diffusers/pr_11438/en/_app/immutable/nodes/10.f45bb362.js

import{s as lu,o as cu,n as D}from"../chunks/scheduler.8c3d61f6.js";import{S as fu,i as pu,g as n,s as r,r as p,A as mu,h as s,f as i,c as t,j as v,u as m,x as f,k as w,y as o,a as x,v as _,d as u,t as h,w as g}from"../chunks/index.da70eac4.js";import{T as S}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.932cdff3.js";import{C as Xd}from"../chunks/CodeBlock.a9c4becf.js";import{E as Nd}from"../chunks/ExampleCodeBlock.c0461b09.js";import{H as O,E as _u}from"../chunks/index.ef90fe87.js";function uu(T){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(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-1fw6lx1"&&(a.innerHTML=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function hu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function gu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Lu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function xu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function bu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function vu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function wu(T){let a,b="Examples:",l,c,y;return c=new Xd({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,l=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),y=!0},p:D,i(d){y||(u(c.$$.fragment,d),y=!0)},o(d){h(c.$$.fragment,d),y=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function $u(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Mu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function yu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Tu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Du(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Su(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Cu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Au(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function ku(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Pu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Hu(T){let a,b="We support loading original format HunyuanVideo LoRA checkpoints.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-gyrs6h"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Ru(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Iu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Vu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Fu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Uu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Wu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Eu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Nu(T){let a,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,y="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=b,l=r(),c=n("p"),c.textContent=y},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-15l1sdn"&&(a.textContent=b),l=t(d),c=s(d,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=y)},m(d,M){x(d,a,M),x(d,l,M),x(d,c,M)},p:D,d(d){d&&(i(a),i(l),i(c))}}}function Xu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function qu(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function zu(T){let a,b="Example:",l,c,y;return c=new Xd({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(<span class="hljs-string">&quot;nerijs/pixel-art-xl&quot;</span>, weight_name=<span class="hljs-string">&quot;pixel-art-xl.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;pixel&quot;</span>)
pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){a=n("p"),a.textContent=b,l=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),y=!0},p:D,i(d){y||(u(c.$$.fragment,d),y=!0)},o(d){h(c.$$.fragment,d),y=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function Bu(T){let a,b="Example:",l,c,y;return c=new Xd({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,l=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-11lpom8"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),y=!0},p:D,i(d){y||(u(c.$$.fragment,d),y=!0)},o(d){h(c.$$.fragment,d),y=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function ju(T){let a,b="This is an experimental API.";return{c(){a=n("p"),a.textContent=b},l(l){a=s(l,"P",{"data-svelte-h":!0}),f(a)!=="svelte-8w79b9"&&(a.textContent=b)},m(l,c){x(l,a,c)},p:D,d(l){l&&i(a)}}}function Gu(T){let a,b="Examples:",l,c,y;return c=new Xd({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,l=r(),p(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),f(a)!=="svelte-kvfsh7"&&(a.textContent=b),l=t(d),m(c.$$.fragment,d)},m(d,M){x(d,a,M),x(d,l,M),_(c,d,M),y=!0},p:D,i(d){y||(u(c.$$.fragment,d),y=!0)},o(d){h(c.$$.fragment,d),y=!1},d(d){d&&(i(a),i(l)),g(c,d)}}}function Ju(T){let a,b,l,c,y,d,M,om='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_11438/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_11438/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Di,Sr,rm='<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>AuraFlowLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/fal/AuraFlow" rel="nofollow">AuraFlow</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>WanLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan" rel="nofollow">Wan</a>.</li> <li><code>CogView4LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4" rel="nofollow">CogView4</a>.</li> <li><code>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/pr_11438/en/api/pipelines/amused#diffusers.AmusedPipeline">AmusedPipeline</a>.</li> <li><code>HiDreamImageLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream" rel="nofollow">HiDream Image</a></li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',Si,ro,Ci,Cr,Ai,I,Ar,qd,Ya,tm=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_11438/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>.`,zd,to,kr,Bd,Qa,am="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",jd,ao,Pr,Gd,Ka,nm="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Jd,Y,Hr,Zd,en,sm=`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>.`,Od,on,im="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Yd,rn,dm=`See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Qd,tn,lm=`See <a href="/docs/diffusers/pr_11438/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>.`,Kd,an,cm=`See <a href="/docs/diffusers/pr_11438/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>.`,el,ue,Rr,ol,nn,fm="Return state dict for lora weights and the network alphas.",rl,no,tl,so,Ir,al,sn,pm="Save the LoRA parameters corresponding to the UNet and text encoder.",ki,Vr,Pi,V,Fr,nl,dn,mm=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_11438/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>.`,sl,io,Ur,il,ln,_m="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",dl,lo,Wr,ll,cn,um="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",cl,Q,Er,fl,fn,hm=`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>.`,pl,pn,gm="All kwargs are forwarded to <code>self.lora_state_dict</code>.",ml,mn,Lm=`See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,_l,_n,xm=`See <a href="/docs/diffusers/pr_11438/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>.`,ul,un,bm=`See <a href="/docs/diffusers/pr_11438/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>.`,hl,he,Nr,gl,hn,vm="Return state dict for lora weights and the network alphas.",Ll,co,xl,fo,Xr,bl,gn,wm="Save the LoRA parameters corresponding to the UNet and text encoder.",Hi,qr,Ri,k,zr,vl,Ln,$m=`Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>,
