diffusers/pr_9017/en/_app/immutable/nodes/9.b48f8580.js

import{s as Cn,o as kn,n as j}from"../chunks/scheduler.8c3d61f6.js";import{S as Pn,i as Rn,g as a,s as o,r as _,A as In,h as n,f as d,c as r,j as v,u as m,x as p,k as $,y as t,a as L,v as u,d as h,t as g,w as x}from"../chunks/index.da70eac4.js";import{T as Ce}from"../chunks/Tip.1d9b8c37.js";import{D as y}from"../chunks/Docstring.ee4b6913.js";import{C as Pa}from"../chunks/CodeBlock.00a903b3.js";import{E as ka}from"../chunks/ExampleCodeBlock.f7bd2c1f.js";import{H as Mt,E as Hn}from"../chunks/EditOnGithub.1e64e623.js";function Un(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}),p(s)!=="svelte-1fw6lx1"&&(s.innerHTML=b)},m(c,l){L(c,s,l)},p:j,d(c){c&&d(s)}}}function En(D){let s,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){s=a("p"),s.textContent=b,c=o(),l=a("p"),l.textContent=M},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(i,w){L(i,s,w),L(i,c,w),L(i,l,w)},p:j,d(i){i&&(d(s),d(c),d(l))}}}function Nn(D){let s,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){s=a("p"),s.textContent=b,c=o(),l=a("p"),l.textContent=M},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(i,w){L(i,s,w),L(i,c,w),L(i,l,w)},p:j,d(i){i&&(d(s),d(c),d(l))}}}function Fn(D){let s,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){s=a("p"),s.textContent=b,c=o(),l=a("p"),l.textContent=M},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(i,w){L(i,s,w),L(i,c,w),L(i,l,w)},p:j,d(i){i&&(d(s),d(c),d(l))}}}function Wn(D){let s,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",c,l,M="This function is experimental and might change in the future.";return{c(){s=a("p"),s.textContent=b,c=o(),l=a("p"),l.textContent=M},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=b),c=r(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=M)},m(i,w){L(i,s,w),L(i,c,w),L(i,l,w)},p:j,d(i){i&&(d(s),d(c),d(l))}}}function Xn(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}),p(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:j,d(c){c&&d(s)}}}function Bn(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}),p(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:j,d(c){c&&d(s)}}}function qn(D){let s,b="Example:",c,l,M;return l=new Pa({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=o(),_(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-11lpom8"&&(s.textContent=b),c=r(i),m(l.$$.fragment,i)},m(i,w){L(i,s,w),L(i,c,w),u(l,i,w),M=!0},p:j,i(i){M||(h(l.$$.fragment,i),M=!0)},o(i){g(l.$$.fragment,i),M=!1},d(i){i&&(d(s),d(c)),x(l,i)}}}function jn(D){let s,b="Example:",c,l,M;return l=new Pa({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=o(),_(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-11lpom8"&&(s.textContent=b),c=r(i),m(l.$$.fragment,i)},m(i,w){L(i,s,w),L(i,c,w),u(l,i,w),M=!0},p:j,i(i){M||(h(l.$$.fragment,i),M=!0)},o(i){g(l.$$.fragment,i),M=!1},d(i){i&&(d(s),d(c)),x(l,i)}}}function Vn(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}),p(s)!=="svelte-8w79b9"&&(s.textContent=b)},m(c,l){L(c,s,l)},p:j,d(c){c&&d(s)}}}function Jn(D){let s,b="Examples:",c,l,M;return l=new Pa({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=o(),_(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-kvfsh7"&&(s.textContent=b),c=r(i),m(l.$$.fragment,i)},m(i,w){L(i,s,w),L(i,c,w),u(l,i,w),M=!0},p:j,i(i){M||(h(l.$$.fragment,i),M=!0)},o(i){g(l.$$.fragment,i),M=!1},d(i){i&&(d(s),d(c)),x(l,i)}}}function Gn(D){let s,b,c,l,M,i,w,Ra='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_9017/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_9017/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',To,ke,Ia='<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>AuraFlowLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/fal/AuraFlow" rel="nofollow">AuraFlow</a>.</li> <li><code>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/pr_9017/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>',So,ae,Ao,Pe,Co,A,Re,er,yt,Ha=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_9017/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>.`,tr,ne,Ie,or,Dt,Ua="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",rr,se,He,ar,Tt,Ea="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",nr,H,Ue,sr,St,Na=`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>.