import{s as Dr,o as Sr,n as Y}from"../chunks/scheduler.8c3d61f6.js";import{S as Tr,i as kr,g as r,s as o,r as m,A as Cr,h as n,f as d,c as a,j as w,u as _,x as p,k as M,y as t,a as b,v as u,d as h,t as g,w as x}from"../chunks/index.da70eac4.js";import{T as Ft}from"../chunks/Tip.1d9b8c37.js";import{D as y}from"../chunks/Docstring.6b390b9a.js";import{C as Na}from"../chunks/CodeBlock.00a903b3.js";import{E as Ua}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as Yt,E as Ar}from"../chunks/EditOnGithub.1e64e623.js";function Rr(T){let s,L='To learn more about how to load LoRA weights, see the <a href="../../using-diffusers/loading_adapters#lora">LoRA</a> loading guide.';return{c(){s=r("p"),s.innerHTML=L},l(f){s=n(f,"P",{"data-svelte-h":!0}),p(s)!=="svelte-1fw6lx1"&&(s.innerHTML=L)},m(f,l){b(f,s,l)},p:Y,d(f){f&&d(s)}}}function Pr(T){let s,L="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",f,l,v="This function is experimental and might change in the future.";return{c(){s=r("p"),s.textContent=L,f=o(),l=r("p"),l.textContent=v},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=L),f=a(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=v)},m(i,$){b(i,s,$),b(i,f,$),b(i,l,$)},p:Y,d(i){i&&(d(s),d(f),d(l))}}}function Ir(T){let s,L="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",f,l,v="This function is experimental and might change in the future.";return{c(){s=r("p"),s.textContent=L,f=o(),l=r("p"),l.textContent=v},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=L),f=a(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=v)},m(i,$){b(i,s,$),b(i,f,$),b(i,l,$)},p:Y,d(i){i&&(d(s),d(f),d(l))}}}function Hr(T){let s,L="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",f,l,v="This function is experimental and might change in the future.";return{c(){s=r("p"),s.textContent=L,f=o(),l=r("p"),l.textContent=v},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=L),f=a(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=v)},m(i,$){b(i,s,$),b(i,f,$),b(i,l,$)},p:Y,d(i){i&&(d(s),d(f),d(l))}}}function Er(T){let s,L="This is an experimental API.";return{c(){s=r("p"),s.textContent=L},l(f){s=n(f,"P",{"data-svelte-h":!0}),p(s)!=="svelte-8w79b9"&&(s.textContent=L)},m(f,l){b(f,s,l)},p:Y,d(f){f&&d(s)}}}function Ur(T){let s,L="Example:",f,l,v;return l=new Na({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(<span class="hljs-string">&quot;nerijs/pixel-art-xl&quot;</span>, weight_name=<span class="hljs-string">&quot;pixel-art-xl.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;pixel&quot;</span>)
pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){s=r("p"),s.textContent=L,f=o(),m(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-11lpom8"&&(s.textContent=L),f=a(i),_(l.$$.fragment,i)},m(i,$){b(i,s,$),b(i,f,$),u(l,i,$),v=!0},p:Y,i(i){v||(h(l.$$.fragment,i),v=!0)},o(i){g(l.$$.fragment,i),v=!1},d(i){i&&(d(s),d(f)),x(l,i)}}}function Nr(T){let s,L="Example:",f,l,v;return l=new Na({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(<span class="hljs-string">&quot;CiroN2022/toy-face&quot;</span>, weight_name=<span class="hljs-string">&quot;toy_face_sdxl.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;toy&quot;</span>)
pipeline.get_active_adapters()`,wrap:!1}}),{c(){s=r("p"),s.textContent=L,f=o(),m(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-11lpom8"&&(s.textContent=L),f=a(i),_(l.$$.fragment,i)},m(i,$){b(i,s,$),b(i,f,$),u(l,i,$),v=!0},p:Y,i(i){v||(h(l.$$.fragment,i),v=!0)},o(i){g(l.$$.fragment,i),v=!1},d(i){i&&(d(s),d(f)),x(l,i)}}}function Xr(T){let s,L="This is an experimental API.";return{c(){s=r("p"),s.textContent=L},l(f){s=n(f,"P",{"data-svelte-h":!0}),p(s)!=="svelte-8w79b9"&&(s.textContent=L)},m(f,l){b(f,s,l)},p:Y,d(f){f&&d(s)}}}function Wr(T){let s,L="Examples:",f,l,v;return l=new Na({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">&gt;&gt;&gt; </span>...',wrap:!1}}),{c(){s=r("p"),s.textContent=L,f=o(),m(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-kvfsh7"&&(s.textContent=L),f=a(i),_(l.$$.fragment,i)},m(i,$){b(i,s,$),b(i,f,$),u(l,i,$),v=!0},p:Y,i(i){v||(h(l.$$.fragment,i),v=!0)},o(i){g(l.$$.fragment,i),v=!1},d(i){i&&(d(s),d(f)),x(l,i)}}}function Br(T){let s,L,f,l,v,i,$,Xa='LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/main/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Kt,be,Wa='<li><code>StableDiffusionLoraLoaderMixin</code> provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.</li> <li><code>StableDiffusionXLLoraLoaderMixin</code> is a <a href="../../api/pipelines/stable_diffusion/stable_diffusion_xl">Stable Diffusion (SDXL)</a> version of the <code>StableDiffusionLoraLoaderMixin</code> class for loading and saving LoRA weights. It can only be used with the SDXL model.</li> <li><code>SD3LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/blog/sd3" rel="nofollow">Stable Diffusion 3</a>.</li> <li><code>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/main/en/api/pipelines/amused#diffusers.AmusedPipeline">AmusedPipeline</a>.