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>, and
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection" rel="nofollow"><code>CLIPTextModelWithProjection</code></a>.`,wl,xn,Mm='Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',$l,po,Br,Ml,bn,ym="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",yl,mo,jr,Tl,vn,Tm="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Dl,oe,Gr,Sl,wn,Dm=`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>.`,Cl,$n,Sm="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Al,Mn,Cm=`See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,kl,yn,Am=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Pl,ge,Jr,Hl,Tn,km="Return state dict for lora weights and the network alphas.",Rl,_o,Il,uo,Zr,Vl,Dn,Pm="Save the LoRA parameters corresponding to the UNet and text encoder.",Fl,Le,Or,Ul,Sn,Hm=`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>.`,Wl,ho,Ii,Yr,Vi,C,Qr,El,Cn,Rm=`Load LoRA layers into <a href="/docs/diffusers/pr_11438/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>.`,Nl,An,Im='Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Xl,go,Kr,ql,kn,Vm="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",zl,Lo,et,Bl,Pn,Fm="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",jl,re,ot,Gl,Hn,Um=`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>.`,Jl,Rn,Wm="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Zl,In,Em=`See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Ol,Vn,Nm=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,Yl,xe,rt,Ql,Fn,Xm="Return state dict for lora weights and the network alphas.",Kl,xo,ec,bo,tt,oc,Un,qm="Save the LoRA parameters corresponding to the UNet and text encoder.",rc,be,at,tc,Wn,zm=`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>.`,ac,vo,nc,ve,nt,sc,En,Bm="Unloads the LoRA parameters.",ic,wo,Fi,st,Ui,F,it,dc,Nn,jm='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',lc,$o,dt,cc,Xn,Gm="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",fc,Mo,lt,pc,qn,Jm=`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_11438/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>.`,mc,we,ct,_c,zn,Zm="Return state dict for lora weights and the network alphas.",uc,yo,hc,To,ft,gc,Bn,Om="Save the LoRA parameters corresponding to the UNet and text encoder.",Lc,$e,pt,xc,jn,Ym=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,bc,Do,Wi,mt,Ei,U,_t,vc,Gn,Qm='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',wc,So,ut,$c,Jn,Km="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Mc,Co,ht,yc,Zn,e_=`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_11438/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>.`,Tc,Me,gt,Dc,On,o_="Return state dict for lora weights and the network alphas.",Sc,Ao,Cc,ko,Lt,Ac,Yn,r_="Save the LoRA parameters corresponding to the UNet and text encoder.",kc,ye,xt,Pc,Qn,t_=`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>.`,Hc,Po,Ni,bt,Xi,W,vt,Rc,Kn,a_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',Ic,Ho,wt,Vc,es,n_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Fc,Ro,$t,Uc,os,s_=`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_11438/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>.`,Wc,Te,Mt,Ec,rs,i_="Return state dict for lora weights and the network alphas.",Nc,Io,Xc,Vo,yt,qc,ts,d_="Save the LoRA parameters corresponding to the UNet and text encoder.",zc,De,Tt,Bc,as,l_=`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>.`,jc,Fo,qi,Dt,zi,E,St,Gc,ns,c_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',Jc,Uo,Ct,Zc,ss,f_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Oc,Wo,At,Yc,is,p_=`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_11438/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>.`,Qc,Se,kt,Kc,ds,m_="Return state dict for lora weights and the network alphas.",ef,Eo,of,No,Pt,rf,ls,__="Save the LoRA parameters corresponding to the UNet and text encoder.",tf,Ce,Ht,af,cs,u_=`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>.`,nf,Xo,Bi,Rt,ji,N,It,sf,fs,h_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',df,qo,Vt,lf,ps,g_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",cf,zo,Ft,ff,ms,L_=`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_11438/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>.`,pf,Ae,Ut,mf,_s,x_="Return state dict for lora weights and the network alphas.",_f,Bo,uf,jo,Wt,hf,us,b_="Save the LoRA parameters corresponding to the UNet and text encoder.",gf,ke,Et,Lf,hs,v_=`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>.`,xf,Go,Gi,Nt,Ji,X,Xt,bf,gs,w_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',vf,Jo,qt,wf,Ls,$_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",$f,Zo,zt,Mf,xs,M_=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,yf,Pe,Bt,Tf,bs,y_="Return state dict for lora weights and the network alphas.",Df,Oo,Sf,Yo,jt,Cf,vs,T_="Save the LoRA parameters corresponding to the UNet and text encoder.",Af,He,Gt,kf,ws,D_=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Pf,Qo,Zi,Jt,Oi,q,Zt,Hf,$s,S_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',Rf,Ko,Ot,If,Ms,C_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Vf,er,Yt,Ff,ys,A_=`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_11438/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>.`,Uf,Re,Qt,Wf,Ts,k_="Return state dict for lora weights and the network alphas.",Ef,or,Nf,rr,Kt,Xf,Ds,P_="Save the LoRA parameters corresponding to the UNet and text encoder.",qf,Ie,ea,zf,Ss,H_=`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>.`,Bf,tr,Yi,oa,Qi,z,ra,jf,Cs,R_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/cogview4#diffusers.CogView4Pipeline">CogView4Pipeline</a>.',Gf,ar,ta,Jf,As,I_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Zf,nr,aa,Of,ks,V_=`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_11438/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>.