import{s as Ce,n as Ke,o as Te}from"../chunks/scheduler.53228c21.js";import{S as Ie,i as Me,e as d,s as i,c as u,h as Ne,a as f,d as o,b as n,f as q,j as l,g,k as L,l as r,m as a,n as _,t as h,o as b,p as v}from"../chunks/index.100fac89.js";import{C as ze}from"../chunks/CopyLLMTxtMenu.20a72e0e.js";import{D as ae}from"../chunks/Docstring.49101787.js";import{H as he,E as Xe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.4d82655d.js";function Ae(be){let m,j,R,B,D,ve="<p>This pipeline is deprecated but it can still be used. However, we won’t test the pipeline anymore and won’t accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.</p>",F,w,Q,$,W,k,De='<a href="https://github.com/crowsonkb/k-diffusion" rel="nofollow">k-diffusion</a> is a popular library created by <a href="https://github.com/crowsonkb/" rel="nofollow">Katherine Crowson</a>. We provide <code>StableDiffusionKDiffusionPipeline</code> and <code>StableDiffusionXLKDiffusionPipeline</code> that allow you to run Stable DIffusion with samplers from k-diffusion.',G,S,xe='Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers <a href="https://huggingface.co/docs/diffusers/api/schedulers/overview" rel="nofollow">here</a>',Y,y,J,s,C,de,X,Pe="Pipeline for text-to-image generation using Stable Diffusion.",fe,A,Le=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,pe,O,we="The pipeline also inherits the following loading methods:",le,E,$e='<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>',me,K,ke="<p>&gt; This is an experimental pipeline and is likely to change in the future.</p>",ce,x,T,ue,H,Se="Encodes the prompt into text encoder hidden states.",Z,I,ee,c,M,ge,P,N,_e,U,ye="Encodes the prompt into text encoder hidden states.",te,z,oe,V,ie;return w=new ze({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),$=new he({props:{title:"K-Diffusion",local:"k-diffusion",headingTag:"h1"}}),y=new he({props:{title:"StableDiffusionKDiffusionPipeline",local:"diffusers.StableDiffusionKDiffusionPipeline",headingTag:"h2"}}),C=new ae({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"}}),T=new ae({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#L202"}}),I=new he({props:{title:"StableDiffusionXLKDiffusionPipeline",local:"diffusers.StableDiffusionXLKDiffusionPipeline",headingTag:"h2"}}),M=new ae({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#L86"}}),N=new ae({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#L202"}}),z=new Xe({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md"}}),{c(){m=d("meta"),j=i(),R=d("p"),B=i(),D=d("blockquote"),D.innerHTML=ve,F=i(),u(w.$$.fragment),Q=i(),u($.$$.fragment),W=i(),k=d("p"),k.innerHTML=De,G=i(),S=d("p"),S.innerHTML=xe,Y=i(),u(y.$$.fragment),J=i(),s=d("div"),u(C.$$.fragment),de=i(),X=d("p"),X.textContent=Pe,fe=i(),A=d("p"),A.innerHTML=Le,pe=i(),O=d("p"),O.textContent=we,le=i(),E=d("ul"),E.innerHTML=$e,me=i(),K=d("blockquote"),K.innerHTML=ke,ce=i(),x=d("div"),u(T.$$.fragment),ue=i(),H=d("p"),H.textContent=Se,Z=i(),u(I.$$.fragment),ee=i(),c=d("div"),u(M.$$.fragment),ge=i(),P=d("div"),u(N.$$.fragment),_e=i(),U=d("p"),U.textContent=ye,te=i(),u(z.