import{s as $e,o as Le,n as we}from"../chunks/scheduler.8c3d61f6.js";import{S as Se,i as ke,g as p,s as i,r as u,A as ye,h as l,f as o,c as s,j as U,u as g,x as D,k as V,y as r,a as f,v as _,d as h,t as b,w as v}from"../chunks/index.da70eac4.js";import{T as Ce}from"../chunks/Tip.1d9b8c37.js";import{D as ie}from"../chunks/Docstring.9419aa1d.js";import{H as ue,E as Ke}from"../chunks/getInferenceSnippets.39110341.js";function Te(j){let a,L="This is an experimental pipeline and is likely to change in the future.";return{c(){a=p("p"),a.textContent=L},l(m){a=l(m,"P",{"data-svelte-h":!0}),D(a)!=="svelte-5fv3pw"&&(a.textContent=L)},m(m,z){f(m,a,z)},p:we,d(m){m&&o(a)}}}function Ie(j){let a,L,m,z,w,F,S,ge='<a href="https://github.com/crowsonkb/k-diffusion" rel="nofollow">k-diffusion</a> is a popular library created by <a href="https://github.com/crowsonkb/" rel="nofollow">Katherine Crowson</a>. We provide <code>StableDiffusionKDiffusionPipeline</code> and <code>StableDiffusionXLKDiffusionPipeline</code> that allow you to run Stable DIffusion with samplers from k-diffusion.',W,k,_e='Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers <a href="https://huggingface.co/docs/diffusers/api/schedulers/overview" rel="nofollow">here</a>',G,y,Y,n,C,se,X,he="Pipeline for text-to-image generation using Stable Diffusion.",re,A,be=`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 the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,ae,O,ve="The pipeline also inherits the following loading methods:",de,E,De='<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>',fe,x,pe,P,K,le,H,xe="Encodes the prompt into text encoder hidden states.",B,T,J,c,I,me,$,M,ce,R,Pe="Encodes the prompt into text encoder hidden states.",Q,N,Z,q,ee;return w=new ue({props:{title:"K-Diffusion",local:"k-diffusion",headingTag:"h1"}}),y=new ue({props:{title:"StableDiffusionKDiffusionPipeline",local:"diffusers.StableDiffusionKDiffusionPipeline",headingTag:"h2"}}),C=new ie({props:{name:"class diffusers.StableDiffusionKDiffusionPipeline",anchor:"diffusers.StableDiffusionKDiffusionPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": typing.Union[transformers.models.clip.tokenization_clip.CLIPTokenizer, transformers.models.clip.tokenization_clip_fast.CLIPTokenizerFast]"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.StableDiffusionKDiffusionPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.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.StableDiffusionKDiffusionPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.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.StableDiffusionKDiffusionPipeline.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
details.`,name:"safety_checker"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py#L66"}}),x=new Ce({props:{warning:!0,$$slots:{default:[Te]},$$scope:{ctx:j}}}),K=new ie({props:{name:"encode_prompt",anchor:"diffusers.StableDiffusionKDiffusionPipeline.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.StableDiffusionKDiffusionPipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
prompt to be encoded`,name:"prompt"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>):
torch device`,name:"device"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.encode_prompt.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>) &#x2014;
number of images that should be generated per prompt`,name:"num_images_per_prompt"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.encode_prompt.do_classifier_free_guidance",description:`<strong>do_classifier_free_guidance</strong> (<code>bool</code>) &#x2014;