</li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',Ot,Q,eo,Le,to,k,ve,$o,nt,Ba=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,wo,K,$e,Mo,st,Ja="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",yo,O,we,Do,it,qa="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",So,I,Me,To,dt,za=`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>.`,ko,lt,ja="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Co,ct,Va=`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.`,Ao,ft,Za=`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>.`,Ro,pt,Ga=`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>.`,Po,B,ye,Io,mt,Fa="Return state dict for lora weights and the network alphas.",Ho,ee,Eo,te,De,Uo,_t,Ya="Save the LoRA parameters corresponding to the UNet and text encoder.",oo,Se,ao,C,Te,No,ut,Qa=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>, and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow"><code>CLIPTextModelWithProjection</code></a>.`,Xo,oe,ke,Wo,ht,Ka="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Bo,ae,Ce,Jo,gt,Oa="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",qo,H,Ae,zo,xt,er=`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>.`,jo,bt,tr="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Vo,Lt,or=`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.`,Zo,vt,ar=`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>.`,Go,$t,rr=`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>.`,Fo,J,Re,Yo,wt,nr="Return state dict for lora weights and the network alphas.",Qo,re,Ko,ne,Pe,Oo,Mt,sr="Save the LoRA parameters corresponding to the UNet and text encoder.",ro,Ie,no,S,He,ea,yt,ir=`Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>, and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow"><code>CLIPTextModelWithProjection</code></a>.`,ta,Dt,dr='Specific to <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',oa,se,Ee,aa,St,lr="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",ra,ie,Ue,na,Tt,cr="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",sa,N,Ne,ia,kt,fr=`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>.`,da,Ct,pr="All kwargs are forwarded to <code>self.lora_state_dict</code>.",la,At,mr=`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.`,ca,Rt,_r=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,fa,q,Xe,pa,Pt,ur="Return state dict for lora weights and the network alphas.",ma,de,_a,le,We,ua,It,hr="Save the LoRA parameters corresponding to the UNet and text encoder.",so,Be,io,G,Je,ha,ce,qe,ga,Ht,gr="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",lo,ze,co,D,je,xa,Et,xr="Utility class for handling LoRAs.",ba,Ut,Ve,La,X,Ze,va,Nt,br="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",$a,fe,wa,pe,Ma,z,Ge,ya,Xt,Lr="Gets the list of the current active adapters.",Da,me,Sa,_e,Fe,Ta,Wt,vr="Gets the current list of all available adapters in the pipeline.",ka,ue,Ye,Ca,Bt,$r=`Moves the LoRAs listed in <code>adapter_names</code> to a target device. Useful for offloading the LoRA to the CPU in case
you want to load multiple adapters and free some GPU memory.`,Aa,j,Qe,Ra,Jt,wr=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Pa,he,Ia,V,Ke,Ha,qt,Mr="Unloads the LoRA parameters.",Ea,ge,fo,Oe,po,Qt,mo;return v=new Yt({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),Q=new Ft({props:{$$slots:{default:[Rr]},$$scope:{ctx:T}}}),Le=new Yt({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),ve=new y({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L69"}}),$e=new y({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"text_encoder",val:""},{name:"prefix",val:" = None"},{name:"lora_scale",val:" = 1.0"},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional <code>text_encoder</code> to distinguish between unet lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) &#x2014;
Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) &#x2014;
How much to scale the output of the lora linear layer before it is added with the output of the regular
lora layer.`,name:"lora_scale"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.Speed",description:"<strong>Speed</strong> up model loading by only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L311"}}),we=new y({props:{name:"load_lora_into_unet",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"unet",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional <code>unet</code> which can be used to distinguish between text
encoder lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.Speed",description:"<strong>Speed</strong> up model loading only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L264"}}),Me=new y({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": Union"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.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.loaders.StableDiffusionLoraLoaderMixin.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L79"}}),ye=new y({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": Union"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.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;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
