diffusers/pr_10725/en/_app/immutable/nodes/10.bc65bda5.js

import{s as _f,o as uf,n as S}from"../chunks/scheduler.8c3d61f6.js";import{S as hf,i as gf,g as n,s as t,r as m,A as Lf,h as s,f as i,c as r,j as v,u as _,x as f,k as w,y as o,a as x,v as u,d as h,t as g,w as L}from"../chunks/index.da70eac4.js";import{T as P}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.6b390b9a.js";import{C as ys}from"../chunks/CodeBlock.00a903b3.js";import{E as $s}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as ne,E as xf}from"../chunks/EditOnGithub.1e64e623.js";function bf(T){let a,b='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=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-1fw6lx1"&&(a.innerHTML=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function vf(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function wf(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function $f(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function yf(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function Mf(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Tf(T){let a,b="Examples:",c,l,M;return l=new ys({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=b,c=t(),m(l.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),c=r(d),_(l.$$.fragment,d)},m(d,y){x(d,a,y),x(d,c,y),u(l,d,y),M=!0},p:S,i(d){M||(h(l.$$.fragment,d),M=!0)},o(d){g(l.$$.fragment,d),M=!1},d(d){d&&(i(a),i(c)),L(l,d)}}}function Df(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function Sf(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Cf(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function kf(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Af(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function Pf(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Rf(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function Hf(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Vf(T){let a,b="We support loading original format HunyuanVideo LoRA checkpoints.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-gyrs6h"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function If(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Uf(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(d),l=s(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(d,y){x(d,a,y),x(d,c,y),x(d,l,y)},p:S,d(d){d&&(i(a),i(c),i(l))}}}function Xf(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Ef(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function Ff(T){let a,b="Example:",c,l,M;return l=new ys({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=b,c=t(),m(l.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),c=r(d),_(l.$$.fragment,d)},m(d,y){x(d,a,y),x(d,c,y),u(l,d,y),M=!0},p:S,i(d){M||(h(l.$$.fragment,d),M=!0)},o(d){g(l.$$.fragment,d),M=!1},d(d){d&&(i(a),i(c)),L(l,d)}}}function Nf(T){let a,b="Example:",c,l,M;return l=new ys({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=b,c=t(),m(l.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),c=r(d),_(l.$$.fragment,d)},m(d,y){x(d,a,y),x(d,c,y),u(l,d,y),M=!0},p:S,i(d){M||(h(l.$$.fragment,d),M=!0)},o(d){g(l.$$.fragment,d),M=!1},d(d){d&&(i(a),i(c)),L(l,d)}}}function Wf(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){x(c,a,l)},p:S,d(c){c&&i(a)}}}function zf(T){let a,b="Examples:",c,l,M;return l=new ys({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=b,c=t(),m(l.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),c=r(d),_(l.$$.fragment,d)},m(d,y){x(d,a,y),x(d,c,y),u(l,d,y),M=!0},p:S,i(d){M||(h(l.$$.fragment,d),M=!0)},o(d){g(l.$$.fragment,d),M=!1},d(d){d&&(i(a),i(c)),L(l,d)}}}function qf(T){let a,b,c,l,M,d,y,Pl='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/pr_10725/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_10725/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Tn,Xo,Rl='<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>FluxLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux" rel="nofollow">Flux</a>.</li> <li><code>CogVideoXLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox" rel="nofollow">CogVideoX</a>.</li> <li><code>Mochi1LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi" rel="nofollow">Mochi</a>.</li> <li><code>LTXVideoLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video" rel="nofollow">LTX-Video</a>.</li> <li><code>SanaLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana" rel="nofollow">Sana</a>.</li> <li><code>HunyuanVideoLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video" rel="nofollow">HunyuanVideo</a>.</li> <li><code>Lumina2LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2" rel="nofollow">Lumina2</a>.</li> <li><code>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/pr_10725/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>',Dn,Re,Sn,Eo,Cn,R,Fo,Ms,Sr,Hl=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_10725/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>.`,Ts,He,No,Ds,Cr,Vl="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ss,Ve,Wo,Cs,kr,Il="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",ks,B,zo,As,Ar,Ul=`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>.`,Ps,Pr,Xl="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Rs,Rr,El=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Hs,Hr,Fl=`See <a href="/docs/diffusers/pr_10725/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>.`,Vs,Vr,Nl=`See <a href="/docs/diffusers/pr_10725/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>.`,Is,ie,qo,Us,Ir,Wl="Return state dict for lora weights and the network alphas.",Xs,Ie,Es,Ue,Bo,Fs,Ur,zl="Save the LoRA parameters corresponding to the UNet and text encoder.",kn,jo,An,H,Go,Ns,Xr,ql=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_10725/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>.`,Ws,Xe,Jo,zs,Er,Bl="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",qs,Ee,Zo,Bs,Fr,jl="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",js,j,Yo,Gs,Nr,Gl=`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>.`,Js,Wr,Jl="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Zs,zr,Zl=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Ys,qr,Yl=`See <a href="/docs/diffusers/pr_10725/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>.`,Os,Br,Ol=`See <a href="/docs/diffusers/pr_10725/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>.`,Qs,de,Oo,Ks,jr,Ql="Return state dict for lora weights and the network alphas.",ei,Fe,oi,Ne,Qo,ti,Gr,Kl="Save the LoRA parameters corresponding to the UNet and text encoder.",Pn,Ko,Rn,A,et,ri,Jr,ec=`Load LoRA layers into <a href="/docs/diffusers/pr_10725/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>.`,ai,Zr,oc='Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',ni,We,ot,si,Yr,tc="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",ii,ze,tt,di,Or,rc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",li,Z,rt,ci,Qr,ac=`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>.`,fi,Kr,nc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",pi,ea,sc=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,mi,oa,ic=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,_i,le,at,ui,ta,dc="Return state dict for lora weights and the network alphas.",hi,qe,gi,Be,nt,Li,ra,lc="Save the LoRA parameters corresponding to the UNet and text encoder.",Hn,st,Vn,D,it,xi,aa,cc=`Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,bi,na,fc='Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',vi,je,dt,wi,sa,pc="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",$i,Ge,lt,yi,ia,mc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Mi,Y,ct,Ti,da,_c=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>.`,Di,la,uc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Si,ca,hc=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Ci,fa,gc=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,ki,ce,ft,Ai,pa,Lc="Return state dict for lora weights and the network alphas.",Pi,Je,Ri,Ze,pt,Hi,ma,xc="Save the LoRA parameters corresponding to the UNet and text encoder.",Vi,fe,mt,Ii,_a,bc=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Ui,Ye,Xi,pe,_t,Ei,ua,vc="Unloads the LoRA parameters.",Fi,Oe,In,ut,Un,V,ht,Ni,ha,wc='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',Wi,Qe,gt,zi,ga,$c="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",qi,Ke,Lt,Bi,La,yc=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,ji,me,xt,Gi,xa,Mc="Return state dict for lora weights and the network alphas.",Ji,eo,Zi,oo,bt,Yi,ba,Tc="Save the LoRA parameters corresponding to the UNet and text encoder.",Oi,_e,vt,Qi,va,Dc=`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>.