whether to use classifier free guidance or not`,name:"do_classifier_free_guidance"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.encode_prompt.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. 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.StableDiffusionKDiffusionPipeline.encode_prompt.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, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.encode_prompt.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, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.encode_prompt.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.`,name:"lora_scale"},{anchor:"diffusers.StableDiffusionKDiffusionPipeline.encode_prompt.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py#L206"}}),T=new ue({props:{title:"StableDiffusionXLKDiffusionPipeline",local:"diffusers.StableDiffusionXLKDiffusionPipeline",headingTag:"h2"}}),I=new ie({props:{name:"class diffusers.StableDiffusionXLKDiffusionPipeline",anchor:"diffusers.StableDiffusionXLKDiffusionPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"text_encoder_2",val:": CLIPTextModelWithProjection"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"tokenizer_2",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"force_zeros_for_empty_prompt",val:": bool = True"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py#L90"}}),M=new ie({props:{name:"encode_prompt",anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt",parameters:[{name:"prompt",val:": str"},{name:"prompt_2",val:": typing.Optional[str] = None"},{name:"device",val:": typing.Optional[torch.device] = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"do_classifier_free_guidance",val:": bool = True"},{name:"negative_prompt",val:": typing.Optional[str] = None"},{name:"negative_prompt_2",val:": typing.Optional[str] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"pooled_prompt_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"negative_pooled_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.StableDiffusionXLKDiffusionPipeline.encode_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
prompt to be encoded`,name:"prompt"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.prompt_2",description:`<strong>prompt_2</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to be sent to the <code>tokenizer_2</code> and <code>text_encoder_2</code>. If not defined, <code>prompt</code> is
used in both text-encoders`,name:"prompt_2"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.device",description:`<strong>device</strong> &#x2014; (<code>torch.device</code>):
torch device`,name:"device"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>) &#x2014;
number of images that should be generated per prompt`,name:"num_images_per_prompt"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.do_classifier_free_guidance",description:`<strong>do_classifier_free_guidance</strong> (<code>bool</code>) &#x2014;
whether to use classifier free guidance or not`,name:"do_classifier_free_guidance"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.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. 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.StableDiffusionXLKDiffusionPipeline.encode_prompt.negative_prompt_2",description:`<strong>negative_prompt_2</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation to be sent to <code>tokenizer_2</code> and
<code>text_encoder_2</code>. If not defined, <code>negative_prompt</code> is used in both text-encoders`,name:"negative_prompt_2"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.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, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.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, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.pooled_prompt_embeds",description:`<strong>pooled_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting.
If not provided, pooled text embeddings will be generated from <code>prompt</code> input argument.`,name:"pooled_prompt_embeds"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.negative_pooled_prompt_embeds",description:`<strong>negative_pooled_prompt_embeds</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from <code>negative_prompt</code>
input argument.`,name:"negative_pooled_prompt_embeds"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, <em>optional</em>) &#x2014;
