diffusers/pr_11739/en/_app/immutable/nodes/10.e89271fe.js

import{s as ch,o as fh,n as D}from"../chunks/scheduler.8c3d61f6.js";import{S as mh,i as ph,g as n,s as t,r as m,A as _h,h as s,f as i,c as r,j as v,u as p,x as f,k as w,y as o,a as x,v as _,d as u,t as h,w as g}from"../chunks/index.da70eac4.js";import{T as A}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.d7448bb3.js";import{C as vl}from"../chunks/CodeBlock.a9c4becf.js";import{E as bl}from"../chunks/ExampleCodeBlock.bdbc5937.js";import{H as O,E as uh}from"../chunks/getInferenceSnippets.1d18021a.js";function hh(M){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(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-1fw6lx1"&&(a.innerHTML=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function gh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Lh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function xh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function bh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function vh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function wh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function $h(M){let a,b="Examples:",l,c,T;return c=new vl({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,l=t(),m(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),l=r(d),p(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),_(c,d,y),T=!0},p:D,i(d){T||(u(c.$$.fragment,d),T=!0)},o(d){h(c.$$.fragment,d),T=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function yh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Mh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Th(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Dh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Ah(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Ch(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Sh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function kh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Rh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Ph(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Hh(M){let a,b="We support loading original format HunyuanVideo LoRA checkpoints.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-gyrs6h"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Ih(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Vh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Wh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Fh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Nh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Eh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Uh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function zh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Xh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function qh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Bh(M){let a,b="Example:",l,c,T;return c=new vl({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,l=t(),m(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),l=r(d),p(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),_(c,d,y),T=!0},p:D,i(d){T||(u(c.$$.fragment,d),T=!0)},o(d){h(c.$$.fragment,d),T=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function jh(M){let a,b="Example:",l,c,T;return c=new vl({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,l=t(),m(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),l=r(d),p(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),_(c,d,y),T=!0},p:D,i(d){T||(u(c.$$.fragment,d),T=!0)},o(d){h(c.$$.fragment,d),T=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function Gh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Oh(M){let a,b="Examples:",l,c,T;return c=new vl({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,l=t(),m(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),l=r(d),p(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),_(c,d,y),T=!0},p:D,i(d){T||(u(c.$$.fragment,d),T=!0)},o(d){h(c.$$.fragment,d),T=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function Jh(M){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,T="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=t(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Zh(M){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Yh(M){let a,b,l,c,T,d,y,Jp='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_11739/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_11739/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',td,Ft,Zp='<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>AuraFlowLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/fal/AuraFlow" rel="nofollow">AuraFlow</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>WanLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan" rel="nofollow">Wan</a>.</li> <li><code>CogView4LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4" rel="nofollow">CogView4</a>.</li> <li><code>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/pr_11739/en/api/pipelines/amused#diffusers.AmusedPipeline">AmusedPipeline</a>.</li> <li><code>HiDreamImageLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream" rel="nofollow">HiDream Image</a></li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',rd,so,ad,Nt,nd,I,Et,wl,gn,Yp=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_11739/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>.`,$l,io,Ut,yl,Ln,Qp="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ml,lo,zt,Tl,xn,Kp="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Dl,K,Xt,Al,bn,e_=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.unet</code> and
<code>self.text_encoder</code>.`,Cl,vn,o_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Sl,wn,t_=`See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,kl,$n,r_=`See <a href="/docs/diffusers/pr_11739/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>.`,Rl,yn,a_=`See <a href="/docs/diffusers/pr_11739/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>.`,Pl,Le,qt,Hl,Mn,n_="Return state dict for lora weights and the network alphas.",Il,co,Vl,fo,Bt,Wl,Tn,s_="Save the LoRA parameters corresponding to the UNet and text encoder.",sd,jt,id,V,Gt,Fl,Dn,i_=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_11739/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>.`,Nl,mo,Ot,El,An,d_="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ul,po,Jt,zl,Cn,l_="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Xl,ee,Zt,ql,Sn,c_=`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>.`,Bl,kn,f_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",jl,Rn,m_=`See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Gl,Pn,p_=`See <a href="/docs/diffusers/pr_11739/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>.`,Ol,Hn,__=`See <a href="/docs/diffusers/pr_11739/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>.`,Jl,xe,Yt,Zl,In,u_="Return state dict for lora weights and the network alphas.",Yl,_o,Ql,uo,Qt,Kl,Vn,h_="Save the LoRA parameters corresponding to the UNet and text encoder.",dd,Kt,ld,k,er,ec,Wn,g_=`Load LoRA layers into <a href="/docs/diffusers/pr_11739/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>.`,oc,Fn,L_='Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',tc,ho,or,rc,Nn,x_="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",ac,go,tr,nc,En,b_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",sc,re,rr,ic,Un,v_=`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>.`,dc,zn,w_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",lc,Xn,$_=`See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,cc,qn,y_=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,fc,be,ar,mc,Bn,M_="Return state dict for lora weights and the network alphas.",pc,Lo,_c,xo,nr,uc,jn,T_="Save the LoRA parameters corresponding to the UNet and text encoder.",hc,ve,sr,gc,Gn,D_=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Lc,bo,cd,ir,fd,C,dr,xc,On,A_=`Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,bc,Jn,C_='Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',vc,vo,lr,wc,Zn,S_="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",$c,wo,cr,yc,Yn,k_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Mc,ae,fr,Tc,Qn,R_=`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>.`,Dc,Kn,P_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Ac,es,H_=`See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Cc,os,I_=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Sc,we,mr,kc,ts,V_="Return state dict for lora weights and the network alphas.",Rc,$o,Pc,yo,pr,Hc,rs,W_="Save the LoRA parameters corresponding to the UNet and text encoder.",Ic,$e,_r,Vc,as,F_=`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>.`,Wc,Mo,Fc,ye,ur,Nc,ns,N_="Unloads the LoRA parameters.",Ec,To,md,hr,pd,W,gr,Uc,ss,E_='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',zc,Do,Lr,Xc,is,U_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",qc,Ao,xr,Bc,ds,z_=`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_11739/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>.`,jc,Me,br,Gc,ls,X_="Return state dict for lora weights and the network alphas.",Oc,Co,Jc,So,vr,Zc,cs,q_="Save the LoRA parameters corresponding to the transformer.",Yc,Te,wr,Qc,fs,B_=`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>.`,Kc,ko,_d,$r,ud,F,yr,ef,ms,j_='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',of,Ro,Mr,tf,ps,G_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",rf,Po,Tr,af,_s,O_=`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_11739/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>.`,nf,De,Dr,sf,us,J_="Return state dict for lora weights and the network alphas.",df,Ho,lf,Io,Ar,cf,hs,Z_="Save the LoRA parameters corresponding to the transformer.",ff,Ae,Cr,mf,gs,Y_=`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>.`,pf,Vo,hd,Sr,gd,N,kr,_f,Ls,Q_='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',uf,Wo,Rr,hf,xs,K_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",gf,Fo,Pr,Lf,bs,eu=`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_11739/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>.`,xf,Ce,Hr,bf,vs,ou="Return state dict for lora weights and the network alphas.",vf,No,wf,Eo,Ir,$f,ws,tu="Save the LoRA parameters corresponding to the transformer.",yf,Se,Vr,Mf,$s,ru=`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>.`,Tf,Uo,Ld,Wr,xd,E,Fr,Df,ys,au='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',Af,zo,Nr,Cf,Ms,nu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Sf,Xo,Er,kf,Ts,su=`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_11739/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>.`,Rf,ke,Ur,Pf,Ds,iu="Return state dict for lora weights and the network alphas.",Hf,qo,If,Bo,zr,Vf,As,du="Save the LoRA parameters corresponding to the transformer.",Wf,Re,Xr,Ff,Cs,lu=`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>.`,Nf,jo,bd,qr,vd,U,Br,Ef,Ss,cu='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',Uf,Go,jr,zf,ks,fu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Xf,Oo,Gr,qf,Rs,mu=`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_11739/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>.`,Bf,Pe,Or,jf,Ps,pu="Return state dict for lora weights and the network alphas.",Gf,Jo,Of,Zo,Jr,Jf,Hs,_u="Save the LoRA parameters corresponding to the transformer.",Zf,He,Zr,Yf,Is,uu=`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>.`,Qf,Yo,wd,Yr,$d,z,Qr,Kf,Vs,hu='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',em,Qo,Kr,om,Ws,gu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",tm,Ko,ea,rm,Fs,Lu=`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_11739/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>.