`,Ki,to,Xn,wt,En,I,$t,ed,wa,Sc='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',od,ro,yt,td,$a,Cc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",rd,ao,Mt,ad,ya,kc=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,nd,ue,Tt,sd,Ma,Ac="Return state dict for lora weights and the network alphas.",id,no,dd,so,Dt,ld,Ta,Pc="Save the LoRA parameters corresponding to the UNet and text encoder.",cd,he,St,fd,Da,Rc=`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>.`,pd,io,Fn,Ct,Nn,U,kt,md,Sa,Hc='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',_d,lo,At,ud,Ca,Vc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",hd,co,Pt,gd,ka,Ic=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Ld,ge,Rt,xd,Aa,Uc="Return state dict for lora weights and the network alphas.",bd,fo,vd,po,Ht,wd,Pa,Xc="Save the LoRA parameters corresponding to the UNet and text encoder.",$d,Le,Vt,yd,Ra,Ec=`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>.`,Md,mo,Wn,It,zn,X,Ut,Td,Ha,Fc='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',Dd,_o,Xt,Sd,Va,Nc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Cd,uo,Et,kd,Ia,Wc=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Ad,xe,Ft,Pd,Ua,zc="Return state dict for lora weights and the network alphas.",Rd,ho,Hd,go,Nt,Vd,Xa,qc="Save the LoRA parameters corresponding to the UNet and text encoder.",Id,be,Wt,Ud,Ea,Bc=`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>.`,Xd,Lo,qn,zt,Bn,E,qt,Ed,Fa,jc='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',Fd,xo,Bt,Nd,Na,Gc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Wd,bo,jt,zd,Wa,Jc=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,qd,ve,Gt,Bd,za,Zc="Return state dict for lora weights and the network alphas.",jd,vo,Gd,wo,Jt,Jd,qa,Yc="Save the LoRA parameters corresponding to the UNet and text encoder.",Zd,we,Zt,Yd,Ba,Oc=`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>.`,Od,$o,jn,Yt,Gn,F,Ot,Qd,ja,Qc='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/lumina2#diffusers.Lumina2Text2ImgPipeline">Lumina2Text2ImgPipeline</a>.',Kd,yo,Qt,el,Ga,Kc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ol,Mo,Kt,tl,Ja,ef=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,rl,$e,er,al,Za,of="Return state dict for lora weights and the network alphas.",nl,To,sl,Do,or,il,Ya,tf="Save the LoRA parameters corresponding to the UNet and text encoder.",dl,ye,tr,ll,Oa,rf=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,cl,So,Jn,rr,Zn,Se,ar,fl,Co,nr,pl,Qa,af="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Yn,sr,On,C,ir,ml,Ka,nf="Utility class for handling LoRAs.",_l,en,dr,ul,se,lr,hl,on,sf="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",gl,ko,Ll,Ao,xl,Me,cr,bl,tn,df="Gets the list of the current active adapters.",vl,Po,wl,Ro,fr,$l,rn,lf="Gets the current list of all available adapters in the pipeline.",yl,Ho,pr,Ml,an,cf=`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.`,Tl,Te,mr,Dl,nn,ff=`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>.`,Sl,Vo,Cl,De,_r,kl,sn,pf="Unloads the LoRA parameters.",Al,Io,Qn,ur,Kn,Mn,es;return M=new ne({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),Re=new P({props:{$$slots:{default:[bf]},$$scope:{ctx:T}}}),Eo=new ne({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Fo=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L69"}}),No=new $({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.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L314"}}),Wo=new $({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.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L264"}}),zo=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = 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/pr_10725/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/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L79"}}),qo=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{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/pr_10725/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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L149"}}),Ie=new P({props:{warning:!0,$$slots:{default:[vf]},$$scope:{ctx:T}}}),Bo=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"unet_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L363"}}),jo=new ne({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),Go=new $({props:{name:"class diffusers.loaders.StableDiffusionXLLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L481"}}),Jo=new $({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.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L754"}}),Zo=new $({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.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L703"}}),Yo=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"**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/pr_10725/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.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.StableDiffusionXLLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L492"}}),Oo=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{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/pr_10725/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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L587"}}),Fe=new P({props:{warning:!0,$$slots:{default:[wf]},$$scope:{ctx:T}}}),Qo=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"unet_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_2_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L804"}}),Ko=new ne({props:{title:"SD3LoraLoaderMixin",local:"diffusers.loaders.SD3LoraLoaderMixin",headingTag:"h2"}}),et=new $({props:{name:"class diffusers.loaders.SD3LoraLoaderMixin",anchor:"diffusers.loaders.SD3LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L930"}}),ot=new $({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.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1161"}}),tt=new $({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.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1125"}}),rt=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = 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/pr_10725/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.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.SD3LoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1040"}}),at=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{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/pr_10725/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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L943"}}),qe=new P({props:{warning:!0,$$slots:{default:[$f]},$$scope:{ctx:T}}}),nt=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_2_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1211"}}),st=new ne({props:{title:"FluxLoraLoaderMixin",local:"diffusers.loaders.FluxLoraLoaderMixin",headingTag:"h2"}}),it=new $({props:{name:"class diffusers.loaders.FluxLoraLoaderMixin",anchor:"diffusers.loaders.FluxLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1338"}}),dt=new $({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.load_lora_into_text_encoder.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1691"}}),lt=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.FluxLoraLoaderMixin.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"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>FluxTransformer2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1594"}}),ct=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.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/pr_10725/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.FluxLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1485"}}),ft=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.FluxLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"return_alphas",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.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/pr_10725/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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1351"}}),Je=new P({props:{warning:!0,$$slots:{default:[yf]},$$scope:{ctx:T}}}),pt=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1741"}}),mt=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) &#x2014; List of LoRA-injectable components to unfuse LoRA from.",name:"components"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1852"}}),Ye=new P({props:{warning:!0,$$slots:{default:[Mf]},$$scope:{ctx:T}}}),_t=new $({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights",parameters:[{name:"reset_to_overwritten_params",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.reset_to_overwritten_params",description:`<strong>reset_to_overwritten_params</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014; Whether to reset the LoRA-loaded modules
