diffusers/pr_11477/en/_app/immutable/nodes/10.0dbefea3.js

import{s as th,o as ah,n as D}from"../chunks/scheduler.8c3d61f6.js";import{S as nh,i as sh,g as n,s as r,r as p,A as ih,h as s,f as i,c as t,j as v,u as m,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 S}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.9419aa1d.js";import{C as gl}from"../chunks/CodeBlock.a9c4becf.js";import{E as hl}from"../chunks/ExampleCodeBlock.1b2603c3.js";import{H as J,E as dh}from"../chunks/getInferenceSnippets.39110341.js";function lh(y){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 ch(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function fh(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function ph(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function mh(y){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(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function uh(y){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(y){let a,b="Examples:",l,c,T;return c=new gl({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=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),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(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Lh(y){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 xh(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function bh(y){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(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function wh(y){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(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Mh(y){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(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Th(y){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 Dh(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-gyrs6h"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Sh(y){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 Ch(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Ah(y){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 kh(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Ph(y){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(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Hh(y){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 Ih(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Vh(y){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 Wh(y){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(y){let a,b="Example:",l,c,T;return c=new gl({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=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),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 Uh(y){let a,b="Example:",l,c,T;return c=new gl({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=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),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 Eh(y){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 Nh(y){let a,b="Examples:",l,c,T;return c=new gl({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=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),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 Xh(y){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=r(),c=n("p"),c.textContent=T},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=T)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function qh(y){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(y){let a,b,l,c,T,d,M,qm='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_11477/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_11477/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Qi,Wr,zm='<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_11477/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>',Ki,so,ed,Fr,od,I,Ur,Ll,un,Bm=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_11477/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>.`,xl,io,Er,bl,hn,jm="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",vl,lo,Nr,wl,gn,Gm="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",$l,K,Xr,Ml,Ln,Jm=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.unet</code> and
<code>self.text_encoder</code>.`,yl,xn,Zm="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Tl,bn,Om=`See <a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Dl,vn,Ym=`See <a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is
loaded into <code>self.unet</code>.`,Sl,wn,Qm=`See <a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder">load_lora_into_text_encoder()</a> for more details on how the state
dict is loaded into <code>self.text_encoder</code>.`,Cl,ge,qr,Al,$n,Km="Return state dict for lora weights and the network alphas.",kl,co,Pl,fo,zr,Rl,Mn,e_="Save the LoRA parameters corresponding to the UNet and text encoder.",rd,Br,td,V,jr,Hl,yn,o_=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_11477/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>.`,Il,po,Gr,Vl,Tn,r_="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Wl,mo,Jr,Fl,Dn,t_="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Ul,ee,Zr,El,Sn,a_=`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>.`,Nl,Cn,n_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Xl,An,s_=`See <a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,ql,kn,i_=`See <a href="/docs/diffusers/pr_11477/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>.`,zl,Pn,d_=`See <a href="/docs/diffusers/pr_11477/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>.`,Bl,Le,Or,jl,Rn,l_="Return state dict for lora weights and the network alphas.",Gl,_o,Jl,uo,Yr,Zl,Hn,c_="Save the LoRA parameters corresponding to the UNet and text encoder.",ad,Qr,nd,k,Kr,Ol,In,f_=`Load LoRA layers into <a href="/docs/diffusers/pr_11477/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>.`,Yl,Vn,p_='Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Ql,ho,et,Kl,Wn,m_="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",ec,go,ot,oc,Fn,__="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",rc,te,rt,tc,Un,u_=`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>.`,ac,En,h_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",nc,Nn,g_=`See <a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,sc,Xn,L_=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,ic,xe,tt,dc,qn,x_="Return state dict for lora weights and the network alphas.",lc,Lo,cc,xo,at,fc,zn,b_="Save the LoRA parameters corresponding to the UNet and text encoder.",pc,be,nt,mc,Bn,v_=`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>.`,_c,bo,sd,st,id,C,it,uc,jn,w_=`Load LoRA layers into <a href="/docs/diffusers/pr_11477/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>.`,hc,Gn,$_='Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',gc,vo,dt,Lc,Jn,M_="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",xc,wo,lt,bc,Zn,y_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",vc,ae,ct,wc,On,T_=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>.