diffusers/pr_11105/en/_app/immutable/nodes/10.e7e73ea7.js

import{s as Pf,o as Hf,n as C}from"../chunks/scheduler.8c3d61f6.js";import{S as Vf,i as If,g as n,s as t,r as p,A as Ff,h as s,f as i,c as a,j as w,u as m,x as f,k as v,y as o,a as L,v as h,d as _,t as u,w as g}from"../chunks/index.da70eac4.js";import{T as P}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.fa488882.js";import{C as Hs}from"../chunks/CodeBlock.a9c4becf.js";import{E as Ps}from"../chunks/ExampleCodeBlock.ec9feb8f.js";import{H as ne,E as Uf}from"../chunks/index.dfbaf638.js";function Xf(T){let r,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(){r=n("p"),r.innerHTML=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-1fw6lx1"&&(r.innerHTML=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function Ef(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function Wf(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function qf(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function Nf(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function zf(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function Bf(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function jf(T){let r,b="Examples:",l,c,M;return c=new Hs({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(){r=n("p"),r.textContent=b,l=t(),p(c.$$.fragment)},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-kvfsh7"&&(r.textContent=b),l=a(d),m(c.$$.fragment,d)},m(d,y){L(d,r,y),L(d,l,y),h(c,d,y),M=!0},p:C,i(d){M||(_(c.$$.fragment,d),M=!0)},o(d){u(c.$$.fragment,d),M=!1},d(d){d&&(i(r),i(l)),g(c,d)}}}function Gf(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function Jf(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function Zf(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function Yf(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function Of(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function Qf(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function Kf(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function ep(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function op(T){let r,b="We support loading original format HunyuanVideo LoRA checkpoints.",l,c,M="This function is experimental and might change in the future.";return{c(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-gyrs6h"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function tp(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function ap(T){let r,b="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(){r=n("p"),r.textContent=b,l=t(),c=n("p"),c.textContent=M},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-15l1sdn"&&(r.textContent=b),l=a(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(d,y){L(d,r,y),L(d,l,y),L(d,c,y)},p:C,d(d){d&&(i(r),i(l),i(c))}}}function rp(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function np(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function sp(T){let r,b="Example:",l,c,M;return c=new Hs({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(){r=n("p"),r.textContent=b,l=t(),p(c.$$.fragment)},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-11lpom8"&&(r.textContent=b),l=a(d),m(c.$$.fragment,d)},m(d,y){L(d,r,y),L(d,l,y),h(c,d,y),M=!0},p:C,i(d){M||(_(c.$$.fragment,d),M=!0)},o(d){u(c.$$.fragment,d),M=!1},d(d){d&&(i(r),i(l)),g(c,d)}}}function ip(T){let r,b="Example:",l,c,M;return c=new Hs({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(){r=n("p"),r.textContent=b,l=t(),p(c.$$.fragment)},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-11lpom8"&&(r.textContent=b),l=a(d),m(c.$$.fragment,d)},m(d,y){L(d,r,y),L(d,l,y),h(c,d,y),M=!0},p:C,i(d){M||(_(c.$$.fragment,d),M=!0)},o(d){u(c.$$.fragment,d),M=!1},d(d){d&&(i(r),i(l)),g(c,d)}}}function dp(T){let r,b="This is an experimental API.";return{c(){r=n("p"),r.textContent=b},l(l){r=s(l,"P",{"data-svelte-h":!0}),f(r)!=="svelte-8w79b9"&&(r.textContent=b)},m(l,c){L(l,r,c)},p:C,d(l){l&&i(r)}}}function lp(T){let r,b="Examples:",l,c,M;return c=new Hs({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(){r=n("p"),r.textContent=b,l=t(),p(c.$$.fragment)},l(d){r=s(d,"P",{"data-svelte-h":!0}),f(r)!=="svelte-kvfsh7"&&(r.textContent=b),l=a(d),m(c.$$.fragment,d)},m(d,y){L(d,r,y),L(d,l,y),h(c,d,y),M=!0},p:C,i(d){M||(_(c.$$.fragment,d),M=!0)},o(d){u(c.$$.fragment,d),M=!1},d(d){d&&(i(r),i(l)),g(c,d)}}}function cp(T){let r,b,l,c,M,d,y,Jl='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:',In,qo,Zl='<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>',Fn,Ie,Un,No,Xn,V,zo,Vs,Ha,Yl=`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>.`,Is,Fe,Bo,Fs,Va,Ol="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Us,Ue,jo,Xs,Ia,Ql="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Es,B,Go,Ws,Fa,Kl=`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>.`,qs,Ua,ec="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Ns,Xa,oc=`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.`,zs,Ea,tc=`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>.