`,am,Ie,oa,nm,Ns,xu="Return state dict for lora weights and the network alphas.",sm,et,im,ot,ta,dm,Es,bu="Save the LoRA parameters corresponding to the transformer.",lm,Ve,ra,cm,Us,vu=`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>.`,fm,tt,yd,aa,Md,X,na,mm,zs,wu='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',pm,rt,sa,_m,Xs,$u="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",um,at,ia,hm,qs,yu=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,gm,We,da,Lm,Bs,Mu="Return state dict for lora weights and the network alphas.",xm,nt,bm,st,la,vm,js,Tu="Save the LoRA parameters corresponding to the transformer.",wm,Fe,ca,$m,Gs,Du=`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>.`,ym,it,Td,fa,Dd,q,ma,Mm,Os,Au='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/cogview4#diffusers.CogView4Pipeline">CogView4Pipeline</a>.',Tm,dt,pa,Dm,Js,Cu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Am,lt,_a,Cm,Zs,Su=`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_11739/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>.`,Sm,Ne,ua,km,Ys,ku="Return state dict for lora weights and the network alphas.",Rm,ct,Pm,ft,ha,Hm,Qs,Ru="Save the LoRA parameters corresponding to the transformer.",Im,Ee,ga,Vm,Ks,Pu=`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>.`,Wm,mt,Ad,La,Cd,B,xa,Fm,ei,Hu='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',Nm,pt,ba,Em,oi,Iu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Um,_t,va,zm,ti,Vu=`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_11739/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>.`,Xm,Ue,wa,qm,ri,Wu="Return state dict for lora weights and the network alphas.",Bm,ut,jm,ht,$a,Gm,ai,Fu="Save the LoRA parameters corresponding to the transformer.",Om,ze,ya,Jm,ni,Nu=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Zm,gt,Sd,Ma,kd,ge,Ta,Ym,Lt,Da,Qm,si,Eu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Km,xt,Aa,ep,ii,Uu="Save the LoRA parameters corresponding to the UNet and text encoder.",Rd,Ca,Pd,j,Sa,op,di,zu='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel">HiDreamImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/hidream#diffusers.HiDreamImagePipeline">HiDreamImagePipeline</a>.',tp,bt,ka,rp,li,Xu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ap,vt,Ra,np,ci,qu=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,sp,Xe,Pa,ip,fi,Bu="Return state dict for lora weights and the network alphas.",dp,wt,lp,$t,Ha,cp,mi,ju="Save the LoRA parameters corresponding to the transformer.",fp,qe,Ia,mp,pi,Gu=`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>.`,pp,yt,Hd,Va,Id,S,Wa,_p,_i,Ou="Utility class for handling LoRAs.",up,ui,Fa,hp,Be,Na,gp,hi,Ju="Enables the possibility to hotswap LoRA adapters.",Lp,gi,Zu=`Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of
the loaded adapters differ.`,xp,he,Ea,bp,Li,Yu="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",vp,Mt,wp,Tt,$p,je,Ua,yp,xi,Qu="Gets the list of the current active adapters.",Mp,Dt,Tp,At,za,Dp,bi,Ku="Gets the current list of all available adapters in the pipeline.",Ap,Ct,Xa,Cp,vi,eh=`Moves the LoRAs listed in <code>adapter_names</code> to a target device. Useful for offloading the LoRA to the CPU in case
you want to load multiple adapters and free some GPU memory.`,Sp,Ge,qa,kp,wi,oh=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Rp,St,Pp,Oe,Ba,Hp,$i,th="Unloads the LoRA parameters.",Ip,kt,Vd,ja,Wd,G,Ga,Vp,yi,rh='Load LoRA layers into <a href="/docs/diffusers/pr_11739/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11739/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',Wp,Rt,Oa,Fp,Mi,ah="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Np,Pt,Ja,Ep,Ti,nh=`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_11739/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>.`,Up,Je,Za,zp,Di,sh="Return state dict for lora weights and the network alphas.",Xp,Ht,qp,It,Ya,Bp,Ai,ih="Save the LoRA parameters corresponding to the transformer.",jp,Ze,Qa,Gp,Ci,dh=`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>.`,Op,Vt,Fd,Ka,Nd,od,Ed;return T=new O({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),so=new A({props:{$$slots:{default:[hh]},$$scope:{ctx:M}}}),Nt=new O({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Et=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L126"}}),Ut=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L414"}}),zt=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L353"}}),Xt=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Defaults to <code>False</code>. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed <code>adapter_name</code> should be the name of an already loaded adapter.</p>
<p>If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L136"}}),qt=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_11739/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"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L237"}}),co=new A({props:{warning:!0,$$slots:{default:[gh]},$$scope:{ctx:M}}}),Bt=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"},{name:"unet_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.unet_lora_adapter_metadata",description:`<strong>unet_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the unet to be serialized with the state dict.`,name:"unet_lora_adapter_metadata"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L472"}}),jt=new O({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),Gt=new $({props:{name:"class diffusers.loaders.StableDiffusionXLLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L610"}}),Ot=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L900"}}),Jt=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L838"}}),Zt=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:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L621"}}),Yt=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_11739/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"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L721"}}),_o=new A({props:{warning:!0,$$slots:{default:[Lh]},$$scope:{ctx:M}}}),Qt=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"},{name:"unet_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"},{name:"text_encoder_2_lora_adapter_metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.unet_lora_adapter_metadata",description:`<strong>unet_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the unet to be serialized with the state dict.`,name:"unet_lora_adapter_metadata"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights.text_encoder_2_lora_adapter_metadata",description:`<strong>text_encoder_2_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the second text encoder to be serialized with the state dict.`,name:"text_encoder_2_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L959"}}),Kt=new O({props:{title:"SD3LoraLoaderMixin",local:"diffusers.loaders.SD3LoraLoaderMixin",headingTag:"h2"}}),er=new $({props:{name:"class diffusers.loaders.SD3LoraLoaderMixin",anchor:"diffusers.loaders.SD3LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1113"}}),or=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1362"}}),tr=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1312"}}),rr=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:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1224"}}),ar=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_11739/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"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1126"}}),Lo=new A({props:{warning:!0,$$slots:{default:[xh]},$$scope:{ctx:M}}}),nr=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, 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"},{name:"transformer_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"},{name:"text_encoder_2_lora_adapter_metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.text_encoder_2_lora_adapter_metadata",description:`<strong>text_encoder_2_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the second text encoder to be serialized with the state dict.`,name:"text_encoder_2_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1421"}}),sr=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SD3LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder', 'text_encoder_2']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.SD3LoraLoaderMixin.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.SD3LoraLoaderMixin.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"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.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_11739/src/diffusers/loaders/lora_pipeline.py#L1559"}}),bo=new A({props:{warning:!0,$$slots:{default:[bh]},$$scope:{ctx:M}}}),ir=new O({props:{title:"FluxLoraLoaderMixin",local:"diffusers.loaders.FluxLoraLoaderMixin",headingTag:"h2"}}),dr=new $({props:{name:"class diffusers.loaders.FluxLoraLoaderMixin",anchor:"diffusers.loaders.FluxLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1922"}}),lr=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L2326"}}),cr=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:"metadata",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = 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"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L2217"}}),fr=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.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"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L2092"}}),mr=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_11739/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"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1935"}}),$o=new A({props:{warning:!0,$$slots:{default:[vh]},$$scope:{ctx:M}}}),pr=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"},{name:"transformer_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L2385"}}),_r=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_11739/src/diffusers/loaders/lora_pipeline.py#L2518"}}),Mo=new A({props:{warning:!0,$$slots:{default:[wh]},$$scope:{ctx:M}}}),ur=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_11739/src/diffusers/loaders/lora_pipeline.py#L2539"}}),To=new bl({props:{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.example",$$slots:{default:[$h]},$$scope:{ctx:M}}}),hr=new O({props:{title:"CogVideoXLoraLoaderMixin",local:"diffusers.loaders.CogVideoXLoraLoaderMixin",headingTag:"h2"}}),gr=new $({props:{name:"class diffusers.loaders.CogVideoXLoraLoaderMixin",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L2994"}}),Lr=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3160"}}),xr=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3101"}}),br=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_11739/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"},{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3002"}}),Co=new A({props:{warning:!0,$$slots:{default:[yh]},$$scope:{ctx:M}}}),vr=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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],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"},{anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3211"}}),wr=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_11739/src/diffusers/loaders/lora_pipeline.py#L3315"}}),ko=new A({props:{warning:!0,$$slots:{default:[Mh]},$$scope:{ctx:M}}}),$r=new O({props:{title:"Mochi1LoraLoaderMixin",local:"diffusers.loaders.Mochi1LoraLoaderMixin",headingTag:"h2"}}),yr=new $({props:{name:"class