`,$c,Yn,D_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Mc,Qn,S_=`See <a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,yc,Kn,C_=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Tc,ve,ft,Dc,es,A_="Return state dict for lora weights and the network alphas.",Sc,$o,Cc,Mo,pt,Ac,os,k_="Save the LoRA parameters corresponding to the UNet and text encoder.",kc,we,mt,Pc,rs,P_=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Rc,yo,Hc,$e,_t,Ic,ts,R_="Unloads the LoRA parameters.",Vc,To,dd,ut,ld,W,ht,Wc,as,H_='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',Fc,Do,gt,Uc,ns,I_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Ec,So,Lt,Nc,ss,V_=`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_11477/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>.`,Xc,Me,xt,qc,is,W_="Return state dict for lora weights and the network alphas.",zc,Co,Bc,Ao,bt,jc,ds,F_="Save the LoRA parameters corresponding to the UNet and text encoder.",Gc,ye,vt,Jc,ls,U_=`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>.`,Zc,ko,cd,wt,fd,F,$t,Oc,cs,E_='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',Yc,Po,Mt,Qc,fs,N_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Kc,Ro,yt,ef,ps,X_=`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_11477/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>.`,of,Te,Tt,rf,ms,q_="Return state dict for lora weights and the network alphas.",tf,Ho,af,Io,Dt,nf,_s,z_="Save the LoRA parameters corresponding to the UNet and text encoder.",sf,De,St,df,us,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>.`,lf,Vo,pd,Ct,md,U,At,cf,hs,j_='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',ff,Wo,kt,pf,gs,G_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",mf,Fo,Pt,_f,Ls,J_=`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_11477/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>.`,uf,Se,Rt,hf,xs,Z_="Return state dict for lora weights and the network alphas.",gf,Uo,Lf,Eo,Ht,xf,bs,O_="Save the LoRA parameters corresponding to the UNet and text encoder.",bf,Ce,It,vf,vs,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>.`,wf,No,_d,Vt,ud,E,Wt,$f,ws,Q_='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',Mf,Xo,Ft,yf,$s,K_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Tf,qo,Ut,Df,Ms,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_11477/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>.`,Sf,Ae,Et,Cf,ys,ou="Return state dict for lora weights and the network alphas.",Af,zo,kf,Bo,Nt,Pf,Ts,ru="Save the LoRA parameters corresponding to the UNet and text encoder.",Rf,ke,Xt,Hf,Ds,tu=`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>.`,If,jo,hd,qt,gd,N,zt,Vf,Ss,au='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',Wf,Go,Bt,Ff,Cs,nu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Uf,Jo,jt,Ef,As,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_11477/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,Pe,Gt,Xf,ks,iu="Return state dict for lora weights and the network alphas.",qf,Zo,zf,Oo,Jt,Bf,Ps,du="Save the LoRA parameters corresponding to the UNet and text encoder.",jf,Re,Zt,Gf,Rs,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>.`,Jf,Yo,Ld,Ot,xd,X,Yt,Zf,Hs,cu='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',Of,Qo,Qt,Yf,Is,fu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Qf,Ko,Kt,Kf,Vs,pu=`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_11477/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>.`,ep,He,ea,op,Ws,mu="Return state dict for lora weights and the network alphas.",rp,er,tp,or,oa,ap,Fs,_u="Save the LoRA parameters corresponding to the UNet and text encoder.",np,Ie,ra,sp,Us,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>.`,ip,rr,bd,ta,vd,q,aa,dp,Es,hu='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',lp,tr,na,cp,Ns,gu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",fp,ar,sa,pp,Xs,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_11477/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>.`,mp,Ve,ia,_p,qs,xu="Return state dict for lora weights and the network alphas.",up,nr,hp,sr,da,gp,zs,bu="Save the LoRA parameters corresponding to the UNet and text encoder.",Lp,We,la,xp,Bs,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>.`,bp,ir,wd,ca,$d,z,fa,vp,js,wu='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/cogview4#diffusers.CogView4Pipeline">CogView4Pipeline</a>.',wp,dr,pa,$p,Gs,$u="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Mp,lr,ma,yp,Js,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_11477/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>.`,Tp,Fe,_a,Dp,Zs,yu="Return state dict for lora weights and the network alphas.",Sp,cr,Cp,fr,ua,Ap,Os,Tu="Save the LoRA parameters corresponding to the UNet and text encoder.",kp,Ue,ha,Pp,Ys,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>.`,Rp,pr,Md,ga,yd,B,La,Hp,Qs,Su='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',Ip,mr,xa,Vp,Ks,Cu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Wp,_r,ba,Fp,ei,Au=`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_11477/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,Ee,va,Ep,oi,ku="Return state dict for lora weights and the network alphas.",Np,ur,Xp,hr,wa,qp,ri,Pu="Save the LoRA parameters corresponding to the UNet and text encoder.",zp,Ne,$a,Bp,ti,Ru=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,jp,gr,Td,Ma,Dd,Oe,ya,Gp,Lr,Ta,Jp,ai,Hu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Sd,Da,Cd,j,Sa,Zp,ni,Iu='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel">HiDreamImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/hidream#diffusers.HiDreamImagePipeline">HiDreamImagePipeline</a>.',Op,xr,Ca,Yp,si,Vu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Qp,br,Aa,Kp,ii,Wu=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,em,Xe,ka,om,di,Fu="Return state dict for lora weights and the network alphas.",rm,vr,tm,wr,Pa,am,li,Uu="Save the LoRA parameters corresponding to the UNet and text encoder.",nm,qe,Ra,sm,ci,Eu=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,im,$r,Ad,Ha,kd,A,Ia,dm,fi,Nu="Utility class for handling LoRAs.",lm,pi,Va,cm,ze,Wa,fm,mi,Xu="Enables the possibility to hotswap LoRA adapters.",pm,_i,qu=`Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of
the loaded adapters differ.`,mm,he,Fa,_m,ui,zu="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",um,Mr,hm,yr,gm,Be,Ua,Lm,hi,Bu="Gets the list of the current active adapters.",xm,Tr,bm,Dr,Ea,vm,gi,ju="Gets the current list of all available adapters in the pipeline.",wm,Sr,Na,$m,Li,Gu=`Moves the LoRAs listed in <code>adapter_names</code> to a target device. Useful for offloading the LoRA to the CPU in case