to their original params. Refer to the <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux" rel="nofollow">Flux
documentation</a> to learn more.`,name:"reset_to_overwritten_params"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L1873"}}),Oe=new $s({props:{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.example",$$slots:{default:[Tf]},$$scope:{ctx:T}}}),ut=new ne({props:{title:"CogVideoXLoraLoaderMixin",local:"diffusers.loaders.CogVideoXLoraLoaderMixin",headingTag:"h2"}}),ht=new $({props:{name:"class diffusers.loaders.CogVideoXLoraLoaderMixin",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2291"}}),gt=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>CogVideoXTransformer3DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2447"}}),Lt=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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/pr_10725/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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2397"}}),xt=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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/pr_10725/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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2299"}}),eo=new P({props:{warning:!0,$$slots:{default:[Df]},$$scope:{ctx:T}}}),bt=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2484"}}),vt=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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.CogVideoXLoraLoaderMixin.unfuse_lora.unfuse_transformer",description:"<strong>unfuse_transformer</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_transformer"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2575"}}),to=new P({props:{warning:!0,$$slots:{default:[Sf]},$$scope:{ctx:T}}}),wt=new ne({props:{title:"Mochi1LoraLoaderMixin",local:"diffusers.loaders.Mochi1LoraLoaderMixin",headingTag:"h2"}}),$t=new $({props:{name:"class diffusers.loaders.Mochi1LoraLoaderMixin",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2593"}}),yt=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>MochiTransformer3DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2750"}}),Mt=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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/pr_10725/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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2700"}}),Tt=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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/pr_10725/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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2601"}}),no=new P({props:{warning:!0,$$slots:{default:[Cf]},$$scope:{ctx:T}}}),Dt=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2787"}}),St=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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.Mochi1LoraLoaderMixin.unfuse_lora.unfuse_transformer",description:"<strong>unfuse_transformer</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_transformer"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2878"}}),io=new P({props:{warning:!0,$$slots:{default:[kf]},$$scope:{ctx:T}}}),Ct=new ne({props:{title:"LTXVideoLoraLoaderMixin",local:"diffusers.loaders.LTXVideoLoraLoaderMixin",headingTag:"h2"}}),kt=new $({props:{name:"class diffusers.loaders.LTXVideoLoraLoaderMixin",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2896"}}),At=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>LTXVideoTransformer3DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3053"}}),Pt=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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/pr_10725/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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3003"}}),Rt=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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/pr_10725/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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2904"}}),fo=new P({props:{warning:!0,$$slots:{default:[Af]},$$scope:{ctx:T}}}),Ht=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3090"}}),Vt=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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.LTXVideoLoraLoaderMixin.unfuse_lora.unfuse_transformer",description:"<strong>unfuse_transformer</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_transformer"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3181"}}),mo=new P({props:{warning:!0,$$slots:{default:[Pf]},$$scope:{ctx:T}}}),It=new ne({props:{title:"SanaLoraLoaderMixin",local:"diffusers.loaders.SanaLoraLoaderMixin",headingTag:"h2"}}),Ut=new $({props:{name:"class diffusers.loaders.SanaLoraLoaderMixin",anchor:"diffusers.loaders.SanaLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3199"}}),Xt=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>SanaTransformer2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3356"}}),Et=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SanaLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.SanaLoraLoaderMixin.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/pr_10725/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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3306"}}),Ft=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SanaLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.SanaLoraLoaderMixin.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/pr_10725/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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3207"}}),ho=new P({props:{warning:!0,$$slots:{default:[Rf]},$$scope:{ctx:T}}}),Nt=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SanaLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3393"}}),Wt=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SanaLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.SanaLoraLoaderMixin.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.SanaLoraLoaderMixin.unfuse_lora.unfuse_transformer",description:"<strong>unfuse_transformer</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_transformer"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3484"}}),Lo=new P({props:{warning:!0,$$slots:{default:[Hf]},$$scope:{ctx:T}}}),zt=new ne({props:{title:"HunyuanVideoLoraLoaderMixin",local:"diffusers.loaders.HunyuanVideoLoraLoaderMixin",headingTag:"h2"}}),qt=new $({props:{name:"class diffusers.loaders.HunyuanVideoLoraLoaderMixin",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3502"}}),Bt=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>HunyuanVideoTransformer3DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3662"}}),jt=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.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/pr_10725/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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3612"}}),Gt=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.