with <a href="/docs/diffusers/main/en/api/models/overview#diffusers.ModelMixin.save_pretrained">ModelMixin.save_pretrained()</a>.</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_or_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.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.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.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.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.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.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.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
won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
<code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&quot;</code>) &#x2014;
The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>, defaults to None) &#x2014;
Name of the serialized state dict file.`,name:"weight_name"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L149"}}),ee=new Ft({props:{warning:!0,$$slots:{default:[Pr]},$$scope:{ctx:T}}}),De=new y({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": Union"},{name:"unet_lora_layers",val:": Dict = None"},{name:"text_encoder_lora_layers",val:": Dict = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory to save LoRA parameters to. Will be created if it doesn&#x2019;t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.unet_lora_layers",description:`<strong>unet_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>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.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
encoder LoRA state dict because it comes from &#x1F917; Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main
process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.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.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L448"}}),Se=new Yt({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),Te=new y({props:{name:"class diffusers.loaders.StableDiffusionXLLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L566"}}),ke=new y({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"text_encoder",val:""},{name:"prefix",val:" = None"},{name:"lora_scale",val:" = 1.0"},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional <code>text_encoder</code> to distinguish between unet lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) &#x2014;
Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) &#x2014;
How much to scale the output of the lora linear layer before it is added with the output of the regular
lora layer.`,name:"lora_scale"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.Speed",description:"<strong>Speed</strong> up model loading by only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L834"}}),Ce=new y({props:{name:"load_lora_into_unet",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"unet",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional <code>unet</code> which can be used to distinguish between text
encoder lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.Speed",description:"<strong>Speed</strong> up model loading only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L786"}}),Ae=new y({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": Union"},{name:"adapter_name",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.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.loaders.StableDiffusionXLLoraLoaderMixin.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights.Speed",description:"<strong>Speed</strong> up model loading by only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L577"}}),Re=new y({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": Union"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.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;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
with <a href="/docs/diffusers/main/en/api/models/overview#diffusers.ModelMixin.save_pretrained">ModelMixin.save_pretrained()</a>.</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_or_dict"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.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.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.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.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.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.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.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
won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
<code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&quot;</code>) &#x2014;
