diffusers/pr_11105/en/_app/immutable/nodes/10.5c496a79.js

import{s as $f,o as yf,n as S}from"../chunks/scheduler.8c3d61f6.js";import{S as Mf,i as Tf,g as n,s as t,r as p,A as Df,h as s,f as i,c as r,j as b,u as _,x as f,k as w,y as o,a as x,v as u,d as h,t as g,w as L}from"../chunks/index.da70eac4.js";import{T as R}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.567bc132.js";import{C as Cs}from"../chunks/CodeBlock.a9c4becf.js";import{E as Ss}from"../chunks/ExampleCodeBlock.15b54358.js";import{H as ne,E as Sf}from"../chunks/index.5d4ab994.js";function Cf(T){let a,v='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=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-1fw6lx1"&&(a.innerHTML=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function kf(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Af(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Pf(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Rf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Hf(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Vf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function If(T){let a,v="Examples:",l,c,M;return c=new Cs({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=v,l=t(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=v),l=r(d),_(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),u(c,d,y),M=!0},p:S,i(d){M||(h(c.$$.fragment,d),M=!0)},o(d){g(c.$$.fragment,d),M=!1},d(d){d&&(i(a),i(l)),L(c,d)}}}function Uf(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Xf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Ef(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Ff(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Nf(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Wf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function qf(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function zf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Bf(T){let a,v="We support loading original format HunyuanVideo LoRA checkpoints.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-gyrs6h"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function jf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Gf(T){let a,v="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=v,l=t(),c=n("p"),c.textContent=M},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=v),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){x(d,a,y),x(d,l,y),x(d,c,y)},p:S,d(d){d&&(i(a),i(l),i(c))}}}function Jf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Zf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Yf(T){let a,v="Example:",l,c,M;return c=new Cs({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=v,l=t(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=v),l=r(d),_(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),u(c,d,y),M=!0},p:S,i(d){M||(h(c.$$.fragment,d),M=!0)},o(d){g(c.$$.fragment,d),M=!1},d(d){d&&(i(a),i(l)),L(c,d)}}}function Of(T){let a,v="Example:",l,c,M;return c=new Cs({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=v,l=t(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=v),l=r(d),_(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),u(c,d,y),M=!0},p:S,i(d){M||(h(c.$$.fragment,d),M=!0)},o(d){g(c.$$.fragment,d),M=!1},d(d){d&&(i(a),i(l)),L(c,d)}}}function Qf(T){let a,v="This is an experimental API.";return{c(){a=n("p"),a.textContent=v},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=v)},m(l,c){x(l,a,c)},p:S,d(l){l&&i(a)}}}function Kf(T){let a,v="Examples:",l,c,M;return c=new Cs({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=v,l=t(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=v),l=r(d),_(c.$$.fragment,d)},m(d,y){x(d,a,y),x(d,l,y),u(c,d,y),M=!0},p:S,i(d){M||(h(c.$$.fragment,d),M=!0)},o(d){g(c.$$.fragment,d),M=!1},d(d){d&&(i(a),i(l)),L(c,d)}}}function em(T){let a,v,l,c,M,d,y,Fl='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_11105/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_11105/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',An,No,Nl='<li><code>StableDiffusionLoraLoaderMixin</code> provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.</li> <li><code>StableDiffusionXLLoraLoaderMixin</code> is a <a href="../../api/pipelines/stable_diffusion/stable_diffusion_xl">Stable Diffusion (SDXL)</a> version of the <code>StableDiffusionLoraLoaderMixin</code> class for loading and saving LoRA weights. It can only be used with the SDXL model.</li> <li><code>SD3LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/blog/sd3" rel="nofollow">Stable Diffusion 3</a>.</li> <li><code>FluxLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux" rel="nofollow">Flux</a>.</li> <li><code>CogVideoXLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox" rel="nofollow">CogVideoX</a>.</li> <li><code>Mochi1LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi" rel="nofollow">Mochi</a>.</li> <li><code>LTXVideoLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video" rel="nofollow">LTX-Video</a>.</li> <li><code>SanaLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana" rel="nofollow">Sana</a>.</li> <li><code>HunyuanVideoLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video" rel="nofollow">HunyuanVideo</a>.</li> <li><code>Lumina2LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2" rel="nofollow">Lumina2</a>.</li> <li><code>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/pr_11105/en/api/pipelines/amused#diffusers.AmusedPipeline">AmusedPipeline</a>.</li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',Pn,Ve,Rn,Wo,Hn,H,qo,ks,Pr,Wl=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_11105/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>.`,As,Ie,zo,Ps,Rr,ql="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Rs,Ue,Bo,Hs,Hr,zl="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Vs,B,jo,Is,Vr,Bl=`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>.`,Us,Ir,jl="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Xs,Ur,Gl=`See <a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Es,Xr,Jl=`See <a href="/docs/diffusers/pr_11105/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>.