$$.fragment),oe=i(),V=d("p"),this.h()},l(e){const t=Ne("svelte-u9bgzb",document.head);m=f(t,"META",{name:!0,content:!0}),t.forEach(o),j=n(e),R=f(e,"P",{}),q(R).forEach(o),B=n(e),D=f(e,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),l(D)!=="svelte-124tjg"&&(D.innerHTML=ve),F=n(e),g(w.$$.fragment,e),Q=n(e),g($.$$.fragment,e),W=n(e),k=f(e,"P",{"data-svelte-h":!0}),l(k)!=="svelte-1u0qckb"&&(k.innerHTML=De),G=n(e),S=f(e,"P",{"data-svelte-h":!0}),l(S)!=="svelte-kxbfc9"&&(S.innerHTML=xe),Y=n(e),g(y.$$.fragment,e),J=n(e),s=f(e,"DIV",{class:!0});var p=q(s);g(C.$$.fragment,p),de=n(p),X=f(p,"P",{"data-svelte-h":!0}),l(X)!=="svelte-35p306"&&(X.textContent=Pe),fe=n(p),A=f(p,"P",{"data-svelte-h":!0}),l(A)!=="svelte-wu1jmh"&&(A.innerHTML=Le),pe=n(p),O=f(p,"P",{"data-svelte-h":!0}),l(O)!=="svelte-14s6m4u"&&(O.textContent=we),le=n(p),E=f(p,"UL",{"data-svelte-h":!0}),l(E)!=="svelte-txtwv6"&&(E.innerHTML=$e),me=n(p),K=f(p,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),l(K)!=="svelte-w27ih6"&&(K.innerHTML=ke),ce=n(p),x=f(p,"DIV",{class:!0});var ne=q(x);g(T.$$.fragment,ne),ue=n(ne),H=f(ne,"P",{"data-svelte-h":!0}),l(H)!=="svelte-16q0ax1"&&(H.textContent=Se),ne.forEach(o),p.forEach(o),Z=n(e),g(I.$$.fragment,e),ee=n(e),c=f(e,"DIV",{class:!0});var se=q(c);g(M.$$.fragment,se),ge=n(se),P=f(se,"DIV",{class:!0});var re=q(P);g(N.$$.fragment,re),_e=n(re),U=f(re,"P",{"data-svelte-h":!0}),l(U)!=="svelte-16q0ax1"&&(U.textContent=ye),re.forEach(o),se.forEach(o),te=n(e),g(z.$$.fragment,e),oe=n(e),V=f(e,"P",{}),q(V).forEach(o),this.h()},h(){L(m,"name","hf:doc:metadata"),L(m,"content",Oe),L(D,"class","warning"),L(K,"class","warning"),L(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),L(s,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),L(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),L(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,m),a(e,j,t),a(e,R,t),a(e,B,t),a(e,D,t),a(e,F,t),_(w,e,t),a(e,Q,t),_($,e,t),a(e,W,t),a(e,k,t),a(e,G,t),a(e,S,t),a(e,Y,t),_(y,e,t),a(e,J,t),a(e,s,t),_(C,s,null),r(s,de),r(s,X),r(s,fe),r(s,A),r(s,pe),r(s,O),r(s,le),r(s,E),r(s,me),r(s,K),r(s,ce),r(s,x),_(T,x,null),r(x,ue),r(x,H),a(e,Z,t),_(I,e,t),a(e,ee,t),a(e,c,t),_(M,c,null),r(c,ge),r(c,P),_(N,P,null),r(P,_e),r(P,U),a(e,te,t),_(z,e,t),a(e,oe,t),a(e,V,t),ie=!0},p:Ke,i(e){ie||(h(w.$$.fragment,e),h($.$$.fragment,e),h(y.$$.fragment,e),h(C.$$.fragment,e),h(T.$$.fragment,e),h(I.$$.fragment,e),h(M.$$.fragment,e),h(N.$$.fragment,e),h(z.$$.fragment,e),ie=!0)},o(e){b(w.$$.fragment,e),b($.$$.fragment,e),b(y.$$.fragment,e),b(C.$$.fragment,e),b(T.$$.fragment,e),b(I.$$.fragment,e),b(M.$$.fragment,e),b(N.$$.fragment,e),b(z.$$.fragment,e),ie=!1},d(e){e&&(o(j),o(R),o(B),o(D),o(F),o(Q),o(W),o(k),o(G),o(S),o(Y),o(J),o(s),o(Z),o(ee),o(c),o(te),o(oe),o(V)),o(m),v(w,e),v($,e),v(y,e),v(C),v(T),v(I,e),v(M),v(N),v(z,e)}}}const Oe='{"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 Ee(be){return Te(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class je extends Ie{constructor(m){super(),Me(this,m,Ee,Ae,Ce,{})}}export{je as component};