The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>, defaults to None) &#x2014;
Name of the serialized state dict file.`,name:"weight_name"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L670"}}),re=new Ft({props:{warning:!0,$$slots:{default:[Ir]},$$scope:{ctx:T}}}),Pe=new y({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": Union"},{name:"unet_lora_layers",val:": Dict = None"},{name:"text_encoder_lora_layers",val:": Dict = None"},{name:"text_encoder_2_lora_layers",val:": Dict = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory to save LoRA parameters to. Will be created if it doesn&#x2019;t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.unet_lora_layers",description:`<strong>unet_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>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.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
encoder LoRA state dict because it comes from &#x1F917; Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.text_encoder_2_lora_layers",description:`<strong>text_encoder_2_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_2</code>. Must explicitly pass the text
encoder LoRA state dict because it comes from &#x1F917; Transformers.`,name:"text_encoder_2_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main
process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.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.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L972"}}),Ie=new Yt({props:{title:"SD3LoraLoaderMixin",local:"diffusers.loaders.SD3LoraLoaderMixin",headingTag:"h2"}}),He=new y({props:{name:"class diffusers.loaders.SD3LoraLoaderMixin",anchor:"diffusers.loaders.SD3LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L1098"}}),Ee=new y({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"text_encoder",val:""},{name:"prefix",val:" = None"},{name:"lora_scale",val:" = 1.0"},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional <code>text_encoder</code> to distinguish between unet lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) &#x2014;
Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) &#x2014;
How much to scale the output of the lora linear layer before it is added with the output of the regular
lora layer.`,name:"lora_scale"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.Speed",description:"<strong>Speed</strong> up model loading by only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L1393"}}),Ue=new y({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional <code>unet</code> which can be used to distinguish between text
encoder lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>SD3Transformer2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer.Speed",description:"<strong>Speed</strong> up model loading by only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L1287"}}),Ne=new y({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": Union"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.SD3LoraLoaderMixin.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.loaders.SD3LoraLoaderMixin.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights.Speed",description:"<strong>Speed</strong> up model loading by only loading the pretrained LoRA weights and not initializing the random weights. &#x2014;",name:"Speed"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L1208"}}),Xe=new y({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": Union"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.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;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
with <a href="/docs/diffusers/main/en/api/models/overview#diffusers.ModelMixin.save_pretrained">ModelMixin.save_pretrained()</a>.</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_or_dict"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.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.loaders.SD3LoraLoaderMixin.lora_state_dict.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.loaders.SD3LoraLoaderMixin.lora_state_dict.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.loaders.SD3LoraLoaderMixin.lora_state_dict.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
won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
<code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&quot;</code>) &#x2014;
The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L1111"}}),de=new Ft({props:{warning:!0,$$slots:{default:[Hr]},$$scope:{ctx:T}}}),We=new y({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": Union"},{name:"transformer_lora_layers",val:": Dict = None"},{name:"text_encoder_lora_layers",val:": Dict = None"},{name:"text_encoder_2_lora_layers",val:": Dict = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory to save LoRA parameters to. Will be created if it doesn&#x2019;t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.transformer_lora_layers",description:`<strong>transformer_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>transformer</code>.`,name:"transformer_lora_layers"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.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