`,Fs,Er,Zl=`See <a href="/docs/diffusers/pr_11105/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>.`,Ns,ie,Go,Ws,Fr,Yl="Return state dict for lora weights and the network alphas.",qs,Xe,zs,Ee,Jo,Bs,Nr,Ol="Save the LoRA parameters corresponding to the UNet and text encoder.",Vn,Zo,In,V,Yo,js,Wr,Ql=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_11105/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>.`,Gs,Fe,Oo,Js,qr,Kl="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Zs,Ne,Qo,Ys,zr,ec="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Os,j,Ko,Qs,Br,oc=`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>.`,Ks,jr,tc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",ei,Gr,rc=`See <a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,oi,Jr,ac=`See <a href="/docs/diffusers/pr_11105/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>.`,ti,Zr,nc=`See <a href="/docs/diffusers/pr_11105/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>.`,ri,de,et,ai,Yr,sc="Return state dict for lora weights and the network alphas.",ni,We,si,qe,ot,ii,Or,ic="Save the LoRA parameters corresponding to the UNet and text encoder.",Un,tt,Xn,C,rt,di,Qr,dc=`Load LoRA layers into <a href="/docs/diffusers/pr_11105/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>.`,li,Kr,lc='Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',ci,ze,at,fi,ea,cc="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",mi,Be,nt,pi,oa,fc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",_i,Z,st,ui,ta,mc=`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>.`,hi,ra,pc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",gi,aa,_c=`See <a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Li,na,uc=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,xi,le,it,vi,sa,hc="Return state dict for lora weights and the network alphas.",bi,je,wi,Ge,dt,$i,ia,gc="Save the LoRA parameters corresponding to the UNet and text encoder.",yi,ce,lt,Mi,da,Lc=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Ti,Je,En,ct,Fn,D,ft,Di,la,xc=`Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,Si,ca,vc='Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Ci,Ze,mt,ki,fa,bc="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ai,Ye,pt,Pi,ma,wc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Ri,Y,_t,Hi,pa,$c=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>.`,Vi,_a,yc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Ii,ua,Mc=`See <a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Ui,ha,Tc=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Xi,fe,ut,Ei,ga,Dc="Return state dict for lora weights and the network alphas.",Fi,Oe,Ni,Qe,ht,Wi,La,Sc="Save the LoRA parameters corresponding to the UNet and text encoder.",qi,me,gt,zi,xa,Cc=`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>.`,Bi,Ke,ji,pe,Lt,Gi,va,kc="Unloads the LoRA parameters.",Ji,eo,Nn,xt,Wn,I,vt,Zi,ba,Ac='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',Yi,oo,bt,Oi,wa,Pc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Qi,to,wt,Ki,$a,Rc=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,ed,_e,$t,od,ya,Hc="Return state dict for lora weights and the network alphas.",td,ro,rd,ao,yt,ad,Ma,Vc="Save the LoRA parameters corresponding to the UNet and text encoder.",nd,ue,Mt,sd,Ta,Ic=`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>.`,id,no,qn,Tt,zn,U,Dt,dd,Da,Uc='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',ld,so,St,cd,Sa,Xc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",fd,io,Ct,md,Ca,Ec=`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_11105/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>.`,pd,he,kt,_d,ka,Fc="Return state dict for lora weights and the network alphas.",ud,lo,hd,co,At,gd,Aa,Nc="Save the LoRA parameters corresponding to the UNet and text encoder.",Ld,ge,Pt,xd,Pa,Wc=`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>.`,vd,fo,Bn,Rt,jn,X,Ht,bd,Ra,qc='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',wd,mo,Vt,$d,Ha,zc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",yd,po,It,Md,Va,Bc=`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_11105/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>.`,Td,Le,Ut,Dd,Ia,jc="Return state dict for lora weights and the network alphas.",Sd,_o,Cd,uo,Xt,kd,Ua,Gc="Save the LoRA parameters corresponding to the UNet and text encoder.",Ad,xe,Et,Pd,Xa,Jc=`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>.`,Rd,ho,Gn,Ft,Jn,E,Nt,Hd,Ea,Zc='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',Vd,go,Wt,Id,Fa,Yc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Ud,Lo,qt,Xd,Na,Oc=`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_11105/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>.`,Ed,ve,zt,Fd,Wa,Qc="Return state dict for lora weights and the network alphas.",Nd,xo,Wd,vo,Bt,qd,qa,Kc="Save the LoRA parameters corresponding to the UNet and text encoder.",zd,be,jt,Bd,za,ef=`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>.`,jd,bo,Zn,Gt,Yn,F,Jt,Gd,Ba,of='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',Jd,wo,Zt,Zd,ja,tf="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Yd,$o,Yt,Od,Ga,rf=`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_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Qd,we,Ot,Kd,Ja,af="Return state dict for lora weights and the network alphas.",el,yo,ol,Mo,Qt,tl,Za,nf="Save the LoRA parameters corresponding to the UNet and text encoder.",rl,$e,Kt,al,Ya,sf=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,nl,To,On,er,Qn,N,or,sl,Oa,df='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',il,Do,tr,dl,Qa,lf="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ll,So,rr,cl,Ka,cf=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,fl,ye,ar,ml,en,ff="Return state dict for lora weights and the network alphas.",pl,Co,_l,ko,nr,ul,on,mf="Save the LoRA parameters corresponding to the UNet and text encoder.",hl,Me,sr,gl,tn,pf=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Ll,Ao,Kn,ir,es,Ce,dr,xl,Po,lr,vl,rn,_f="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",os,cr,ts,k,fr,bl,an,uf="Utility class for handling LoRAs.",wl,nn,mr,$l,se,pr,yl,sn,hf="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Ml,Ro,Tl,Ho,Dl,Te,_r,Sl,dn,gf="Gets the list of the current active adapters.",Cl,Vo,kl,Io,ur,Al,ln,Lf="Gets the current list of all available adapters in the pipeline.",Pl,Uo,hr,Rl,cn,xf=`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.