diffusers.loaders.Mochi1LoraLoaderMixin",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3333"}}),Mr=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3500"}}),Tr=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3441"}}),Dr=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_11739/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"},{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3341"}}),Ho=new A({props:{warning:!0,$$slots:{default:[Th]},$$scope:{ctx:M}}}),Ar=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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],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"},{anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3551"}}),Cr=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_11739/src/diffusers/loaders/lora_pipeline.py#L3657"}}),Vo=new A({props:{warning:!0,$$slots:{default:[Dh]},$$scope:{ctx:M}}}),Sr=new O({props:{title:"AuraFlowLoraLoaderMixin",local:"diffusers.loaders.AuraFlowLoraLoaderMixin",headingTag:"h2"}}),kr=new $({props:{name:"class diffusers.loaders.AuraFlowLoraLoaderMixin",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1580"}}),Rr=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>AuraFlowTransformer2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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"},{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1747"}}),Pr=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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_11739/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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1688"}}),Hr=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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_11739/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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1588"}}),No=new A({props:{warning:!0,$$slots:{default:[Ah]},$$scope:{ctx:M}}}),Ir=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],parametersDescription:[{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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"},{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L1798"}}),Vr=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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.AuraFlowLoraLoaderMixin.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_11739/src/diffusers/loaders/lora_pipeline.py#L1904"}}),Uo=new A({props:{warning:!0,$$slots:{default:[Ch]},$$scope:{ctx:M}}}),Wr=new O({props:{title:"LTXVideoLoraLoaderMixin",local:"diffusers.loaders.LTXVideoLoraLoaderMixin",headingTag:"h2"}}),Fr=new $({props:{name:"class diffusers.loaders.LTXVideoLoraLoaderMixin",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3675"}}),Nr=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3844"}}),Er=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3785"}}),Ur=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_11739/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"},{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3683"}}),qo=new A({props:{warning:!0,$$slots:{default:[Sh]},$$scope:{ctx:M}}}),zr=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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],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"},{anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L3895"}}),Xr=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_11739/src/diffusers/loaders/lora_pipeline.py#L4001"}}),jo=new A({props:{warning:!0,$$slots:{default:[kh]},$$scope:{ctx:M}}}),qr=new O({props:{title:"SanaLoraLoaderMixin",local:"diffusers.loaders.SanaLoraLoaderMixin",headingTag:"h2"}}),Br=new $({props:{name:"class diffusers.loaders.SanaLoraLoaderMixin",anchor:"diffusers.loaders.SanaLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4019"}}),jr=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.SanaLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.SanaLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4186"}}),Gr=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4127"}}),Or=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_11739/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"},{anchor:"diffusers.loaders.SanaLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4027"}}),Jo=new A({props:{warning:!0,$$slots:{default:[Rh]},$$scope:{ctx:M}}}),Jr=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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],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"},{anchor:"diffusers.loaders.SanaLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4237"}}),Zr=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_11739/src/diffusers/loaders/lora_pipeline.py#L4343"}}),Yo=new A({props:{warning:!0,$$slots:{default:[Ph]},$$scope:{ctx:M}}}),Yr=new O({props:{title:"HunyuanVideoLoraLoaderMixin",local:"diffusers.loaders.HunyuanVideoLoraLoaderMixin",headingTag:"h2"}}),Qr=new $({props:{name:"class diffusers.loaders.HunyuanVideoLoraLoaderMixin",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4361"}}),Kr=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4530"}}),ea=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4471"}}),oa=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_11739/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"},{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4369"}}),et=new A({props:{warning:!0,$$slots:{default:[Hh]},$$scope:{ctx:M}}}),ta=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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],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"},{anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4581"}}),ra=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_11739/src/diffusers/loaders/lora_pipeline.py#L4687"}}),tt=new A({props:{warning:!0,$$slots:{default:[Ih]},$$scope:{ctx:M}}}),aa=new O({props:{title:"Lumina2LoraLoaderMixin",local:"diffusers.loaders.Lumina2LoraLoaderMixin",headingTag:"h2"}}),na=new $({props:{name:"class diffusers.loaders.Lumina2LoraLoaderMixin",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4705"}}),sa=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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],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"},{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4875"}}),ia=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:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{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_11739/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.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{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_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4816"}}),da=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_11739/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"},{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4713"}}),nt=new A({props:{warning:!0,$$slots:{default:[Vh]},$$scope:{ctx:M}}}),la=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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],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"},{anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L4926"}}),ca=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_11739/src/diffusers/loaders/lora_pipeline.py#L5032"}}),it=new A({props:{warning:!0,$$slots:{default:[Wh]},$$scope:{ctx:M}}}),fa=new O({props:{title:"CogView4LoraLoaderMixin",local:"diffusers.loaders.CogView4LoraLoaderMixin",headingTag:"h2"}}),ma=new $({props:{name:"class diffusers.loaders.CogView4LoraLoaderMixin",anchor:"diffusers.loaders.CogView4LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5445"}}),pa=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>CogView4Transformer2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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"},{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5612"}}),_a=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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_11739/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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5553"}}),ua=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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_11739/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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5453"}}),ct=new A({props:{warning:!0,$$slots:{default:[Fh]},$$scope:{ctx:M}}}),ha=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],parametersDescription:[{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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"},{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5663"}}),ga=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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.CogView4LoraLoaderMixin.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_11739/src/diffusers/loaders/lora_pipeline.py#L5769"}}),mt=new A({props:{warning:!0,$$slots:{default:[Nh]},$$scope:{ctx:M}}}),La=new O({props:{title:"WanLoraLoaderMixin",local:"diffusers.loaders.WanLoraLoaderMixin",headingTag:"h2"}}),xa=new $({props:{name:"class diffusers.loaders.WanLoraLoaderMixin",anchor:"diffusers.loaders.WanLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5050"}}),ba=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.WanLoraLoaderMixin.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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>WanTransformer3DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5270"}}),va=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.WanLoraLoaderMixin.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:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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_11739/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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5207"}}),wa=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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_11739/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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5058"}}),ut=new A({props:{warning:!0,$$slots:{default:[Eh]},$$scope:{ctx:M}}}),$a=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.WanLoraLoaderMixin.