import{s as nr,o as sr,n as Se}from"../chunks/scheduler.8c3d61f6.js";import{S as ir,i as dr,g as n,s as o,r as _,A as lr,h as s,f as d,c as r,j as y,u as h,x as u,k as T,y as t,a as v,v as g,d as x,t as L,w as b}from"../chunks/index.589a98e8.js";import{T as Dt}from"../chunks/Tip.42aa8582.js";import{D as C}from"../chunks/Docstring.27406313.js";import{C as ko}from"../chunks/CodeBlock.36627b28.js";import{E as Co}from"../chunks/ExampleCodeBlock.3dc467a7.js";import{H as Do,E as cr}from"../chunks/EditOnGithub.e5a8d9cb.js";function fr(D){let a,$='To learn more about how to load LoRA weights, see the <a href="../../using-diffusers/loading_adapters#lora">LoRA</a> loading guide.';return{c(){a=n("p"),a.innerHTML=$},l(l){a=s(l,"P",{"data-svelte-h":!0}),u(a)!=="svelte-1fw6lx1"&&(a.innerHTML=$)},m(l,c){v(l,a,c)},p:Se,d(l){l&&d(a)}}}function pr(D){let a,$="This is an experimental API.";return{c(){a=n("p"),a.textContent=$},l(l){a=s(l,"P",{"data-svelte-h":!0}),u(a)!=="svelte-8w79b9"&&(a.textContent=$)},m(l,c){v(l,a,c)},p:Se,d(l){l&&d(a)}}}function mr(D){let a,$="Example:",l,c,w;return c=new ko({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(){a=n("p"),a.textContent=$,l=o(),_(c.$$.fragment)},l(i){a=s(i,"P",{"data-svelte-h":!0}),u(a)!=="svelte-11lpom8"&&(a.textContent=$),l=r(i),h(c.$$.fragment,i)},m(i,M){v(i,a,M),v(i,l,M),g(c,i,M),w=!0},p:Se,i(i){w||(x(c.$$.fragment,i),w=!0)},o(i){L(c.$$.fragment,i),w=!1},d(i){i&&(d(a),d(l)),b(c,i)}}}function ur(D){let a,$="Example:",l,c,w;return c=new ko({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(){a=n("p"),a.textContent=$,l=o(),_(c.$$.fragment)},l(i){a=s(i,"P",{"data-svelte-h":!0}),u(a)!=="svelte-11lpom8"&&(a.textContent=$),l=r(i),h(c.$$.fragment,i)},m(i,M){v(i,a,M),v(i,l,M),g(c,i,M),w=!0},p:Se,i(i){w||(x(c.$$.fragment,i),w=!0)},o(i){L(c.$$.fragment,i),w=!1},d(i){i&&(d(a),d(l)),b(c,i)}}}function _r(D){let a,$="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,w="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=$,l=o(),c=n("p"),c.textContent=w},l(i){a=s(i,"P",{"data-svelte-h":!0}),u(a)!=="svelte-15l1sdn"&&(a.textContent=$),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),u(c)!=="svelte-3fufvn"&&(c.textContent=w)},m(i,M){v(i,a,M),v(i,l,M),v(i,c,M)},p:Se,d(i){i&&(d(a),d(l),d(c))}}}function hr(D){let a,$="This is an experimental API.";return{c(){a=n("p"),a.textContent=$},l(l){a=s(l,"P",{"data-svelte-h":!0}),u(a)!=="svelte-8w79b9"&&(a.textContent=$)},m(l,c){v(l,a,c)},p:Se,d(l){l&&d(a)}}}function gr(D){let a,$="Examples:",l,c,w;return c=new ko({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">&gt;&gt;&gt; </span>...',wrap:!1}}),{c(){a=n("p"),a.textContent=$,l=o(),_(c.$$.fragment)},l(i){a=s(i,"P",{"data-svelte-h":!0}),u(a)!=="svelte-kvfsh7"&&(a.textContent=$),l=r(i),h(c.$$.fragment,i)},m(i,M){v(i,a,M),v(i,l,M),g(c,i,M),w=!0},p:Se,i(i){w||(x(c.$$.fragment,i),w=!0)},o(i){L(c.