diffusers/pr_10725/en/_app/immutable/nodes/10.e2a4e5d8.js

import{s as Ui,o as Fi,n as N}from"../chunks/scheduler.8c3d61f6.js";import{S as Vi,i as Xi,g as n,s as t,r as _,A as Ni,h as s,f as d,c as r,j as v,u as m,x as f,k as w,y as o,a as L,v as u,d as h,t as g,w as x}from"../chunks/index.da70eac4.js";import{T as Y}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.6b390b9a.js";import{C as ua}from"../chunks/CodeBlock.00a903b3.js";import{E as ma}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as me,E as Wi}from"../chunks/EditOnGithub.1e64e623.js";function zi(D){let a,b='To learn more about how to load LoRA weights, see the <a href="../../using-diffusers/loading_adapters#lora">LoRA</a> loading guide.';return{c(){a=n("p"),a.innerHTML=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-1fw6lx1"&&(a.innerHTML=b)},m(c,l){L(c,a,l)},p:N,d(c){c&&d(a)}}}function qi(D){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=y},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(i),l=s(i,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(i,M){L(i,a,M),L(i,c,M),L(i,l,M)},p:N,d(i){i&&(d(a),d(c),d(l))}}}function Bi(D){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=y},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(i),l=s(i,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(i,M){L(i,a,M),L(i,c,M),L(i,l,M)},p:N,d(i){i&&(d(a),d(c),d(l))}}}function ji(D){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=y},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(i),l=s(i,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(i,M){L(i,a,M),L(i,c,M),L(i,l,M)},p:N,d(i){i&&(d(a),d(c),d(l))}}}function Gi(D){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=y},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(i),l=s(i,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(i,M){L(i,a,M),L(i,c,M),L(i,l,M)},p:N,d(i){i&&(d(a),d(c),d(l))}}}function Ji(D){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){L(c,a,l)},p:N,d(c){c&&d(a)}}}function Zi(D){let a,b="Examples:",c,l,y;return l=new ua({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">&gt;&gt;&gt; </span>...',wrap:!1}}),{c(){a=n("p"),a.textContent=b,c=t(),_(l.$$.fragment)},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),c=r(i),m(l.$$.fragment,i)},m(i,M){L(i,a,M),L(i,c,M),u(l,i,M),y=!0},p:N,i(i){y||(h(l.$$.fragment,i),y=!0)},o(i){g(l.$$.fragment,i),y=!1},d(i){i&&(d(a),d(c)),x(l,i)}}}function Yi(D){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=y},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(i),l=s(i,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(i,M){L(i,a,M),L(i,c,M),L(i,l,M)},p:N,d(i){i&&(d(a),d(c),d(l))}}}function Qi(D){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){L(c,a,l)},p:N,d(c){c&&d(a)}}}function Oi(D){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,c=t(),l=n("p"),l.textContent=y},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),c=r(i),l=s(i,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(i,M){L(i,a,M),L(i,c,M),L(i,l,M)},p:N,d(i){i&&(d(a),d(c),d(l))}}}function Ki(D){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){L(c,a,l)},p:N,d(c){c&&d(a)}}}function ed(D){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){L(c,a,l)},p:N,d(c){c&&d(a)}}}function od(D){let a,b="Example:",c,l,y;return l=new ua({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEFwaXBlbGluZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJzdGFiaWxpdHlhaSUyRnN0YWJsZS1kaWZmdXNpb24teGwtYmFzZS0xLjAlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMEEpLnRvKCUyMmN1ZGElMjIpJTBBcGlwZWxpbmUubG9hZF9sb3JhX3dlaWdodHMoJTIybmVyaWpzJTJGcGl4ZWwtYXJ0LXhsJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJwaXhlbC1hcnQteGwuc2FmZXRlbnNvcnMlMjIlMkMlMjBhZGFwdGVyX25hbWUlM0QlMjJwaXhlbCUyMiklMEFwaXBlbGluZS5mdXNlX2xvcmEobG9yYV9zY2FsZSUzRDAuNyk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(<span class="hljs-string">&quot;nerijs/pixel-art-xl&quot;</span>, weight_name=<span class="hljs-string">&quot;pixel-art-xl.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;pixel&quot;</span>)
pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){a=n("p"),a.textContent=b,c=t(),_(l.$$.fragment)},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),c=r(i),m(l.$$.fragment,i)},m(i,M){L(i,a,M),L(i,c,M),u(l,i,M),y=!0},p:N,i(i){y||(h(l.$$.fragment,i),y=!0)},o(i){g(l.$$.fragment,i),y=!1},d(i){i&&(d(a),d(c)),x(l,i)}}}function td(D){let a,b="Example:",c,l,y;return l=new ua({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(<span class="hljs-string">&quot;CiroN2022/toy-face&quot;</span>, weight_name=<span class="hljs-string">&quot;toy_face_sdxl.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;toy&quot;</span>)