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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5321"}}),ya=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.WanLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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_11739/src/diffusers/loaders/lora_pipeline.py#L5427"}}),gt=new A({props:{warning:!0,$$slots:{default:[Uh]},$$scope:{ctx:M}}}),Ma=new O({props:{title:"AmusedLoraLoaderMixin",local:"diffusers.loaders.AmusedLoraLoaderMixin",headingTag:"h2"}}),Ta=new $({props:{name:"class diffusers.loaders.AmusedLoraLoaderMixin",anchor:"diffusers.loaders.AmusedLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L2819"}}),Da=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:"metadata",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = 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"},{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L2824"}}),Aa=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.AmusedLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"transformer_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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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_11739/src/diffusers/loaders/lora_pipeline.py#L2939"}}),Ca=new O({props:{title:"HiDreamImageLoraLoaderMixin",local:"diffusers.loaders.HiDreamImageLoraLoaderMixin",headingTag:"h2"}}),Sa=new $({props:{name:"class diffusers.loaders.HiDreamImageLoraLoaderMixin",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5787"}}),ka=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>HiDreamImageTransformer2DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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"},{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5956"}}),Ra=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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_11739/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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5897"}}),Pa=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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_11739/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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5795"}}),wt=new A({props:{warning:!0,$$slots:{default:[zh]},$$scope:{ctx:M}}}),Ha=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],parametersDescription:[{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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"},{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L6007"}}),Ia=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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.HiDreamImageLoraLoaderMixin.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_11739/src/diffusers/loaders/lora_pipeline.py#L6113"}}),yt=new A({props:{warning:!0,$$slots:{default:[Xh]},$$scope:{ctx:M}}}),Va=new O({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),Wa=new $({props:{name:"class diffusers.loaders.lora_base.LoraBaseMixin",anchor:"diffusers.loaders.lora_base.LoraBaseMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_base.py#L450"}}),Fa=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_11739/src/diffusers/loaders/lora_base.py#L769"}}),Na=new $({props:{name:"enable_lora_hotswap",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora_hotswap",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora_hotswap.target_rank",description:`<strong>target_rank</strong> (<code>int</code>) &#x2014;
The highest rank among all the adapters that will be loaded.`,name:"target_rank"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora_hotswap.check_compiled",description:`<strong>check_compiled</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;error&quot;</code>) &#x2014;
How to handle the case when the model is already compiled, which should generally be avoided. The
options are:<ul>
<li>&#x201C;error&#x201D; (default): raise an error</li>
<li>&#x201C;warn&#x201D;: issue a warning</li>
<li>&#x201C;ignore&#x201D;: do nothing</li>
</ul>`,name:"check_compiled"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_base.py#L936"}}),Ea=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_11739/src/diffusers/loaders/lora_base.py#L517"}}),Mt=new A({props:{warning:!0,$$slots:{default:[qh]},$$scope:{ctx:M}}}),Tt=new bl({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[Bh]},$$scope:{ctx:M}}}),Ua=new $({props:{name:"get_active_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_base.py#L791"}}),Dt=new bl({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[jh]},$$scope:{ctx:M}}}),za=new $({props:{name:"get_list_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_base.py#L824"}}),Xa=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_11739/src/diffusers/loaders/lora_base.py#L846"}}),qa=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_11739/src/diffusers/loaders/lora_base.py#L607"}}),St=new A({props:{warning:!0,$$slots:{default:[Gh]},$$scope:{ctx:M}}}),Ba=new $({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_base.py#L494"}}),kt=new bl({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[Oh]},$$scope:{ctx:M}}}),ja=new O({props:{title:"WanLoraLoaderMixin",local:"diffusers.loaders.WanLoraLoaderMixin",headingTag:"h2"}}),Ga=new $({props:{name:"class diffusers.loaders.WanLoraLoaderMixin",anchor:"diffusers.loaders.WanLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5050"}}),Oa=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.WanLoraLoaderMixin.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"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>WanTransformer3DModel</code>) &#x2014;
The Transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5270"}}),Ja=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.WanLoraLoaderMixin.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:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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_11739/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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11739/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5207"}}),Za=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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_11739/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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5058"}}),Ht=new A({props:{warning:!0,$$slots:{default:[Jh]},$$scope:{ctx:M}}}),Ya=new $({props:{name:"save_lora_weights",anchor:"diffusers.loaders.WanLoraLoaderMixin.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"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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"},{anchor:"diffusers.loaders.WanLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> &#x2014;
LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/loaders/lora_pipeline.py#L5321"}}),Qa=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.WanLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.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.WanLoraLoaderMixin.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_11739/src/diffusers/loaders/lora_pipeline.py#L5427"}}),Vt=new A({props:{warning:!0,$$slots:{default:[Zh]},$$scope:{ctx:M}}}),Ka=new uh({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/loaders/lora.md"}}),{c(){a=n("meta"),b=t(),l=n("p"),c=t(),m(T.$$.fragment),d=t(),y=n("p"),y.innerHTML=Jp,td=t(),Ft=n("ul"),Ft.innerHTML=Zp,rd=t(),m(so.$$.fragment),ad=t(),m(Nt.$$.fragment),nd=t(),I=n("div"),m(Et.$$.fragment),wl=t(),gn=n("p"),gn.innerHTML=Yp,$l=t(),io=n("div"),m(Ut.$$.fragment),yl=t(),Ln=n("p"),Ln.innerHTML=Qp,Ml=t(),lo=n("div"),m(zt.$$.fragment),Tl=t(),xn=n("p"),xn.innerHTML=Kp,Dl=t(),K=n("div"),m(Xt.$$.fragment),Al=t(),bn=n("p"),bn.innerHTML=e_,Cl=t(),vn=n("p"),vn.innerHTML=o_,Sl=t(),wn=n("p"),wn.innerHTML=t_,kl=t(),$n=n("p"),$n.innerHTML=r_,Rl=t(),yn=n("p"),yn.innerHTML=a_,Pl=t(),Le=n("div"),m(qt.$$.fragment),Hl=t(),Mn=n("p"),Mn.textContent=n_,Il=t(),m(co.$$.fragment),Vl=t(),fo=n("div"),m(Bt.$$.fragment),Wl=t(),Tn=n("p"),Tn.textContent=s_,sd=t(),m(jt.$$.fragment),id=t(),V=n("div"),m(Gt.$$.fragment),Fl=t(),Dn=n("p"),Dn.innerHTML=i_,Nl=t(),mo=n("div"),m(Ot.$$.fragment),El=t(),An=n("p"),An.innerHTML=d_,Ul=t(),po=n("div"),m(Jt.$$.fragment),zl=t(),Cn=n("p"),Cn.innerHTML=l_,Xl=t(),ee=n("div"),m(Zt.$$.fragment),ql=t(),Sn=n("p"),Sn.innerHTML=c_,Bl=t(),kn=n("p"),kn.innerHTML=f_,jl=t(),Rn=n("p"),Rn.innerHTML=m_,Gl=t(),Pn=n("p"),Pn.innerHTML=p_,Ol=t(),Hn=n("p"),Hn.innerHTML=__,Jl=t(),xe=n("div"),m(Yt.$$.fragment),Zl=t(),In=n("p"),In.textContent=u_,Yl=t(),m(_o.$$.fragment),Ql=t(),uo=n("div"),m(Qt.$$.fragment),Kl=t(),Vn=n("p"),Vn.textContent=h_,dd=t(),m(Kt.$$.fragment),ld=t(),k=n("div"),m(er.$$.fragment),ec=t(),Wn=n("p"),Wn.innerHTML=g_,oc=t(),Fn=n("p"),Fn.innerHTML=L_,tc=t(),ho=n("div"),m(or.$$.fragment),rc=t(),Nn=n("p"),Nn.innerHTML=x_,ac=t(),go=n("div"),m(tr.$$.fragment),nc=t(),En=n("p"),En.innerHTML=b_,sc=t(),re=n("div"),m(rr.$$.fragment),ic=t(),Un=n("p"),Un.innerHTML=v_,dc=t(),zn=n("p"),zn.innerHTML=w_,lc=t(),Xn=n("p"),Xn.innerHTML=$_,cc=t(),qn=n("p"),qn.innerHTML=y_,fc=t(),be=n("div"),m(ar.$$.fragment),mc=t(),Bn=n("p"),Bn.textContent=M_,pc=t(),m(Lo.$$.fragment),_c=t(),xo=n("div"),m(nr.$$.fragment),uc=t(),jn=n("p"),jn.textContent=T_,hc=t(),ve=n("div"),m(sr.$$.fragment),gc=t(),Gn=n("p"),Gn.innerHTML=D_,Lc=t(),m(bo.$$.fragment),cd=t(),m(ir.$$.fragment),fd=t(),C=n("div"),m(dr.$$.fragment),xc=t(),On=n("p"),On.innerHTML=A_,bc=t(),Jn=n("p"),Jn.innerHTML=C_,vc=t(),vo=n("div"),m(lr.$$.fragment),wc=t(),Zn=n("p"),Zn.innerHTML=S_,$c=t(),wo=n("div"),m(cr.$$.fragment),yc=t(),Yn=n("p"),Yn.innerHTML=k_,Mc=t(),ae=n("div"),m(fr.$$.fragment),Tc=t(),Qn=n("p"),Qn.innerHTML=R_,Dc=t(),Kn=n("p"),Kn.innerHTML=P_,Ac=t(),es=n("p"),es.innerHTML=H_,Cc=t(),os=n("p"),os.innerHTML=I_,Sc=t(),we=n("div"),m(mr.$$.fragment),kc=t(),ts=n("p"),ts.textContent=V_,Rc=t(),m($o.$$.fragment),Pc=t(),yo=n("div"),m(pr.$$.fragment),Hc=t(),rs=n("p"),rs.textContent=W_,Ic=t(),$e=n("div"),m(_r.$$.fragment),Vc=t(),as=n("p"),as.innerHTML=F_,Wc=t(),m(Mo.$$.fragment),Fc=t(),ye=n("div"),m(ur.$$.fragment),Nc=t(),ns=n("p"),ns.textContent=N_,Ec=t(),m(To.$$.fragment),md=t(),m(hr.$$.fragment),pd=t(),W=n("div"),m(gr.$$.fragment),Uc=t(),ss=n("p"),ss.innerHTML=E_,zc=t(),Do=n("div"),m(Lr.$$.fragment),Xc=t(),is=n("p"),is.innerHTML=U_,qc=t(),Ao=n("div"),m(xr.$$.fragment),Bc=t(),ds=n("p"),ds.innerHTML=z_,jc=t(),Me=n("div"),m(br.$$.fragment),Gc=t(),ls=n("p"),ls.textContent=X_,Oc=t(),m(Co.$$.fragment),Jc=t(),So=n("div"),m(vr.$$.fragment),Zc=t(),cs=n("p"),cs.textContent=q_,Yc=t(),Te=n("div"),m(wr.$$.fragment),Qc=t(),fs=n("p"),fs.innerHTML=B_,Kc=t(),m(ko.$$.fragment),_d=t(),m($r.$$.fragment),ud=t(),F=n("div"),m(yr.$$.fragment),ef=t(),ms=n("p"),ms.innerHTML=j_,of=t(),Ro=n("div"),m(Mr.$$.fragment),tf=t(),ps=n("p"),ps.innerHTML=G_,rf=t(),Po=n("div"),m(Tr.$$.fragment),af=t(),_s=n("p"),_s.innerHTML=O_,nf=t(),De=n("div"),m(Dr.$$.fragment),sf=t(),us=n("p"),us.textContent=J_,df=t(),m(Ho.$$.fragment),lf=t(),Io=n("div"),m(Ar.$$.fragment),cf=t(),hs=n("p"),hs.textContent=Z_,ff=t(),Ae=n("div"),m(Cr.$$.fragment),mf=t(),gs=n("p"),gs.innerHTML=Y_,pf=t(),m(Vo.$$.fragment),hd=t(),m(Sr.$$.fragment),gd=t(),N=n("div"),m(kr.$$.fragment),_f=t(),Ls=n("p"),Ls.innerHTML=Q_,uf=t(),Wo=n("div"),m(Rr.$$.fragment),hf=t(),xs=n("p"),xs.innerHTML=K_,gf=t(),Fo=n("div"),m(Pr.$$.fragment),Lf=t(),bs=n("p"),bs.innerHTML=eu,xf=t(),Ce=n("div"),m(Hr.$$.fragment),bf=t(),vs=n("p"),vs.textContent=ou,vf=t(),m(No.$$.fragment),wf=t(),Eo=n("div"),m(Ir.$$.fragment),$f=t(),ws=n("p"),ws.textContent=tu,yf=t(),Se=n("div"),m(Vr.$$.fragment),Mf=t(),$s=n("p"),$s.innerHTML=ru,Tf=t(),m(Uo.$$.fragment),Ld=t(),m(Wr.$$.fragment),xd=t(),E=n("div"),m(Fr.$$.fragment),Df=t(),ys=n("p"),ys.innerHTML=au,Af=t(),zo=n("div"),m(Nr.$$.fragment),Cf=t(),Ms=n("p"),Ms.innerHTML=nu,Sf=t(),Xo=n("div"),m(Er.$$.fragment),kf=t(),Ts=n("p"),Ts.innerHTML=su,Rf=t(),ke=n("div"),m(Ur.$$.fragment),Pf=t(),Ds=n("p"),Ds.textContent=iu,Hf=t(),m(qo.$$.fragment),If=t(),Bo=n("div"),m(zr.$$.fragment),Vf=t(),As=n("p"),As.textContent=du,Wf=t(),Re=n("div"),m(Xr.$$.fragment),Ff=t(),Cs=n("p"),Cs.innerHTML=lu,Nf=t(),m(jo.$$.fragment),bd=t(),m(qr.$$.fragment),vd=t(),U=n("div"),m(Br.$$.fragment),Ef=t(),Ss=n("p"),Ss.innerHTML=cu,Uf=t(),Go=n("div"),m(jr.$$.fragment),zf=t(),ks=n("p"),ks.innerHTML=fu,Xf=t(),Oo=n("div"),m(Gr.$$.fragment),qf=t(),Rs=n("p"),Rs.innerHTML=mu,Bf=t(),Pe=n("div"),m(Or.$$.fragment),jf=t(),Ps=n("p"),Ps.textContent=pu,Gf=t(),m(Jo.$$.fragment),Of=t(),Zo=n("div"),m(Jr.$$.fragment),Jf=t(),Hs=n("p"),Hs.textContent=_u,Zf=t(),He=n("div"),m(Zr.$$.fragment),Yf=t(),Is=n("p"),Is.innerHTML=uu,Qf=t(),m(Yo.$$.fragment),wd=t(),m(Yr.$$.fragment),$d=t(),z=n("div"),m(Qr.$$.fragment),Kf=t(),Vs=n("p"),Vs.innerHTML=hu,em=t(),Qo=n("div"),m(Kr.$$.fragment),om=t(),Ws=n("p"),Ws.innerHTML=gu,tm=t(),Ko=n("div"),m(ea.$$.fragment),rm=t(),Fs=n("p"),Fs.innerHTML=Lu,am=t(),Ie=n("div"),m(oa.