diffusers/pr_10312/en/_app/immutable/nodes/9.7f094a79.js

import{s as Si,o as Ci,n as z}from"../chunks/scheduler.8c3d61f6.js";import{S as ki,i as Ai,g as a,s as t,r as _,A as Ri,h as n,f as i,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 Z}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.6b390b9a.js";import{C as gs}from"../chunks/CodeBlock.00a903b3.js";import{E as hs}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as _e,E as Pi}from"../chunks/EditOnGithub.1e64e623.js";function Ii(D){let s,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(){s=a("p"),s.innerHTML=b},l(c){s=n(c,"P",{"data-svelte-h":!0}),f(s)!=="svelte-1fw6lx1"&&(s.innerHTML=b)},m(c,l){L(c,s,l)},p:z,d(c){c&&i(s)}}}function Hi(D){let s,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(){s=a("p"),s.textContent=b,c=t(),l=a("p"),l.textContent=y},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(d),l=n(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(d,M){L(d,s,M),L(d,c,M),L(d,l,M)},p:z,d(d){d&&(i(s),i(c),i(l))}}}function Vi(D){let s,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(){s=a("p"),s.textContent=b,c=t(),l=a("p"),l.textContent=y},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(d),l=n(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(d,M){L(d,s,M),L(d,c,M),L(d,l,M)},p:z,d(d){d&&(i(s),i(c),i(l))}}}function Ei(D){let s,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(){s=a("p"),s.textContent=b,c=t(),l=a("p"),l.textContent=y},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(d),l=n(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(d,M){L(d,s,M),L(d,c,M),L(d,l,M)},p:z,d(d){d&&(i(s),i(c),i(l))}}}function Xi(D){let s,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(){s=a("p"),s.textContent=b,c=t(),l=a("p"),l.textContent=y},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(d),l=n(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(d,M){L(d,s,M),L(d,c,M),L(d,l,M)},p:z,d(d){d&&(i(s),i(c),i(l))}}}function Ui(D){let s,b="This is an experimental API.";return{c(){s=a("p"),s.textContent=b},l(c){s=n(c,"P",{"data-svelte-h":!0}),f(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:z,d(c){c&&i(s)}}}function Fi(D){let s,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(){s=a("p"),s.textContent=b,c=t(),l=a("p"),l.textContent=y},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(d),l=n(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(d,M){L(d,s,M),L(d,c,M),L(d,l,M)},p:z,d(d){d&&(i(s),i(c),i(l))}}}function Ni(D){let s,b="This is an experimental API.";return{c(){s=a("p"),s.textContent=b},l(c){s=n(c,"P",{"data-svelte-h":!0}),f(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:z,d(c){c&&i(s)}}}function Wi(D){let s,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(){s=a("p"),s.textContent=b,c=t(),l=a("p"),l.textContent=y},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(d),l=n(d,"P",{"data-svelte-h":!0}),f(l)!=="svelte-3fufvn"&&(l.textContent=y)},m(d,M){L(d,s,M),L(d,c,M),L(d,l,M)},p:z,d(d){d&&(i(s),i(c),i(l))}}}function zi(D){let s,b="This is an experimental API.";return{c(){s=a("p"),s.textContent=b},l(c){s=n(c,"P",{"data-svelte-h":!0}),f(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:z,d(c){c&&i(s)}}}function qi(D){let s,b="This is an experimental API.";return{c(){s=a("p"),s.textContent=b},l(c){s=n(c,"P",{"data-svelte-h":!0}),f(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:z,d(c){c&&i(s)}}}function Bi(D){let s,b="Example:",c,l,y;return l=new gs({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(){s=a("p"),s.textContent=b,c=t(),_(l.$$.fragment)},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-11lpom8"&&(s.textContent=b),c=r(d),m(l.$$.fragment,d)},m(d,M){L(d,s,M),L(d,c,M),u(l,d,M),y=!0},p:z,i(d){y||(h(l.$$.fragment,d),y=!0)},o(d){g(l.$$.fragment,d),y=!1},d(d){d&&(i(s),i(c)),x(l,d)}}}function ji(D){let s,b="Example:",c,l,y;return l=new gs({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(){s=a("p"),s.textContent=b,c=t(),_(l.$$.fragment)},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-11lpom8"&&(s.textContent=b),c=r(d),m(l.$$.fragment,d)},m(d,M){L(d,s,M),L(d,c,M),u(l,d,M),y=!0},p:z,i(d){y||(h(l.$$.fragment,d),y=!0)},o(d){g(l.$$.fragment,d),y=!1},d(d){d&&(i(s),i(c)),x(l,d)}}}function Ji(D){let s,b="This is an experimental API.";return{c(){s=a("p"),s.textContent=b},l(c){s=n(c,"P",{"data-svelte-h":!0}),f(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:z,d(c){c&&i(s)}}}function Gi(D){let s,b="Examples:",c,l,y;return l=new gs({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(){s=a("p"),s.textContent=b,c=t(),_(l.$$.fragment)},l(d){s=n(d,"P",{"data-svelte-h":!0}),f(s)!=="svelte-kvfsh7"&&(s.textContent=b),c=r(d),m(l.$$.fragment,d)},m(d,M){L(d,s,M),L(d,c,M),u(l,d,M),y=!0},p:z,i(d){y||(h(l.$$.fragment,d),y=!0)},o(d){g(l.$$.fragment,d),y=!1},d(d){d&&(i(s),i(c)),x(l,d)}}}function Zi(D){let s,b,c,l,y,d,M,xs='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_10312/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_10312/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Sr,Qe,Ls='<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_10312/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>',Cr,me,kr,Oe,Ar,k,Ke,la,lt,bs=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_10312/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>.`,ca,ue,eo,fa,ct,vs="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",pa,he,oo,_a,ft,ws="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",ma,U,to,ua,pt,$s=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.unet</code> and
<code>self.text_encoder</code>.`,ha,_t,Ms="All kwargs are forwarded to <code>self.lora_state_dict</code>.",ga,mt,ys=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,xa,ut,Ds=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is
loaded into <code>self.unet</code>.`,La,ht,Ts=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder">load_lora_into_text_encoder()</a> for more details on how the state