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/pr_10725/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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3510"}}),vo=new P({props:{warning:!0,$$slots:{default:[Vf]},$$scope:{ctx:T}}}),Jt=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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.HunyuanVideoLoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3699"}}),Zt=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) &#x2014; List of LoRA-injectable components to unfuse LoRA from.",name:"components"},{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.unfuse_lora.unfuse_transformer",description:"<strong>unfuse_transformer</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_transformer"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3790"}}),$o=new P({props:{warning:!0,$$slots:{default:[If]},$$scope:{ctx:T}}}),Yt=new ne({props:{title:"Lumina2LoraLoaderMixin",local:"diffusers.loaders.Lumina2LoraLoaderMixin",headingTag:"h2"}}),Ot=new $({props:{name:"class diffusers.loaders.Lumina2LoraLoaderMixin",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3808"}}),Qt=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>Lumina2Transformer2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3965"}}),Kt=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.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/pr_10725/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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3915"}}),er=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.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/pr_10725/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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L3816"}}),To=new P({props:{warning:!0,$$slots:{default:[Uf]},$$scope:{ctx:T}}}),or=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.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/vr_10725/src/diffusers/loaders/lora_pipeline.py#L4002"}}),tr=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.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.Lumina2LoraLoaderMixin.unfuse_lora.unfuse_transformer",description:"<strong>unfuse_transformer</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_transformer"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L4095"}}),So=new P({props:{warning:!0,$$slots:{default:[Xf]},$$scope:{ctx:T}}}),rr=new ne({props:{title:"AmusedLoraLoaderMixin",local:"diffusers.loaders.AmusedLoraLoaderMixin",headingTag:"h2"}}),ar=new $({props:{name:"class diffusers.loaders.AmusedLoraLoaderMixin",anchor:"diffusers.loaders.AmusedLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2137"}}),nr=new $({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"},{name:"low_cpu_mem_usage",val:" = False"}],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.transformer",description:`<strong>transformer</strong> (<code>UVit2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{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"},{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L2142"}}),sr=new ne({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),ir=new $({props:{name:"class diffusers.loaders.lora_base.LoraBaseMixin",anchor:"diffusers.loaders.lora_base.LoraBaseMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_base.py#L455"}}),dr=new $({props:{name:"delete_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"}],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/vr_10725/src/diffusers/loaders/lora_base.py#L756"}}),lr=new $({props:{name:"fuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.components",description:"<strong>components</strong> &#x2014; (<code>List[str]</code>): List of LoRA-injectable components to fuse the LoRAs into.",name:"components"},{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/vr_10725/src/diffusers/loaders/lora_base.py#L522"}}),ko=new P({props:{warning:!0,$$slots:{default:[Ef]},$$scope:{ctx:T}}}),Ao=new $s({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[Ff]},$$scope:{ctx:T}}}),cr=new $({props:{name:"get_active_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_base.py#L778"}}),Po=new $s({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[Nf]},$$scope:{ctx:T}}}),fr=new $({props:{name:"get_list_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_base.py#L811"}}),pr=new $({props:{name:"set_lora_device",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device",parameters:[{name:"adapter_names",val:": typing.List[str]"},{name:"device",val:": typing.Union[torch.device, str, int]"}],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/vr_10725/src/diffusers/loaders/lora_base.py#L833"}}),mr=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{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/vr_10725/src/diffusers/loaders/lora_base.py#L603"}}),Vo=new P({props:{warning:!0,$$slots:{default:[Wf]},$$scope:{ctx:T}}}),_r=new $({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_base.py#L499"}}),Io=new $s({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[zf]},$$scope:{ctx:T}}}),ur=new xf({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/loaders/lora.md"}}),{c(){a=n("meta"),b=t(),c=n("p"),l=t(),m(M.$$.fragment),d=t(),y=n("p"),y.innerHTML=Pl,Tn=t(),Xo=n("ul"),Xo.innerHTML=Rl,Dn=t(),m(Re.$$.fragment),Sn=t(),m(Eo.$$.fragment),Cn=t(),R=n("div"),m(Fo.$$.fragment),Ms=t(),Sr=n("p"),Sr.innerHTML=Hl,Ts=t(),He=n("div"),m(No.$$.fragment),Ds=t(),Cr=n("p"),Cr.innerHTML=Vl,Ss=t(),Ve=n("div"),m(Wo.$$.fragment),Cs=t(),kr=n("p"),kr.innerHTML=Il,ks=t(),B=n("div"),m(zo.$$.fragment),As=t(),Ar=n("p"),Ar.innerHTML=Ul,Ps=t(),Pr=n("p"),Pr.innerHTML=Xl,Rs=t(),Rr=n("p"),Rr.innerHTML=El,Hs=t(),Hr=n("p"),Hr.innerHTML=Fl,Vs=t(),Vr=n("p"),Vr.innerHTML=Nl,Is=t(),ie=n("div"),m(qo.$$.fragment),Us=t(),Ir=n("p"),Ir.textContent=Wl,Xs=t(),m(Ie.$$.fragment),Es=t(),Ue=n("div"),m(Bo.$$.fragment),Fs=t(),Ur=n("p"),Ur.textContent=zl,kn=t(),m(jo.$$.fragment),An=t(),H=n("div"),m(Go.$$.fragment),Ns=t(),Xr=n("p"),Xr.innerHTML=ql,Ws=t(),Xe=n("div"),m(Jo.$$.fragment),zs=t(),Er=n("p"),Er.innerHTML=Bl,qs=t(),Ee=n("div"),m(Zo.$$.fragment),Bs=t(),Fr=n("p"),Fr.innerHTML=jl,js=t(),j=n("div"),m(Yo.$$.fragment),Gs=t(),Nr=n("p"),Nr.innerHTML=Gl,Js=t(),Wr=n("p"),Wr.innerHTML=Jl,Zs=t(),zr=n("p"),zr.innerHTML=Zl,Ys=t(),qr=n("p"),qr.innerHTML=Yl,Os=t(),Br=n("p"),Br.innerHTML=Ol,Qs=t(),de=n("div"),m(Oo.$$.fragment),Ks=t(),jr=n("p"),jr.textContent=Ql,ei=t(),m(Fe.$$.fragment),oi=t(),Ne=n("div"),m(Qo.$$.fragment),ti=t(),Gr=n("p"),Gr.textContent=Kl,Pn=t(),m(Ko.$$.fragment),Rn=t(),A=n("div"),m(et.$$.fragment),ri=t(),Jr=n("p"),Jr.innerHTML=ec,ai=t(),Zr=n("p"),Zr.innerHTML=oc,ni=t(),We=n("div"),m(ot.$$.fragment),si=t(),Yr=n("p"),Yr.innerHTML=tc,ii=t(),ze=n("div"),m(tt.$$.fragment),di=t(),Or=n("p"),Or.innerHTML=rc,li=t(),Z=n("div"),m(rt.$$.fragment),ci=t(),Qr=n("p"),Qr.innerHTML=ac,fi=t(),Kr=n("p"),Kr.innerHTML=nc,pi=t(),ea=n("p"),ea.innerHTML=sc,mi=t(),oa=n("p"),oa.innerHTML=ic,_i=t(),le=n("div"),m(at.$$.fragment),ui=t(),ta=n("p"),ta.textContent=dc,hi=t(),m(qe.$$.fragment),gi=t(),Be=n("div"),m(nt.$$.fragment),Li=t(),ra=n("p"),ra.textContent=lc,Hn=t(),m(st.$$.fragment),Vn=t(),D=n("div"),m(it.$$.fragment),xi=t(),aa=n("p"),aa.innerHTML=cc,bi=t(),na=n("p"),na.innerHTML=fc,vi=t(),je=n("div"),m(dt.$$.fragment),wi=t(),sa=n("p"),sa.innerHTML=pc,$i=t(),Ge=n("div"),m(lt.$$.fragment),yi=t(),ia=n("p"),ia.innerHTML=mc,Mi=t(),Y=n("div"),m(ct.$$.fragment),Ti=t(),da=n("p"),da.innerHTML=_c,Di=t(),la=n("p"),la.innerHTML=uc,Si=t(),ca=n("p"),ca.innerHTML=hc,Ci=t(),fa=n("p"),fa.innerHTML=gc,ki=t(),ce=n("div"),m(ft.$$.fragment),Ai=t(),pa=n("p"),pa.textContent=Lc,Pi=t(),m(Je.$$.fragment),Ri=t(),Ze=n("div"),m(pt.$$.fragment),Hi=t(),ma=n("p"),ma.textContent=xc,Vi=t(),fe=n("div"),m(mt.$$.fragment),Ii=t(),_a=n("p"),_a.innerHTML=bc,Ui=t(),m(Ye.$$.fragment),Xi=t(),pe=n("div"),m(_t.$$.fragment),Ei=t(),ua=n("p"),ua.textContent=vc,Fi=t(),m(Oe.$$.fragment),In=t(),m(ut.$$.fragment),Un=t(),V=n("div"),m(ht.$$.fragment),Ni=t(),ha=n("p"),ha.innerHTML=wc,Wi=t(),Qe=n("div"),m(gt.$$.fragment),zi=t(),ga=n("p"),ga.innerHTML=$c,qi=t(),Ke=n("div"),m(Lt.$$.fragment),Bi=t(),La=n("p"),La.innerHTML=yc,ji=t(),me=n("div"),m(xt.$$.fragment),Gi=t(),xa=n("p"),xa.textContent=Mc,Ji=t(),m(eo.