encoder LoRA state dict because it comes from &#x1F917; Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.text_encoder_2_lora_layers",description:`<strong>text_encoder_2_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_2</code>. Must explicitly pass the text
encoder LoRA state dict because it comes from &#x1F917; Transformers.`,name:"text_encoder_2_lora_layers"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main
process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.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.loaders.SD3LoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L1531"}}),Be=new Yt({props:{title:"AmusedLoraLoaderMixin",local:"diffusers.loaders.AmusedLoraLoaderMixin",headingTag:"h2"}}),Je=new y({props:{name:"class diffusers.loaders.AmusedLoraLoaderMixin",anchor:"diffusers.loaders.AmusedLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L2239"}}),qe=new y({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional <code>unet</code> which can be used to distinguish between text
encoder lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L2244"}}),ze=new Yt({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),je=new y({props:{name:"class diffusers.loaders.lora_base.LoraBaseMixin",anchor:"diffusers.loaders.lora_base.LoraBaseMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L184"}}),Ve=new y({props:{name:"delete_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters",parameters:[{name:"adapter_names",val:": Union"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.Deletes",description:`<strong>Deletes</strong> the LoRA layers of <code>adapter_name</code> for the unet and text-encoder(s). &#x2014;
adapter_names (<code>Union[List[str], str]</code>):
The names of the adapter to delete. Can be a single string or a list of strings`,name:"Deletes"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L607"}}),Ze=new y({props:{name:"fuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora",parameters:[{name:"components",val:": List = []"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, defaults to 1.0) &#x2014;
Controls how much to influence the outputs with the LoRA parameters.`,name:"lora_scale"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.safe_fusing",description:`<strong>safe_fusing</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.`,name:"safe_fusing"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L379"}}),fe=new Ft({props:{warning:!0,$$slots:{default:[Er]},$$scope:{ctx:T}}}),pe=new Ua({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[Ur]},$$scope:{ctx:T}}}),Ge=new y({props:{name:"get_active_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L629"}}),me=new Ua({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[Nr]},$$scope:{ctx:T}}}),Fe=new y({props:{name:"get_list_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L662"}}),Ye=new y({props:{name:"set_lora_device",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device",parameters:[{name:"adapter_names",val:": List"},{name:"device",val:": Union"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>) &#x2014;
List of adapters to send device to.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.device",description:`<strong>device</strong> (<code>Union[torch.device, str, int]</code>) &#x2014;
Device to send the adapters to. Can be either a torch device, a str or an integer.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L684"}}),Qe=new y({props:{name:"unfuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora",parameters:[{name:"components",val:": List = []"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) &#x2014; List of LoRA-injectable components to unfuse LoRA from.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_unet",description:"<strong>unfuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_unet"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_text_encoder",description:`<strong>unfuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn&#x2019;t monkey-patched with the
LoRA parameters then it won&#x2019;t have any effect.`,name:"unfuse_text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L460"}}),he=new Ft({props:{warning:!0,$$slots:{default:[Xr]},$$scope:{ctx:T}}}),Ke=new y({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L356"}}),ge=new Ua({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[Wr]},$$scope:{ctx:T}}}),Oe=new Ar({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/loaders/lora.md"}}),{c(){s=r("meta"),L=o(),f=r("p"),l=o(),m(v.$$.fragment),i=o(),$=r("p"),$.innerHTML=Xa,Kt=o(),be=r("ul"),be.innerHTML=Wa,Ot=o(),m(Q.$$.fragment),eo=o(),m(Le.$$.fragment),to=o(),k=r("div"),m(ve.$$.fragment),$o=o(),nt=r("p"),nt.innerHTML=Ba,wo=o(),K=r("div"),m($e.