$$.fragment),nm=t(),Ns=n("p"),Ns.textContent=xu,sm=t(),m(et.$$.fragment),im=t(),ot=n("div"),m(ta.$$.fragment),dm=t(),Es=n("p"),Es.textContent=bu,lm=t(),Ve=n("div"),m(ra.$$.fragment),cm=t(),Us=n("p"),Us.innerHTML=vu,fm=t(),m(tt.$$.fragment),yd=t(),m(aa.$$.fragment),Md=t(),X=n("div"),m(na.$$.fragment),mm=t(),zs=n("p"),zs.innerHTML=wu,pm=t(),rt=n("div"),m(sa.$$.fragment),_m=t(),Xs=n("p"),Xs.innerHTML=$u,um=t(),at=n("div"),m(ia.$$.fragment),hm=t(),qs=n("p"),qs.innerHTML=yu,gm=t(),We=n("div"),m(da.$$.fragment),Lm=t(),Bs=n("p"),Bs.textContent=Mu,xm=t(),m(nt.$$.fragment),bm=t(),st=n("div"),m(la.$$.fragment),vm=t(),js=n("p"),js.textContent=Tu,wm=t(),Fe=n("div"),m(ca.$$.fragment),$m=t(),Gs=n("p"),Gs.innerHTML=Du,ym=t(),m(it.$$.fragment),Td=t(),m(fa.$$.fragment),Dd=t(),q=n("div"),m(ma.$$.fragment),Mm=t(),Os=n("p"),Os.innerHTML=Au,Tm=t(),dt=n("div"),m(pa.$$.fragment),Dm=t(),Js=n("p"),Js.innerHTML=Cu,Am=t(),lt=n("div"),m(_a.$$.fragment),Cm=t(),Zs=n("p"),Zs.innerHTML=Su,Sm=t(),Ne=n("div"),m(ua.$$.fragment),km=t(),Ys=n("p"),Ys.textContent=ku,Rm=t(),m(ct.$$.fragment),Pm=t(),ft=n("div"),m(ha.$$.fragment),Hm=t(),Qs=n("p"),Qs.textContent=Ru,Im=t(),Ee=n("div"),m(ga.$$.fragment),Vm=t(),Ks=n("p"),Ks.innerHTML=Pu,Wm=t(),m(mt.$$.fragment),Ad=t(),m(La.$$.fragment),Cd=t(),B=n("div"),m(xa.$$.fragment),Fm=t(),ei=n("p"),ei.innerHTML=Hu,Nm=t(),pt=n("div"),m(ba.$$.fragment),Em=t(),oi=n("p"),oi.innerHTML=Iu,Um=t(),_t=n("div"),m(va.$$.fragment),zm=t(),ti=n("p"),ti.innerHTML=Vu,Xm=t(),Ue=n("div"),m(wa.$$.fragment),qm=t(),ri=n("p"),ri.textContent=Wu,Bm=t(),m(ut.$$.fragment),jm=t(),ht=n("div"),m($a.$$.fragment),Gm=t(),ai=n("p"),ai.textContent=Fu,Om=t(),ze=n("div"),m(ya.$$.fragment),Jm=t(),ni=n("p"),ni.innerHTML=Nu,Zm=t(),m(gt.$$.fragment),Sd=t(),m(Ma.$$.fragment),kd=t(),ge=n("div"),m(Ta.$$.fragment),Ym=t(),Lt=n("div"),m(Da.$$.fragment),Qm=t(),si=n("p"),si.innerHTML=Eu,Km=t(),xt=n("div"),m(Aa.$$.fragment),ep=t(),ii=n("p"),ii.textContent=Uu,Rd=t(),m(Ca.$$.fragment),Pd=t(),j=n("div"),m(Sa.$$.fragment),op=t(),di=n("p"),di.innerHTML=zu,tp=t(),bt=n("div"),m(ka.$$.fragment),rp=t(),li=n("p"),li.innerHTML=Xu,ap=t(),vt=n("div"),m(Ra.$$.fragment),np=t(),ci=n("p"),ci.innerHTML=qu,sp=t(),Xe=n("div"),m(Pa.$$.fragment),ip=t(),fi=n("p"),fi.textContent=Bu,dp=t(),m(wt.$$.fragment),lp=t(),$t=n("div"),m(Ha.$$.fragment),cp=t(),mi=n("p"),mi.textContent=ju,fp=t(),qe=n("div"),m(Ia.$$.fragment),mp=t(),pi=n("p"),pi.innerHTML=Gu,pp=t(),m(yt.$$.fragment),Hd=t(),m(Va.$$.fragment),Id=t(),S=n("div"),m(Wa.$$.fragment),_p=t(),_i=n("p"),_i.textContent=Ou,up=t(),ui=n("div"),m(Fa.$$.fragment),hp=t(),Be=n("div"),m(Na.$$.fragment),gp=t(),hi=n("p"),hi.textContent=Ju,Lp=t(),gi=n("p"),gi.textContent=Zu,xp=t(),he=n("div"),m(Ea.$$.fragment),bp=t(),Li=n("p"),Li.textContent=Yu,vp=t(),m(Mt.$$.fragment),wp=t(),m(Tt.$$.fragment),$p=t(),je=n("div"),m(Ua.$$.fragment),yp=t(),xi=n("p"),xi.textContent=Qu,Mp=t(),m(Dt.$$.fragment),Tp=t(),At=n("div"),m(za.$$.fragment),Dp=t(),bi=n("p"),bi.textContent=Ku,Ap=t(),Ct=n("div"),m(Xa.$$.fragment),Cp=t(),vi=n("p"),vi.innerHTML=eh,Sp=t(),Ge=n("div"),m(qa.$$.fragment),kp=t(),wi=n("p"),wi.innerHTML=oh,Rp=t(),m(St.$$.fragment),Pp=t(),Oe=n("div"),m(Ba.$$.fragment),Hp=t(),$i=n("p"),$i.textContent=th,Ip=t(),m(kt.$$.fragment),Vd=t(),m(ja.$$.fragment),Wd=t(),G=n("div"),m(Ga.$$.fragment),Vp=t(),yi=n("p"),yi.innerHTML=rh,Wp=t(),Rt=n("div"),m(Oa.$$.fragment),Fp=t(),Mi=n("p"),Mi.innerHTML=ah,Np=t(),Pt=n("div"),m(Ja.$$.fragment),Ep=t(),Ti=n("p"),Ti.innerHTML=nh,Up=t(),Je=n("div"),m(Za.$$.fragment),zp=t(),Di=n("p"),Di.textContent=sh,Xp=t(),m(Ht.$$.fragment),qp=t(),It=n("div"),m(Ya.$$.fragment),Bp=t(),Ai=n("p"),Ai.textContent=ih,jp=t(),Ze=n("div"),m(Qa.$$.fragment),Gp=t(),Ci=n("p"),Ci.innerHTML=dh,Op=t(),m(Vt.$$.fragment),Fd=t(),m(Ka.$$.fragment),Nd=t(),od=n("p"),this.h()},l(e){const L=_h("svelte-u9bgzb",document.head);a=s(L,"META",{name:!0,content:!0}),L.forEach(i),b=r(e),l=s(e,"P",{}),v(l).forEach(i),c=r(e),p(T.$$.fragment,e),d=r(e),y=s(e,"P",{"data-svelte-h":!0}),f(y)!=="svelte-src06x"&&(y.innerHTML=Jp),td=r(e),Ft=s(e,"UL",{"data-svelte-h":!0}),f(Ft)!=="svelte-1tf7qyo"&&(Ft.innerHTML=Zp),rd=r(e),p(so.$$.fragment,e),ad=r(e),p(Nt.$$.fragment,e),nd=r(e),I=s(e,"DIV",{class:!0});var J=v(I);p(Et.$$.fragment,J),wl=r(J),gn=s(J,"P",{"data-svelte-h":!0}),f(gn)!=="svelte-x92bek"&&(gn.innerHTML=Yp),$l=r(J),io=s(J,"DIV",{class:!0});var en=v(io);p(Ut.$$.fragment,en),yl=r(en),Ln=s(en,"P",{"data-svelte-h":!0}),f(Ln)!=="svelte-1062ci4"&&(Ln.innerHTML=Qp),en.forEach(i),Ml=r(J),lo=s(J,"DIV",{class:!0});var on=v(lo);p(zt.$$.fragment,on),Tl=r(on),xn=s(on,"P",{"data-svelte-h":!0}),f(xn)!=="svelte-u3q4so"&&(xn.innerHTML=Kp),on.forEach(i),Dl=r(J),K=s(J,"DIV",{class:!0});var oe=v(K);p(Xt.$$.fragment,oe),Al=r(oe),bn=s(oe,"P",{"data-svelte-h":!0}),f(bn)!=="svelte-vs7s0z"&&(bn.innerHTML=e_),Cl=r(oe),vn=s(oe,"P",{"data-svelte-h":!0}),f(vn)!=="svelte-15b960v"&&(vn.innerHTML=o_),Sl=r(oe),wn=s(oe,"P",{"data-svelte-h":!0}),f(wn)!=="svelte-18r91jl"&&(wn.innerHTML=t_),kl=r(oe),$n=s(oe,"P",{"data-svelte-h":!0}),f($n)!=="svelte-bcd41e"&&($n.innerHTML=r_),Rl=r(oe),yn=s(oe,"P",{"data-svelte-h":!0}),f(yn)!=="svelte-cgqsww"&&(yn.innerHTML=a_),oe.forEach(i),Pl=r(J),Le=s(J,"DIV",{class:!0});var Ye=v(Le);p(qt.$$.fragment,Ye),Hl=r(Ye),Mn=s(Ye,"P",{"data-svelte-h":!0}),f(Mn)!=="svelte-flusvq"&&(Mn.textContent=n_),Il=r(Ye),p(co.$$.fragment,Ye),Ye.forEach(i),Vl=r(J),fo=s(J,"DIV",{class:!0});var tn=v(fo);p(Bt.$$.fragment,tn),Wl=r(tn),Tn=s(tn,"P",{"data-svelte-h":!0}),f(Tn)!=="svelte-1ufq5ot"&&(Tn.textContent=s_),tn.forEach(i),J.forEach(i),sd=r(e),p(jt.$$.fragment,e),id=r(e),V=s(e,"DIV",{class:!0});var Z=v(V);p(Gt.$$.fragment,Z),Fl=r(Z),Dn=s(Z,"P",{"data-svelte-h":!0}),f(Dn)!=="svelte-h47735"&&(Dn.innerHTML=i_),Nl=r(Z),mo=s(Z,"DIV",{class:!0});var rn=v(mo);p(Ot.$$.fragment,rn),El=r(rn),An=s(rn,"P",{"data-svelte-h":!0}),f(An)!=="svelte-1062ci4"&&(An.innerHTML=d_),rn.forEach(i),Ul=r(Z),po=s(Z,"DIV",{class:!0});var an=v(po);p(Jt.$$.fragment,an),zl=r(an),Cn=s(an,"P",{"data-svelte-h":!0}),f(Cn)!=="svelte-u3q4so"&&(Cn.innerHTML=l_),an.forEach(i),Xl=r(Z),ee=s(Z,"DIV",{class:!0});var te=v(ee);p(Zt.$$.fragment,te),ql=r(te),Sn=s(te,"P",{"data-svelte-h":!0}),f(Sn)!=="svelte-vs7s0z"&&(Sn.innerHTML=c_),Bl=r(te),kn=s(te,"P",{"data-svelte-h":!0}),f(kn)!=="svelte-15b960v"&&(kn.innerHTML=f_),jl=r(te),Rn=s(te,"P",{"data-svelte-h":!0}),f(Rn)!=="svelte-18r91jl"&&(Rn.innerHTML=m_),Gl=r(te),Pn=s(te,"P",{"data-svelte-h":!0}),f(Pn)!=="svelte-bcd41e"&&(Pn.innerHTML=p_),Ol=r(te),Hn=s(te,"P",{"data-svelte-h":!0}),f(Hn)!=="svelte-cgqsww"&&(Hn.innerHTML=__),te.forEach(i),Jl=r(Z),xe=s(Z,"DIV",{class:!0});var Qe=v(xe);p(Yt.$$.fragment,Qe),Zl=r(Qe),In=s(Qe,"P",{"data-svelte-h":!0}),f(In)!=="svelte-flusvq"&&(In.textContent=u_),Yl=r(Qe),p(_o.$$.fragment,Qe),Qe.forEach(i),Ql=r(Z),uo=s(Z,"DIV",{class:!0});var nn=v(uo);p(Qt.$$.fragment,nn),Kl=r(nn),Vn=s(nn,"P",{"data-svelte-h":!0}),f(Vn)!=="svelte-1ufq5ot"&&(Vn.textContent=h_),nn.forEach(i),Z.forEach(i),dd=r(e),p(Kt.$$.fragment,e),ld=r(e),k=s(e,"DIV",{class:!0});var P=v(k);p(er.$$.fragment,P),ec=r(P),Wn=s(P,"P",{"data-svelte-h":!0}),f(Wn)!=="svelte-1cvkxig"&&(Wn.innerHTML=g_),oc=r(P),Fn=s(P,"P",{"data-svelte-h":!0}),f(Fn)!=="svelte-1glmuti"&&(Fn.innerHTML=L_),tc=r(P),ho=s(P,"DIV",{class:!0});var sn=v(ho);p(or.$$.fragment,sn),rc=r(sn),Nn=s(sn,"P",{"data-svelte-h":!0}),f(Nn)!=="svelte-1062ci4"&&(Nn.innerHTML=x_),sn.forEach(i),ac=r(P),go=s(P,"DIV",{class:!0});var dn=v(go);p(tr.$$.fragment,dn),nc=r(dn),En=s(dn,"P",{"data-svelte-h":!0}),f(En)!=="svelte-1lgbsz7"&&(En.innerHTML=b_),dn.forEach(i),sc=r(P),re=s(P,"DIV",{class:!0});var _e=v(re);p(rr.$$.fragment,_e),ic=r(_e),Un=s(_e,"P",{"data-svelte-h":!0}),f(Un)!=="svelte-vs7s0z"&&(Un.innerHTML=v_),dc=r(_e),zn=s(_e,"P",{"data-svelte-h":!0}),f(zn)!=="svelte-15b960v"&&(zn.innerHTML=w_),lc=r(_e),Xn=s(_e,"P",{"data-svelte-h":!0}),f(Xn)!=="svelte-18r91jl"&&(Xn.innerHTML=$_),cc=r(_e),qn=s(_e,"P",{"data-svelte-h":!0}),f(qn)!=="svelte-1ukghd4"&&(qn.innerHTML=y_),_e.forEach(i),fc=r(P),be=s(P,"DIV",{class:!0});var Ke=v(be);p(ar.$$.fragment,Ke),mc=r(Ke),Bn=s(Ke,"P",{"data-svelte-h":!0}),f(Bn)!=="svelte-flusvq"&&(Bn.textContent=M_),pc=r(Ke),p(Lo.$$.fragment,Ke),Ke.forEach(i),_c=r(P),xo=s(P,"DIV",{class:!0});var ln=v(xo);p(nr.$$.fragment,ln),uc=r(ln),jn=s(ln,"P",{"data-svelte-h":!0}),f(jn)!=="svelte-1ufq5ot"&&(jn.textContent=T_),ln.forEach(i),hc=r(P),ve=s(P,"DIV",{class:!0});var eo=v(ve);p(sr.$$.fragment,eo),gc=r(eo),Gn=s(eo,"P",{"data-svelte-h":!0}),f(Gn)!=="svelte-ioswce"&&(Gn.innerHTML=D_),Lc=r(eo),p(bo.$$.fragment,eo),eo.forEach(i),P.forEach(i),cd=r(e),p(ir.$$.fragment,e),fd=r(e),C=s(e,"DIV",{class:!0});var R=v(C);p(dr.$$.fragment,R),xc=r(R),On=s(R,"P",{"data-svelte-h":!0}),f(On)!=="svelte-1irbhz9"&&(On.innerHTML=A_),bc=r(R),Jn=s(R,"P",{"data-svelte-h":!0}),f(Jn)!=="svelte-1glmuti"&&(Jn.innerHTML=C_),vc=r(R),vo=s(R,"DIV",{class:!0});var cn=v(vo);p(lr.$$.fragment,cn),wc=r(cn),Zn=s(cn,"P",{"data-svelte-h":!0}),f(Zn)!=="svelte-1062ci4"&&(Zn.innerHTML=S_),cn.forEach(i),$c=r(R),wo=s(R,"DIV",{class:!0});var fn=v(wo);p(cr.$$.fragment,fn),yc=r(fn),Yn=s(fn,"P",{"data-svelte-h":!0}),f(Yn)!=="svelte-1lgbsz7"&&(Yn.innerHTML=k_),fn.forEach(i),Mc=r(R),ae=s(R,"DIV",{class:!0});var ue=v(ae);p(fr.$$.fragment,ue),Tc=r(ue),Qn=s(ue,"P",{"data-svelte-h":!0}),f(Qn)!=="svelte-178gcly"&&(Qn.innerHTML=R_),Dc=r(ue),Kn=s(ue,"P",{"data-svelte-h":!0}),f(Kn)!=="svelte-15b960v"&&(Kn.innerHTML=P_),Ac=r(ue),es=s(ue,"P",{"data-svelte-h":!0}),f(es)!=="svelte-18r91jl"&&(es.innerHTML=H_),Cc=r(ue),os=s(ue,"P",{"data-svelte-h":!0}),f(os)!=="svelte-1ukghd4"&&(os.innerHTML=I_),ue.forEach(i),Sc=r(R),we=s(R,"DIV",{class:!0});var oo=v(we);p(mr.$$.fragment,oo),kc=r(oo),ts=s(oo,"P",{"data-svelte-h":!0}),f(ts)!=="svelte-flusvq"&&(ts.textContent=V_),Rc=r(oo),p($o.$$.fragment,oo),oo.forEach(i),Pc=r(R),yo=s(R,"DIV",{class:!0});var mn=v(yo);p(pr.$$.fragment,mn),Hc=r(mn),rs=s(mn,"P",{"data-svelte-h":!0}),f(rs)!=="svelte-1ufq5ot"&&(rs.textContent=W_),mn.forEach(i),Ic=r(R),$e=s(R,"DIV",{class:!0});var to=v($e);p(_r.$$.fragment,to),Vc=r(to),as=s(to,"P",{"data-svelte-h":!0}),f(as)!=="svelte-ioswce"&&(as.innerHTML=F_),Wc=r(to),p(Mo.$$.fragment,to),to.forEach(i),Fc=r(R),ye=s(R,"DIV",{class:!0});var ro=v(ye);p(ur.$$.fragment,ro),Nc=r(ro),ns=s(ro,"P",{"data-svelte-h":!0}),f(ns)!=="svelte-119cgd9"&&(ns.textContent=N_),Ec=r(ro),p(To.$$.fragment,ro),ro.forEach(i),R.forEach(i),md=r(e),p(hr.$$.fragment,e),pd=r(e),W=s(e,"DIV",{class:!0});var Y=v(W);p(gr.$$.fragment,Y),Uc=r(Y),ss=s(Y,"P",{"data-svelte-h":!0}),f(ss)!=="svelte-1o0hzap"&&(ss.innerHTML=E_),zc=r(Y),Do=s(Y,"DIV",{class:!0});var pn=v(Do);p(Lr.$$.fragment,pn),Xc=r(pn),is=s(pn,"P",{"data-svelte-h":!0}),f(is)!=="svelte-1lgbsz7"&&(is.innerHTML=U_),pn.forEach(i),qc=r(Y),Ao=s(Y,"DIV",{class:!0});var _n=v(Ao);p(xr.$$.fragment,_n),Bc=r(_n),ds=s(_n,"P",{"data-svelte-h":!0}),f(ds)!=="svelte-1ciyxu4"&&(ds.innerHTML=z_),_n.forEach(i),jc=r(Y),Me=s(Y,"DIV",{class:!0});var ao=v(Me);p(br.$$.fragment,ao),Gc=r(ao),ls=s(ao,"P",{"data-svelte-h":!0}),f(ls)!=="svelte-flusvq"&&(ls.textContent=X_),Oc=r(ao),p(Co.$$.fragment,ao),ao.forEach(i),Jc=r(Y),So=s(Y,"DIV",{class:!0});var un=v(So);p(vr.$$.fragment,un),Zc=r(un),cs=s(un,"P",{"data-svelte-h":!0}),f(cs)!=="svelte-1gl6t2c"&&(cs.textContent=q_),un.forEach(i),Yc=r(Y),Te=s(Y,"DIV",{class:!0});var no=v(Te);p(wr.$$.fragment,no),Qc=r(no),fs=s(no,"P",{"data-svelte-h":!0}),f(fs)!=="svelte-ioswce"&&(fs.innerHTML=B_),Kc=r(no),p(ko.$$.fragment,no),no.forEach(i),Y.forEach(i),_d=r(e),p($r.$$.fragment,e),ud=r(e),F=s(e,"DIV",{class:!0});var Q=v(F);p(yr.$$.fragment,Q),ef=r(Q),ms=s(Q,"P",{"data-svelte-h":!0}),f(ms)!