$$.fragment),Zi=t(),oo=n("div"),m(bt.$$.fragment),Yi=t(),ba=n("p"),ba.textContent=Tc,Oi=t(),_e=n("div"),m(vt.$$.fragment),Qi=t(),va=n("p"),va.innerHTML=Dc,Ki=t(),m(to.$$.fragment),Xn=t(),m(wt.$$.fragment),En=t(),I=n("div"),m($t.$$.fragment),ed=t(),wa=n("p"),wa.innerHTML=Sc,od=t(),ro=n("div"),m(yt.$$.fragment),td=t(),$a=n("p"),$a.innerHTML=Cc,rd=t(),ao=n("div"),m(Mt.$$.fragment),ad=t(),ya=n("p"),ya.innerHTML=kc,nd=t(),ue=n("div"),m(Tt.$$.fragment),sd=t(),Ma=n("p"),Ma.textContent=Ac,id=t(),m(no.$$.fragment),dd=t(),so=n("div"),m(Dt.$$.fragment),ld=t(),Ta=n("p"),Ta.textContent=Pc,cd=t(),he=n("div"),m(St.$$.fragment),fd=t(),Da=n("p"),Da.innerHTML=Rc,pd=t(),m(io.$$.fragment),Fn=t(),m(Ct.$$.fragment),Nn=t(),U=n("div"),m(kt.$$.fragment),md=t(),Sa=n("p"),Sa.innerHTML=Hc,_d=t(),lo=n("div"),m(At.$$.fragment),ud=t(),Ca=n("p"),Ca.innerHTML=Vc,hd=t(),co=n("div"),m(Pt.$$.fragment),gd=t(),ka=n("p"),ka.innerHTML=Ic,Ld=t(),ge=n("div"),m(Rt.$$.fragment),xd=t(),Aa=n("p"),Aa.textContent=Uc,bd=t(),m(fo.$$.fragment),vd=t(),po=n("div"),m(Ht.$$.fragment),wd=t(),Pa=n("p"),Pa.textContent=Xc,$d=t(),Le=n("div"),m(Vt.$$.fragment),yd=t(),Ra=n("p"),Ra.innerHTML=Ec,Md=t(),m(mo.$$.fragment),Wn=t(),m(It.$$.fragment),zn=t(),X=n("div"),m(Ut.$$.fragment),Td=t(),Ha=n("p"),Ha.innerHTML=Fc,Dd=t(),_o=n("div"),m(Xt.$$.fragment),Sd=t(),Va=n("p"),Va.innerHTML=Nc,Cd=t(),uo=n("div"),m(Et.$$.fragment),kd=t(),Ia=n("p"),Ia.innerHTML=Wc,Ad=t(),xe=n("div"),m(Ft.$$.fragment),Pd=t(),Ua=n("p"),Ua.textContent=zc,Rd=t(),m(ho.$$.fragment),Hd=t(),go=n("div"),m(Nt.$$.fragment),Vd=t(),Xa=n("p"),Xa.textContent=qc,Id=t(),be=n("div"),m(Wt.$$.fragment),Ud=t(),Ea=n("p"),Ea.innerHTML=Bc,Xd=t(),m(Lo.$$.fragment),qn=t(),m(zt.$$.fragment),Bn=t(),E=n("div"),m(qt.$$.fragment),Ed=t(),Fa=n("p"),Fa.innerHTML=jc,Fd=t(),xo=n("div"),m(Bt.$$.fragment),Nd=t(),Na=n("p"),Na.innerHTML=Gc,Wd=t(),bo=n("div"),m(jt.$$.fragment),zd=t(),Wa=n("p"),Wa.innerHTML=Jc,qd=t(),ve=n("div"),m(Gt.$$.fragment),Bd=t(),za=n("p"),za.textContent=Zc,jd=t(),m(vo.$$.fragment),Gd=t(),wo=n("div"),m(Jt.$$.fragment),Jd=t(),qa=n("p"),qa.textContent=Yc,Zd=t(),we=n("div"),m(Zt.$$.fragment),Yd=t(),Ba=n("p"),Ba.innerHTML=Oc,Od=t(),m($o.$$.fragment),jn=t(),m(Yt.$$.fragment),Gn=t(),F=n("div"),m(Ot.$$.fragment),Qd=t(),ja=n("p"),ja.innerHTML=Qc,Kd=t(),yo=n("div"),m(Qt.$$.fragment),el=t(),Ga=n("p"),Ga.innerHTML=Kc,ol=t(),Mo=n("div"),m(Kt.$$.fragment),tl=t(),Ja=n("p"),Ja.innerHTML=ef,rl=t(),$e=n("div"),m(er.$$.fragment),al=t(),Za=n("p"),Za.textContent=of,nl=t(),m(To.$$.fragment),sl=t(),Do=n("div"),m(or.$$.fragment),il=t(),Ya=n("p"),Ya.textContent=tf,dl=t(),ye=n("div"),m(tr.$$.fragment),ll=t(),Oa=n("p"),Oa.innerHTML=rf,cl=t(),m(So.$$.fragment),Jn=t(),m(rr.$$.fragment),Zn=t(),Se=n("div"),m(ar.$$.fragment),fl=t(),Co=n("div"),m(nr.$$.fragment),pl=t(),Qa=n("p"),Qa.innerHTML=af,Yn=t(),m(sr.$$.fragment),On=t(),C=n("div"),m(ir.$$.fragment),ml=t(),Ka=n("p"),Ka.textContent=nf,_l=t(),en=n("div"),m(dr.$$.fragment),ul=t(),se=n("div"),m(lr.$$.fragment),hl=t(),on=n("p"),on.textContent=sf,gl=t(),m(ko.$$.fragment),Ll=t(),m(Ao.$$.fragment),xl=t(),Me=n("div"),m(cr.$$.fragment),bl=t(),tn=n("p"),tn.textContent=df,vl=t(),m(Po.$$.fragment),wl=t(),Ro=n("div"),m(fr.$$.fragment),$l=t(),rn=n("p"),rn.textContent=lf,yl=t(),Ho=n("div"),m(pr.$$.fragment),Ml=t(),an=n("p"),an.innerHTML=cf,Tl=t(),Te=n("div"),m(mr.$$.fragment),Dl=t(),nn=n("p"),nn.innerHTML=ff,Sl=t(),m(Vo.$$.fragment),Cl=t(),De=n("div"),m(_r.$$.fragment),kl=t(),sn=n("p"),sn.textContent=pf,Al=t(),m(Io.$$.fragment),Qn=t(),m(ur.$$.fragment),Kn=t(),Mn=n("p"),this.h()},l(e){const p=Lf("svelte-u9bgzb",document.head);a=s(p,"META",{name:!0,content:!0}),p.forEach(i),b=r(e),c=s(e,"P",{}),v(c).forEach(i),l=r(e),_(M.$$.fragment,e),d=r(e),y=s(e,"P",{"data-svelte-h":!0}),f(y)!=="svelte-12kvmzd"&&(y.innerHTML=Pl),Tn=r(e),Xo=s(e,"UL",{"data-svelte-h":!0}),f(Xo)!=="svelte-1ntmlg8"&&(Xo.innerHTML=Rl),Dn=r(e),_(Re.$$.fragment,e),Sn=r(e),_(Eo.$$.fragment,e),Cn=r(e),R=s(e,"DIV",{class:!0});var z=v(R);_(Fo.$$.fragment,z),Ms=r(z),Sr=s(z,"P",{"data-svelte-h":!0}),f(Sr)!=="svelte-17skqt4"&&(Sr.innerHTML=Hl),Ts=r(z),He=s(z,"DIV",{class:!0});var hr=v(He);_(No.$$.fragment,hr),Ds=r(hr),Cr=s(hr,"P",{"data-svelte-h":!0}),f(Cr)!=="svelte-1062ci4"&&(Cr.innerHTML=Vl),hr.forEach(i),Ss=r(z),Ve=s(z,"DIV",{class:!0});var gr=v(Ve);_(Wo.$$.fragment,gr),Cs=r(gr),kr=s(gr,"P",{"data-svelte-h":!0}),f(kr)!=="svelte-u3q4so"&&(kr.innerHTML=Il),gr.forEach(i),ks=r(z),B=s(z,"DIV",{class:!0});var G=v(B);_(zo.$$.fragment,G),As=r(G),Ar=s(G,"P",{"data-svelte-h":!0}),f(Ar)!=="svelte-vs7s0z"&&(Ar.innerHTML=Ul),Ps=r(G),Pr=s(G,"P",{"data-svelte-h":!0}),f(Pr)!=="svelte-15b960v"&&(Pr.innerHTML=Xl),Rs=r(G),Rr=s(G,"P",{"data-svelte-h":!0}),f(Rr)!=="svelte-1tazhb9"&&(Rr.innerHTML=El),Hs=r(G),Hr=s(G,"P",{"data-svelte-h":!0}),f(Hr)!=="svelte-1boeg66"&&(Hr.innerHTML=Fl),Vs=r(G),Vr=s(G,"P",{"data-svelte-h":!0}),f(Vr)!=="svelte-19p3dv0"&&(Vr.innerHTML=Nl),G.forEach(i),Is=r(z),ie=s(z,"DIV",{class:!0});var Ce=v(ie);_(qo.$$.fragment,Ce),Us=r(Ce),Ir=s(Ce,"P",{"data-svelte-h":!0}),f(Ir)!=="svelte-flusvq"&&(Ir.textContent=Wl),Xs=r(Ce),_(Ie.$$.fragment,Ce),Ce.forEach(i),Es=r(z),Ue=s(z,"DIV",{class:!0});var Lr=v(Ue);_(Bo.$$.fragment,Lr),Fs=r(Lr),Ur=s(Lr,"P",{"data-svelte-h":!0}),f(Ur)!=="svelte-1ufq5ot"&&(Ur.textContent=zl),Lr.forEach(i),z.forEach(i),kn=r(e),_(jo.$$.fragment,e),An=r(e),H=s(e,"DIV",{class:!0});var q=v(H);_(Go.$$.fragment,q),Ns=r(q),Xr=s(q,"P",{"data-svelte-h":!0}),f(Xr)!=="svelte-1gg62p9"&&(Xr.innerHTML=ql),Ws=r(q),Xe=s(q,"DIV",{class:!0});var xr=v(Xe);_(Jo.$$.fragment,xr),zs=r(xr),Er=s(xr,"P",{"data-svelte-h":!0}),f(Er)!=="svelte-1062ci4"&&(Er.innerHTML=Bl),xr.forEach(i),qs=r(q),Ee=s(q,"DIV",{class:!0});var br=v(Ee);_(Zo.$$.fragment,br),Bs=r(br),Fr=s(br,"P",{"data-svelte-h":!0}),f(Fr)!=="svelte-u3q4so"&&(Fr.innerHTML=jl),br.forEach(i),js=r(q),j=s(q,"DIV",{class:!0});var J=v(j);_(Yo.$$.fragment,J),Gs=r(J),Nr=s(J,"P",{"data-svelte-h":!0}),f(Nr)!=="svelte-vs7s0z"&&(Nr.innerHTML=Gl),Js=r(J),Wr=s(J,"P",{"data-svelte-h":!0}),f(Wr)!=="svelte-15b960v"&&(Wr.innerHTML=Jl),Zs=r(J),zr=s(J,"P",{"data-svelte-h":!0}),f(zr)!=="svelte-1tazhb9"&&(zr.innerHTML=Zl),Ys=r(J),qr=s(J,"P",{"data-svelte-h":!0}),f(qr)!=="svelte-1boeg66"&&(qr.innerHTML=Yl),Os=r(J),Br=s(J,"P",{"data-svelte-h":!0}),f(Br)!=="svelte-19p3dv0"&&(Br.innerHTML=Ol),J.forEach(i),Qs=r(q),de=s(q,"DIV",{class:!0});var ke=v(de);_(Oo.$$.fragment,ke),Ks=r(ke),jr=s(ke,"P",{"data-svelte-h":!0}),f(jr)!=="svelte-flusvq"&&(jr.textContent=Ql),ei=r(ke),_(Fe.$$.fragment,ke),ke.forEach(i),oi=r(q),Ne=s(q,"DIV",{class:!0});var vr=v(Ne);_(Qo.$$.fragment,vr),ti=r(vr),Gr=s(vr,"P",{"data-svelte-h":!0}),f(Gr)!=="svelte-1ufq5ot"&&(Gr.textContent=Kl),vr.forEach(i),q.forEach(i),Pn=r(e),_(Ko.$$.fragment,e),Rn=r(e),A=s(e,"DIV",{class:!0});var N=v(A);_(et.$$.fragment,N),ri=r(N),Jr=s(N,"P",{"data-svelte-h":!0}),f(Jr)!=="svelte-joqbj8"&&(Jr.innerHTML=ec),ai=r(N),Zr=s(N,"P",{"data-svelte-h":!0}),f(Zr)!=="svelte-1wbipxu"&&(Zr.innerHTML=oc),ni=r(N),We=s(N,"DIV",{class:!0});var wr=v(We);_(ot.$$.fragment,wr),si=r(wr),Yr=s(wr,"P",{"data-svelte-h":!0}),f(Yr)!=="svelte-1062ci4"&&(Yr.innerHTML=tc),wr.forEach(i),ii=r(N),ze=s(N,"DIV",{class:!0});var $r=v(ze);_(tt.$$.fragment,$r),di=r($r),Or=s($r,"P",{"data-svelte-h":!0}),f(Or)!=="svelte-1lgbsz7"&&(Or.innerHTML=rc),$r.forEach(i),li=r(N),Z=s(N,"DIV",{class:!0});var re=v(Z);_(rt.$$.fragment,re),ci=r(re),Qr=s(re,"P",{"data-svelte-h":!0}),f(Qr)!=="svelte-vs7s0z"&&(Qr.innerHTML=ac),fi=r(re),Kr=s(re,"P",{"data-svelte-h":!0}),f(Kr)!=="svelte-15b960v"&&(Kr.innerHTML=nc),pi=r(re),ea=s(re,"P",{"data-svelte-h":!0}),f(ea)!=="svelte-1tazhb9"&&(ea.innerHTML=sc),mi=r(re),oa=s(re,"P",{"data-svelte-h":!0}),f(oa)!=="svelte-1ukghd4"&&(oa.innerHTML=ic),re.forEach(i),_i=r(N),le=s(N,"DIV",{class:!0});var Ae=v(le);_(at.$$.fragment,Ae),ui=r(Ae),ta=s(Ae,"P",{"data-svelte-h":!0}),f(ta)!=="svelte-flusvq"&&(ta.textContent=dc),hi=r(Ae),_(qe.$$.fragment,Ae),Ae.forEach(i),gi=r(N),Be=s(N,"DIV",{class:!0});var yr=v(Be);_(nt.$$.fragment,yr),Li=r(yr),ra=s(yr,"P",{"data-svelte-h":!0}),f(ra)!=="svelte-1ufq5ot"&&(ra.textContent=lc),yr.forEach(i),N.forEach(i),Hn=r(e),_(st.$$.fragment,e),Vn=r(e),D=s(e,"DIV",{class:!0});var k=v(D);_(it.