$$.fragment,i),w=!1},d(i){i&&(d(a),d(l)),b(c,i)}}}function xr(D){let a,$,l,c,w,i,M,Ro="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 UNet, text encoder or both. There are two classes for loading LoRA weights:",pt,ne,Ao='<li><code>LoraLoaderMixin</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>LoraLoaderMixin</code> class for loading and saving LoRA weights. It can only be used with the SDXL model.</li>',mt,j,ut,se,_t,f,ie,kt,Ie,So=`Load LoRA layers into <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>.`,Rt,Ue,de,At,Z,le,St,Pe,Io="Disables the LoRA layers for the text encoder.",It,X,ce,Ut,Ne,Uo="Enables the LoRA layers for the text encoder.",Pt,S,fe,Nt,Ee,Po="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Et,G,Ht,V,Jt,N,pe,Wt,He,No="Gets the list of the current active adapters.",jt,q,Zt,z,me,Xt,Je,Eo="Gets the current list of all available adapters in the pipeline.",Gt,B,ue,Vt,We,Ho="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",qt,F,_e,zt,je,Jo="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Bt,Y,he,Ft,Ze,Wo="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Yt,k,ge,Qt,Xe,jo=`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>.`,Ot,Ge,Zo="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Kt,Ve,Xo='See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.',eo,qe,Go=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is loaded into
<code>self.unet</code>.`,to,ze,Vo=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.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>.`,oo,E,xe,ro,Be,qo="Return state dict for lora weights and the network alphas.",ao,Q,no,O,Le,so,Fe,zo="Save the LoRA parameters corresponding to the UNet and text encoder.",io,K,be,lo,Ye,Bo="Sets the adapter layers for the text encoder.",co,ee,ve,fo,Qe,Fo=`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.`,po,H,$e,mo,Oe,Yo=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,uo,te,_o,J,we,ho,Ke,Qo="Unloads the LoRA parameters.",go,oe,ht,Me,gt,A,ye,xo,et,Oo="This class overrides <code>LoraLoaderMixin</code> with LoRA loading/saving code that’s specific to SDXL",Lo,R,Te,bo,tt,Ko=`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>.`,vo,ot,er="All kwargs are forwarded to <code>self.lora_state_dict</code>.",$o,rt,tr='See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.',wo,at,or=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is loaded into
<code>self.unet</code>.`,Mo,nt,rr=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.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>.`,yo,re,Ce,To,st,ar="Save the LoRA parameters corresponding to the UNet and text encoder.",xt,De,Lt,ct,bt;return w=new Do({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),j=new Dt({props:{$$slots:{default:[fr]},$$scope:{ctx:D}}}),se=new Do({props:{title:"LoraLoaderMixin",local:"diffusers.loaders.LoraLoaderMixin",headingTag:"h2"}}),ie=new C({props:{name:"class diffusers.loaders.LoraLoaderMixin",anchor:"diffusers.loaders.LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L69"}}),de=new C({props:{name:"delete_adapters",anchor:"diffusers.loaders.LoraLoaderMixin.delete_adapters",parameters:[{name:"adapter_names",val:": Union"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.