=="svelte-1qtk6tp"&&(ms.innerHTML=j_),of=r(Q),Ro=s(Q,"DIV",{class:!0});var hn=v(Ro);p(Mr.$$.fragment,hn),tf=r(hn),ps=s(hn,"P",{"data-svelte-h":!0}),f(ps)!=="svelte-1lgbsz7"&&(ps.innerHTML=G_),hn.forEach(i),rf=r(Q),Po=s(Q,"DIV",{class:!0});var Ud=v(Po);p(Tr.$$.fragment,Ud),af=r(Ud),_s=s(Ud,"P",{"data-svelte-h":!0}),f(_s)!=="svelte-1ciyxu4"&&(_s.innerHTML=O_),Ud.forEach(i),nf=r(Q),De=s(Q,"DIV",{class:!0});var Si=v(De);p(Dr.$$.fragment,Si),sf=r(Si),us=s(Si,"P",{"data-svelte-h":!0}),f(us)!=="svelte-flusvq"&&(us.textContent=J_),df=r(Si),p(Ho.$$.fragment,Si),Si.forEach(i),lf=r(Q),Io=s(Q,"DIV",{class:!0});var zd=v(Io);p(Ar.$$.fragment,zd),cf=r(zd),hs=s(zd,"P",{"data-svelte-h":!0}),f(hs)!=="svelte-1gl6t2c"&&(hs.textContent=Z_),zd.forEach(i),ff=r(Q),Ae=s(Q,"DIV",{class:!0});var ki=v(Ae);p(Cr.$$.fragment,ki),mf=r(ki),gs=s(ki,"P",{"data-svelte-h":!0}),f(gs)!=="svelte-ioswce"&&(gs.innerHTML=Y_),pf=r(ki),p(Vo.$$.fragment,ki),ki.forEach(i),Q.forEach(i),hd=r(e),p(Sr.$$.fragment,e),gd=r(e),N=s(e,"DIV",{class:!0});var ne=v(N);p(kr.$$.fragment,ne),_f=r(ne),Ls=s(ne,"P",{"data-svelte-h":!0}),f(Ls)!=="svelte-1plz0co"&&(Ls.innerHTML=Q_),uf=r(ne),Wo=s(ne,"DIV",{class:!0});var Xd=v(Wo);p(Rr.$$.fragment,Xd),hf=r(Xd),xs=s(Xd,"P",{"data-svelte-h":!0}),f(xs)!=="svelte-1lgbsz7"&&(xs.innerHTML=K_),Xd.forEach(i),gf=r(ne),Fo=s(ne,"DIV",{class:!0});var qd=v(Fo);p(Pr.$$.fragment,qd),Lf=r(qd),bs=s(qd,"P",{"data-svelte-h":!0}),f(bs)!=="svelte-1ciyxu4"&&(bs.innerHTML=eu),qd.forEach(i),xf=r(ne),Ce=s(ne,"DIV",{class:!0});var Ri=v(Ce);p(Hr.$$.fragment,Ri),bf=r(Ri),vs=s(Ri,"P",{"data-svelte-h":!0}),f(vs)!=="svelte-flusvq"&&(vs.textContent=ou),vf=r(Ri),p(No.$$.fragment,Ri),Ri.forEach(i),wf=r(ne),Eo=s(ne,"DIV",{class:!0});var Bd=v(Eo);p(Ir.$$.fragment,Bd),$f=r(Bd),ws=s(Bd,"P",{"data-svelte-h":!0}),f(ws)!=="svelte-1gl6t2c"&&(ws.textContent=tu),Bd.forEach(i),yf=r(ne),Se=s(ne,"DIV",{class:!0});var Pi=v(Se);p(Vr.$$.fragment,Pi),Mf=r(Pi),$s=s(Pi,"P",{"data-svelte-h":!0}),f($s)!=="svelte-ioswce"&&($s.innerHTML=ru),Tf=r(Pi),p(Uo.$$.fragment,Pi),Pi.forEach(i),ne.forEach(i),Ld=r(e),p(Wr.$$.fragment,e),xd=r(e),E=s(e,"DIV",{class:!0});var se=v(E);p(Fr.$$.fragment,se),Df=r(se),ys=s(se,"P",{"data-svelte-h":!0}),f(ys)!=="svelte-m7t6up"&&(ys.innerHTML=au),Af=r(se),zo=s(se,"DIV",{class:!0});var jd=v(zo);p(Nr.$$.fragment,jd),Cf=r(jd),Ms=s(jd,"P",{"data-svelte-h":!0}),f(Ms)!=="svelte-1lgbsz7"&&(Ms.innerHTML=nu),jd.forEach(i),Sf=r(se),Xo=s(se,"DIV",{class:!0});var Gd=v(Xo);p(Er.$$.fragment,Gd),kf=r(Gd),Ts=s(Gd,"P",{"data-svelte-h":!0}),f(Ts)!=="svelte-1ciyxu4"&&(Ts.innerHTML=su),Gd.forEach(i),Rf=r(se),ke=s(se,"DIV",{class:!0});var Hi=v(ke);p(Ur.$$.fragment,Hi),Pf=r(Hi),Ds=s(Hi,"P",{"data-svelte-h":!0}),f(Ds)!=="svelte-flusvq"&&(Ds.textContent=iu),Hf=r(Hi),p(qo.$$.fragment,Hi),Hi.forEach(i),If=r(se),Bo=s(se,"DIV",{class:!0});var Od=v(Bo);p(zr.$$.fragment,Od),Vf=r(Od),As=s(Od,"P",{"data-svelte-h":!0}),f(As)!=="svelte-1gl6t2c"&&(As.textContent=du),Od.forEach(i),Wf=r(se),Re=s(se,"DIV",{class:!0});var Ii=v(Re);p(Xr.$$.fragment,Ii),Ff=r(Ii),Cs=s(Ii,"P",{"data-svelte-h":!0}),f(Cs)!=="svelte-ioswce"&&(Cs.innerHTML=lu),Nf=r(Ii),p(jo.$$.fragment,Ii),Ii.forEach(i),se.forEach(i),bd=r(e),p(qr.$$.fragment,e),vd=r(e),U=s(e,"DIV",{class:!0});var ie=v(U);p(Br.$$.fragment,ie),Ef=r(ie),Ss=s(ie,"P",{"data-svelte-h":!0}),f(Ss)!=="svelte-1hgo1j4"&&(Ss.innerHTML=cu),Uf=r(ie),Go=s(ie,"DIV",{class:!0});var Jd=v(Go);p(jr.$$.fragment,Jd),zf=r(Jd),ks=s(Jd,"P",{"data-svelte-h":!0}),f(ks)!=="svelte-1lgbsz7"&&(ks.innerHTML=fu),Jd.forEach(i),Xf=r(ie),Oo=s(ie,"DIV",{class:!0});var Zd=v(Oo);p(Gr.$$.fragment,Zd),qf=r(Zd),Rs=s(Zd,"P",{"data-svelte-h":!0}),f(Rs)!=="svelte-1ciyxu4"&&(Rs.innerHTML=mu),Zd.forEach(i),Bf=r(ie),Pe=s(ie,"DIV",{class:!0});var Vi=v(Pe);p(Or.$$.fragment,Vi),jf=r(Vi),Ps=s(Vi,"P",{"data-svelte-h":!0}),f(Ps)!=="svelte-flusvq"&&(Ps.textContent=pu),Gf=r(Vi),p(Jo.$$.fragment,Vi),Vi.forEach(i),Of=r(ie),Zo=s(ie,"DIV",{class:!0});var Yd=v(Zo);p(Jr.$$.fragment,Yd),Jf=r(Yd),Hs=s(Yd,"P",{"data-svelte-h":!0}),f(Hs)!=="svelte-1gl6t2c"&&(Hs.textContent=_u),Yd.forEach(i),Zf=r(ie),He=s(ie,"DIV",{class:!0});var Wi=v(He);p(Zr.$$.fragment,Wi),Yf=r(Wi),Is=s(Wi,"P",{"data-svelte-h":!0}),f(Is)!=="svelte-ioswce"&&(Is.innerHTML=uu),Qf=r(Wi),p(Yo.$$.fragment,Wi),Wi.forEach(i),ie.forEach(i),wd=r(e),p(Yr.$$.fragment,e),$d=r(e),z=s(e,"DIV",{class:!0});var de=v(z);p(Qr.$$.fragment,de),Kf=r(de),Vs=s(de,"P",{"data-svelte-h":!0}),f(Vs)!=="svelte-b4h7p0"&&(Vs.innerHTML=hu),em=r(de),Qo=s(de,"DIV",{class:!0});var Qd=v(Qo);p(Kr.$$.fragment,Qd),om=r(Qd),Ws=s(Qd,"P",{"data-svelte-h":!0}),f(Ws)!=="svelte-1lgbsz7"&&(Ws.innerHTML=gu),Qd.forEach(i),tm=r(de),Ko=s(de,"DIV",{class:!0});var Kd=v(Ko);p(ea.$$.fragment,Kd),rm=r(Kd),Fs=s(Kd,"P",{"data-svelte-h":!0}),f(Fs)!=="svelte-1ciyxu4"&&(Fs.innerHTML=Lu),Kd.forEach(i),am=r(de),Ie=s(de,"DIV",{class:!0});var Fi=v(Ie);p(oa.$$.fragment,Fi),nm=r(Fi),Ns=s(Fi,"P",{"data-svelte-h":!0}),f(Ns)!=="svelte-flusvq"&&(Ns.textContent=xu),sm=r(Fi),p(et.$$.fragment,Fi),Fi.forEach(i),im=r(de),ot=s(de,"DIV",{class:!0});var el=v(ot);p(ta.$$.fragment,el),dm=r(el),Es=s(el,"P",{"data-svelte-h":!0}),f(Es)!=="svelte-1gl6t2c"&&(Es.textContent=bu),el.forEach(i),lm=r(de),Ve=s(de,"DIV",{class:!0});var Ni=v(Ve);p(ra.$$.fragment,Ni),cm=r(Ni),Us=s(Ni,"P",{"data-svelte-h":!0}),f(Us)!=="svelte-ioswce"&&(Us.innerHTML=vu),fm=r(Ni),p(tt.$$.fragment,Ni),Ni.forEach(i),de.forEach(i),yd=r(e),p(aa.$$.fragment,e),Md=r(e),X=s(e,"DIV",{class:!0});var le=v(X);p(na.$$.fragment,le),mm=r(le),zs=s(le,"P",{"data-svelte-h":!0}),f(zs)!=="svelte-vt2ekb"&&(zs.innerHTML=wu),pm=r(le),rt=s(le,"DIV",{class:!0});var ol=v(rt);p(sa.$$.fragment,ol),_m=r(ol),Xs=s(ol,"P",{"data-svelte-h":!0}),f(Xs)!=="svelte-1lgbsz7"&&(Xs.innerHTML=$u),ol.forEach(i),um=r(le),at=s(le,"DIV",{class:!0});var tl=v(at);p(ia.$$.fragment,tl),hm=r(tl),qs=s(tl,"P",{"data-svelte-h":!0}),f(qs)!=="svelte-1ciyxu4"&&(qs.innerHTML=yu),tl.forEach(i),gm=r(le),We=s(le,"DIV",{class:!0});var Ei=v(We);p(da.$$.fragment,Ei),Lm=r(Ei),Bs=s(Ei,"P",{"data-svelte-h":!0}),f(Bs)!=="svelte-flusvq"&&(Bs.textContent=Mu),xm=r(Ei),p(nt.$$.fragment,Ei),Ei.forEach(i),bm=r(le),st=s(le,"DIV",{class:!0});var rl=v(st);p(la.$$.fragment,rl),vm=r(rl),js=s(rl,"P",{"data-svelte-h":!0}),f(js)!=="svelte-1gl6t2c"&&(js.textContent=Tu),rl.forEach(i),wm=r(le),Fe=s(le,"DIV",{class:!0});var Ui=v(Fe);p(ca.$$.fragment,Ui),$m=r(Ui),Gs=s(Ui,"P",{"data-svelte-h":!0}),f(Gs)!=="svelte-ioswce"&&(Gs.innerHTML=Du),ym=r(Ui),p(it.$$.fragment,Ui),Ui.forEach(i),le.forEach(i),Td=r(e),p(fa.$$.fragment,e),Dd=r(e),q=s(e,"DIV",{class:!0});var ce=v(q);p(ma.$$.fragment,ce),Mm=r(ce),Os=s(ce,"P",{"data-svelte-h":!0}),f(Os)!=="svelte-1x7harm"&&(Os.innerHTML=Au),Tm=r(ce),dt=s(ce,"DIV",{class:!0});var al=v(dt);p(pa.$$.fragment,al),Dm=r(al),Js=s(al,"P",{"data-svelte-h":!0}),f(Js)!=="svelte-1lgbsz7"&&(Js.innerHTML=Cu),al.forEach(i),Am=r(ce),lt=s(ce,"DIV",{class:!0});var nl=v(lt);p(_a.$$.fragment,nl),Cm=r(nl),Zs=s(nl,"P",{"data-svelte-h":!0}),f(Zs)!=="svelte-1ciyxu4"&&(Zs.innerHTML=Su),nl.forEach(i),Sm=r(ce),Ne=s(ce,"DIV",{class:!0});var zi=v(Ne);p(ua.$$.fragment,zi),km=r(zi),Ys=s(zi,"P",{"data-svelte-h":!0}),f(Ys)!=="svelte-flusvq"&&(Ys.textContent=ku),Rm=r(zi),p(ct.$$.fragment,zi),zi.forEach(i),Pm=r(ce),ft=s(ce,"DIV",{class:!0});var sl=v(ft);p(ha.$$.fragment,sl),Hm=r(sl),Qs=s(sl,"P",{"data-svelte-h":!0}),f(Qs)!=="svelte-1gl6t2c"&&(Qs.textContent=Ru),sl.forEach(i),Im=r(ce),Ee=s(ce,"DIV",{class:!0});var Xi=v(Ee);p(ga.$$.fragment,Xi),Vm=r(Xi),Ks=s(Xi,"P",{"data-svelte-h":!0}),f(Ks)!=="svelte-ioswce"&&(Ks.innerHTML=Pu),Wm=r(Xi),p(mt.$$.fragment,Xi),Xi.forEach(i),ce.forEach(i),Ad=r(e),p(La.$$.fragment,e),Cd=r(e),B=s(e,"DIV",{class:!0});var fe=v(B);p(xa.$$.fragment,fe),Fm=r(fe),ei=s(fe,"P",{"data-svelte-h":!0}),f(ei)!=="svelte-97yy4x"&&(ei.innerHTML=Hu),Nm=r(fe),pt=s(fe,"DIV",{class:!0});var il=v(pt);p(ba.$$.fragment,il),Em=r(il),oi=s(il,"P",{"data-svelte-h":!0}),f(oi)!=="svelte-1lgbsz7"&&(oi.innerHTML=Iu),il.forEach(i),Um=r(fe),_t=s(fe,"DIV",{class:!0});var dl=v(_t);p(va.$$.fragment,dl),zm=r(dl),ti=s(dl,"P",{"data-svelte-h":!0}),f(ti)!=="svelte-1ciyxu4"&&(ti.innerHTML=Vu),dl.forEach(i),Xm=r(fe),Ue=s(fe,"DIV",{class:!0});var qi=v(Ue);p(wa.$$.fragment,qi),qm=r(qi),ri=s(qi,"P",{"data-svelte-h":!0}),f(ri)!=="svelte-flusvq"&&(ri.textContent=Wu),Bm=r(qi),p(ut.$$.fragment,qi),qi.forEach(i),jm=r(fe),ht=s(fe,"DIV",{class:!0});var ll=v(ht);p($a.$$.fragment,ll),Gm=r(ll),ai=s(ll,"P",{"data-svelte-h":!0}),f(ai)!=="svelte-1gl6t2c"&&(ai.textContent=Fu),ll.forEach(i),Om=r(fe),ze=s(fe,"DIV",{class:!0});var Bi=v(ze);p(ya.$$.fragment,Bi),Jm=r(Bi),ni=s(Bi,"P",{"data-svelte-h":!0}),f(ni)!=="svelte-ioswce"&&(ni.innerHTML=Nu),Zm=r(Bi),p(gt.$$.fragment,Bi),Bi.forEach(i),fe.forEach(i),Sd=r(e),p(Ma.$$.fragment,e),kd=r(e),ge=s(e,"DIV",{class:!0});var ji=v(ge);p(Ta.$$.fragment,ji),Ym=r(ji),Lt=s(ji,"DIV",{class:!0});var cl=v(Lt);p(Da.$$.fragment,cl),Qm=r(cl),si=s(cl,"P",{"data-svelte-h":!0}),f(si)!=="svelte-1lgbsz7"&&(si.innerHTML=Eu),cl.forEach(i),Km=r(ji),xt=s(ji,"DIV",{class:!0});var fl=v(xt);p(Aa.$$.fragment,fl),ep=r(fl),ii=s(fl,"P",{"data-svelte-h":!0}),f(ii)!=="svelte-1ufq5ot"&&(ii.textContent=Uu),fl.forEach(i),ji.forEach(i),Rd=r(e),p(Ca.$$.fragment,e),Pd=r(e),j=s(e,"DIV",{class:!0});var me=v(j);p(Sa.$$.fragment,me),op=r(me),di=s(me,"P",{"data-svelte-h":!0}),f(di)!=="svelte-ows5s0"&&(di.innerHTML=zu),tp=r(me),bt=s(me,"DIV",{class:!0});var ml=v(bt);p(ka.$$.fragment,ml),rp=r(ml),li=s(ml,"P",{"data-svelte-h":!0}),f(li)!=="svelte-1lgbsz7"&&(li.innerHTML=Xu),ml.forEach(i),ap=r(me),vt=s(me,"DIV",{class:!0});var pl=v(vt);p(Ra.$$.fragment,pl),np=r(pl),ci=s(pl,"P",{"data-svelte-h":!0}),f(ci)!=="svelte-1ciyxu4"&&(ci.innerHTML=qu),pl.forEach(i),sp=r(me),Xe=s(me,"DIV",{class:!0});var Gi=v(Xe);p(Pa.$$.fragment,Gi),ip=r(Gi),fi=s(Gi,"P",{"data-svelte-h":!0}),f(fi)!=="svelte-flusvq"&&(fi.textContent=Bu),dp=r(Gi),p(wt.$$.fragment,Gi),Gi.forEach(i),lp=r(me),$t=s(me,"DIV",{class:!0});var _l=v($t);p(Ha.$$.fragment,_l),cp=r(_l),mi=s(_l,"P",{"data-svelte-h":!0}),f(mi)!=="svelte-1gl6t2c"&&(mi.textContent=ju),_l.forEach(i),fp=r(me),qe=s(me,"DIV",{class:!0});var Oi=v(qe);p(Ia.$$.fragment,Oi),mp=r(Oi),pi=s(Oi,"P",{"data-svelte-h":!0}),f(pi)!=="svelte-ioswce"&&(pi.innerHTML=Gu),pp=r(Oi),p(yt.$$.fragment,Oi),Oi.forEach(i),me.forEach(i),Hd=r(e),p(Va.$$.fragment,e),Id=r(e),S=s(e,"DIV",{class:!0});var H=v(S);p(Wa.$$.fragment,H),_p=r(H),_i=s(H,"P",{"data-svelte-h":!0}),f(_i)!=="svelte-1q4bbx"&&(_i.textContent=Ou),up=r(H),ui=s(H,"DIV",{class:!0});var lh=v(ui);p(Fa.$$.fragment,lh),lh.forEach(i),hp=r(H),Be=s(H,"DIV",{class:!0});var Ji=v(Be);p(Na.$$.fragment,Ji),gp=r(Ji),hi=s(Ji,"P",{"data-svelte-h":!0}),f(hi)!=="svelte-w22v11"&&(hi.textContent=Ju),Lp=r(Ji),gi=s(Ji,"P",{"data-svelte-h":!0}),f(gi)!=="svelte-zdadya"&&(gi.textContent=Zu),Ji.forEach(i),xp=r(H),he=s(H,"DIV",{class:!0});var Wt=v(he);p(Ea.$$.fragment,Wt),bp=r(Wt),Li=s(Wt,"P",{"data-svelte-h":!0}),f(Li)!=="svelte-1nr2dy0"&&(Li.textContent=Yu),vp=r(Wt),p(Mt.$$.fragment,Wt),wp=r(Wt),p(Tt.$$.fragment,Wt),Wt.forEach(i),$p=r(H),je=s(H,"DIV",{class:!0});var Zi=v(je);p(Ua.$$.fragment,Zi),yp=r(Zi),xi=s(Zi,"P",{"data-svelte-h":!0}),f(xi)!=="svelte-h0os0v"&&(xi.textContent=Qu),Mp=r(Zi),p(Dt.$$.fragment,Zi),Zi.forEach(i),Tp=r(H),At=s(H,"DIV",{class:!0});var ul=v(At);p(za.$$.fragment,ul),Dp=r(ul),bi=s(ul,"P",{"data-svelte-h":!0}),f(bi)!=="svelte-1825k9e"&&(bi.textContent=Ku),ul.forEach(i),Ap=r(H),Ct=s(H,"DIV",{class:!0});var hl=v(Ct);p(Xa.$$.fragment,hl),Cp=r(hl),vi=s(hl,"P",{"data-svelte-h":!0}),f(vi)!=="svelte-rvubqa"&&(vi.innerHTML=eh),hl.forEach(i),Sp=r(H),Ge=s(H,"DIV",{class:!0});var Yi=v(Ge);p(qa.