</code>, <em>optional</em>) &#x2014;
Custom sigmas to use for the denoising process with schedulers which support a <code>sigmas</code> argument in
their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is passed
will be used.`,name:"sigmas"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__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.StableDiffusionInpaintPipeline.__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.StableDiffusionInpaintPipeline.__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.StableDiffusionInpaintPipeline.__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://huggingface.co/papers/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.StableDiffusionInpaintPipeline.__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.StableDiffusionInpaintPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</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.StableDiffusionInpaintPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</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.StableDiffusionInpaintPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.Tensor</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.StableDiffusionInpaintPipeline.__call__.ip_adapter_image",description:"<strong>ip_adapter_image</strong> &#x2014; (<code>PipelineImageInput</code>, <em>optional</em>): Optional image input to work with IP Adapters.",name:"ip_adapter_image"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.ip_adapter_image_embeds",description:`<strong>ip_adapter_image_embeds</strong> (<code>List[torch.Tensor]</code>, <em>optional</em>) &#x2014;
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
IP-adapters. Each element should be a tensor of shape <code>(batch_size, num_images, emb_dim)</code>. It should
contain the negative image embedding if <code>do_classifier_free_guidance</code> is set to <code>True</code>. If not
provided, embeddings are computed from the <code>ip_adapter_image</code> input argument.`,name:"ip_adapter_image_embeds"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__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.StableDiffusionInpaintPipeline.__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/text2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__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"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.clip_skip",description:`<strong>clip_skip</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.`,name:"clip_skip"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <code>PipelineCallback</code>, <code>MultiPipelineCallbacks</code>, <em>optional</em>) &#x2014;
A function or a subclass of <code>PipelineCallback</code> or <code>MultiPipelineCallbacks</code> that is called at the end of
each denoising step during the inference. with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a
list of all tensors as specified by <code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L880",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#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
“not-safe-for-work” (nsfw) content.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),R=new Xe({props:{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.example",$$slots:{default:[In]},$$scope:{ctx:$}}}),se=new C({props:{name:"enable_attention_slicing",anchor:"diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing",parameters:[{name:"slice_size",val:": typing.Union[int, str, NoneType] = 'auto'"}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.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#L1867"}}),X=new Et({props:{warning:!0,$$slots:{default:[Mn]},$$scope:{ctx:$}}}),z=new Xe({props:{anchor:"diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing.example",$$slots:{default:[$n]},$$scope:{ctx:$}}}),re=new C({props:{name:"disable_attention_slicing",anchor:"diffusers.StableDiffusionInpaintPipeline.disable_attention_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1907"}}),le=new C({props:{name:"enable_xformers_memory_efficient_attention",anchor:"diffusers.StableDiffusionInpaintPipeline.enable_xformers_memory_efficient_attention",parameters:[{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.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#L1806"}}),H=new Et({props:{warning:!0,$$slots:{default:[Tn]},$$scope:{ctx:$}}}),F=new Xe({props:{anchor:"diffusers.StableDiffusionInpaintPipeline.enable_xformers_memory_efficient_attention.example",$$slots:{default:[kn]},$$scope:{ctx:$}}}),de=new C({props:{name:"disable_xformers_memory_efficient_attention",anchor:"diffusers.StableDiffusionInpaintPipeline.disable_xformers_memory_efficient_attention",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1841"}}),pe=new C({props:{name:"load_textual_inversion",anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]]"},{name:"token",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"tokenizer",val:": typing.Optional[ForwardRef('PreTrainedTokenizer')] = None"},{name:"text_encoder",val:": typing.Optional[ForwardRef('PreTrainedModel')] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code> or <code>List[str or os.PathLike]</code> or <code>Dict</code> or <code>List[Dict]</code>) &#x2014;
Can be either one of the following or a list of them:</p>
<ul>
<li>A string, the <em>model id</em> (for example <code>sd-concepts-library/low-poly-hd-logos-icons</code>) of a
pretrained model hosted on the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_text_inversion_directory/</code>) containing the textual
inversion weights.</li>
<li>A path to a <em>file</em> (for example <code>./my_text_inversions.pt</code>) containing textual inversion weights.</li>
<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
dict</a>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.token",description:`<strong>token</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
Override the token to use for the textual inversion weights. If <code>pretrained_model_name_or_path</code> is a
list, then <code>token</code> must also be a list of equal length.`,name:"token"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.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>, <em>optional</em>) &#x2014;
Frozen text-encoder (<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a>).