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.py#L1049"}}),le=new C({props:{name:"disable_lora_for_text_encoder",anchor:"diffusers.loaders.LoraLoaderMixin.disable_lora_for_text_encoder",parameters:[{name:"text_encoder",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.disable_lora_for_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>torch.nn.Module</code>, <em>optional</em>) &#x2014;
The text encoder module to disable the LoRA layers for. If <code>None</code>, it will try to get the
<code>text_encoder</code> attribute.`,name:"text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L913"}}),ce=new C({props:{name:"enable_lora_for_text_encoder",anchor:"diffusers.loaders.LoraLoaderMixin.enable_lora_for_text_encoder",parameters:[{name:"text_encoder",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.enable_lora_for_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>torch.nn.Module</code>, <em>optional</em>) &#x2014;
The text encoder module to enable the LoRA layers for. If <code>None</code>, it will try to get the <code>text_encoder</code>
attribute.`,name:"text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L930"}}),fe=new C({props:{name:"fuse_lora",anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora",parameters:[{name:"fuse_unet",val:": bool = True"},{name:"fuse_text_encoder",val:": bool = True"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.fuse_unet",description:"<strong>fuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to fuse the UNet LoRA parameters.",name:"fuse_unet"},{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.fuse_text_encoder",description:`<strong>fuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to fuse 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:"fuse_text_encoder"},{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.py#L746"}}),G=new Dt({props:{warning:!0,$$slots:{default:[pr]},$$scope:{ctx:D}}}),V=new Co({props:{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.example",$$slots:{default:[mr]},$$scope:{ctx:D}}}),pe=new C({props:{name:"get_active_adapters",anchor:"diffusers.loaders.LoraLoaderMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L1073"}}),q=new Co({props:{anchor:"diffusers.loaders.LoraLoaderMixin.get_active_adapters.example",$$slots:{default:[ur]},$$scope:{ctx:D}}}),me=new C({props:{name:"get_list_adapters",anchor:"diffusers.loaders.LoraLoaderMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L1105"}}),ue=new C({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.LoraLoaderMixin.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"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