`,Bs,Wa,ac=`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>.`,js,ie,Jo,Gs,qa,rc="Return state dict for lora weights and the network alphas.",Js,Xe,Zs,Ee,Zo,Ys,Na,nc="Save the LoRA parameters corresponding to the UNet and text encoder.",En,Yo,Wn,I,Oo,Os,za,sc=`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>.`,Qs,We,Qo,Ks,Ba,ic="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",ei,qe,Ko,oi,ja,dc="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",ti,j,et,ai,Ga,lc=`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>.`,ri,Ja,cc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",ni,Za,fc=`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.`,si,Ya,pc=`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>.`,ii,Oa,mc=`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>.`,di,de,ot,li,Qa,hc="Return state dict for lora weights and the network alphas.",ci,Ne,fi,ze,tt,pi,Ka,_c="Save the LoRA parameters corresponding to the UNet and text encoder.",qn,at,Nn,k,rt,mi,er,uc=`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>.`,hi,or,gc='Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',_i,Be,nt,ui,tr,xc="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",gi,je,st,xi,ar,Lc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Li,Z,it,bi,rr,bc=`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>.`,wi,nr,wc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",vi,sr,vc=`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.`,$i,ir,$c=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,yi,le,dt,Mi,dr,yc="Return state dict for lora weights and the network alphas.",Ti,Ge,Di,Je,lt,Si,lr,Mc="Save the LoRA parameters corresponding to the UNet and text encoder.",Ci,ce,ct,ki,cr,Tc=`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>.`,Ai,Ze,zn,ft,Bn,D,pt,Ri,fr,Dc=`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>.`,Pi,pr,Sc='Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Hi,Ye,mt,Vi,mr,Cc="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ii,Oe,ht,Fi,hr,kc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Ui,Y,_t,Xi,_r,Ac=`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>.`,Ei,ur,Rc="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Wi,gr,Pc=`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.`,qi,xr,Hc=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Ni,fe,ut,zi,Lr,Vc="Return state dict for lora weights and the network alphas.",Bi,Qe,ji,Ke,gt,Gi,br,Ic="Save the LoRA parameters corresponding to the UNet and text encoder.",Ji,pe,xt,Zi,wr,Fc=`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>.`,Yi,eo,Oi,me,Lt,Qi,vr,Uc="Unloads the LoRA parameters.",Ki,oo,jn,bt,Gn,F,wt,ed,$r,Xc='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11105/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',od,to,vt,td,yr,Ec="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ad,ao,$t,rd,Mr,Wc=`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>.`,nd,he,yt,sd,Tr,qc="Return state dict for lora weights and the network alphas.",id,ro,dd,no,Mt,ld,Dr,Nc="Save the LoRA parameters corresponding to the UNet and text encoder.",cd,_e,Tt,fd,Sr,zc=`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>.`,pd,so,Jn,Dt,Zn,U,St,md,Cr,Bc='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>.',hd,io,Ct,_d,kr,jc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ud,lo,kt,gd,Ar,Gc=`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>.`,xd,ue,At,Ld,Rr,Jc="Return state dict for lora weights and the network alphas.",bd,co,wd,fo,Rt,vd,Pr,Zc="Save the LoRA parameters corresponding to the UNet and text encoder.",$d,ge,Pt,yd,Hr,Yc=`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>.`,Md,po,Yn,Ht,On,X,Vt,Td,Vr,Oc='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>.',Dd,mo,It,Sd,Ir,Qc="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Cd,ho,Ft,kd,Fr,Kc=`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>.`,Ad,xe,Ut,Rd,Ur,ef="Return state dict for lora weights and the network alphas.",Pd,_o,Hd,uo,Xt,Vd,Xr,of="Save the LoRA parameters corresponding to the UNet and text encoder.",Id,Le,Et,Fd,Er,tf=`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>.`,Ud,go,Qn,Wt,Kn,E,qt,Xd,Wr,af='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>.',Ed,xo,Nt,Wd,qr,rf="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",qd,Lo,zt,Nd,Nr,nf=`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>.`,zd,be,Bt,Bd,zr,sf="Return state dict for lora weights and the network alphas.",jd,bo,Gd,wo,jt,Jd,Br,df="Save the LoRA parameters corresponding to the UNet and text encoder.",Zd,we,Gt,Yd,jr,lf=`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>.`,Od,vo,es,Jt,os,W,Zt,Qd,Gr,cf='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>.',Kd,$o,Yt,el,Jr,ff="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ol,yo,Ot,tl,Zr,pf=`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>.