$$.fragment,k),xi=r(k),aa=s(k,"P",{"data-svelte-h":!0}),f(aa)!=="svelte-x2x6ex"&&(aa.innerHTML=cc),bi=r(k),na=s(k,"P",{"data-svelte-h":!0}),f(na)!=="svelte-1wbipxu"&&(na.innerHTML=fc),vi=r(k),je=s(k,"DIV",{class:!0});var Mr=v(je);_(dt.$$.fragment,Mr),wi=r(Mr),sa=s(Mr,"P",{"data-svelte-h":!0}),f(sa)!=="svelte-1062ci4"&&(sa.innerHTML=pc),Mr.forEach(i),$i=r(k),Ge=s(k,"DIV",{class:!0});var Tr=v(Ge);_(lt.$$.fragment,Tr),yi=r(Tr),ia=s(Tr,"P",{"data-svelte-h":!0}),f(ia)!=="svelte-1lgbsz7"&&(ia.innerHTML=mc),Tr.forEach(i),Mi=r(k),Y=s(k,"DIV",{class:!0});var ae=v(Y);_(ct.$$.fragment,ae),Ti=r(ae),da=s(ae,"P",{"data-svelte-h":!0}),f(da)!=="svelte-178gcly"&&(da.innerHTML=_c),Di=r(ae),la=s(ae,"P",{"data-svelte-h":!0}),f(la)!=="svelte-15b960v"&&(la.innerHTML=uc),Si=r(ae),ca=s(ae,"P",{"data-svelte-h":!0}),f(ca)!=="svelte-1tazhb9"&&(ca.innerHTML=hc),Ci=r(ae),fa=s(ae,"P",{"data-svelte-h":!0}),f(fa)!=="svelte-1ukghd4"&&(fa.innerHTML=gc),ae.forEach(i),ki=r(k),ce=s(k,"DIV",{class:!0});var Pe=v(ce);_(ft.$$.fragment,Pe),Ai=r(Pe),pa=s(Pe,"P",{"data-svelte-h":!0}),f(pa)!=="svelte-flusvq"&&(pa.textContent=Lc),Pi=r(Pe),_(Je.$$.fragment,Pe),Pe.forEach(i),Ri=r(k),Ze=s(k,"DIV",{class:!0});var Dr=v(Ze);_(pt.$$.fragment,Dr),Hi=r(Dr),ma=s(Dr,"P",{"data-svelte-h":!0}),f(ma)!=="svelte-1ufq5ot"&&(ma.textContent=xc),Dr.forEach(i),Vi=r(k),fe=s(k,"DIV",{class:!0});var dn=v(fe);_(mt.$$.fragment,dn),Ii=r(dn),_a=s(dn,"P",{"data-svelte-h":!0}),f(_a)!=="svelte-ioswce"&&(_a.innerHTML=bc),Ui=r(dn),_(Ye.$$.fragment,dn),dn.forEach(i),Xi=r(k),pe=s(k,"DIV",{class:!0});var ln=v(pe);_(_t.$$.fragment,ln),Ei=r(ln),ua=s(ln,"P",{"data-svelte-h":!0}),f(ua)!=="svelte-119cgd9"&&(ua.textContent=vc),Fi=r(ln),_(Oe.$$.fragment,ln),ln.forEach(i),k.forEach(i),In=r(e),_(ut.$$.fragment,e),Un=r(e),V=s(e,"DIV",{class:!0});var O=v(V);_(ht.$$.fragment,O),Ni=r(O),ha=s(O,"P",{"data-svelte-h":!0}),f(ha)!=="svelte-9gd1zd"&&(ha.innerHTML=wc),Wi=r(O),Qe=s(O,"DIV",{class:!0});var os=v(Qe);_(gt.$$.fragment,os),zi=r(os),ga=s(os,"P",{"data-svelte-h":!0}),f(ga)!=="svelte-1lgbsz7"&&(ga.innerHTML=$c),os.forEach(i),qi=r(O),Ke=s(O,"DIV",{class:!0});var ts=v(Ke);_(Lt.$$.fragment,ts),Bi=r(ts),La=s(ts,"P",{"data-svelte-h":!0}),f(La)!=="svelte-i68oio"&&(La.innerHTML=yc),ts.forEach(i),ji=r(O),me=s(O,"DIV",{class:!0});var cn=v(me);_(xt.$$.fragment,cn),Gi=r(cn),xa=s(cn,"P",{"data-svelte-h":!0}),f(xa)!=="svelte-flusvq"&&(xa.textContent=Mc),Ji=r(cn),_(eo.$$.fragment,cn),cn.forEach(i),Zi=r(O),oo=s(O,"DIV",{class:!0});var rs=v(oo);_(bt.$$.fragment,rs),Yi=r(rs),ba=s(rs,"P",{"data-svelte-h":!0}),f(ba)!=="svelte-1ufq5ot"&&(ba.textContent=Tc),rs.forEach(i),Oi=r(O),_e=s(O,"DIV",{class:!0});var fn=v(_e);_(vt.$$.fragment,fn),Qi=r(fn),va=s(fn,"P",{"data-svelte-h":!0}),f(va)!=="svelte-ioswce"&&(va.innerHTML=Dc),Ki=r(fn),_(to.$$.fragment,fn),fn.forEach(i),O.forEach(i),Xn=r(e),_(wt.$$.fragment,e),En=r(e),I=s(e,"DIV",{class:!0});var Q=v(I);_($t.$$.fragment,Q),ed=r(Q),wa=s(Q,"P",{"data-svelte-h":!0}),f(wa)!=="svelte-evmoh1"&&(wa.innerHTML=Sc),od=r(Q),ro=s(Q,"DIV",{class:!0});var as=v(ro);_(yt.$$.fragment,as),td=r(as),$a=s(as,"P",{"data-svelte-h":!0}),f($a)!=="svelte-1lgbsz7"&&($a.innerHTML=Cc),as.forEach(i),rd=r(Q),ao=s(Q,"DIV",{class:!0});var ns=v(ao);_(Mt.$$.fragment,ns),ad=r(ns),ya=s(ns,"P",{"data-svelte-h":!0}),f(ya)!=="svelte-i68oio"&&(ya.innerHTML=kc),ns.forEach(i),nd=r(Q),ue=s(Q,"DIV",{class:!0});var pn=v(ue);_(Tt.$$.fragment,pn),sd=r(pn),Ma=s(pn,"P",{"data-svelte-h":!0}),f(Ma)!=="svelte-flusvq"&&(Ma.textContent=Ac),id=r(pn),_(no.$$.fragment,pn),pn.forEach(i),dd=r(Q),so=s(Q,"DIV",{class:!0});var ss=v(so);_(Dt.$$.fragment,ss),ld=r(ss),Ta=s(ss,"P",{"data-svelte-h":!0}),f(Ta)!=="svelte-1ufq5ot"&&(Ta.textContent=Pc),ss.forEach(i),cd=r(Q),he=s(Q,"DIV",{class:!0});var mn=v(he);_(St.$$.fragment,mn),fd=r(mn),Da=s(mn,"P",{"data-svelte-h":!0}),f(Da)!=="svelte-ioswce"&&(Da.innerHTML=Rc),pd=r(mn),_(io.$$.fragment,mn),mn.forEach(i),Q.forEach(i),Fn=r(e),_(Ct.$$.fragment,e),Nn=r(e),U=s(e,"DIV",{class:!0});var K=v(U);_(kt.$$.fragment,K),md=r(K),Sa=s(K,"P",{"data-svelte-h":!0}),f(Sa)!=="svelte-1jnp34p"&&(Sa.innerHTML=Hc),_d=r(K),lo=s(K,"DIV",{class:!0});var is=v(lo);_(At.$$.fragment,is),ud=r(is),Ca=s(is,"P",{"data-svelte-h":!0}),f(Ca)!=="svelte-1lgbsz7"&&(Ca.innerHTML=Vc),is.forEach(i),hd=r(K),co=s(K,"DIV",{class:!0});var ds=v(co);_(Pt.$$.fragment,ds),gd=r(ds),ka=s(ds,"P",{"data-svelte-h":!0}),f(ka)!=="svelte-i68oio"&&(ka.innerHTML=Ic),ds.forEach(i),Ld=r(K),ge=s(K,"DIV",{class:!0});var _n=v(ge);_(Rt.$$.fragment,_n),xd=r(_n),Aa=s(_n,"P",{"data-svelte-h":!0}),f(Aa)!=="svelte-flusvq"&&(Aa.textContent=Uc),bd=r(_n),_(fo.$$.fragment,_n),_n.forEach(i),vd=r(K),po=s(K,"DIV",{class:!0});var ls=v(po);_(Ht.$$.fragment,ls),wd=r(ls),Pa=s(ls,"P",{"data-svelte-h":!0}),f(Pa)!=="svelte-1ufq5ot"&&(Pa.textContent=Xc),ls.forEach(i),$d=r(K),Le=s(K,"DIV",{class:!0});var un=v(Le);_(Vt.$$.fragment,un),yd=r(un),Ra=s(un,"P",{"data-svelte-h":!0}),f(Ra)!=="svelte-ioswce"&&(Ra.innerHTML=Ec),Md=r(un),_(mo.$$.fragment,un),un.forEach(i),K.forEach(i),Wn=r(e),_(It.$$.fragment,e),zn=r(e),X=s(e,"DIV",{class:!0});var ee=v(X);_(Ut.$$.fragment,ee),Td=r(ee),Ha=s(ee,"P",{"data-svelte-h":!0}),f(Ha)!=="svelte-mql9bc"&&(Ha.innerHTML=Fc),Dd=r(ee),_o=s(ee,"DIV",{class:!0});var cs=v(_o);_(Xt.$$.fragment,cs),Sd=r(cs),Va=s(cs,"P",{"data-svelte-h":!0}),f(Va)!=="svelte-1lgbsz7"&&(Va.innerHTML=Nc),cs.forEach(i),Cd=r(ee),uo=s(ee,"DIV",{class:!0});var fs=v(uo);_(Et.$$.fragment,fs),kd=r(fs),Ia=s(fs,"P",{"data-svelte-h":!0}),f(Ia)!=="svelte-i68oio"&&(Ia.innerHTML=Wc),fs.forEach(i),Ad=r(ee),xe=s(ee,"DIV",{class:!0});var hn=v(xe);_(Ft.$$.fragment,hn),Pd=r(hn),Ua=s(hn,"P",{"data-svelte-h":!0}),f(Ua)!=="svelte-flusvq"&&(Ua.textContent=zc),Rd=r(hn),_(ho.$$.fragment,hn),hn.forEach(i),Hd=r(ee),go=s(ee,"DIV",{class:!0});var ps=v(go);_(Nt.$$.fragment,ps),Vd=r(ps),Xa=s(ps,"P",{"data-svelte-h":!0}),f(Xa)!=="svelte-1ufq5ot"&&(Xa.textContent=qc),ps.forEach(i),Id=r(ee),be=s(ee,"DIV",{class:!0});var gn=v(be);_(Wt.$$.fragment,gn),Ud=r(gn),Ea=s(gn,"P",{"data-svelte-h":!0}),f(Ea)!=="svelte-ioswce"&&(Ea.innerHTML=Bc),Xd=r(gn),_(Lo.$$.fragment,gn),gn.forEach(i),ee.forEach(i),qn=r(e),_(zt.$$.fragment,e),Bn=r(e),E=s(e,"DIV",{class:!0});var oe=v(E);_(qt.$$.fragment,oe),Ed=r(oe),Fa=s(oe,"P",{"data-svelte-h":!0}),f(Fa)!=="svelte-edp284"&&(Fa.innerHTML=jc),Fd=r(oe),xo=s(oe,"DIV",{class:!0});var ms=v(xo);_(Bt.$$.fragment,ms),Nd=r(ms),Na=s(ms,"P",{"data-svelte-h":!0}),f(Na)!=="svelte-1lgbsz7"&&(Na.innerHTML=Gc),ms.forEach(i),Wd=r(oe),bo=s(oe,"DIV",{class:!0});var _s=v(bo);_(jt.$$.fragment,_s),zd=r(_s),Wa=s(_s,"P",{"data-svelte-h":!0}),f(Wa)!=="svelte-i68oio"&&(Wa.innerHTML=Jc),_s.forEach(i),qd=r(oe),ve=s(oe,"DIV",{class:!0});var Ln=v(ve);_(Gt.$$.fragment,Ln),Bd=r(Ln),za=s(Ln,"P",{"data-svelte-h":!0}),f(za)!=="svelte-flusvq"&&(za.textContent=Zc),jd=r(Ln),_(vo.$$.fragment,Ln),Ln.forEach(i),Gd=r(oe),wo=s(oe,"DIV",{class:!0});var us=v(wo);_(Jt.$$.fragment,us),Jd=r(us),qa=s(us,"P",{"data-svelte-h":!0}),f(qa)!=="svelte-1ufq5ot"&&(qa.textContent=Yc),us.forEach(i),Zd=r(oe),we=s(oe,"DIV",{class:!0});var xn=v(we);_(Zt.$$.fragment,xn),Yd=r(xn),Ba=s(xn,"P",{"data-svelte-h":!0}),f(Ba)!=="svelte-ioswce"&&(Ba.innerHTML=Oc),Od=r(xn),_($o.$$.fragment,xn),xn.forEach(i),oe.forEach(i),jn=r(e),_(Yt.$$.fragment,e),Gn=r(e),F=s(e,"DIV",{class:!0});var te=v(F);_(Ot.$$.fragment,te),Qd=r(te),ja=s(te,"P",{"data-svelte-h":!0}),f(ja)!=="svelte-1hduwtw"&&(ja.innerHTML=Qc),Kd=r(te),yo=s(te,"DIV",{class:!0});var hs=v(yo);_(Qt.$$.fragment,hs),el=r(hs),Ga=s(hs,"P",{"data-svelte-h":!0}),f(Ga)!=="svelte-1lgbsz7"&&(Ga.innerHTML=Kc),hs.forEach(i),ol=r(te),Mo=s(te,"DIV",{class:!0});var gs=v(Mo);_(Kt.$$.fragment,gs),tl=r(gs),Ja=s(gs,"P",{"data-svelte-h":!0}),f(Ja)!=="svelte-i68oio"&&(Ja.innerHTML=ef),gs.forEach(i),rl=r(te),$e=s(te,"DIV",{class:!0});var bn=v($e);_(er.$$.fragment,bn),al=r(bn),Za=s(bn,"P",{"data-svelte-h":!0}),f(Za)!=="svelte-flusvq"&&(Za.textContent=of),nl=r(bn),_(To.$$.fragment,bn),bn.forEach(i),sl=r(te),Do=s(te,"DIV",{class:!0});var Ls=v(Do);_(or.$$.fragment,Ls),il=r(Ls),Ya=s(Ls,"P",{"data-svelte-h":!0}),f(Ya)!=="svelte-1ufq5ot"&&(Ya.textContent=tf),Ls.forEach(i),dl=r(te),ye=s(te,"DIV",{class:!0});var vn=v(ye);_(tr.$$.fragment,vn),ll=r(vn),Oa=s(vn,"P",{"data-svelte-h":!0}),f(Oa)!