See <code>LoRALinearLayer</code> for more details.`,name:"network_alphas"},{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L406"}}),_e=new C({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.load_lora_into_transformer.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
See <code>LoRALinearLayer</code> for more details.`,name:"network_alphas"},{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.py#L524"}}),he=new C({props:{name:"load_lora_into_unet",anchor:"diffusers.loaders.LoraLoaderMixin.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"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L371"}}),ge=new C({props:{name:"load_lora_weights",anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"},{anchor:"diffusers.loaders.LoraLoaderMixin.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L80"}}),xe=new C({props:{name:"lora_state_dict",anchor:"diffusers.loaders.LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": Union"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
of Diffusers.`,name:"force_download"},{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.py#L138"}}),Q=new Dt({props:{warning:!0,$$slots:{default:[_r]},$$scope:{ctx:D}}}),Le=new C({props:{name:"save_lora_weights",anchor:"diffusers.loaders.LoraLoaderMixin.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:"transformer_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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.LoraLoaderMixin.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.py#L626"}}),be=new C({props:{name:"set_adapters_for_text_encoder",anchor:"diffusers.loaders.LoraLoaderMixin.set_adapters_for_text_encoder",parameters:[{name:"adapter_names",val:": Union"},{name:"text_encoder",val:": Optional = None"},{name:"text_encoder_weights",val:": Union = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.set_adapters_for_text_encoder.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code> or <code>str</code>) &#x2014;
The names of the adapters to use.`,name:"adapter_names"},{anchor:"diffusers.loaders.LoraLoaderMixin.set_adapters_for_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>torch.nn.Module</code>, <em>optional</em>) &#x2014;
The text encoder module to set the adapter layers for. If <code>None</code>, it will try to get the <code>text_encoder</code>
attribute.`,name:"text_encoder"},{anchor:"diffusers.loaders.LoraLoaderMixin.set_adapters_for_text_encoder.text_encoder_weights",description:`<strong>text_encoder_weights</strong> (<code>List[float]</code>, <em>optional</em>) &#x2014;
The weights to use for the text encoder. If <code>None</code>, the weights are set to <code>1.0</code> for all the adapters.