`,ir,At,Fa="All kwargs are forwarded to <code>self.lora_state_dict</code>.",dr,Ct,Wa=`See <a href="/docs/diffusers/pr_9017/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,lr,kt,Xa=`See <a href="/docs/diffusers/pr_9017/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>.`,cr,Pt,Ba=`See <a href="/docs/diffusers/pr_9017/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>.`,fr,V,Ee,pr,Rt,qa="Return state dict for lora weights and the network alphas.",_r,ie,mr,de,Ne,ur,It,ja="Save the LoRA parameters corresponding to the UNet and text encoder.",ko,Fe,Po,C,We,hr,Ht,Va=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_9017/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>.`,gr,le,Xe,xr,Ut,Ja="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Lr,ce,Be,br,Et,Ga="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",vr,U,qe,$r,Nt,Za=`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>.`,wr,Ft,za="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Mr,Wt,Ya=`See <a href="/docs/diffusers/pr_9017/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,yr,Xt,Qa=`See <a href="/docs/diffusers/pr_9017/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>.`,Dr,Bt,Oa=`See <a href="/docs/diffusers/pr_9017/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>.`,Tr,J,je,Sr,qt,Ka="Return state dict for lora weights and the network alphas.",Ar,fe,Cr,pe,Ve,kr,jt,en="Save the LoRA parameters corresponding to the UNet and text encoder.",Ro,Je,Io,S,Ge,Pr,Vt,tn=`Load LoRA layers into <a href="/docs/diffusers/pr_9017/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>.`,Rr,Jt,on='Specific to <a href="/docs/diffusers/pr_9017/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Ir,_e,Ze,Hr,Gt,rn="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ur,me,ze,Er,Zt,an="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Nr,W,Ye,Fr,zt,nn=`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>.`,Wr,Yt,sn="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Xr,Qt,dn=`See <a href="/docs/diffusers/pr_9017/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Br,Ot,ln=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,qr,G,Qe,jr,Kt,cn="Return state dict for lora weights and the network alphas.",Vr,ue,Jr,he,Oe,Gr,eo,fn="Save the LoRA parameters corresponding to the UNet and text encoder.",Ho,Ke,Uo,k,et,Zr,to,pn=`Load LoRA layers into <a href="/docs/diffusers/pr_9017/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a>
Specific to <a href="/docs/diffusers/pr_9017/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.`,zr,ge,tt,Yr,oo,_n="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Qr,X,ot,Or,ro,mn="Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code>",Kr,ao,un="All kwargs are forwarded to <code>self.lora_state_dict</code>.",ea,no,hn=`See <a href="/docs/diffusers/pr_9017/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,ta,so,gn=`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,Z,rt,ra,io,xn="Return state dict for lora weights and the network alphas.",aa,xe,na,Le,at,sa,lo,Ln="Save the LoRA parameters corresponding to the UNet and text encoder.",ia,z,nt,da,co,bn=`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>.`,la,be,Eo,st,No,te,it,ca,ve,dt,fa,fo,vn="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Fo,lt,Wo,T,ct,pa,po,$n="Utility class for handling LoRAs.",_a,_o,ft,ma,q,pt,ua,mo,wn="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",ha,$e,ga,we,xa,Y,_t,La,uo,Mn="Gets the list of the current active adapters.",ba,Me,va,ye,mt,$a,ho,yn="Gets the current list of all available adapters in the pipeline.",wa,De,ut,Ma,go,Dn=`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.`,ya,Q,ht,Da,xo,Tn=`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>.`,Ta,Te,Sa,O,gt,Aa,Lo,Sn="Unloads the LoRA parameters.",Ca,Se,Xo,xt,Bo,Do,qo;return M=new Mt({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),ae=new Ce({props:{$$slots:{default:[Un]},$$scope:{ctx:D}}}),Pe=new Mt({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Re=new y({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_9017/src/diffusers/loaders/lora_pipeline.py#L50"}}),Ie=new y({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"}],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;
See <code>LoRALinearLayer</code> for more details.`,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
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