pipeline.get_active_adapters()`,wrap:!1}}),{c(){a=n("p"),a.textContent=b,c=t(),_(l.$$.fragment)},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),c=r(i),m(l.$$.fragment,i)},m(i,M){L(i,a,M),L(i,c,M),u(l,i,M),y=!0},p:N,i(i){y||(h(l.$$.fragment,i),y=!0)},o(i){g(l.$$.fragment,i),y=!1},d(i){i&&(d(a),d(c)),x(l,i)}}}function rd(D){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(c){a=s(c,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(c,l){L(c,a,l)},p:N,d(c){c&&d(a)}}}function ad(D){let a,b="Examples:",c,l,y;return l=new ua({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">&gt;&gt;&gt; </span>...',wrap:!1}}),{c(){a=n("p"),a.textContent=b,c=t(),_(l.$$.fragment)},l(i){a=s(i,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),c=r(i),m(l.$$.fragment,i)},m(i,M){L(i,a,M),L(i,c,M),u(l,i,M),y=!0},p:N,i(i){y||(h(l.$$.fragment,i),y=!0)},o(i){g(l.$$.fragment,i),y=!1},d(i){i&&(d(a),d(c)),x(l,i)}}}function nd(D){let a,b,c,l,y,i,M,Ds='LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet (<a href="/docs/diffusers/pr_10725/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_10725/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Pr,Ke,Ts='<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>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/pr_10725/en/api/pipelines/amused#diffusers.AmusedPipeline">AmusedPipeline</a>.</li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',Ir,ue,Hr,eo,Er,A,oo,ha,pt,Ss=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_10725/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,ga,he,to,xa,_t,Cs="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",La,ge,ro,ba,mt,ks="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",va,V,ao,wa,ut,As=`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>.`,$a,ht,Rs="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Ma,gt,Ps=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,ya,xt,Is=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is
loaded into <code>self.unet</code>.`,Da,Lt,Hs=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder">load_lora_into_text_encoder()</a> for more details on how the state
dict is loaded into <code>self.text_encoder</code>.`,Ta,Q,no,Sa,bt,Es="Return state dict for lora weights and the network alphas.",Ca,xe,ka,Le,so,Aa,vt,Us="Save the LoRA parameters corresponding to the UNet and text encoder.",Ur,io,Fr,R,lo,Ra,wt,Fs=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_10725/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>, and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow"><code>CLIPTextModelWithProjection</code></a>.`,Pa,be,co,Ia,$t,Vs="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ha,ve,fo,Ea,Mt,Xs="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Ua,X,po,Fa,yt,Ns=`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>.`,Va,Dt,Ws="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Xa,Tt,zs=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Na,St,qs=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is
loaded into <code>self.unet</code>.`,Wa,Ct,Bs=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder">load_lora_into_text_encoder()</a> for more details on how the state
dict is loaded into <code>self.text_encoder</code>.`,za,O,_o,qa,kt,js="Return state dict for lora weights and the network alphas.",Ba,we,ja,$e,mo,Ga,At,Gs="Save the LoRA parameters corresponding to the UNet and text encoder.",Vr,uo,Xr,C,ho,Ja,Rt,Js=`Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>, and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow"><code>CLIPTextModelWithProjection</code></a>.`,Za,Pt,Zs='Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Ya,Me,go,Qa,It,Ys="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Oa,ye,xo,Ka,Ht,Qs="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",en,q,Lo,on,Et,Os=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.unet</code> and
<code>self.text_encoder</code>.`,tn,Ut,Ks="All kwargs are forwarded to <code>self.lora_state_dict</code>.",rn,Ft,ei=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,an,Vt,oi=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,nn,K,bo,sn,Xt,ti="Return state dict for lora weights and the network alphas.",dn,De,ln,Te,vo,cn,Nt,ri="Save the LoRA parameters corresponding to the UNet and text encoder.",Nr,wo,Wr,T,$o,fn,Wt,ai=`Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,pn,zt,ni='Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',_n,Se,Mo,mn,qt,si="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",un,Ce,yo,hn,Bt,ii="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",gn,B,Do,xn,jt,di=`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>.`,Ln,Gt,li="All kwargs are forwarded to <code>self.lora_state_dict</code>.",bn,Jt,ci=`See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,vn,Zt,fi=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,wn,ee,To,$n,Yt,pi="Return state dict for lora weights and the network alphas.",Mn,ke,yn,Ae,So,Dn,Qt,_i="Save the LoRA parameters corresponding to the UNet and text encoder.",Tn,oe,Co,Sn,Ot,mi=`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>.