`,name:"text_encoder_weights"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L866"}}),ve=new C({props:{name:"set_lora_device",anchor:"diffusers.loaders.LoraLoaderMixin.set_lora_device",parameters:[{name:"adapter_names",val:": List"},{name:"device",val:": Union"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.py#L1128"}}),$e=new C({props:{name:"unfuse_lora",anchor:"diffusers.loaders.LoraLoaderMixin.unfuse_lora",parameters:[{name:"unfuse_unet",val:": bool = True"},{name:"unfuse_text_encoder",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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.py#L828"}}),te=new Dt({props:{warning:!0,$$slots:{default:[hr]},$$scope:{ctx:D}}}),we=new C({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.LoraLoaderMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L725"}}),oe=new Co({props:{anchor:"diffusers.loaders.LoraLoaderMixin.unload_lora_weights.example",$$slots:{default:[gr]},$$scope:{ctx:D}}}),Me=new Do({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),ye=new C({props:{name:"class diffusers.loaders.StableDiffusionXLLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L1182"}}),Te=new C({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.LoraLoaderMixin.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.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora.py#L1186"}}),Ce=new C({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.py#L1263"}}),De=new cr({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/loaders/lora.md"}}),{c(){a=n("meta"),$=o(),l=n("p"),c=o(),_(w.$$.fragment),i=o(),M=n("p"),M.textContent=Ro,pt=o(),ne=n("ul"),ne.innerHTML=Ao,mt=o(),_(j.$$.fragment),ut=o(),_(se.$$.fragment),_t=o(),f=n("div"),_(ie.$$.fragment),kt=o(),Ie=n("p"),Ie.innerHTML=So,Rt=o(),Ue=n("div"),_(de.$$.fragment),At=o(),Z=n("div"),_(le.$$.fragment),St=o(),Pe=n("p"),Pe.textContent=Io,It=o(),X=n("div"),_(ce.$$.fragment),Ut=o(),Ne=n("p"),Ne.textContent=Uo,Pt=o(),S=n("div"),_(fe.$$.fragment),Nt=o(),Ee=n("p"),Ee.textContent=Po,Et=o(),_(G.$$.fragment),Ht=o(),_(V.$$.fragment),Jt=o(),N=n("div"),_(pe.$$.fragment),Wt=o(),He=n("p"),He.textContent=No,jt=o(),_(q.$$.fragment),Zt=o(),z=n("div"),_(me.$$.fragment),Xt=o(),Je=n("p"),Je.textContent=Eo,Gt=o(),B=n("div"),_(ue.$$.fragment),Vt=o(),We=n("p"),We.innerHTML=Ho,qt=o(),F=n("div"),_(_e.$$.fragment),zt=o(),je=n("p"),je.innerHTML=Jo,Bt=o(),Y=n("div"),_(he.$$.fragment),Ft=o(),Ze=n("p"),Ze.innerHTML=Wo,Yt=o(),k=n("div"),_(ge.$$.fragment),Qt=o(),Xe=n("p"),Xe.innerHTML=jo,Ot=o(),Ge=n("p"),Ge.innerHTML=Zo,Kt=o(),Ve=n("p"),Ve.innerHTML=Xo,eo=o(),qe=n("p"),qe.innerHTML=Go,to=o(),ze=n("p"),ze.innerHTML=Vo,oo=o(),E=n("div"),_(xe.$$.fragment),ro=o(),Be=n("p"),Be.textContent=qo,ao=o(),_(Q.$$.fragment),no=o(),O=n("div"),_(Le.$$.fragment),so=o(),Fe=n("p"),Fe.textContent=zo,io=o(),K=n("div"),_(be.$$.fragment),lo=o(),Ye=n("p"),Ye.textContent=Bo,co=o(),ee=n("div"),_(ve.$$.fragment),fo=o(),Qe=n("p"),Qe.innerHTML=Fo,po=o(),H=n("div"),_($e.$$.fragment),mo=o(),Oe=n("p"),Oe.innerHTML=Yo,uo=o(),_(te.$$.fragment),_o=o(),J=n("div"),_(we.$$.fragment),ho=o(),Ke=n("p"),Ke.textContent=Qo,go=o(),_(oe.$$.fragment),ht=o(),_(Me.$$.fragment),gt=o(),A=n("div"),_(ye.$$.fragment),xo=o(),et=n("p"),et.innerHTML=Oo,Lo=o(),R=n("div"),_(Te.