$$.fragment,Yi),kp=r(Yi),wi=s(Yi,"P",{"data-svelte-h":!0}),f(wi)!=="svelte-ioswce"&&(wi.innerHTML=oh),Rp=r(Yi),p(St.$$.fragment,Yi),Yi.forEach(i),Pp=r(H),Oe=s(H,"DIV",{class:!0});var Qi=v(Oe);p(Ba.$$.fragment,Qi),Hp=r(Qi),$i=s(Qi,"P",{"data-svelte-h":!0}),f($i)!=="svelte-119cgd9"&&($i.textContent=th),Ip=r(Qi),p(kt.$$.fragment,Qi),Qi.forEach(i),H.forEach(i),Vd=r(e),p(ja.$$.fragment,e),Wd=r(e),G=s(e,"DIV",{class:!0});var pe=v(G);p(Ga.$$.fragment,pe),Vp=r(pe),yi=s(pe,"P",{"data-svelte-h":!0}),f(yi)!=="svelte-97yy4x"&&(yi.innerHTML=rh),Wp=r(pe),Rt=s(pe,"DIV",{class:!0});var gl=v(Rt);p(Oa.$$.fragment,gl),Fp=r(gl),Mi=s(gl,"P",{"data-svelte-h":!0}),f(Mi)!=="svelte-1lgbsz7"&&(Mi.innerHTML=ah),gl.forEach(i),Np=r(pe),Pt=s(pe,"DIV",{class:!0});var Ll=v(Pt);p(Ja.$$.fragment,Ll),Ep=r(Ll),Ti=s(Ll,"P",{"data-svelte-h":!0}),f(Ti)!=="svelte-1ciyxu4"&&(Ti.innerHTML=nh),Ll.forEach(i),Up=r(pe),Je=s(pe,"DIV",{class:!0});var Ki=v(Je);p(Za.$$.fragment,Ki),zp=r(Ki),Di=s(Ki,"P",{"data-svelte-h":!0}),f(Di)!=="svelte-flusvq"&&(Di.textContent=sh),Xp=r(Ki),p(Ht.$$.fragment,Ki),Ki.forEach(i),qp=r(pe),It=s(pe,"DIV",{class:!0});var xl=v(It);p(Ya.$$.fragment,xl),Bp=r(xl),Ai=s(xl,"P",{"data-svelte-h":!0}),f(Ai)!=="svelte-1gl6t2c"&&(Ai.textContent=ih),xl.forEach(i),jp=r(pe),Ze=s(pe,"DIV",{class:!0});var ed=v(Ze);p(Qa.$$.fragment,ed),Gp=r(ed),Ci=s(ed,"P",{"data-svelte-h":!0}),f(Ci)!=="svelte-ioswce"&&(Ci.innerHTML=dh),Op=r(ed),p(Vt.$$.fragment,ed),ed.forEach(i),pe.forEach(i),Fd=r(e),p(Ka.$$.fragment,e),Nd=r(e),od=s(e,"P",{}),v(od).forEach(i),this.h()},h(){w(a,"name","hf:doc:metadata"),w(a,"content",Qh),w(io,"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(K,"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(fo,"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(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(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(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(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(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(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(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(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(re,"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(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(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(k,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(vo,"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(ae,"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(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($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(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(C,"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(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(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(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(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(W,"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(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(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(Io,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ae,"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(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(Fo,"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(Eo,"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(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(zo,"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(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(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(Re,"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(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(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(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(Zo,"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(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(Qo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ko,"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(ot,"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(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(rt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(at,"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(st,"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(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(dt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(lt,"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(ft,"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(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(pt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(_t,"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(ht,"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(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(Lt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(xt,"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(bt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(vt,"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($t,"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(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(ui,"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(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(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(At,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ct,"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(Oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Rt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Pt,"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(It,"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(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,L){o(document.head,a),x(e,b,L),x(e,l,L),x(e,c,L),_(T,e,L),x(e,d,L),x(e,y,L),x(e,td,L),x(e,Ft,L),x(e,rd,L),_(so,e,L),x(e,ad,L),_(Nt,e,L),x(e,nd,L),x(e,I,L),_(Et,I,null),o(I,wl),o(I,gn),o(I,$l),o(I,io),_(Ut,io,null),o(io,yl),o(io,Ln),o(I,Ml),o(I,lo),_(zt,lo,null),o(lo,Tl),o(lo,xn),o(I,Dl),o(I,K),_(Xt,K,null),o(K,Al),o(K,bn),o(K,Cl),o(K,vn),o(K,Sl),o(K,wn),o(K,kl),o(K,$n),o(K,Rl),o(K,yn),o(I,Pl),o(I,Le),_(qt,Le,null),o(Le,Hl),o(Le,Mn),o(Le,Il),_(co,Le,null),o(I,Vl),o(I,fo),_(Bt,fo,null),o(fo,Wl),o(fo,Tn),x(e,sd,L),_(jt,e,L),x(e,id,L),x(e,V,L),_(Gt,V,null),o(V,Fl),o(V,Dn),o(V,Nl),o(V,mo),_(Ot,mo,null),o(mo,El),o(mo,An),o(V,Ul),o(V,po),_(Jt,po,null),o(po,zl),o(po,Cn),o(V,Xl),o(V,ee),_(Zt,ee,null),o(ee,ql),o(ee,Sn),o(ee,Bl),o(ee,kn),o(ee,jl),o(ee,Rn),o(ee,Gl),o(ee,Pn),o(ee,Ol),o(ee,Hn),o(V,Jl),o(V,xe),_(Yt,xe,null),o(xe,Zl),o(xe,In),o(xe,Yl),_(_o,xe,null),o(V,Ql),o(V,uo),_(Qt,uo,null),o(uo,Kl),o(uo,Vn),x(e,dd,L),_(Kt,e,L),x(e,ld,L),x(e,k,L),_(er,k,null),o(k,ec),o(k,Wn),o(k,oc),o(k,Fn),o(k,tc),o(k,ho),_(or,ho,null),o(ho,rc),o(ho,Nn),o(k,ac),o(k,go),_(tr,go,null),o(go,nc),o(go,En),o(k,sc),o(k,re),_(rr,re,null),o(re,ic),o(re,Un),o(re,dc),o(re,zn),o(re,lc),o(re,Xn),o(re,cc),o(re,qn),o(k,fc),o(k,be),_(ar,be,null),o(be,mc),o(be,Bn),o(be,pc),_(Lo,be,null),o(k,_c),o(k,xo),_(nr,xo,null),o(xo,uc),o(xo,jn),o(k,hc),o(k,ve),_(sr,ve,null),o(ve,gc),o(ve,Gn),o(ve,Lc),_(bo,ve,null),x(e,cd,L),_(ir,e,L),x(e,fd,L),x(e,C,L),_(dr,C,null),o(C,xc),o(C,On),o(C,bc),o(C,Jn),o(C,vc),o(C,vo),_(lr,vo,null),o(vo,wc),o(vo,Zn),o(C,$c),o(C,wo),_(cr,wo,null),o(wo,yc),o(wo,Yn),o(C,Mc),o(C,ae),_(fr,ae,null),o(ae,Tc),o(ae,Qn),o(ae,Dc),o(ae,Kn),o(ae,Ac),o(ae,es),o(ae,Cc),o(ae,os),o(C,Sc),o(C,we),_(mr,we,null),o(we,kc),o(we,ts),o(we,Rc),_($o,we,null),o(C,Pc),o(C,yo),_(pr,yo,null),o(yo,Hc),o(yo,rs),o(C,Ic),o(C,$e),_(_r,$e,null),o($e,Vc),o($e,as),o($e,Wc),_(Mo,$e,null),o(C,Fc),o(C,ye),_(ur,ye,null),o(ye,Nc),o(ye,ns),o(ye,Ec),_(To,ye,null),x(e,md,L),_(hr,e,L),x(e,pd,L),x(e,W,L),_(gr,W,null),o(W,Uc),o(W,ss),o(W,zc),o(W,Do),_(Lr,Do,null),o(Do,Xc),o(Do,is),o(W,qc),o(W,Ao),_(xr,Ao,null),o(Ao,Bc),o(Ao,ds),o(W,jc),o(W,Me),_(br,Me,null),o(Me,Gc),o(Me,ls),o(Me,Oc),_(Co,Me,null),o(W,Jc),o(W,So),_(vr,So,null),o(So,Zc),o(So,cs),o(W,Yc),o(W,Te),_(wr,Te,null),o(Te,Qc),o(Te,fs),o(Te,Kc),_(ko,Te,null),x(e,_d,L),_($r,e,L),x(e,ud,L),x(e,F,L),_(yr,F,null),o(F,ef),o(F,ms),o(F,of),o(F,Ro),_(Mr,Ro,null),o(Ro,tf),o(Ro,ps),o(F,rf),o(F,Po),_(Tr,Po,null),o(Po,af),o(Po,_s),o(F,nf),o(F,De),_(Dr,De,null),o(De,sf),o(De,us),o(De,df),_(Ho,De,null),o(F,lf),o(F,Io),_(Ar,Io,null),o(Io,cf),o(Io,hs),o(F,ff),o(F,Ae),_(Cr,Ae,null),o(Ae,mf),o(Ae,gs),o(Ae,pf),_(Vo,Ae,null),x(e,hd,L),_(Sr,e,L),x(e,gd,L),x(e,N,L),_(kr,N,null),o(N,_f),o(N,Ls),o(N,uf),o(N,Wo),_(Rr,Wo,null),o(Wo,hf),o(Wo,xs),o(N,gf),o(N,Fo),_(Pr,Fo,null),o(Fo,Lf),o(Fo,bs),o(N,xf),o(N,Ce),_(Hr,Ce,null),o(Ce,bf),o(Ce,vs),o(Ce,vf),_(No,Ce,null),o(N,wf),o(N,Eo),_(Ir,Eo,null),o(Eo,$f),o(Eo,ws),o(N,yf),o(N,Se),_(Vr,Se,null),o(Se,Mf),o(Se,$s),o(Se,Tf),_(Uo,Se,null),x(e,Ld,L),_(Wr,e,L),x(e,xd,L),x(e,E,L),_(Fr,E,null),o(E,Df),o(E,ys),o(E,Af),o(E,zo),_(Nr,zo,null),o(zo,Cf),o(zo,Ms),o(E,Sf),o(E,Xo),_(Er,Xo,null),o(Xo,kf),o(Xo,Ts),o(E,Rf),o(E,ke),_(Ur,ke,null),o(ke,Pf),o(ke,Ds),o(ke,Hf),_(qo,ke,null),o(E,If),o(E,Bo),_(zr,Bo,null),o(Bo,Vf),o(Bo,As),o(E,Wf),o(E,Re),_(Xr,Re,null),o(Re,Ff),o(Re,Cs),o(Re,Nf),_(jo,Re,null),x(e,bd,L),_(qr,e,L),x(e,vd,L),x(e,U,L),_(Br,U,null),o(U,Ef),o(U,Ss),o(U,Uf),o(U,Go),_(jr,Go,null),o(Go,zf),o(Go,ks),o(U,Xf),o(U,Oo),_(Gr,Oo,null),o(Oo,qf),o(Oo,Rs),o(U,Bf),o(U,Pe),_(Or,Pe,null),o(Pe,jf),o(Pe,Ps),o(Pe,Gf),_(Jo,Pe,null),o(U,Of),o(U,Zo),_(Jr,Zo,null),o(Zo,Jf),o(Zo,Hs),o(U,Zf),o(U,He),_(Zr,He,null),o(He,Yf),o(He,Is),o(He,Qf),_(Yo,He,null),x(e,wd,L),_(Yr,e,L),x(e,$d,L),x(e,z,L),_(Qr,z,null),o(z,Kf),o(z,Vs),o(z,em),o(z,Qo),_(Kr,Qo,null),o(Qo,om),o(Qo,Ws),o(z,tm),o(z,Ko),_(ea,Ko,null),o(Ko,rm),o(Ko,Fs),o(z,am),o(z,Ie),_(oa,Ie,null),o(Ie,nm),o(Ie,Ns),o(Ie,sm),_(et,Ie,null),o(z,im),o(z,ot),_(ta,ot,null),o(ot,dm),o(ot,Es),o(z,lm),o(z,Ve),_(ra,Ve,null),o(Ve,cm),o(Ve,Us),o(Ve,fm),_(tt,Ve,null),x(e,yd,L),_(aa,e,L),x(e,Md,L),x(e,X,L),_(na,X,null),o(X,mm),o(X,zs),o(X,pm),o(X,rt),_(sa,rt,null),o(rt,_m),o(rt,Xs),o(X,um),o(X,at),_(ia,at,null),o(at,hm),o(at,qs),o(X,gm),o(X,We),_(da,We,null),o(We,Lm),o(We,Bs),o(We,xm),_(nt,We,null),o(X,bm),o(X,st),_(la,st,null),o(st,vm),o(st,js),o(X,wm),o(X,Fe),_(ca,Fe,null),o(Fe,$m),o(Fe,Gs),o(Fe,ym),_(it,Fe,null),x(e,Td,L),_(fa,e,L),x(e,Dd,L),x(e,q,L),_(ma,q,null),o(q,Mm),o(q,Os),o(q,Tm),o(q,dt),_(pa,dt,null),o(dt,Dm),o(dt,Js),o(q,Am),o(q,lt),_(_a,lt,null),o(lt,Cm),o(lt,Zs),o(q,Sm),o(q,Ne),_(ua,Ne,null),o(Ne,km),o(Ne,Ys),o(Ne,Rm),_(ct,Ne,null),o(q,Pm),o(q,ft),_(ha,ft,null),o(ft,Hm),o(ft,Qs),o(q,Im),o(q,Ee),_(ga,Ee,null),o(Ee,Vm),o(Ee,Ks),o(Ee,Wm),_(mt,Ee,null),x(e,Ad,L),_(La,e,L),x(e,Cd,L),x(e,B,L),_(xa,B,null),o(B,Fm),o(B,ei),o(B,Nm),o(B,pt),_(ba,pt,null),o(pt,Em),o(pt,oi),o(B,Um),o(B,_t),_(va,_t,null),o(_t,zm),o(_t,ti),o(B,Xm),o(B,Ue),_(wa,Ue,null),o(Ue,qm),o(Ue,ri),o(Ue,Bm),_(ut,Ue,null),o(B,jm),o(B,ht),_($a,ht,null),o(ht,Gm),o(ht,ai),o(B,Om),o(B,ze),_(ya,ze,null),o(ze,Jm),o(ze,ni),o(ze,Zm),_(gt,ze,null),x(e,Sd,L),_(Ma,e,L),x(e,kd,L),x(e,ge,L),_(Ta,ge,null),o(ge,Ym),o(ge,Lt),_(Da,Lt,null),o(Lt,Qm),o(Lt,si),o(ge,Km),o(ge,xt),_(Aa,xt,null),o(xt,ep),o(xt,ii),x(e,Rd,L),_(Ca,e,L),x(e,Pd,L),x(e,j,L),_(Sa,j,null),o(j,op),o(j,di),o(j,tp),o(j,bt),_(ka,bt,null),o(bt,rp),o(bt,li),o(j,ap),o(j,vt),_(Ra,vt,null),o(vt,np),o(vt,ci),o(j,sp),o(j,Xe),_(Pa,Xe,null),o(Xe,ip),o(Xe,fi),o(Xe,dp),_(wt,Xe,null),o(j,lp),o(j,$t),_(Ha,$t,null),o($t,cp),o($t,mi),o(j,fp),o(j,qe),_(Ia,qe,null),o(qe,mp),o(qe,pi),o(qe,pp),_(yt,qe,null),x(e,Hd,L),_(Va,e,L),x(e,Id,L),x(e,S,L),_(Wa,S,null),o(S,_p),o(S,_i),o(S,up),o(S,ui),_(Fa,ui,null),o(S,hp),o(S,Be),_(Na,Be,null),o(Be,gp),o(Be,hi),o(Be,Lp),o(Be,gi),o(S,xp),o(S,he),_(Ea,he,null),o(he,bp),o(he,Li),o(he,vp),_(Mt,he,null),o(he,wp),_(Tt,he,null),o(S,$p),o(S,je),_(Ua,je,null),o(je,yp),o(je,xi),o(je,Mp),_(Dt,je,null),o(S,Tp),o(S,At),_(za,At,null),o(At,Dp),o(At,bi),o(S,Ap),o(S,Ct),_(Xa,Ct,null),o(Ct,Cp),o(Ct,vi),o(S,Sp),o(S,Ge),_(qa,Ge,null),o(Ge,kp),o(Ge,wi),o(Ge,Rp),_(St,Ge,null),o(S,Pp),o(S,Oe),_(Ba,Oe,null),o(Oe,Hp),o(Oe,$i),o(Oe,Ip),_(kt,Oe,null),x(e,Vd,L),_(ja,e,L),x(e,Wd,L),x(e,G,L),_(Ga,G,null),o(G,Vp),o(G,yi),o(G,Wp),o(G,Rt),_(Oa,Rt,null),o(Rt,Fp),o(Rt,Mi),o(G,Np),o(G,Pt),_(Ja,Pt,null),o(Pt,Ep),o(Pt,Ti),o(G,Up),o(G,Je),_(Za,Je,null),o(Je,zp),o(Je,Di),o(Je,Xp),_(Ht,Je,null),o(G,qp),o(G,It),_(Ya,It,null),o(It,Bp),o(It,Ai),o(G,jp),o(G,Ze),_(Qa,Ze,null),o(Ze,Gp),o(Ze,Ci),o(Ze,Op),_(Vt,Ze,null),x(e,Fd,L),_(Ka,e,L),x(e,Nd,L),x(e,od,L),Ed=!0},p(e,[L]){const J={};L&2&&(J.