dict is loaded into <code>self.text_encoder</code>.`,ba,Y,ro,va,gt,Ss="Return state dict for lora weights and the network alphas.",wa,ge,$a,xe,ao,Ma,xt,Cs="Save the LoRA parameters corresponding to the UNet and text encoder.",Rr,no,Pr,A,so,ya,Lt,ks=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_10312/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>.`,Da,Le,io,Ta,bt,As="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Sa,be,lo,Ca,vt,Rs="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",ka,F,co,Aa,wt,Ps=`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>.`,Ra,$t,Is="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Pa,Mt,Hs=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Ia,yt,Vs=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is
loaded into <code>self.unet</code>.`,Ha,Dt,Es=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder">load_lora_into_text_encoder()</a> for more details on how the state
dict is loaded into <code>self.text_encoder</code>.`,Va,Q,fo,Ea,Tt,Xs="Return state dict for lora weights and the network alphas.",Xa,ve,Ua,we,po,Fa,St,Us="Save the LoRA parameters corresponding to the UNet and text encoder.",Ir,_o,Hr,C,mo,Na,Ct,Fs=`Load LoRA layers into <a href="/docs/diffusers/pr_10312/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>, and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow"><code>CLIPTextModelWithProjection</code></a>.`,Wa,kt,Ns='Specific to <a href="/docs/diffusers/pr_10312/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',za,$e,uo,qa,At,Ws="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ba,Me,ho,ja,Rt,zs="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Ja,q,go,Ga,Pt,qs=`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>.`,Za,It,Bs="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Ya,Ht,js=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Qa,Vt,Js=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Oa,O,xo,Ka,Et,Gs="Return state dict for lora weights and the network alphas.",en,ye,on,De,Lo,tn,Xt,Zs="Save the LoRA parameters corresponding to the UNet and text encoder.",Vr,bo,Er,T,vo,rn,Ut,Ys=`Load LoRA layers into <a href="/docs/diffusers/pr_10312/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>.`,an,Ft,Qs='Specific to <a href="/docs/diffusers/pr_10312/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',nn,Te,wo,sn,Nt,Os="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",dn,Se,$o,ln,Wt,Ks="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",cn,B,Mo,fn,zt,ei=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>.`,pn,qt,oi="All kwargs are forwarded to <code>self.lora_state_dict</code>.",_n,Bt,ti=`See <a href="/docs/diffusers/pr_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,mn,jt,ri=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,un,K,yo,hn,Jt,ai="Return state dict for lora weights and the network alphas.",gn,Ce,xn,ke,Do,Ln,Gt,ni="Save the LoRA parameters corresponding to the UNet and text encoder.",bn,ee,To,vn,Zt,si=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,wn,Ae,Xr,So,Ur,R,Co,$n,Yt,ii='Load LoRA layers into <a href="/docs/diffusers/pr_10312/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10312/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',Mn,Re,ko,yn,Qt,di="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Dn,Pe,Ao,Tn,Ot,li=`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_10312/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>.`,Sn,oe,Ro,Cn,Kt,ci="Return state dict for lora weights and the network alphas.",kn,Ie,An,He,Po,Rn,er,fi="Save the LoRA parameters corresponding to the UNet and text encoder.",Pn,te,Io,In,or,pi=`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>.`,Hn,Ve,Fr,Ho,Nr,P,Vo,Vn,tr,_i='Load LoRA layers into <a href="/docs/diffusers/pr_10312/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_10312/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',En,Ee,Eo,Xn,rr,mi="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Un,Xe,Xo,Fn,ar,ui=`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_10312/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>.`,Nn,re,Uo,Wn,nr,hi="Return state dict for lora weights and the network alphas.",zn,Ue,qn,Fe,Fo,Bn,sr,gi="Save the LoRA parameters corresponding to the UNet and text encoder.",jn,ae,No,Jn,ir,xi=`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>.`,Gn,Ne,Wr,Wo,zr,ce,zo,Zn,We,qo,Yn,dr,Li="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",qr,Bo,Br,S,jo,Qn,lr,bi="Utility class for handling LoRAs.",On,cr,Jo,Kn,G,Go,es,fr,vi="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",os,ze,ts,qe,rs,ne,Zo,as,pr,wi="Gets the list of the current active adapters.",ns,Be,ss,je,Yo,is,_r,$i="Gets the current list of all available adapters in the pipeline.",ds,Je,Qo,ls,mr,Mi=`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.`,cs,se,Oo,fs,ur,yi=`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>.`,ps,Ge,_s,ie,Ko,ms,hr,Di="Unloads the LoRA parameters.",us,Ze,jr,et,Jr,Tr,Gr;return y=new _e({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),me=new Z({props:{$$slots:{default:[Ii]},$$scope:{ctx:D}}}),Oe=new _e({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Ke=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10312/src/diffusers/loaders/lora_pipeline.py#L69"}}),eo=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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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;
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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
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Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
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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
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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_10312/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
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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_10312/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_10312/src/diffusers/loaders/lora_pipeline.py#L79"}}),ro=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_10312/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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State dict of the LoRA layers corresponding to the <code>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) &#x2014;
State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text
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