=="svelte-ioswce"&&(Oa.innerHTML=rf),cl=r(vn),_(So.$$.fragment,vn),vn.forEach(i),te.forEach(i),Jn=r(e),_(rr.$$.fragment,e),Zn=r(e),Se=s(e,"DIV",{class:!0});var xs=v(Se);_(ar.$$.fragment,xs),fl=r(xs),Co=s(xs,"DIV",{class:!0});var bs=v(Co);_(nr.$$.fragment,bs),pl=r(bs),Qa=s(bs,"P",{"data-svelte-h":!0}),f(Qa)!=="svelte-1lgbsz7"&&(Qa.innerHTML=af),bs.forEach(i),xs.forEach(i),Yn=r(e),_(sr.$$.fragment,e),On=r(e),C=s(e,"DIV",{class:!0});var W=v(C);_(ir.$$.fragment,W),ml=r(W),Ka=s(W,"P",{"data-svelte-h":!0}),f(Ka)!=="svelte-1q4bbx"&&(Ka.textContent=nf),_l=r(W),en=s(W,"DIV",{class:!0});var mf=v(en);_(dr.$$.fragment,mf),mf.forEach(i),ul=r(W),se=s(W,"DIV",{class:!0});var Uo=v(se);_(lr.$$.fragment,Uo),hl=r(Uo),on=s(Uo,"P",{"data-svelte-h":!0}),f(on)!=="svelte-1nr2dy0"&&(on.textContent=sf),gl=r(Uo),_(ko.$$.fragment,Uo),Ll=r(Uo),_(Ao.$$.fragment,Uo),Uo.forEach(i),xl=r(W),Me=s(W,"DIV",{class:!0});var wn=v(Me);_(cr.$$.fragment,wn),bl=r(wn),tn=s(wn,"P",{"data-svelte-h":!0}),f(tn)!=="svelte-h0os0v"&&(tn.textContent=df),vl=r(wn),_(Po.$$.fragment,wn),wn.forEach(i),wl=r(W),Ro=s(W,"DIV",{class:!0});var vs=v(Ro);_(fr.$$.fragment,vs),$l=r(vs),rn=s(vs,"P",{"data-svelte-h":!0}),f(rn)!=="svelte-1825k9e"&&(rn.textContent=lf),vs.forEach(i),yl=r(W),Ho=s(W,"DIV",{class:!0});var ws=v(Ho);_(pr.$$.fragment,ws),Ml=r(ws),an=s(ws,"P",{"data-svelte-h":!0}),f(an)!=="svelte-rvubqa"&&(an.innerHTML=cf),ws.forEach(i),Tl=r(W),Te=s(W,"DIV",{class:!0});var $n=v(Te);_(mr.$$.fragment,$n),Dl=r($n),nn=s($n,"P",{"data-svelte-h":!0}),f(nn)!=="svelte-ioswce"&&(nn.innerHTML=ff),Sl=r($n),_(Vo.$$.fragment,$n),$n.forEach(i),Cl=r(W),De=s(W,"DIV",{class:!0});var yn=v(De);_(_r.$$.fragment,yn),kl=r(yn),sn=s(yn,"P",{"data-svelte-h":!0}),f(sn)!=="svelte-119cgd9"&&(sn.textContent=pf),Al=r(yn),_(Io.$$.fragment,yn),yn.forEach(i),W.forEach(i),Qn=r(e),_(ur.$$.fragment,e),Kn=r(e),Mn=s(e,"P",{}),v(Mn).forEach(i),this.h()},h(){w(a,"name","hf:doc:metadata"),w(a,"content",Bf),w(He,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Xe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(We,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ze,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(A,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(je,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ze,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Qe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(oo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(_e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ro,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ao,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(so,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(he,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(lo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(co,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(po,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(_o,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(uo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(xe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(go,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(xo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(bo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(wo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(we,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(yo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Mo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w($e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Do,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Co,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(en,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ro,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ho,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(De,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(C,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,p){o(document.head,a),x(e,b,p),x(e,c,p),x(e,l,p),u(M,e,p),x(e,d,p),x(e,y,p),x(e,Tn,p),x(e,Xo,p),x(e,Dn,p),u(Re,e,p),x(e,Sn,p),u(Eo,e,p),x(e,Cn,p),x(e,R,p),u(Fo,R,null),o(R,Ms),o(R,Sr),o(R,Ts),o(R,He),u(No,He,null),o(He,Ds),o(He,Cr),o(R,Ss),o(R,Ve),u(Wo,Ve,null),o(Ve,Cs),o(Ve,kr),o(R,ks),o(R,B),u(zo,B,null),o(B,As),o(B,Ar),o(B,Ps),o(B,Pr),o(B,Rs),o(B,Rr),o(B,Hs),o(B,Hr),o(B,Vs),o(B,Vr),o(R,Is),o(R,ie),u(qo,ie,null),o(ie,Us),o(ie,Ir),o(ie,Xs),u(Ie,ie,null),o(R,Es),o(R,Ue),u(Bo,Ue,null),o(Ue,Fs),o(Ue,Ur),x(e,kn,p),u(jo,e,p),x(e,An,p),x(e,H,p),u(Go,H,null),o(H,Ns),o(H,Xr),o(H,Ws),o(H,Xe),u(Jo,Xe,null),o(Xe,zs),o(Xe,Er),o(H,qs),o(H,Ee),u(Zo,Ee,null),o(Ee,Bs),o(Ee,Fr),o(H,js),o(H,j),u(Yo,j,null),o(j,Gs),o(j,Nr),o(j,Js),o(j,Wr),o(j,Zs),o(j,zr),o(j,Ys),o(j,qr),o(j,Os),o(j,Br),o(H,Qs),o(H,de),u(Oo,de,null),o(de,Ks),o(de,jr),o(de,ei),u(Fe,de,null),o(H,oi),o(H,Ne),u(Qo,Ne,null),o(Ne,ti),o(Ne,Gr),x(e,Pn,p),u(Ko,e,p),x(e,Rn,p),x(e,A,p),u(et,A,null),o(A,ri),o(A,Jr),o(A,ai),o(A,Zr),o(A,ni),o(A,We),u(ot,We,null),o(We,si),o(We,Yr),o(A,ii),o(A,ze),u(tt,ze,null),o(ze,di),o(ze,Or),o(A,li),o(A,Z),u(rt,Z,null),o(Z,ci),o(Z,Qr),o(Z,fi),o(Z,Kr),o(Z,pi),o(Z,ea),o(Z,mi),o(Z,oa),o(A,_i),o(A,le),u(at,le,null),o(le,ui),o(le,ta),o(le,hi),u(qe,le,null),o(A,gi),o(A,Be),u(nt,Be,null),o(Be,Li),o(Be,ra),x(e,Hn,p),u(st,e,p),x(e,Vn,p),x(e,D,p),u(it,D,null),o(D,xi),o(D,aa),o(D,bi),o(D,na),o(D,vi),o(D,je),u(dt,je,null),o(je,wi),o(je,sa),o(D,$i),o(D,Ge),u(lt,Ge,null),o(Ge,yi),o(Ge,ia),o(D,Mi),o(D,Y),u(ct,Y,null),o(Y,Ti),o(Y,da),o(Y,Di),o(Y,la),o(Y,Si),o(Y,ca),o(Y,Ci),o(Y,fa),o(D,ki),o(D,ce),u(ft,ce,null),o(ce,Ai),o(ce,pa),o(ce,Pi),u(Je,ce,null),o(D,Ri),o(D,Ze),u(pt,Ze,null),o(Ze,Hi),o(Ze,ma),o(D,Vi),o(D,fe),u(mt,fe,null),o(fe,Ii),o(fe,_a),o(fe,Ui),u(Ye,fe,null),o(D,Xi),o(D,pe),u(_t,pe,null),o(pe,Ei),o(pe,ua),o(pe,Fi),u(Oe,pe,null),x(e,In,p),u(ut,e,p),x(e,Un,p),x(e,V,p),u(ht,V,null),o(V,Ni),o(V,ha),o(V,Wi),o(V,Qe),u(gt,Qe,null),o(Qe,zi),o(Qe,ga),o(V,qi),o(V,Ke),u(Lt,Ke,null),o(Ke,Bi),o(Ke,La),o(V,ji),o(V,me),u(xt,me,null),o(me,Gi),o(me,xa),o(me,Ji),u(eo,me,null),o(V,Zi),o(V,oo),u(bt,oo,null),o(oo,Yi),o(oo,ba),o(V,Oi),o(V,_e),u(vt,_e,null),o(_e,Qi),o(_e,va),o(_e,Ki),u(to,_e,null),x(e,Xn,p),u(wt,e,p),x(e,En,p),x(e,I,p),u($t,I,null),o(I,ed),o(I,wa),o(I,od),o(I,ro),u(yt,ro,null),o(ro,td),o(ro,$a),o(I,rd),o(I,ao),u(Mt,ao,null),o(ao,ad),o(ao,ya),o(I,nd),o(I,ue),u(Tt,ue,null),o(ue,sd),o(ue,Ma),o(ue,id),u(no,ue,null),o(I,dd),o(I,so),u(Dt,so,null),o(so,ld),o(so,Ta),o(I,cd),o(I,he),u(St,he,null),o(he,fd),o(he,Da),o(he,pd),u(io,he,null),x(e,Fn,p),u(Ct,e,p),x(e,Nn,p),x(e,U,p),u(kt,U,null),o(U,md),o(U,Sa),o(U,_d),o(U,lo),u(At,lo,null),o(lo,ud),o(lo,Ca),o(U,hd),o(U,co),u(Pt,co,null),o(co,gd),o(co,ka),o(U,Ld),o(U,ge),u(Rt,ge,null),o(ge,xd),o(ge,Aa),o(ge,bd),u(fo,ge,null),o(U,vd),o(U,po),u(Ht,po,null),o(po,wd),o(po,Pa),o(U,$d),o(U,Le),u(Vt,Le,null),o(Le,yd),o(Le,Ra),o(Le,Md),u(mo,Le,null),x(e,Wn,p),u(It,e,p),x(e,zn,p),x(e,X,p),u(Ut,X,null),o(X,Td),o(X,Ha),o(X,Dd),o(X,_o),u(Xt,_o,null),o(_o,Sd),o(_o,Va),o(X,Cd),o(X,uo),u(Et,uo,null),o(uo,kd),o(uo,Ia),o(X,Ad),o(X,xe),u(Ft,xe,null),o(xe,Pd),o(xe,Ua),o(xe,Rd),u(ho,xe,null),o(X,Hd),o(X,go),u(Nt,go,null),o(go,Vd),o(go,Xa),o(X,Id),o(X,be),u(Wt,be,null),o(be,Ud),o(be,Ea),o(be,Xd),u(Lo,be,null),x(e,qn,p),u(zt,e,p),x(e,Bn,p),x(e,E,p),u(qt,E,null),o(E,Ed),o(E,Fa),o(E,Fd),o(E,xo),u(Bt,xo,null),o(xo,Nd),o(xo,Na),o(E,Wd),o(E,bo),u(jt,bo,null),o(bo,zd),o(bo,Wa),o(E,qd),o(E,ve),u(Gt,ve,null),o(ve,Bd),o(ve,za),o(ve,jd),u(vo,ve,null),o(E,Gd),o(E,wo),u(Jt,wo,null),o(wo,Jd),o(wo,qa),o(E,Zd),o(E,we),u(Zt,we,null),o(we,Yd),o(we,Ba),o(we,Od),u($o,we,null),x(e,jn,p),u(Yt,e,p),x(e,Gn,p),x(e,F,p),u(Ot,F,null),o(F,Qd),o(F,ja),o(F,Kd),o(F,yo),u(Qt,yo,null),o(yo,el),o(yo,Ga),o(F,ol),o(F,Mo),u(Kt,Mo,null),o(Mo,tl),o(Mo,Ja),o(F,rl),o(F,$e),u(er,$e,null),o($e,al),o($e,Za),o($e,nl),u(To,$e,null),o(F,sl),o(F,Do),u(or,Do,null),o(Do,il),o(Do,Ya),o(F,dl),o(F,ye),u(tr,ye,null),o(ye,ll),o(ye,Oa),o(ye,cl),u(So,ye,null),x(e,Jn,p),u(rr,e,p),x(e,Zn,p),x(e,Se,p),u(ar,Se,null),o(Se,fl),o(Se,Co),u(nr,Co,null),o(Co,pl),o(Co,Qa),x(e,Yn,p),u(sr,e,p),x(e,On,p),x(e,C,p),u(ir,C,null),o(C,ml),o(C,Ka),o(C,_l),o(C,en),u(dr,en,null),o(C,ul),o(C,se),u(lr,se,null),o(se,hl),o(se,on),o(se,gl),u(ko,se,null),o(se,Ll),u(Ao,se,null),o(C,xl),o(C,Me),u(cr,Me,null),o(Me,bl),o(Me,tn),o(Me,vl),u(Po,Me,null),o(C,wl),o(C,Ro),u(fr,Ro,null),o(Ro,$l),o(Ro,rn),o(C,yl),o(C,Ho),u(pr,Ho,null),o(Ho,Ml),o(Ho,an),o(C,Tl),o(C,Te),u(mr,Te,null),o(Te,Dl),o(Te,nn),o(Te,Sl),u(Vo,Te,null),o(C,Cl),o(C,De),u(_r,De,null),o(De,kl),o(De,sn),o(De,Al),u(Io,De,null),x(e,Qn,p),u(ur,e,p),x(e,Kn,p),x(e,Mn,p),es=!0},p(e,[p]){const z={};p&2&&(z.