$$.fragment),Mo=o(),st=r("p"),st.innerHTML=Ja,yo=o(),O=r("div"),m(we.$$.fragment),Do=o(),it=r("p"),it.innerHTML=qa,So=o(),I=r("div"),m(Me.$$.fragment),To=o(),dt=r("p"),dt.innerHTML=za,ko=o(),lt=r("p"),lt.innerHTML=ja,Co=o(),ct=r("p"),ct.innerHTML=Va,Ao=o(),ft=r("p"),ft.innerHTML=Za,Ro=o(),pt=r("p"),pt.innerHTML=Ga,Po=o(),B=r("div"),m(ye.$$.fragment),Io=o(),mt=r("p"),mt.textContent=Fa,Ho=o(),m(ee.$$.fragment),Eo=o(),te=r("div"),m(De.$$.fragment),Uo=o(),_t=r("p"),_t.textContent=Ya,oo=o(),m(Se.$$.fragment),ao=o(),C=r("div"),m(Te.$$.fragment),No=o(),ut=r("p"),ut.innerHTML=Qa,Xo=o(),oe=r("div"),m(ke.$$.fragment),Wo=o(),ht=r("p"),ht.innerHTML=Ka,Bo=o(),ae=r("div"),m(Ce.$$.fragment),Jo=o(),gt=r("p"),gt.innerHTML=Oa,qo=o(),H=r("div"),m(Ae.$$.fragment),zo=o(),xt=r("p"),xt.innerHTML=er,jo=o(),bt=r("p"),bt.innerHTML=tr,Vo=o(),Lt=r("p"),Lt.innerHTML=or,Zo=o(),vt=r("p"),vt.innerHTML=ar,Go=o(),$t=r("p"),$t.innerHTML=rr,Fo=o(),J=r("div"),m(Re.$$.fragment),Yo=o(),wt=r("p"),wt.textContent=nr,Qo=o(),m(re.$$.fragment),Ko=o(),ne=r("div"),m(Pe.$$.fragment),Oo=o(),Mt=r("p"),Mt.textContent=sr,ro=o(),m(Ie.$$.fragment),no=o(),S=r("div"),m(He.$$.fragment),ea=o(),yt=r("p"),yt.innerHTML=ir,ta=o(),Dt=r("p"),Dt.innerHTML=dr,oa=o(),se=r("div"),m(Ee.$$.fragment),aa=o(),St=r("p"),St.innerHTML=lr,ra=o(),ie=r("div"),m(Ue.$$.fragment),na=o(),Tt=r("p"),Tt.innerHTML=cr,sa=o(),N=r("div"),m(Ne.$$.fragment),ia=o(),kt=r("p"),kt.innerHTML=fr,da=o(),Ct=r("p"),Ct.innerHTML=pr,la=o(),At=r("p"),At.innerHTML=mr,ca=o(),Rt=r("p"),Rt.innerHTML=_r,fa=o(),q=r("div"),m(Xe.$$.fragment),pa=o(),Pt=r("p"),Pt.textContent=ur,ma=o(),m(de.$$.fragment),_a=o(),le=r("div"),m(We.$$.fragment),ua=o(),It=r("p"),It.textContent=hr,so=o(),m(Be.$$.fragment),io=o(),G=r("div"),m(Je.$$.fragment),ha=o(),ce=r("div"),m(qe.$$.fragment),ga=o(),Ht=r("p"),Ht.innerHTML=gr,lo=o(),m(ze.$$.fragment),co=o(),D=r("div"),m(je.$$.fragment),xa=o(),Et=r("p"),Et.textContent=xr,ba=o(),Ut=r("div"),m(Ve.$$.fragment),La=o(),X=r("div"),m(Ze.$$.fragment),va=o(),Nt=r("p"),Nt.textContent=br,$a=o(),m(fe.$$.fragment),wa=o(),m(pe.$$.fragment),Ma=o(),z=r("div"),m(Ge.$$.fragment),ya=o(),Xt=r("p"),Xt.textContent=Lr,Da=o(),m(me.$$.fragment),Sa=o(),_e=r("div"),m(Fe.$$.fragment),Ta=o(),Wt=r("p"),Wt.textContent=vr,ka=o(),ue=r("div"),m(Ye.$$.fragment),Ca=o(),Bt=r("p"),Bt.innerHTML=$r,Aa=o(),j=r("div"),m(Qe.$$.fragment),Ra=o(),Jt=r("p"),Jt.innerHTML=wr,Pa=o(),m(he.$$.fragment),Ia=o(),V=r("div"),m(Ke.$$.fragment),Ha=o(),qt=r("p"),qt.textContent=Mr,Ea=o(),m(ge.$$.fragment),fo=o(),m(Oe.$$.fragment),po=o(),Qt=r("p"),this.h()},l(e){const c=Cr("svelte-u9bgzb",document.head);s=n(c,"META",{name:!0,content:!0}),c.forEach(d),L=a(e),f=n(e,"P",{}),w(f).forEach(d),l=a(e),_(v.$$.fragment,e),i=a(e),$=n(e,"P",{"data-svelte-h":!0}),p($)!=="svelte-1gbgnc1"&&($.innerHTML=Xa),Kt=a(e),be=n(e,"UL",{"data-svelte-h":!0}),p(be)!=="svelte-zczuv6"&&(be.innerHTML=Wa),Ot=a(e),_(Q.$$.fragment,e),eo=a(e),_(Le.$$.fragment,e),to=a(e),k=n(e,"DIV",{class:!0});var R=w(k);_(ve.$$.fragment,R),$o=a(R),nt=n(R,"P",{"data-svelte-h":!0}),p(nt)!=="svelte-1xpk7vh"&&(nt.innerHTML=Ba),wo=a(R),K=n(R,"DIV",{class:!0});var et=w(K);_($e.$$.fragment,et),Mo=a(et),st=n(et,"P",{"data-svelte-h":!0}),p(st)!=="svelte-1062ci4"&&(st.innerHTML=Ja),et.forEach(d),yo=a(R),O=n(R,"DIV",{class:!0});var tt=w(O);_(we.$$.fragment,tt),Do=a(tt),it=n(tt,"P",{"data-svelte-h":!0}),p(it)!=="svelte-u3q4so"&&(it.innerHTML=qa),tt.forEach(d),So=a(R),I=n(R,"DIV",{class:!0});var U=w(I);_(Me.$$.fragment,U),To=a(U),dt=n(U,"P",{"data-svelte-h":!0}),p(dt)!=="svelte-vs7s0z"&&(dt.innerHTML=za),ko=a(U),lt=n(U,"P",{"data-svelte-h":!0}),p(lt)!=="svelte-15b960v"&&(lt.innerHTML=ja),Co=a(U),ct=n(U,"P",{"data-svelte-h":!0}),p(ct)!=="svelte-15mw932"&&(ct.innerHTML=Va),Ao=a(U),ft=n(U,"P",{"data-svelte-h":!0}),p(ft)!=="svelte-qyr8zl"&&(ft.innerHTML=Za),Ro=a(U),pt=n(U,"P",{"data-svelte-h":!0}),p(pt)!=="svelte-9uqd0r"&&(pt.innerHTML=Ga),U.forEach(d),Po=a(R),B=n(R,"DIV",{class:!0});var F=w(B);_(ye.$$.fragment,F),Io=a(F),mt=n(F,"P",{"data-svelte-h":!0}),p(mt)!=="svelte-flusvq"&&(mt.textContent=Fa),Ho=a(F),_(ee.$$.fragment,F),F.forEach(d),Eo=a(R),te=n(R,"DIV",{class:!0});var ot=w(te);_(De.$$.fragment,ot),Uo=a(ot),_t=n(ot,"P",{"data-svelte-h":!0}),p(_t)!=="svelte-1ufq5ot"&&(_t.textContent=Ya),ot.forEach(d),R.forEach(d),oo=a(e),_(Se.$$.fragment,e),ao=a(e),C=n(e,"DIV",{class:!0});var P=w(C);_(Te.$$.fragment,P),No=a(P),ut=n(P,"P",{"data-svelte-h":!0}),p(ut)!=="svelte-46vapy"&&(ut.innerHTML=Qa),Xo=a(P),oe=n(P,"DIV",{class:!0});var at=w(oe);_(ke.$$.fragment,at),Wo=a(at),ht=n(at,"P",{"data-svelte-h":!0}),p(ht)!=="svelte-1062ci4"&&(ht.innerHTML=Ka),at.forEach(d),Bo=a(P),ae=n(P,"DIV",{class:!0});var rt=w(ae);_(Ce.$$.fragment,rt),Jo=a(rt),gt=n(rt,"P",{"data-svelte-h":!0}),p(gt)!=="svelte-u3q4so"&&(gt.innerHTML=Oa),rt.forEach(d),qo=a(P),H=n(P,"DIV",{class:!0});var W=w(H);_(Ae.$$.fragment,W),zo=a(W),xt=n(W,"P",{"data-svelte-h":!0}),p(xt)!=="svelte-vs7s0z"&&(xt.innerHTML=er),jo=a(W),bt=n(W,"P",{"data-svelte-h":!0}),p(bt)!