`,Cn,Re,kn,te,ko,An,Kt,ui="Unloads the LoRA parameters.",Rn,Pe,zr,Ao,qr,P,Ro,Pn,er,hi='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',In,Ie,Po,Hn,or,gi="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",En,He,Io,Un,tr,xi=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Fn,re,Ho,Vn,rr,Li="Return state dict for lora weights and the network alphas.",Xn,Ee,Nn,Ue,Eo,Wn,ar,bi="Save the LoRA parameters corresponding to the UNet and text encoder.",zn,ae,Uo,qn,nr,vi=`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>.`,Bn,Fe,Br,Fo,jr,I,Vo,jn,sr,wi='Load LoRA layers into <a href="/docs/diffusers/pr_10725/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10725/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',Gn,Ve,Xo,Jn,ir,$i="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Zn,Xe,No,Yn,dr,Mi=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Qn,ne,Wo,On,lr,yi="Return state dict for lora weights and the network alphas.",Kn,Ne,es,We,zo,os,cr,Di="Save the LoRA parameters corresponding to the UNet and text encoder.",ts,se,qo,rs,fr,Ti=`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>.`,as,ze,Gr,Bo,Jr,fe,jo,ns,qe,Go,ss,pr,Si="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Zr,Jo,Yr,S,Zo,is,_r,Ci="Utility class for handling LoRAs.",ds,mr,Yo,ls,Z,Qo,cs,ur,ki="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",fs,Be,ps,je,_s,ie,Oo,ms,hr,Ai="Gets the list of the current active adapters.",us,Ge,hs,Je,Ko,gs,gr,Ri="Gets the current list of all available adapters in the pipeline.",xs,Ze,et,Ls,xr,Pi=`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.`,bs,de,ot,vs,Lr,Ii=`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>.`,ws,Ye,$s,le,tt,Ms,br,Hi="Unloads the LoRA parameters.",ys,Qe,Qr,rt,Or,Rr,Kr;return y=new me({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),ue=new Y({props:{$$slots:{default:[zi]},$$scope:{ctx:D}}}),eo=new me({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),oo=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L69"}}),to=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;
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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;
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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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<ul>
<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
dict</a>.</li>
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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.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
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The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
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State dict of the LoRA layers corresponding to the <code>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) &#x2014;
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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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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 only loading the pretrained LoRA weights and not initializing the random
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See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10725/src/diffusers/loaders/lora_pipeline.py#L492"}}),_o=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) &#x2014;
Can be either:</p>
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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
the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
with <a href="/docs/diffusers/pr_10725/en/api/models/overview#diffusers.ModelMixin.save_pretrained">ModelMixin.save_pretrained()</a>.</li>
<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
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The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
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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>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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The function to use to save the state dictionary. Useful during distributed training when you need to
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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.SD3LoraLoaderMixin.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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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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See <a href="/docs/diffusers/pr_10725/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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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_10725/en/api/models/overview#diffusers.ModelMixin.save_pretrained">ModelMixin.save_pretrained()</a>.</li>
<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
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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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Can be either:</p>
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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
dict</a>.</li>
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