you want to load multiple adapters and free some GPU memory.`,Mm,je,Xa,ym,xi,Ju=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Tm,Cr,Dm,Ge,qa,Sm,bi,Zu="Unloads the LoRA parameters.",Cm,Ar,Pd,za,Rd,G,Ba,Am,vi,Ou='Load LoRA layers into <a href="/docs/diffusers/pr_11477/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11477/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',km,kr,ja,Pm,wi,Yu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Rm,Pr,Ga,Hm,$i,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_11477/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>.`,Im,Je,Ja,Vm,Mi,Ku="Return state dict for lora weights and the network alphas.",Wm,Rr,Fm,Hr,Za,Um,yi,eh="Save the LoRA parameters corresponding to the UNet and text encoder.",Em,Ze,Oa,Nm,Ti,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>.`,Xm,Ir,Hd,Ya,Id,Yi,Vd;return T=new J({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),so=new S({props:{$$slots:{default:[lh]},$$scope:{ctx:y}}}),Fr=new J({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Ur=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11477/src/diffusers/loaders/lora_pipeline.py#L120"}}),Er=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"}],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_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11477/src/diffusers/loaders/lora_pipeline.py#L399"}}),Nr=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"}],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_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11477/src/diffusers/loaders/lora_pipeline.py#L343"}}),Xr=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_11477/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_11477/src/diffusers/loaders/lora_pipeline.py#L130"}}),qr=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_11477/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;
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Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
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See <a href="/docs/diffusers/pr_11477/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
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See <a href="/docs/diffusers/pr_11477/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;
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<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_11477/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
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Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
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Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
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The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
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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
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State dict of the LoRA layers corresponding to the <code>text_encoder_2</code>. Must explicitly pass the text
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Whether the process calling this is the main process or not. Useful during distributed training and you
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The function to use to save the state dictionary. Useful during distributed training when you need to
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<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;
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A standard state dict containing the lora layer parameters. The key should be prefixed with an
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The value of the network alpha used for stable learning and preventing underflow. This value has the
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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;
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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
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Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
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See <a href="/docs/diffusers/pr_11477/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11477/src/diffusers/loaders/lora_pipeline.py#L2150"}}),lt=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{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
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The value of the network alpha used for stable learning and preventing underflow. This value has the
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Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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See <a href="/docs/diffusers/pr_11477/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;
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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_11477/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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
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<li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
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<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
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<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
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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;
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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;
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<li>&#x201C;warn&#x201D;: issue a warning</li>
<li>&#x201C;ignore&#x201D;: do nothing</li>
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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_11477/src/diffusers/loaders/lora_base.py#L621"}}),Cr=new S({props:{warning:!0,$$slots:{default:[Eh]},$$scope:{ctx:y}}}),qa=new $({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11477/src/diffusers/loaders/lora_base.py#L508"}}),Ar=new hl({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[Nh]},$$scope:{ctx:y}}}),za=new J({props:{title:"WanLoraLoaderMixin",local:"diffusers.loaders.WanLoraLoaderMixin",headingTag:"h2"}}),Ba=new $({props:{name:"class diffusers.loaders.WanLoraLoaderMixin",anchor:"diffusers.loaders.WanLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11477/src/diffusers/loaders/lora_pipeline.py#L4697"}}),ja=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"}],parametersDescription:[{anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_into_transformer.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
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Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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<li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
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<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
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<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
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