$$.fragment),bo=o(),tt=n("p"),tt.innerHTML=Ko,vo=o(),ot=n("p"),ot.innerHTML=er,$o=o(),rt=n("p"),rt.innerHTML=tr,wo=o(),at=n("p"),at.innerHTML=or,Mo=o(),nt=n("p"),nt.innerHTML=rr,yo=o(),re=n("div"),_(Ce.$$.fragment),To=o(),st=n("p"),st.textContent=ar,xt=o(),_(De.$$.fragment),Lt=o(),ct=n("p"),this.h()},l(e){const p=lr("svelte-u9bgzb",document.head);a=s(p,"META",{name:!0,content:!0}),p.forEach(d),$=r(e),l=s(e,"P",{}),y(l).forEach(d),c=r(e),h(w.$$.fragment,e),i=r(e),M=s(e,"P",{"data-svelte-h":!0}),u(M)!=="svelte-99vqvv"&&(M.textContent=Ro),pt=r(e),ne=s(e,"UL",{"data-svelte-h":!0}),u(ne)!=="svelte-1x6k2ez"&&(ne.innerHTML=Ao),mt=r(e),h(j.$$.fragment,e),ut=r(e),h(se.$$.fragment,e),_t=r(e),f=s(e,"DIV",{class:!0});var m=y(f);h(ie.$$.fragment,m),kt=r(m),Ie=s(m,"P",{"data-svelte-h":!0}),u(Ie)!=="svelte-1pb3n03"&&(Ie.innerHTML=So),Rt=r(m),Ue=s(m,"DIV",{class:!0});var ft=y(Ue);h(de.$$.fragment,ft),ft.forEach(d),At=r(m),Z=s(m,"DIV",{class:!0});var ke=y(Z);h(le.$$.fragment,ke),St=r(ke),Pe=s(ke,"P",{"data-svelte-h":!0}),u(Pe)!=="svelte-tbr0r9"&&(Pe.textContent=Io),ke.forEach(d),It=r(m),X=s(m,"DIV",{class:!0});var Re=y(X);h(ce.$$.fragment,Re),Ut=r(Re),Ne=s(Re,"P",{"data-svelte-h":!0}),u(Ne)!=="svelte-ujvdgi"&&(Ne.textContent=Uo),Re.forEach(d),Pt=r(m),S=s(m,"DIV",{class:!0});var P=y(S);h(fe.$$.fragment,P),Nt=r(P),Ee=s(P,"P",{"data-svelte-h":!0}),u(Ee)!=="svelte-1nr2dy0"&&(Ee.textContent=Po),Et=r(P),h(G.$$.fragment,P),Ht=r(P),h(V.$$.fragment,P),P.forEach(d),Jt=r(m),N=s(m,"DIV",{class:!0});var W=y(N);h(pe.$$.fragment,W),Wt=r(W),He=s(W,"P",{"data-svelte-h":!0}),u(He)!=="svelte-h0os0v"&&(He.textContent=No),jt=r(W),h(q.$$.fragment,W),W.forEach(d),Zt=r(m),z=s(m,"DIV",{class:!0});var Ae=y(z);h(me.$$.fragment,Ae),Xt=r(Ae),Je=s(Ae,"P",{"data-svelte-h":!0}),u(Je)!=="svelte-1825k9e"&&(Je.textContent=Eo),Ae.forEach(d),Gt=r(m),B=s(m,"DIV",{class:!0});var vt=y(B);h(ue.$$.fragment,vt),Vt=r(vt),We=s(vt,"P",{"data-svelte-h":!0}),u(We)!=="svelte-1062ci4"&&(We.innerHTML=Ho),vt.forEach(d),qt=r(m),F=s(m,"DIV",{class:!0});var $t=y(F);h(_e.$$.fragment,$t),zt=r($t),je=s($t,"P",{"data-svelte-h":!0}),u(je)!=="svelte-1lgbsz7"&&(je.innerHTML=Jo),$t.forEach(d),Bt=r(m),Y=s(m,"DIV",{class:!0});var wt=y(Y);h(he.$$.fragment,wt),Ft=r(wt),Ze=s(wt,"P",{"data-svelte-h":!0}),u(Ze)!=="svelte-u3q4so"&&(Ze.innerHTML=Wo),wt.forEach(d),Yt=r(m),k=s(m,"DIV",{class:!0});var I=y(k);h(ge.$$.fragment,I),Qt=r(I),Xe=s(I,"P",{"data-svelte-h":!0}),u(Xe)!=="svelte-vs7s0z"&&(Xe.innerHTML=jo),Ot=r(I),Ge=s(I,"P",{"data-svelte-h":!0}),u(Ge)!=="svelte-15b960v"&&(Ge.innerHTML=Zo),Kt=r(I),Ve=s(I,"P",{"data-svelte-h":!0}),u(Ve)!=="svelte-12uyc98"&&(Ve.innerHTML=Xo),eo=r(I),qe=s(I,"P",{"data-svelte-h":!0}),u(qe)!=="svelte-1q3yzk7"&&(qe.innerHTML=Go),to=r(I),ze=s(I,"P",{"data-svelte-h":!0}),u(ze)!=="svelte-10xxjud"&&(ze.innerHTML=Vo),I.forEach(d),oo=r(m),E=s(m,"DIV",{class:!0});var it=y(E);h(xe.$$.fragment,it),ro=r(it),Be=s(it,"P",{"data-svelte-h":!0}),u(Be)!=="svelte-flusvq"&&(Be.textContent=qo),ao=r(it),h(Q.