If not specified, function will take self.tokenizer.`,name:"text_encoder"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>, <em>optional</em>) &#x2014;
A <code>CLIPTokenizer</code> to tokenize text. If not specified, function will take self.tokenizer.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Name of a custom weight file. This should be used when:</p>
<ul>
<li>The saved textual inversion file is in &#x1F917; Diffusers format, but was saved under a specific weight
name such as <code>text_inv.bin</code>.</li>
<li>The saved textual inversion file is in the Automatic1111 format.</li>
</ul>`,name:"weight_name"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.cache_dir",description:`<strong>cache_dir</strong> (<code>Union[str, os.PathLike]</code>, <em>optional</em>) &#x2014;
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.`,name:"cache_dir"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
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The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
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The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
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The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.mirror",description:`<strong>mirror</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Mirror source to resolve accessibility issues if you&#x2019;re downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.`,name:"mirror"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/textual_inversion.py#L263"}}),Y=new Xe({props:{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.example",$$slots:{default:[Pn]},$$scope:{ctx:$}}}),A=new Xe({props:{anchor:"diffusers.StableDiffusionInpaintPipeline.load_textual_inversion.example-2",$$slots:{default:[Sn]},$$scope:{ctx:$}}}),ce=new C({props:{name:"load_lora_weights",anchor:"diffusers.StableDiffusionInpaintPipeline.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.load_lora_weights.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) &#x2014;
See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.StableDiffusionInpaintPipeline.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed <code>adapter_name</code> should be the name of an already loaded adapter.</p>
<p>If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
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State dict of the LoRA layers corresponding to the <code>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.StableDiffusionInpaintPipeline.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) &#x2014;
State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text
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process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.StableDiffusionInpaintPipeline.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) &#x2014;
The function to use to save the state dictionary. Useful during distributed training when you need to
replace <code>torch.save</code> with another method. Can be configured with the environment variable
<code>DIFFUSERS_SAVE_MODE</code>.`,name:"save_function"},{anchor:"diffusers.StableDiffusionInpaintPipeline.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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LoRA adapter metadata associated with the unet to be serialized with the state dict.`,name:"unet_lora_adapter_metadata"},{anchor:"diffusers.StableDiffusionInpaintPipeline.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L474"}}),me=new C({props:{name:"encode_prompt",anchor:"diffusers.StableDiffusionInpaintPipeline.encode_prompt",parameters:[{name:"prompt",val:""},{name:"device",val:""},{name:"num_images_per_prompt",val:""},{name:"do_classifier_free_guidance",val:""},{name:"negative_prompt",val:" = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"lora_scale",val:": typing.Optional[float] = None"},{name:"clip_skip",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
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torch device`,name:"device"},{anchor:"diffusers.StableDiffusionInpaintPipeline.encode_prompt.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>) &#x2014;
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
<code>negative_prompt_embeds</code> instead. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is
less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableDiffusionInpaintPipeline.encode_prompt.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
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Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.`,name:"clip_skip"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L312"}}),ue=new C({props:{name:"get_guidance_scale_embedding",anchor:"diffusers.StableDiffusionInpaintPipeline.get_guidance_scale_embedding",parameters:[{name:"w",val:": Tensor"},{name:"embedding_dim",val:": int = 512"},{name:"dtype",val:": dtype = torch.float32"}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.get_guidance_scale_embedding.w",description:`<strong>w</strong> (<code>torch.Tensor</code>) &#x2014;
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