`,Yf,Ve,na,Qf,Ps,F_="Return state dict for lora weights and the network alphas.",Kf,sr,ep,ir,sa,op,Hs,U_="Save the LoRA parameters corresponding to the UNet and text encoder.",rp,Fe,ia,tp,Rs,W_=`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>.`,ap,dr,Ki,da,ed,B,la,np,Is,E_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',sp,lr,ca,ip,Vs,N_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",dp,cr,fa,lp,Fs,X_=`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_11438/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>.`,cp,Ue,pa,fp,Us,q_="Return state dict for lora weights and the network alphas.",pp,fr,mp,pr,ma,_p,Ws,z_="Save the LoRA parameters corresponding to the UNet and text encoder.",up,We,_a,hp,Es,B_=`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>.`,gp,mr,od,ua,rd,je,ha,Lp,_r,ga,xp,Ns,j_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",td,La,ad,j,xa,bp,Xs,G_='Load LoRA layers into <a href="/docs/diffusers/pr_11438/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel">HiDreamImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_11438/en/api/pipelines/hidream#diffusers.HiDreamImagePipeline">HiDreamImagePipeline</a>.',vp,ur,ba,wp,qs,J_="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",$p,hr,va,Mp,zs,Z_=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,yp,Ee,wa,Tp,Bs,O_="Return state dict for lora weights and the network alphas.",Dp,gr,Sp,Lr,$a,Cp,js,Y_="Save the LoRA parameters corresponding to the UNet and text encoder.",Ap,Ne,Ma,kp,Gs,Q_=`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>.`,Pp,xr,nd,ya,sd,A,Ta,Hp,Js,K_="Utility class for handling LoRAs.",Rp,Zs,Da,Ip,Xe,Sa,Vp,Os,eu="Enables the possibility to hotswap LoRA adapters.",Fp,Ys,ou=`Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of
the loaded adapters differ.`,Up,_e,Ca,Wp,Qs,ru="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Ep,br,Np,vr,Xp,qe,Aa,qp,Ks,tu="Gets the list of the current active adapters.",zp,wr,Bp,$r,ka,jp,ei,au="Gets the current list of all available adapters in the pipeline.",Gp,Mr,Pa,Jp,oi,nu=`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.`,Zp,ze,Ha,Op,ri,su=`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>.`,Yp,yr,Qp,Be,Ra,Kp,ti,iu="Unloads the LoRA parameters.",em,Tr,id,Ia,dd,Ti,ld;return y=new O({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),ro=new S({props:{$$slots:{default:[uu]},$$scope:{ctx:T}}}),Cr=new O({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Ar=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_11438/src/diffusers/loaders/lora_pipeline.py#L120"}}),kr=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;
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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
weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11438/src/diffusers/loaders/lora_pipeline.py#L399"}}),Pr=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
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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
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link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
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Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.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> (<code>bool</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11438/src/diffusers/loaders/lora_pipeline.py#L343"}}),Hr=new $({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) &#x2014;
See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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_11438/src/diffusers/loaders/lora_pipeline.py#L130"}}),Rr=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_11438/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
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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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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.StableDiffusionXLLoraLoaderMixin.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
<code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
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See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11438/src/diffusers/loaders/lora_pipeline.py#L854"}}),Wr=new $({props:{name:"load_lora_into_unet",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.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.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.state_dict",description:`<strong>state_dict</strong> (<code>dict</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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<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_11438/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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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.StableDiffusionXLLoraLoaderMixin.lora_state_dict.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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 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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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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lora layer.`,name:"lora_scale"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"}],source:"https://github.com/huggingface/diffusers/blob/vr_11438/src/diffusers/loaders/lora_pipeline.py#L1271"}}),jr=new $({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",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.SD3LoraLoaderMixin.load_lora_into_transformer.state_dict",description:`<strong>state_dict</strong> (<code>dict</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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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_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/pr_11438/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_11438/src/diffusers/loaders/lora_pipeline.py#L1148"}}),Jr=new $({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) &#x2014;
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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_11438/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>
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Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
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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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