$$.fragment,it),it.forEach(d),no=r(m),O=s(m,"DIV",{class:!0});var Mt=y(O);h(Le.$$.fragment,Mt),so=r(Mt),Fe=s(Mt,"P",{"data-svelte-h":!0}),u(Fe)!=="svelte-1ufq5ot"&&(Fe.textContent=zo),Mt.forEach(d),io=r(m),K=s(m,"DIV",{class:!0});var yt=y(K);h(be.$$.fragment,yt),lo=r(yt),Ye=s(yt,"P",{"data-svelte-h":!0}),u(Ye)!=="svelte-kmejci"&&(Ye.textContent=Bo),yt.forEach(d),co=r(m),ee=s(m,"DIV",{class:!0});var Tt=y(ee);h(ve.$$.fragment,Tt),fo=r(Tt),Qe=s(Tt,"P",{"data-svelte-h":!0}),u(Qe)!=="svelte-rvubqa"&&(Qe.innerHTML=Fo),Tt.forEach(d),po=r(m),H=s(m,"DIV",{class:!0});var dt=y(H);h($e.$$.fragment,dt),mo=r(dt),Oe=s(dt,"P",{"data-svelte-h":!0}),u(Oe)!=="svelte-56a0bo"&&(Oe.innerHTML=Yo),uo=r(dt),h(te.$$.fragment,dt),dt.forEach(d),_o=r(m),J=s(m,"DIV",{class:!0});var lt=y(J);h(we.$$.fragment,lt),ho=r(lt),Ke=s(lt,"P",{"data-svelte-h":!0}),u(Ke)!=="svelte-119cgd9"&&(Ke.textContent=Qo),go=r(lt),h(oe.$$.fragment,lt),lt.forEach(d),m.forEach(d),ht=r(e),h(Me.$$.fragment,e),gt=r(e),A=s(e,"DIV",{class:!0});var ae=y(A);h(ye.$$.fragment,ae),xo=r(ae),et=s(ae,"P",{"data-svelte-h":!0}),u(et)!=="svelte-1powver"&&(et.innerHTML=Oo),Lo=r(ae),R=s(ae,"DIV",{class:!0});var U=y(R);h(Te.$$.fragment,U),bo=r(U),tt=s(U,"P",{"data-svelte-h":!0}),u(tt)!=="svelte-vs7s0z"&&(tt.innerHTML=Ko),vo=r(U),ot=s(U,"P",{"data-svelte-h":!0}),u(ot)!=="svelte-15b960v"&&(ot.innerHTML=er),$o=r(U),rt=s(U,"P",{"data-svelte-h":!0}),u(rt)!=="svelte-12uyc98"&&(rt.innerHTML=tr),wo=r(U),at=s(U,"P",{"data-svelte-h":!0}),u(at)!=="svelte-1q3yzk7"&&(at.innerHTML=or),Mo=r(U),nt=s(U,"P",{"data-svelte-h":!0}),u(nt)!=="svelte-10xxjud"&&(nt.innerHTML=rr),U.forEach(d),yo=r(ae),re=s(ae,"DIV",{class:!0});var Ct=y(re);h(Ce.$$.fragment,Ct),To=r(Ct),st=s(Ct,"P",{"data-svelte-h":!0}),u(st)!=="svelte-1ufq5ot"&&(st.textContent=ar),Ct.forEach(d),ae.forEach(d),xt=r(e),h(De.$$.fragment,e),Lt=r(e),ct=s(e,"P",{}),y(ct).forEach(d),this.h()},h(){T(a,"name","hf:doc:metadata"),T(a,"content",Lr),T(Ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(k,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(f,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),T(A,"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,p){t(document.head,a),v(e,$,p),v(e,l,p),v(e,c,p),g(w,e,p),v(e,i,p),v(e,M,p),v(e,pt,p),v(e,ne,p),v(e,mt,p),g(j,e,p),v(e,ut,p),g(se,e,p),v(e,_t,p),v(e,f,p),g(ie,f,null),t(f,kt),t(f,Ie),t(f,Rt),t(f,Ue),g(de,Ue,null),t(f,At),t(f,Z),g(le,Z,null),t(Z,St),t(Z,Pe),t(f,It),t(f,X),g(ce,X,null),t(X,Ut),t(X,Ne),t(f,Pt),t(f,S),g(fe,S,null),t(S,Nt),t(S,Ee),t(S,Et),g(G,S,null),t(S,Ht),g(V,S,null),t(f,Jt),t(f,N),g(pe,N,null),t(N,Wt),t(N,He),t(N,jt),g(q,N,null),t(f,Zt),t(f,z),g(me,z,null),t(z,Xt),t(z,Je),t(f,Gt),t(f,B),g(ue,B,null),t(B,Vt),t(B,We),t(f,qt),t(f,F),g(_e,F,null),t(F,zt),t(F,je),t(f,Bt),t(f,Y),g(he,Y,null),t(Y,Ft),t(Y,Ze),t(f,Yt),t(f,k),g(ge,k,null),t(k,Qt),t(k,Xe),t(k,Ot),t(k,Ge),t(k,Kt),t(k,Ve),t(k,eo),t(k,qe),t(k,to),t(k,ze),t(f,oo),t(f,E),g(xe,E,null),t(E,ro),t(E,Be),t(E,ao),g(Q,E,null),t(f,no),