$$scope={dirty:p,ctx:e}),Re.$set(z);const hr={};p&2&&(hr.$$scope={dirty:p,ctx:e}),Ie.$set(hr);const gr={};p&2&&(gr.$$scope={dirty:p,ctx:e}),Fe.$set(gr);const G={};p&2&&(G.$$scope={dirty:p,ctx:e}),qe.$set(G);const Ce={};p&2&&(Ce.$$scope={dirty:p,ctx:e}),Je.$set(Ce);const Lr={};p&2&&(Lr.$$scope={dirty:p,ctx:e}),Ye.$set(Lr);const q={};p&2&&(q.$$scope={dirty:p,ctx:e}),Oe.$set(q);const xr={};p&2&&(xr.$$scope={dirty:p,ctx:e}),eo.$set(xr);const br={};p&2&&(br.$$scope={dirty:p,ctx:e}),to.$set(br);const J={};p&2&&(J.$$scope={dirty:p,ctx:e}),no.$set(J);const ke={};p&2&&(ke.$$scope={dirty:p,ctx:e}),io.$set(ke);const vr={};p&2&&(vr.$$scope={dirty:p,ctx:e}),fo.$set(vr);const N={};p&2&&(N.$$scope={dirty:p,ctx:e}),mo.$set(N);const wr={};p&2&&(wr.$$scope={dirty:p,ctx:e}),ho.$set(wr);const $r={};p&2&&($r.$$scope={dirty:p,ctx:e}),Lo.$set($r);const re={};p&2&&(re.$$scope={dirty:p,ctx:e}),vo.$set(re);const Ae={};p&2&&(Ae.$$scope={dirty:p,ctx:e}),$o.$set(Ae);const yr={};p&2&&(yr.$$scope={dirty:p,ctx:e}),To.$set(yr);const k={};p&2&&(k.$$scope={dirty:p,ctx:e}),So.$set(k);const Mr={};p&2&&(Mr.$$scope={dirty:p,ctx:e}),ko.$set(Mr);const Tr={};p&2&&(Tr.$$scope={dirty:p,ctx:e}),Ao.$set(Tr);const ae={};p&2&&(ae.$$scope={dirty:p,ctx:e}),Po.$set(ae);const Pe={};p&2&&(Pe.$$scope={dirty:p,ctx:e}),Vo.$set(Pe);const Dr={};p&2&&(Dr.$$scope={dirty:p,ctx:e}),Io.$set(Dr)},i(e){es||(h(M.$$.fragment,e),h(Re.$$.fragment,e),h(Eo.$$.fragment,e),h(Fo.$$.fragment,e),h(No.$$.fragment,e),h(Wo.$$.fragment,e),h(zo.$$.fragment,e),h(qo.$$.fragment,e),h(Ie.$$.fragment,e),h(Bo.$$.fragment,e),h(jo.$$.fragment,e),h(Go.$$.fragment,e),h(Jo.$$.fragment,e),h(Zo.$$.fragment,e),h(Yo.$$.fragment,e),h(Oo.$$.fragment,e),h(Fe.$$.fragment,e),h(Qo.$$.fragment,e),h(Ko.$$.fragment,e),h(et.$$.fragment,e),h(ot.$$.fragment,e),h(tt.$$.fragment,e),h(rt.$$.fragment,e),h(at.$$.fragment,e),h(qe.$$.fragment,e),h(nt.$$.fragment,e),h(st.$$.fragment,e),h(it.$$.fragment,e),h(dt.$$.fragment,e),h(lt.$$.fragment,e),h(ct.$$.fragment,e),h(ft.$$.fragment,e),h(Je.$$.fragment,e),h(pt.$$.fragment,e),h(mt.$$.fragment,e),h(Ye.$$.fragment,e),h(_t.$$.fragment,e),h(Oe.$$.fragment,e),h(ut.$$.fragment,e),h(ht.$$.fragment,e),h(gt.$$.fragment,e),h(Lt.$$.fragment,e),h(xt.$$.fragment,e),h(eo.$$.fragment,e),h(bt.$$.fragment,e),h(vt.$$.fragment,e),h(to.$$.fragment,e),h(wt.$$.fragment,e),h($t.$$.fragment,e),h(yt.$$.fragment,e),h(Mt.$$.fragment,e),h(Tt.$$.fragment,e),h(no.$$.fragment,e),h(Dt.$$.fragment,e),h(St.$$.fragment,e),h(io.$$.fragment,e),h(Ct.$$.fragment,e),h(kt.$$.fragment,e),h(At.$$.fragment,e),h(Pt.$$.fragment,e),h(Rt.$$.fragment,e),h(fo.$$.fragment,e),h(Ht.$$.fragment,e),h(Vt.$$.fragment,e),h(mo.$$.fragment,e),h(It.$$.fragment,e),h(Ut.$$.fragment,e),h(Xt.$$.fragment,e),h(Et.$$.fragment,e),h(Ft.$$.fragment,e),h(ho.$$.fragment,e),h(Nt.$$.fragment,e),h(Wt.$$.fragment,e),h(Lo.$$.fragment,e),h(zt.$$.fragment,e),h(qt.$$.fragment,e),h(Bt.$$.fragment,e),h(jt.$$.fragment,e),h(Gt.$$.fragment,e),h(vo.$$.fragment,e),h(Jt.$$.fragment,e),h(Zt.$$.fragment,e),h($o.$$.fragment,e),h(Yt.$$.fragment,e),h(Ot.$$.fragment,e),h(Qt.$$.fragment,e),h(Kt.$$.fragment,e),h(er.$$.fragment,e),h(To.$$.fragment,e),h(or.$$.fragment,e),h(tr.$$.fragment,e),h(So.$$.fragment,e),h(rr.$$.fragment,e),h(ar.$$.fragment,e),h(nr.$$.fragment,e),h(sr.$$.fragment,e),h(ir.$$.fragment,e),h(dr.$$.fragment,e),h(lr.$$.fragment,e),h(ko.$$.fragment,e),h(Ao.$$.fragment,e),h(cr.$$.fragment,e),h(Po.$$.fragment,e),h(fr.$$.fragment,e),h(pr.$$.fragment,e),h(mr.$$.fragment,e),h(Vo.$$.fragment,e),h(_r.$$.fragment,e),h(Io.$$.fragment,e),h(ur.$$.fragment,e),es=!0)},o(e){g(M.$$.fragment,e),g(Re.$$.fragment,e),g(Eo.$$.fragment,e),g(Fo.$$.fragment,e),g(No.$$.fragment,e),g(Wo.$$.fragment,e),g(zo.$$.fragment,e),g(qo.$$.fragment,e),g(Ie.$$.fragment,e),g(Bo.$$.fragment,e),g(jo.$$.fragment,e),g(Go.$$.fragment,e),g(Jo.$$.fragment,e),g(Zo.$$.fragment,e),g(Yo.$$.fragment,e),g(Oo.$$.fragment,e),g(Fe.$$.fragment,e),g(Qo.$$.fragment,e),g(Ko.$$.fragment,e),g(et.$$.fragment,e),g(ot.$$.fragment,e),g(tt.$$.fragment,e),g(rt.$$.fragment,e),g(at.$$.fragment,e),g(qe.$$.fragment,e),g(nt.$$.fragment,e),g(st.$$.fragment,e),g(it.$$.fragment,e),g(dt.$$.fragment,e),g(lt.$$.fragment,e),g(ct.$$.fragment,e),g(ft.$$.fragment,e),g(Je.$$.fragment,e),g(pt.$$.fragment,e),g(mt.$$.fragment,e),g(Ye.$$.fragment,e),g(_t.$$.fragment,e),g(Oe.$$.fragment,e),g(ut.$$.fragment,e),g(ht.$$.fragment,e),g(gt.$$.fragment,e),g(Lt.$$.fragment,e),g(xt.$$.fragment,e),g(eo.$$.fragment,e),g(bt.$$.fragment,e),g(vt.$$.fragment,e),g(to.$$.fragment,e),g(wt.$$.fragment,e),g($t.$$.fragment,e),g(yt.$$.fragment,e),g(Mt.$$.fragment,e),g(Tt.$$.fragment,e),g(no.$$.fragment,e),g(Dt.$$.fragment,e),g(St.$$.fragment,e),g(io.$$.fragment,e),g(Ct.$$.fragment,e),g(kt.$$.fragment,e),g(At.$$.fragment,e),g(Pt.$$.fragment,e),g(Rt.$$.fragment,e),g(fo.$$.fragment,e),g(Ht.$$.fragment,e),g(Vt.$$.fragment,e),g(mo.$$.fragment,e),g(It.$$.fragment,e),g(Ut.$$.fragment,e),g(Xt.$$.fragment,e),g(Et.$$.fragment,e),g(Ft.$$.fragment,e),g(ho.$$.fragment,e),g(Nt.$$.fragment,e),g(Wt.$$.fragment,e),g(Lo.$$.fragment,e),g(zt.$$.fragment,e),g(qt.$$.fragment,e),g(Bt.$$.fragment,e),g(jt.$$.fragment,e),g(Gt.$$.fragment,e),g(vo.$$.fragment,e),g(Jt.$$.fragment,e),g(Zt.$$.fragment,e),g($o.$$.fragment,e),g(Yt.$$.fragment,e),g(Ot.$$.fragment,e),g(Qt.$$.fragment,e),g(Kt.$$.fragment,e),g(er.$$.fragment,e),g(To.$$.fragment,e),g(or.$$.fragment,e),g(tr.$$.fragment,e),g(So.$$.fragment,e),g(rr.$$.fragment,e),g(ar.$$.fragment,e),g(nr.$$.fragment,e),g(sr.$$.fragment,e),g(ir.$$.fragment,e),g(dr.$$.fragment,e),g(lr.$$.fragment,e),g(ko.$$.fragment,e),g(Ao.$$.fragment,e),g(cr.$$.fragment,e),g(Po.$$.fragment,e),g(fr.$$.fragment,e),g(pr.$$.fragment,e),g(mr.$$.fragment,e),g(Vo.$$.fragment,e),g(_r.$$.fragment,e),g(Io.$$.fragment,e),g(ur.$$.fragment,e),es=!1},d(e){e&&(i(b),i(c),i(l),i(d),i(y),i(Tn),i(Xo),i(Dn),i(Sn),i(Cn),i(R),i(kn),i(An),i(H),i(Pn),i(Rn),i(A),i(Hn),i(Vn),i(D),i(In),i(Un),i(V),i(Xn),i(En),i(I),i(Fn),i(Nn),i(U),i(Wn),i(zn),i(X),i(qn),i(Bn),i(E),i(jn),i(Gn),i(F),i(Jn),i(Zn),i(Se),i(Yn),i(On),i(C),i(Qn),i(Kn),i(Mn)),i(a),L(M,e),L(Re,e),L(Eo,e),L(Fo),L(No),L(Wo),L(zo),L(qo),L(Ie),L(Bo),L(jo,e),L(Go),L(Jo),L(Zo),L(Yo),L(Oo),L(Fe),L(Qo),L(Ko,e),L(et),L(ot),L(tt),L(rt),L(at),L(qe),L(nt),L(st,e),L(it),L(dt),L(lt),L(ct),L(ft),L(Je),L(pt),L(mt),L(Ye),L(_t),L(Oe),L(ut,e),L(ht),L(gt),L(Lt),L(xt),L(eo),L(bt),L(vt),L(to),L(wt,e),L($t),L(yt),L(Mt),L(Tt),L(no),L(Dt),L(St),L(io),L(Ct,e),L(kt),L(At),L(Pt),L(Rt),L(fo),L(Ht),L(Vt),L(mo),L(It,e),L(Ut),L(Xt),L(Et),L(Ft),L(ho),L(Nt),L(Wt),L(Lo),L(zt,e),L(qt),L(Bt),L(jt),L(Gt),L(vo),L(Jt),L(Zt),L($o),L(Yt,e),L(Ot),L(Qt),L(Kt),L(er),L(To),L(or),L(tr),L(So),L(rr,e),L(ar),L(nr),L(sr,e),L(ir),L(dr),L(lr),L(ko),L(Ao),L(cr),L(Po),L(fr),L(pr),L(mr),L(Vo),L(_r),L(Io),L(ur,e)}}}const Bf='{"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":"FluxLoraLoaderMixin","local":"diffusers.loaders.FluxLoraLoaderMixin","sections":[],"depth":2},{"title":"CogVideoXLoraLoaderMixin","local":"diffusers.loaders.CogVideoXLoraLoaderMixin","sections":[],"depth":2},{"title":"Mochi1LoraLoaderMixin","local":"diffusers.loaders.Mochi1LoraLoaderMixin","sections":[],"depth":2},{"title":"LTXVideoLoraLoaderMixin","local":"diffusers.loaders.LTXVideoLoraLoaderMixin","sections":[],"depth":2},{"title":"SanaLoraLoaderMixin","local":"diffusers.loaders.SanaLoraLoaderMixin","sections":[],"depth":2},{"title":"HunyuanVideoLoraLoaderMixin","local":"diffusers.loaders.HunyuanVideoLoraLoaderMixin","sections":[],"depth":2},{"title":"Lumina2LoraLoaderMixin","local":"diffusers.loaders.Lumina2LoraLoaderMixin","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 jf(T){return uf(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ep extends hf{constructor(a){super(),gf(this,a,jf,qf,_f,{})}}export{ep as component};