=="svelte-15b960v"&&(bt.innerHTML=tr),Vo=a(W),Lt=n(W,"P",{"data-svelte-h":!0}),p(Lt)!=="svelte-15mw932"&&(Lt.innerHTML=or),Zo=a(W),vt=n(W,"P",{"data-svelte-h":!0}),p(vt)!=="svelte-qyr8zl"&&(vt.innerHTML=ar),Go=a(W),$t=n(W,"P",{"data-svelte-h":!0}),p($t)!=="svelte-9uqd0r"&&($t.innerHTML=rr),W.forEach(d),Fo=a(P),J=n(P,"DIV",{class:!0});var zt=w(J);_(Re.$$.fragment,zt),Yo=a(zt),wt=n(zt,"P",{"data-svelte-h":!0}),p(wt)!=="svelte-flusvq"&&(wt.textContent=nr),Qo=a(zt),_(re.$$.fragment,zt),zt.forEach(d),Ko=a(P),ne=n(P,"DIV",{class:!0});var _o=w(ne);_(Pe.$$.fragment,_o),Oo=a(_o),Mt=n(_o,"P",{"data-svelte-h":!0}),p(Mt)!=="svelte-1ufq5ot"&&(Mt.textContent=sr),_o.forEach(d),P.forEach(d),ro=a(e),_(Ie.$$.fragment,e),no=a(e),S=n(e,"DIV",{class:!0});var E=w(S);_(He.$$.fragment,E),ea=a(E),yt=n(E,"P",{"data-svelte-h":!0}),p(yt)!=="svelte-j7diyz"&&(yt.innerHTML=ir),ta=a(E),Dt=n(E,"P",{"data-svelte-h":!0}),p(Dt)!=="svelte-dges5l"&&(Dt.innerHTML=dr),oa=a(E),se=n(E,"DIV",{class:!0});var uo=w(se);_(Ee.$$.fragment,uo),aa=a(uo),St=n(uo,"P",{"data-svelte-h":!0}),p(St)!=="svelte-1062ci4"&&(St.innerHTML=lr),uo.forEach(d),ra=a(E),ie=n(E,"DIV",{class:!0});var ho=w(ie);_(Ue.$$.fragment,ho),na=a(ho),Tt=n(ho,"P",{"data-svelte-h":!0}),p(Tt)!=="svelte-1lgbsz7"&&(Tt.innerHTML=cr),ho.forEach(d),sa=a(E),N=n(E,"DIV",{class:!0});var Z=w(N);_(Ne.$$.fragment,Z),ia=a(Z),kt=n(Z,"P",{"data-svelte-h":!0}),p(kt)!=="svelte-vs7s0z"&&(kt.innerHTML=fr),da=a(Z),Ct=n(Z,"P",{"data-svelte-h":!0}),p(Ct)!=="svelte-15b960v"&&(Ct.innerHTML=pr),la=a(Z),At=n(Z,"P",{"data-svelte-h":!0}),p(At)!=="svelte-15mw932"&&(At.innerHTML=mr),ca=a(Z),Rt=n(Z,"P",{"data-svelte-h":!0}),p(Rt)!=="svelte-1ukghd4"&&(Rt.innerHTML=_r),Z.forEach(d),fa=a(E),q=n(E,"DIV",{class:!0});var jt=w(q);_(Xe.$$.fragment,jt),pa=a(jt),Pt=n(jt,"P",{"data-svelte-h":!0}),p(Pt)!=="svelte-flusvq"&&(Pt.textContent=ur),ma=a(jt),_(de.$$.fragment,jt),jt.forEach(d),_a=a(E),le=n(E,"DIV",{class:!0});var go=w(le);_(We.$$.fragment,go),ua=a(go),It=n(go,"P",{"data-svelte-h":!0}),p(It)!=="svelte-1ufq5ot"&&(It.textContent=hr),go.forEach(d),E.forEach(d),so=a(e),_(Be.$$.fragment,e),io=a(e),G=n(e,"DIV",{class:!0});var xo=w(G);_(Je.$$.fragment,xo),ha=a(xo),ce=n(xo,"DIV",{class:!0});var bo=w(ce);_(qe.$$.fragment,bo),ga=a(bo),Ht=n(bo,"P",{"data-svelte-h":!0}),p(Ht)!=="svelte-1lgbsz7"&&(Ht.innerHTML=gr),bo.forEach(d),xo.forEach(d),lo=a(e),_(ze.$$.fragment,e),co=a(e),D=n(e,"DIV",{class:!0});var A=w(D);_(je.$$.fragment,A),xa=a(A),Et=n(A,"P",{"data-svelte-h":!0}),p(Et)!=="svelte-1q4bbx"&&(Et.textContent=xr),ba=a(A),Ut=n(A,"DIV",{class:!0});var yr=w(Ut);_(Ve.$$.fragment,yr),yr.forEach(d),La=a(A),X=n(A,"DIV",{class:!0});var xe=w(X);_(Ze.$$.fragment,xe),va=a(xe),Nt=n(xe,"P",{"data-svelte-h":!0}),p(Nt)!=="svelte-1nr2dy0"&&(Nt.textContent=br),$a=a(xe),_(fe.$$.fragment,xe),wa=a(xe),_(pe.$$.fragment,xe),xe.forEach(d),Ma=a(A),z=n(A,"DIV",{class:!0});var Vt=w(z);_(Ge.$$.fragment,Vt),ya=a(Vt),Xt=n(Vt,"P",{"data-svelte-h":!0}),p(Xt)!=="svelte-h0os0v"&&(Xt.textContent=Lr),Da=a(Vt),_(me.$$.fragment,Vt),Vt.forEach(d),Sa=a(A),_e=n(A,"DIV",{class:!0});var Lo=w(_e);_(Fe.$$.fragment,Lo),Ta=a(Lo),Wt=n(Lo,"P",{"data-svelte-h":!0}),p(Wt)!=="svelte-1825k9e"&&(Wt.textContent=vr),Lo.forEach(d),ka=a(A),ue=n(A,"DIV",{class:!0});var vo=w(ue);_(Ye.$$.fragment,vo),Ca=a(vo),Bt=n(vo,"P",{"data-svelte-h":!0}),p(Bt)!=="svelte-rvubqa"&&(Bt.innerHTML=$r),vo.forEach(d),Aa=a(A),j=n(A,"DIV",{class:!0});var Zt=w(j);_(Qe.$$.fragment,Zt),Ra=a(Zt),Jt=n(Zt,"P",{"data-svelte-h":!0}),p(Jt)!=="svelte-ioswce"&&(Jt.innerHTML=wr),Pa=a(Zt),_(he.$$.fragment,Zt),Zt.forEach(d),Ia=a(A),V=n(A,"DIV",{class:!0});var Gt=w(V);_(Ke.$$.fragment,Gt),Ha=a(Gt),qt=n(Gt,"P",{"data-svelte-h":!0}),p(qt)!=="svelte-119cgd9"&&(qt.textContent=Mr),Ea=a(Gt),_(ge.$$.fragment,Gt),Gt.forEach(d),A.forEach(d),fo=a(e),_(Oe.$$.fragment,e),po=a(e),Qt=n(e,"P",{}),w(Qt).forEach(d),this.h()},h(){M(s,"name","hf:doc:metadata"),M(s,"content",Jr),M(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(k,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(ae,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(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(se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(Ut,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(z,"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,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),M(D,"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,c){t(document.head,s),b(e,L,c),b(e,f,c),b(e,l,c),u(v,e,c),b(e,i,c),b(e,$,c),b(e,Kt,c),b(e,be,c),b(e,Ot,c),u(Q,e,c),b(e,eo,c),u(Le,e,c),b(e,to,c),b(e,k,c),u(ve,k,null),t(k,$o),t(k,nt),t(k,wo),t(k,K),u($e,K,null),t(K,Mo),t(K,st),t(k,yo),t(k,O),u(we,O,null),t(O,Do),t(O,it),t(k,So),t(k,I),u(Me,I,null),t(I,To),t(I,dt),t(I,ko),t(I,lt),t(I,Co),t(I,ct),t(I,Ao),t(I,ft),t(I,Ro),t(I,pt),t(k,Po),t(k,B),u(ye,B,null),t(B,Io),t(B,mt),t(B,Ho),u(ee,B,null),t(k,Eo),t(k,te),u(De,te,null),t(te,Uo),t(te,_t),b(e,