$$scope={dirty:L,ctx:e}),so.$set(J);const en={};L&2&&(en.$$scope={dirty:L,ctx:e}),co.$set(en);const on={};L&2&&(on.$$scope={dirty:L,ctx:e}),_o.$set(on);const oe={};L&2&&(oe.$$scope={dirty:L,ctx:e}),Lo.$set(oe);const Ye={};L&2&&(Ye.$$scope={dirty:L,ctx:e}),bo.$set(Ye);const tn={};L&2&&(tn.$$scope={dirty:L,ctx:e}),$o.$set(tn);const Z={};L&2&&(Z.$$scope={dirty:L,ctx:e}),Mo.$set(Z);const rn={};L&2&&(rn.$$scope={dirty:L,ctx:e}),To.$set(rn);const an={};L&2&&(an.$$scope={dirty:L,ctx:e}),Co.$set(an);const te={};L&2&&(te.$$scope={dirty:L,ctx:e}),ko.$set(te);const Qe={};L&2&&(Qe.$$scope={dirty:L,ctx:e}),Ho.$set(Qe);const nn={};L&2&&(nn.$$scope={dirty:L,ctx:e}),Vo.$set(nn);const P={};L&2&&(P.$$scope={dirty:L,ctx:e}),No.$set(P);const sn={};L&2&&(sn.$$scope={dirty:L,ctx:e}),Uo.$set(sn);const dn={};L&2&&(dn.$$scope={dirty:L,ctx:e}),qo.$set(dn);const _e={};L&2&&(_e.$$scope={dirty:L,ctx:e}),jo.$set(_e);const Ke={};L&2&&(Ke.$$scope={dirty:L,ctx:e}),Jo.$set(Ke);const ln={};L&2&&(ln.$$scope={dirty:L,ctx:e}),Yo.$set(ln);const eo={};L&2&&(eo.$$scope={dirty:L,ctx:e}),et.$set(eo);const R={};L&2&&(R.$$scope={dirty:L,ctx:e}),tt.$set(R);const cn={};L&2&&(cn.$$scope={dirty:L,ctx:e}),nt.$set(cn);const fn={};L&2&&(fn.$$scope={dirty:L,ctx:e}),it.$set(fn);const ue={};L&2&&(ue.$$scope={dirty:L,ctx:e}),ct.$set(ue);const oo={};L&2&&(oo.$$scope={dirty:L,ctx:e}),mt.$set(oo);const mn={};L&2&&(mn.$$scope={dirty:L,ctx:e}),ut.$set(mn);const to={};L&2&&(to.$$scope={dirty:L,ctx:e}),gt.$set(to);const ro={};L&2&&(ro.$$scope={dirty:L,ctx:e}),wt.$set(ro);const Y={};L&2&&(Y.$$scope={dirty:L,ctx:e}),yt.$set(Y);const pn={};L&2&&(pn.$$scope={dirty:L,ctx:e}),Mt.$set(pn);const _n={};L&2&&(_n.$$scope={dirty:L,ctx:e}),Tt.$set(_n);const ao={};L&2&&(ao.$$scope={dirty:L,ctx:e}),Dt.$set(ao);const un={};L&2&&(un.$$scope={dirty:L,ctx:e}),St.$set(un);const no={};L&2&&(no.$$scope={dirty:L,ctx:e}),kt.$set(no);const Q={};L&2&&(Q.$$scope={dirty:L,ctx:e}),Ht.$set(Q);const hn={};L&2&&(hn.$$scope={dirty:L,ctx:e}),Vt.$set(hn)},i(e){Ed||(u(T.$$.fragment,e),u(so.$$.fragment,e),u(Nt.$$.fragment,e),u(Et.$$.fragment,e),u(Ut.$$.fragment,e),u(zt.$$.fragment,e),u(Xt.$$.fragment,e),u(qt.$$.fragment,e),u(co.$$.fragment,e),u(Bt.$$.fragment,e),u(jt.$$.fragment,e),u(Gt.$$.fragment,e),u(Ot.$$.fragment,e),u(Jt.$$.fragment,e),u(Zt.$$.fragment,e),u(Yt.$$.fragment,e),u(_o.$$.fragment,e),u(Qt.$$.fragment,e),u(Kt.$$.fragment,e),u(er.$$.fragment,e),u(or.$$.fragment,e),u(tr.$$.fragment,e),u(rr.$$.fragment,e),u(ar.$$.fragment,e),u(Lo.$$.fragment,e),u(nr.$$.fragment,e),u(sr.$$.fragment,e),u(bo.$$.fragment,e),u(ir.$$.fragment,e),u(dr.$$.fragment,e),u(lr.$$.fragment,e),u(cr.$$.fragment,e),u(fr.$$.fragment,e),u(mr.$$.fragment,e),u($o.$$.fragment,e),u(pr.$$.fragment,e),u(_r.$$.fragment,e),u(Mo.$$.fragment,e),u(ur.$$.fragment,e),u(To.$$.fragment,e),u(hr.$$.fragment,e),u(gr.$$.fragment,e),u(Lr.$$.fragment,e),u(xr.$$.fragment,e),u(br.$$.fragment,e),u(Co.$$.fragment,e),u(vr.$$.fragment,e),u(wr.$$.fragment,e),u(ko.$$.fragment,e),u($r.$$.fragment,e),u(yr.$$.fragment,e),u(Mr.$$.fragment,e),u(Tr.$$.fragment,e),u(Dr.$$.fragment,e),u(Ho.$$.fragment,e),u(Ar.$$.fragment,e),u(Cr.$$.fragment,e),u(Vo.$$.fragment,e),u(Sr.$$.fragment,e),u(kr.$$.fragment,e),u(Rr.$$.fragment,e),u(Pr.$$.fragment,e),u(Hr.$$.fragment,e),u(No.$$.fragment,e),u(Ir.$$.fragment,e),u(Vr.$$.fragment,e),u(Uo.$$.fragment,e),u(Wr.$$.fragment,e),u(Fr.$$.fragment,e),u(Nr.$$.fragment,e),u(Er.$$.fragment,e),u(Ur.$$.fragment,e),u(qo.$$.fragment,e),u(zr.$$.fragment,e),u(Xr.$$.fragment,e),u(jo.$$.fragment,e),u(qr.$$.fragment,e),u(Br.$$.fragment,e),u(jr.$$.fragment,e),u(Gr.$$.fragment,e),u(Or.$$.fragment,e),u(Jo.$$.fragment,e),u(Jr.$$.fragment,e),u(Zr.$$.fragment,e),u(Yo.$$.fragment,e),u(Yr.$$.fragment,e),u(Qr.$$.fragment,e),u(Kr.$$.fragment,e),u(ea.$$.fragment,e),u(oa.$$.fragment,e),u(et.$$.fragment,e),u(ta.$$.fragment,e),u(ra.$$.fragment,e),u(tt.$$.fragment,e),u(aa.$$.fragment,e),u(na.$$.fragment,e),u(sa.$$.fragment,e),u(ia.$$.fragment,e),u(da.$$.fragment,e),u(nt.$$.fragment,e),u(la.$$.fragment,e),u(ca.$$.fragment,e),u(it.$$.fragment,e),u(fa.$$.fragment,e),u(ma.$$.fragment,e),u(pa.$$.fragment,e),u(_a.$$.fragment,e),u(ua.$$.fragment,e),u(ct.$$.fragment,e),u(ha.$$.fragment,e),u(ga.$$.fragment,e),u(mt.$$.fragment,e),u(La.$$.fragment,e),u(xa.$$.fragment,e),u(ba.$$.fragment,e),u(va.$$.fragment,e),u(wa.$$.fragment,e),u(ut.$$.fragment,e),u($a.$$.fragment,e),u(ya.$$.fragment,e),u(gt.$$.fragment,e),u(Ma.$$.fragment,e),u(Ta.$$.fragment,e),u(Da.$$.fragment,e),u(Aa.$$.fragment,e),u(Ca.$$.fragment,e),u(Sa.$$.fragment,e),u(ka.$$.fragment,e),u(Ra.$$.fragment,e),u(Pa.$$.fragment,e),u(wt.$$.fragment,e),u(Ha.$$.fragment,e),u(Ia.$$.fragment,e),u(yt.$$.fragment,e),u(Va.$$.fragment,e),u(Wa.$$.fragment,e),u(Fa.$$.fragment,e),u(Na.$$.fragment,e),u(Ea.$$.fragment,e),u(Mt.$$.fragment,e),u(Tt.$$.fragment,e),u(Ua.$$.fragment,e),u(Dt.$$.fragment,e),u(za.$$.fragment,e),u(Xa.$$.fragment,e),u(qa.$$.fragment,e),u(St.$$.fragment,e),u(Ba.$$.fragment,e),u(kt.$$.fragment,e),u(ja.$$.fragment,e),u(Ga.$$.fragment,e),u(Oa.$$.fragment,e),u(Ja.$$.fragment,e),u(Za.$$.fragment,e),u(Ht.$$.fragment,e),u(Ya.$$.fragment,e),u(Qa.$$.fragment,e),u(Vt.$$.fragment,e),u(Ka.$$.fragment,e),Ed=!0)},o(e){h(T.$$.fragment,e),h(so.$$.fragment,e),h(Nt.$$.fragment,e),h(Et.$$.fragment,e),h(Ut.$$.fragment,e),h(zt.$$.fragment,e),h(Xt.$$.fragment,e),h(qt.$$.fragment,e),h(co.$$.fragment,e),h(Bt.$$.fragment,e),h(jt.$$.fragment,e),h(Gt.$$.fragment,e),h(Ot.$$.fragment,e),h(Jt.$$.fragment,e),h(Zt.$$.fragment,e),h(Yt.$$.fragment,e),h(_o.$$.fragment,e),h(Qt.$$.fragment,e),h(Kt.$$.fragment,e),h(er.$$.fragment,e),h(or.$$.fragment,e),h(tr.$$.fragment,e),h(rr.$$.fragment,e),h(ar.$$.fragment,e),h(Lo.$$.fragment,e),h(nr.$$.fragment,e),h(sr.$$.fragment,e),h(bo.$$.fragment,e),h(ir.$$.fragment,e),h(dr.$$.fragment,e),h(lr.$$.fragment,e),h(cr.$$.fragment,e),h(fr.$$.fragment,e),h(mr.$$.fragment,e),h($o.$$.fragment,e),h(pr.$$.fragment,e),h(_r.$$.fragment,e),h(Mo.$$.fragment,e),h(ur.$$.fragment,e),h(To.$$.fragment,e),h(hr.$$.fragment,e),h(gr.$$.fragment,e),h(Lr.$$.fragment,e),h(xr.$$.fragment,e),h(br.$$.fragment,e),h(Co.$$.fragment,e),h(vr.$$.fragment,e),h(wr.$$.fragment,e),h(ko.$$.fragment,e),h($r.$$.fragment,e),h(yr.$$.fragment,e),h(Mr.$$.fragment,e),h(Tr.$$.fragment,e),h(Dr.$$.fragment,e),h(Ho.$$.fragment,e),h(Ar.$$.fragment,e),h(Cr.$$.fragment,e),h(Vo.$$.fragment,e),h(Sr.$$.fragment,e),h(kr.$$.fragment,e),h(Rr.$$.fragment,e),h(Pr.$$.fragment,e),h(Hr.$$.fragment,e),h(No.$$.fragment,e),h(Ir.$$.fragment,e),h(Vr.$$.fragment,e),h(Uo.$$.fragment,e),h(Wr.$$.fragment,e),h(Fr.$$.fragment,e),h(Nr.$$.fragment,e),h(Er.$$.fragment,e),h(Ur.$$.fragment,e),h(qo.$$.fragment,e),h(zr.$$.fragment,e),h(Xr.$$.fragment,e),h(jo.$$.fragment,e),h(qr.$$.fragment,e),h(Br.$$.fragment,e),h(jr.$$.fragment,e),h(Gr.$$.fragment,e),h(Or.$$.fragment,e),h(Jo.$$.fragment,e),h(Jr.$$.fragment,e),h(Zr.$$.fragment,e),h(Yo.$$.fragment,e),h(Yr.$$.fragment,e),h(Qr.$$.fragment,e),h(Kr.$$.fragment,e),h(ea.$$.fragment,e),h(oa.$$.fragment,e),h(et.$$.fragment,e),h(ta.$$.fragment,e),h(ra.$$.fragment,e),h(tt.$$.fragment,e),h(aa.$$.fragment,e),h(na.$$.fragment,e),h(sa.$$.fragment,e),h(ia.$$.fragment,e),h(da.$$.fragment,e),h(nt.$$.fragment,e),h(la.$$.fragment,e),h(ca.$$.fragment,e),h(it.$$.fragment,e),h(fa.$$.fragment,e),h(ma.$$.fragment,e),h(pa.$$.fragment,e),h(_a.$$.fragment,e),h(ua.$$.fragment,e),h(ct.$$.fragment,e),h(ha.$$.fragment,e),h(ga.$$.fragment,e),h(mt.$$.fragment,e),h(La.$$.fragment,e),h(xa.$$.fragment,e),h(ba.$$.fragment,e),h(va.$$.fragment,e),h(wa.$$.fragment,e),h(ut.$$.fragment,e),h($a.$$.fragment,e),h(ya.$$.fragment,e),h(gt.$$.fragment,e),h(Ma.$$.fragment,e),h(Ta.$$.fragment,e),h(Da.$$.fragment,e),h(Aa.$$.fragment,e),h(Ca.$$.fragment,e),h(Sa.$$.fragment,e),h(ka.$$.fragment,e),h(Ra.$$.fragment,e),h(Pa.$$.fragment,e),h(wt.$$.fragment,e),h(Ha.$$.fragment,e),h(Ia.$$.fragment,e),h(yt.$$.fragment,e),h(Va.$$.fragment,e),h(Wa.$$.fragment,e),h(Fa.$$.fragment,e),h(Na.$$.fragment,e),h(Ea.$$.fragment,e),h(Mt.$$.fragment,e),h(Tt.$$.fragment,e),h(Ua.$$.fragment,e),h(Dt.$$.fragment,e),h(za.$$.fragment,e),h(Xa.$$.fragment,e),h(qa.$$.fragment,e),h(St.$$.fragment,e),h(Ba.$$.fragment,e),h(kt.$$.fragment,e),h(ja.$$.fragment,e),h(Ga.$$.fragment,e),h(Oa.$$.fragment,e),h(Ja.$$.fragment,e),h(Za.$$.fragment,e),h(Ht.$$.fragment,e),h(Ya.$$.fragment,e),h(Qa.$$.fragment,e),h(Vt.$$.fragment,e),h(Ka.$$.fragment,e),Ed=!1},d(e){e&&(i(b),i(l),i(c),i(d),i(y),i(td),i(Ft),i(rd),i(ad),i(nd),i(I),i(sd),i(id),i(V),i(dd),i(ld),i(k),i(cd),i(fd),i(C),i(md),i(pd),i(W),i(_d),i(ud),i(F),i(hd),i(gd),i(N),i(Ld),i(xd),i(E),i(bd),i(vd),i(U),i(wd),i($d),i(z),i(yd),i(Md),i(X),i(Td),i(Dd),i(q),i(Ad),i(Cd),i(B),i(Sd),i(kd),i(ge),i(Rd),i(Pd),i(j),i(Hd),i(Id),i(S),i(Vd),i(Wd),i(G),i(Fd),i(Nd),i(od)),i(a),g(T,e),g(so,e),g(Nt,e),g(Et),g(Ut),g(zt),g(Xt),g(qt),g(co),g(Bt),g(jt,e),g(Gt),g(Ot),g(Jt),g(Zt),g(Yt),g(_o),g(Qt),g(Kt,e),g(er),g(or),g(tr),g(rr),g(ar),g(Lo),g(nr),g(sr),g(bo),g(ir,e),g(dr),g(lr),g(cr),g(fr),g(mr),g($o),g(pr),g(_r),g(Mo),g(ur),g(To),g(hr,e),g(gr),g(Lr),g(xr),g(br),g(Co),g(vr),g(wr),g(ko),g($r,e),g(yr),g(Mr),g(Tr),g(Dr),g(Ho),g(Ar),g(Cr),g(Vo),g(Sr,e),g(kr),g(Rr),g(Pr),g(Hr),g(No),g(Ir),g(Vr),g(Uo),g(Wr,e),g(Fr),g(Nr),g(Er),g(Ur),g(qo),g(zr),g(Xr),g(jo),g(qr,e),g(Br),g(jr),g(Gr),g(Or),g(Jo),g(Jr),g(Zr),g(Yo),g(Yr,e),g(Qr),g(Kr),g(ea),g(oa),g(et),g(ta),g(ra),g(tt),g(aa,e),g(na),g(sa),g(ia),g(da),g(nt),g(la),g(ca),g(it),g(fa,e),g(ma),g(pa),g(_a),g(ua),g(ct),g(ha),g(ga),g(mt),g(La,e),g(xa),g(ba),g(va),g(wa),g(ut),g($a),g(ya),g(gt),g(Ma,e),g(Ta),g(Da),g(Aa),g(Ca,e),g(Sa),g(ka),g(Ra),g(Pa),g(wt),g(Ha),g(Ia),g(yt),g(Va,e),g(Wa),g(Fa),g(Na),g(Ea),g(Mt),g(Tt),g(Ua),g(Dt),g(za),g(Xa),g(qa),g(St),g(Ba),g(kt),g(ja,e),g(Ga),g(Oa),g(Ja),g(Za),g(Ht),g(Ya),g(Qa),g(Vt),g(Ka,e)}}}const Qh='{"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":"AuraFlowLoraLoaderMixin","local":"diffusers.loaders.AuraFlowLoraLoaderMixin","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":"CogView4LoraLoaderMixin","local":"diffusers.loaders.CogView4LoraLoaderMixin","sections":[],"depth":2},{"title":"WanLoraLoaderMixin","local":"diffusers.loaders.WanLoraLoaderMixin","sections":[],"depth":2},{"title":"AmusedLoraLoaderMixin","local":"diffusers.loaders.AmusedLoraLoaderMixin","sections":[],"depth":2},{"title":"HiDreamImageLoraLoaderMixin","local":"diffusers.loaders.HiDreamImageLoraLoaderMixin","sections":[],"depth":2},{"title":"LoraBaseMixin","local":"diffusers.loaders.lora_base.LoraBaseMixin","sections":[],"depth":2},{"title":"WanLoraLoaderMixin","local":"diffusers.loaders.WanLoraLoaderMixin","sections":[],"depth":2}],"depth":1}';function Kh(M){return fh(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ig extends mh{constructor(a){super(),ph(this,a,Kh,Yh,ch,{})}}export{ig as component};