`,al,ve,Qt,rl,Yr,mf="Return state dict for lora weights and the network alphas.",nl,Mo,sl,To,Kt,il,Or,hf="Save the LoRA parameters corresponding to the UNet and text encoder.",dl,$e,ea,ll,Qr,_f=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,cl,Do,ts,oa,as,q,ta,fl,Kr,uf='Load LoRA layers into <a href="/docs/diffusers/pr_11105/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',pl,So,aa,ml,en,gf="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",hl,Co,ra,_l,on,xf=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11105/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,ul,ye,na,gl,tn,Lf="Return state dict for lora weights and the network alphas.",xl,ko,Ll,Ao,sa,bl,an,bf="Save the LoRA parameters corresponding to the UNet and text encoder.",wl,Me,ia,vl,rn,wf=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,$l,Ro,rs,da,ns,ke,la,yl,Po,ca,Ml,nn,vf="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ss,fa,is,S,pa,Tl,sn,$f="Utility class for handling LoRAs.",Dl,dn,ma,Sl,Te,ha,Cl,ln,yf="Enables the possibility to hotswap LoRA adapters.",kl,cn,Mf=`Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of
the loaded adapters differ.`,Al,se,_a,Rl,fn,Tf="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Pl,Ho,Hl,Vo,Vl,De,ua,Il,pn,Df="Gets the list of the current active adapters.",Fl,Io,Ul,Fo,ga,Xl,mn,Sf="Gets the current list of all available adapters in the pipeline.",El,Uo,xa,Wl,hn,Cf=`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.`,ql,Se,La,Nl,_n,kf=`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>.`,zl,Xo,Bl,Ce,ba,jl,un,Af="Unloads the LoRA parameters.",Gl,Eo,ds,wa,ls,Vn,cs;return M=new ne({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),Ie=new P({props:{$$slots:{default:[Xf]},$$scope:{ctx:T}}}),No=new ne({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),zo=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"}}),Bo=new $({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"text_encoder",val:""},{name:"prefix",val:" = None"},{name:"lora_scale",val:" = 1.0"},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional <code>text_encoder</code> to distinguish between unet lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) &#x2014;
Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) &#x2014;
How much to scale the output of the lora linear layer before it is added with the output of the regular
lora layer.`,name:"lora_scale"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong>  &#x2014; (<code>bool</code>, <em>optional</em>)
Defaults to <code>False</code>. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed <code>adapter_name</code> should be the name of an already loaded adapter.</p>
<p>If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11105/src/diffusers/loaders/lora_pipeline.py#L371"}}),jo=new $({props:{name:"load_lora_into_unet",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"unet",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional <code>unet</code> which can be used to distinguish between text
encoder lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.hotswap",description:`<strong>hotswap</strong>  &#x2014; (<code>bool</code>, <em>optional</em>)
Defaults to <code>False</code>. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed <code>adapter_name</code> should be the name of an already loaded adapter.</p>
<p>If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
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adapter weights and replace them with the weights of the new adapter. This can be faster and more
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adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
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<p>If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
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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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Defaults to <code>False</code>. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
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adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed <code>adapter_name</code> should be the name of an already loaded adapter.</p>
<p>If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
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adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
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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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adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
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<li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
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<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
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<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
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pp(T){return Hf(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class bp extends Vf{constructor(r){super(),If(this,r,pp,cp,Pf,{})}}export{bp as component};