`,Hl,De,gr,Vl,fn,vf=`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>.`,Il,Xo,Ul,Se,Lr,Xl,mn,bf="Unloads the LoRA parameters.",El,Eo,rs,xr,as,kn,ns;return M=new ne({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),Ve=new R({props:{$$slots:{default:[Cf]},$$scope:{ctx:T}}}),Wo=new ne({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),qo=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L71"}}),zo=new $({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"text_encoder",val:""},{name:"prefix",val:" = None"},{name:"lora_scale",val:" = 1.0"},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional <code>text_encoder</code> to distinguish between unet lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) &#x2014;
Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) &#x2014;
How much to scale the output of the lora linear layer before it is added with the output of the regular
lora layer.`,name:"lora_scale"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"}],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L312"}}),Bo=new $({props:{name:"load_lora_into_unet",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"unet",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional <code>unet</code> which can be used to distinguish between text
encoder lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"}],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L266"}}),jo=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) &#x2014;
See <a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
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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
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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
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A standard state dict containing the lora layer parameters. The keys can either be indexed directly
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encoder lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>SD3Transformer2DModel</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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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_11105/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
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See <a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L1037"}}),it=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_11105/en/api/models/overview#diffusers.ModelMixin.save_pretrained">ModelMixin.save_pretrained()</a>.</li>
<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
dict</a>.</li>
</ul>`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.cache_dir",description:`<strong>cache_dir</strong> (<code>Union[str, os.PathLike]</code>, <em>optional</em>) &#x2014;
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.`,name:"cache_dir"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
<code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&quot;</code>) &#x2014;
The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"}],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L940"}}),je=new R({props:{warning:!0,$$slots:{default:[Pf]},$$scope:{ctx:T}}}),dt=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"}],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
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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
replace <code>torch.save</code> with another method. Can be configured with the environment variable
<code>DIFFUSERS_SAVE_MODE</code>.`,name:"save_function"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"}],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L1198"}}),lt=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_11105/src/diffusers/loaders/lora_pipeline.py#L1310"}}),Je=new R({props:{warning:!0,$$slots:{default:[Rf]},$$scope:{ctx:T}}}),ct=new ne({props:{title:"FluxLoraLoaderMixin",local:"diffusers.loaders.FluxLoraLoaderMixin",headingTag:"h2"}}),ft=new $({props:{name:"class diffusers.loaders.FluxLoraLoaderMixin",anchor:"diffusers.loaders.FluxLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L1331"}}),mt=new $({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"text_encoder",val:""},{name:"prefix",val:" = None"},{name:"lora_scale",val:" = 1.0"},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
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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;
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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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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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A standard state dict containing the lora layer parameters. The keys can either be indexed directly
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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
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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
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See <a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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\`Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
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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_11105/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
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