t(f,O),g(Le,O,null),t(O,so),t(O,Fe),t(f,io),t(f,K),g(be,K,null),t(K,lo),t(K,Ye),t(f,co),t(f,ee),g(ve,ee,null),t(ee,fo),t(ee,Qe),t(f,po),t(f,H),g($e,H,null),t(H,mo),t(H,Oe),t(H,uo),g(te,H,null),t(f,_o),t(f,J),g(we,J,null),t(J,ho),t(J,Ke),t(J,go),g(oe,J,null),v(e,ht,p),g(Me,e,p),v(e,gt,p),v(e,A,p),g(ye,A,null),t(A,xo),t(A,et),t(A,Lo),t(A,R),g(Te,R,null),t(R,bo),t(R,tt),t(R,vo),t(R,ot),t(R,$o),t(R,rt),t(R,wo),t(R,at),t(R,Mo),t(R,nt),t(A,yo),t(A,re),g(Ce,re,null),t(re,To),t(re,st),v(e,xt,p),g(De,e,p),v(e,Lt,p),v(e,ct,p),bt=!0},p(e,[p]){const m={};p&2&&(m.$$scope={dirty:p,ctx:e}),j.$set(m);const ft={};p&2&&(ft.$$scope={dirty:p,ctx:e}),G.$set(ft);const ke={};p&2&&(ke.$$scope={dirty:p,ctx:e}),V.$set(ke);const Re={};p&2&&(Re.$$scope={dirty:p,ctx:e}),q.$set(Re);const P={};p&2&&(P.$$scope={dirty:p,ctx:e}),Q.$set(P);const W={};p&2&&(W.$$scope={dirty:p,ctx:e}),te.$set(W);const Ae={};p&2&&(Ae.$$scope={dirty:p,ctx:e}),oe.$set(Ae)},i(e){bt||(x(w.$$.fragment,e),x(j.$$.fragment,e),x(se.$$.fragment,e),x(ie.$$.fragment,e),x(de.$$.fragment,e),x(le.$$.fragment,e),x(ce.$$.fragment,e),x(fe.$$.fragment,e),x(G.$$.fragment,e),x(V.$$.fragment,e),x(pe.$$.fragment,e),x(q.$$.fragment,e),x(me.$$.fragment,e),x(ue.$$.fragment,e),x(_e.$$.fragment,e),x(he.$$.fragment,e),x(ge.$$.fragment,e),x(xe.$$.fragment,e),x(Q.$$.fragment,e),x(Le.$$.fragment,e),x(be.$$.fragment,e),x(ve.$$.fragment,e),x($e.$$.fragment,e),x(te.$$.fragment,e),x(we.$$.fragment,e),x(oe.$$.fragment,e),x(Me.$$.fragment,e),x(ye.$$.fragment,e),x(Te.$$.fragment,e),x(Ce.$$.fragment,e),x(De.$$.fragment,e),bt=!0)},o(e){L(w.$$.fragment,e),L(j.$$.fragment,e),L(se.$$.fragment,e),L(ie.$$.fragment,e),L(de.$$.fragment,e),L(le.$$.fragment,e),L(ce.$$.fragment,e),L(fe.$$.fragment,e),L(G.$$.fragment,e),L(V.$$.fragment,e),L(pe.$$.fragment,e),L(q.$$.fragment,e),L(me.$$.fragment,e),L(ue.$$.fragment,e),L(_e.$$.fragment,e),L(he.$$.fragment,e),L(ge.$$.fragment,e),L(xe.$$.fragment,e),L(Q.$$.fragment,e),L(Le.$$.fragment,e),L(be.$$.fragment,e),L(ve.$$.fragment,e),L($e.$$.fragment,e),L(te.$$.fragment,e),L(we.$$.fragment,e),L(oe.$$.fragment,e),L(Me.$$.fragment,e),L(ye.$$.fragment,e),L(Te.$$.fragment,e),L(Ce.$$.fragment,e),L(De.$$.fragment,e),bt=!1},d(e){e&&(d($),d(l),d(c),d(i),d(M),d(pt),d(ne),d(mt),d(ut),d(_t),d(f),d(ht),d(gt),d(A),d(xt),d(Lt),d(ct)),d(a),b(w,e),b(j,e),b(se,e),b(ie),b(de),b(le),b(ce),b(fe),b(G),b(V),b(pe),b(q),b(me),b(ue),b(_e),b(he),b(ge),b(xe),b(Q),b(Le),b(be),b(ve),b($e),b(te),b(we),b(oe),b(Me,e),b(ye),b(Te),b(Ce),b(De,e)}}}const Lr='{"title":"LoRA","local":"lora","sections":[{"title":"LoraLoaderMixin","local":"diffusers.loaders.LoraLoaderMixin","sections":[],"depth":2},{"title":"StableDiffusionXLLoraLoaderMixin","local":"diffusers.loaders.StableDiffusionXLLoraLoaderMixin","sections":[],"depth":2}],"depth":1}';function br(D){return sr(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Dr extends ir{constructor(a){super(),dr(this,a,br,xr,nr,{})}}export{Dr as component};