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.`,name:"lora_scale"},{anchor:"diffusers.StableDiffusionXLKDiffusionPipeline.encode_prompt.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py#L206"}}),N=new Ke({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md"}}),{c(){a=p("meta"),L=i(),m=p("p"),z=i(),u(w.$$.fragment),F=i(),S=p("p"),S.innerHTML=ge,W=i(),k=p("p"),k.innerHTML=_e,G=i(),u(y.$$.fragment),Y=i(),n=p("div"),u(C.$$.fragment),se=i(),X=p("p"),X.textContent=he,re=i(),A=p("p"),A.innerHTML=be,ae=i(),O=p("p"),O.textContent=ve,de=i(),E=p("ul"),E.innerHTML=De,fe=i(),u(x.$$.fragment),pe=i(),P=p("div"),u(K.$$.fragment),le=i(),H=p("p"),H.textContent=xe,B=i(),u(T.$$.fragment),J=i(),c=p("div"),u(I.$$.fragment),me=i(),$=p("div"),u(M.$$.fragment),ce=i(),R=p("p"),R.textContent=Pe,Q=i(),u(N.$$.fragment),Z=i(),q=p("p"),this.h()},l(e){const t=ye("svelte-u9bgzb",document.head);a=l(t,"META",{name:!0,content:!0}),t.forEach(o),L=s(e),m=l(e,"P",{}),U(m).forEach(o),z=s(e),g(w.$$.fragment,e),F=s(e),S=l(e,"P",{"data-svelte-h":!0}),D(S)!=="svelte-1u0qckb"&&(S.innerHTML=ge),W=s(e),k=l(e,"P",{"data-svelte-h":!0}),D(k)!=="svelte-kxbfc9"&&(k.innerHTML=_e),G=s(e),g(y.$$.fragment,e),Y=s(e),n=l(e,"DIV",{class:!0});var d=U(n);g(C.$$.fragment,d),se=s(d),X=l(d,"P",{"data-svelte-h":!0}),D(X)!=="svelte-35p306"&&(X.textContent=he),re=s(d),A=l(d,"P",{"data-svelte-h":!0}),D(A)!=="svelte-wu1jmh"&&(A.innerHTML=be),ae=s(d),O=l(d,"P",{"data-svelte-h":!0}),D(O)!=="svelte-14s6m4u"&&(O.textContent=ve),de=s(d),E=l(d,"UL",{"data-svelte-h":!0}),D(E)!=="svelte-txtwv6"&&(E.innerHTML=De),fe=s(d),g(x.$$.fragment,d),pe=s(d),P=l(d,"DIV",{class:!0});var te=U(P);g(K.$$.fragment,te),le=s(te),H=l(te,"P",{"data-svelte-h":!0}),D(H)!=="svelte-16q0ax1"&&(H.textContent=xe),te.forEach(o),d.forEach(o),B=s(e),g(T.$$.fragment,e),J=s(e),c=l(e,"DIV",{class:!0});var oe=U(c);g(I.$$.fragment,oe),me=s(oe),$=l(oe,"DIV",{class:!0});var ne=U($);g(M.$$.fragment,ne),ce=s(ne),R=l(ne,"P",{"data-svelte-h":!0}),D(R)!=="svelte-16q0ax1"&&(R.textContent=Pe),ne.forEach(o),oe.forEach(o),Q=s(e),g(N.$$.fragment,e),Z=s(e),q=l(e,"P",{}),U(q).forEach(o),this.h()},h(){V(a,"name","hf:doc:metadata"),V(a,"content",Me),V(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),V(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),V($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),V(c,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,t){r(document.head,a),f(e,L,t),f(e,m,t),f(e,z,t),_(w,e,t),f(e,F,t),f(e,S,t),f(e,W,t),f(e,k,t),f(e,G,t),_(y,e,t),f(e,Y,t),f(e,n,t),_(C,n,null),r(n,se),r(n,X),r(n,re),r(n,A),r(n,ae),r(n,O),r(n,de),r(n,E),r(n,fe),_(x,n,null),r(n,pe),r(n,P),_(K,P,null),r(P,le),r(P,H),f(e,B,t),_(T,e,t),f(e,J,t),f(e,c,t),_(I,c,null),r(c,me),r(c,$),_(M,$,null),r($,ce),r($,R),f(e,Q,t),_(N,e,t),f(e,Z,t),f(e,q,t),ee=!0},p(e,[t]){const d={};t&2&&(d.$$scope={dirty:t,ctx:e}),x.$set(d)},i(e){ee||(h(w.$$.fragment,e),h(y.$$.fragment,e),h(C.$$.fragment,e),h(x.$$.fragment,e),h(K.$$.fragment,e),h(T.$$.fragment,e),h(I.$$.fragment,e),h(M.$$.fragment,e),h(N.$$.fragment,e),ee=!0)},o(e){b(w.$$.fragment,e),b(y.$$.fragment,e),b(C.$$.fragment,e),b(x.$$.fragment,e),b(K.$$.fragment,e),b(T.$$.fragment,e),b(I.$$.fragment,e),b(M.$$.fragment,e),b(N.$$.fragment,e),ee=!1},d(e){e&&(o(L),o(m),o(z),o(F),o(S),o(W),o(k),o(G),o(Y),o(n),o(B),o(J),o(c),o(Q),o(Z),o(q)),o(a),v(w,e),v(y,e),v(C),v(x),v(K),v(T,e),v(I),v(M),v(N,e)}}}const Me='{"title":"K-Diffusion","local":"k-diffusion","sections":[{"title":"StableDiffusionKDiffusionPipeline","local":"diffusers.StableDiffusionKDiffusionPipeline","sections":[],"depth":2},{"title":"StableDiffusionXLKDiffusionPipeline","local":"diffusers.StableDiffusionXLKDiffusionPipeline","sections":[],"depth":2}],"depth":1}';function Ne(j){return Le(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class He extends Se{constructor(a){super(),ke(this,a,Ne,Ie,$e,{})}}export{He as component};