oo,c),u(Se,e,c),b(e,ao,c),b(e,C,c),u(Te,C,null),t(C,No),t(C,ut),t(C,Xo),t(C,oe),u(ke,oe,null),t(oe,Wo),t(oe,ht),t(C,Bo),t(C,ae),u(Ce,ae,null),t(ae,Jo),t(ae,gt),t(C,qo),t(C,H),u(Ae,H,null),t(H,zo),t(H,xt),t(H,jo),t(H,bt),t(H,Vo),t(H,Lt),t(H,Zo),t(H,vt),t(H,Go),t(H,$t),t(C,Fo),t(C,J),u(Re,J,null),t(J,Yo),t(J,wt),t(J,Qo),u(re,J,null),t(C,Ko),t(C,ne),u(Pe,ne,null),t(ne,Oo),t(ne,Mt),b(e,ro,c),u(Ie,e,c),b(e,no,c),b(e,S,c),u(He,S,null),t(S,ea),t(S,yt),t(S,ta),t(S,Dt),t(S,oa),t(S,se),u(Ee,se,null),t(se,aa),t(se,St),t(S,ra),t(S,ie),u(Ue,ie,null),t(ie,na),t(ie,Tt),t(S,sa),t(S,N),u(Ne,N,null),t(N,ia),t(N,kt),t(N,da),t(N,Ct),t(N,la),t(N,At),t(N,ca),t(N,Rt),t(S,fa),t(S,q),u(Xe,q,null),t(q,pa),t(q,Pt),t(q,ma),u(de,q,null),t(S,_a),t(S,le),u(We,le,null),t(le,ua),t(le,It),b(e,so,c),u(Be,e,c),b(e,io,c),b(e,G,c),u(Je,G,null),t(G,ha),t(G,ce),u(qe,ce,null),t(ce,ga),t(ce,Ht),b(e,lo,c),u(ze,e,c),b(e,co,c),b(e,D,c),u(je,D,null),t(D,xa),t(D,Et),t(D,ba),t(D,Ut),u(Ve,Ut,null),t(D,La),t(D,X),u(Ze,X,null),t(X,va),t(X,Nt),t(X,$a),u(fe,X,null),t(X,wa),u(pe,X,null),t(D,Ma),t(D,z),u(Ge,z,null),t(z,ya),t(z,Xt),t(z,Da),u(me,z,null),t(D,Sa),t(D,_e),u(Fe,_e,null),t(_e,Ta),t(_e,Wt),t(D,ka),t(D,ue),u(Ye,ue,null),t(ue,Ca),t(ue,Bt),t(D,Aa),t(D,j),u(Qe,j,null),t(j,Ra),t(j,Jt),t(j,Pa),u(he,j,null),t(D,Ia),t(D,V),u(Ke,V,null),t(V,Ha),t(V,qt),t(V,Ea),u(ge,V,null),b(e,fo,c),u(Oe,e,c),b(e,po,c),b(e,Qt,c),mo=!0},p(e,[c]){const R={};c&2&&(R.$$scope={dirty:c,ctx:e}),Q.$set(R);const et={};c&2&&(et.$$scope={dirty:c,ctx:e}),ee.$set(et);const tt={};c&2&&(tt.$$scope={dirty:c,ctx:e}),re.$set(tt);const U={};c&2&&(U.$$scope={dirty:c,ctx:e}),de.$set(U);const F={};c&2&&(F.$$scope={dirty:c,ctx:e}),fe.$set(F);const ot={};c&2&&(ot.$$scope={dirty:c,ctx:e}),pe.$set(ot);const P={};c&2&&(P.$$scope={dirty:c,ctx:e}),me.$set(P);const at={};c&2&&(at.$$scope={dirty:c,ctx:e}),he.$set(at);const rt={};c&2&&(rt.$$scope={dirty:c,ctx:e}),ge.$set(rt)},i(e){mo||(h(v.$$.fragment,e),h(Q.$$.fragment,e),h(Le.$$.fragment,e),h(ve.$$.fragment,e),h($e.$$.fragment,e),h(we.$$.fragment,e),h(Me.$$.fragment,e),h(ye.$$.fragment,e),h(ee.$$.fragment,e),h(De.$$.fragment,e),h(Se.$$.fragment,e),h(Te.$$.fragment,e),h(ke.$$.fragment,e),h(Ce.$$.fragment,e),h(Ae.$$.fragment,e),h(Re.$$.fragment,e),h(re.$$.fragment,e),h(Pe.$$.fragment,e),h(Ie.$$.fragment,e),h(He.$$.fragment,e),h(Ee.$$.fragment,e),h(Ue.$$.fragment,e),h(Ne.$$.fragment,e),h(Xe.$$.fragment,e),h(de.$$.fragment,e),h(We.$$.fragment,e),h(Be.$$.fragment,e),h(Je.$$.fragment,e),h(qe.$$.fragment,e),h(ze.$$.fragment,e),h(je.$$.fragment,e),h(Ve.$$.fragment,e),h(Ze.$$.fragment,e),h(fe.$$.fragment,e),h(pe.$$.fragment,e),h(Ge.$$.fragment,e),h(me.$$.fragment,e),h(Fe.$$.fragment,e),h(Ye.$$.fragment,e),h(Qe.$$.fragment,e),h(he.$$.fragment,e),h(Ke.$$.fragment,e),h(ge.$$.fragment,e),h(Oe.$$.fragment,e),mo=!0)},o(e){g(v.$$.fragment,e),g(Q.$$.fragment,e),g(Le.$$.fragment,e),g(ve.$$.fragment,e),g($e.$$.fragment,e),g(we.$$.fragment,e),g(Me.$$.fragment,e),g(ye.$$.fragment,e),g(ee.$$.fragment,e),g(De.$$.fragment,e),g(Se.$$.fragment,e),g(Te.$$.fragment,e),g(ke.$$.fragment,e),g(Ce.$$.fragment,e),g(Ae.$$.fragment,e),g(Re.$$.fragment,e),g(re.$$.fragment,e),g(Pe.$$.fragment,e),g(Ie.$$.fragment,e),g(He.$$.fragment,e),g(Ee.$$.fragment,e),g(Ue.$$.fragment,e),g(Ne.$$.fragment,e),g(Xe.$$.fragment,e),g(de.$$.fragment,e),g(We.$$.fragment,e),g(Be.$$.fragment,e),g(Je.$$.fragment,e),g(qe.$$.fragment,e),g(ze.$$.fragment,e),g(je.$$.fragment,e),g(Ve.$$.fragment,e),g(Ze.$$.fragment,e),g(fe.$$.fragment,e),g(pe.$$.fragment,e),g(Ge.$$.fragment,e),g(me.$$.fragment,e),g(Fe.$$.fragment,e),g(Ye.$$.fragment,e),g(Qe.$$.fragment,e),g(he.$$.fragment,e),g(Ke.$$.fragment,e),g(ge.$$.fragment,e),g(Oe.$$.fragment,e),mo=!1},d(e){e&&(d(L),d(f),d(l),d(i),d($),d(Kt),d(be),d(Ot),d(eo),d(to),d(k),d(oo),d(ao),d(C),d(ro),d(no),d(S),d(so),d(io),d(G),d(lo),d(co),d(D),d(fo),d(po),d(Qt)),d(s),x(v,e),x(Q,e),x(Le,e),x(ve),x($e),x(we),x(Me),x(ye),x(ee),x(De),x(Se,e),x(Te),x(ke),x(Ce),x(Ae),x(Re),x(re),x(Pe),x(Ie,e),x(He),x(Ee),x(Ue),x(Ne),x(Xe),x(de),x(We),x(Be,e),x(Je),x(qe),x(ze,e),x(je),x(Ve),x(Ze),x(fe),x(pe),x(Ge),x(me),x(Fe),x(Ye),x(Qe),x(he),x(Ke),x(ge),x(Oe,e)}}}const Jr='{"title":"LoRA","local":"lora","sections":[{"title":"StableDiffusionLoraLoaderMixin","local":"diffusers.loaders.StableDiffusionLoraLoaderMixin","sections":[],"depth":2},{"title":"StableDiffusionXLLoraLoaderMixin","local":"diffusers.loaders.StableDiffusionXLLoraLoaderMixin","sections":[],"depth":2},{"title":"SD3LoraLoaderMixin","local":"diffusers.loaders.SD3LoraLoaderMixin","sections":[],"depth":2},{"title":"AmusedLoraLoaderMixin","local":"diffusers.loaders.AmusedLoraLoaderMixin","sections":[],"depth":2},{"title":"LoraBaseMixin","local":"diffusers.loaders.lora_base.LoraBaseMixin","sections":[],"depth":2}],"depth":1}';function qr(T){return Sr(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Qr extends Tr{constructor(s){super(),kr(this,s,qr,Br,Dr,{})}}export{Qr as component};
