import{s as Qh,o as Oh,n as D}from"../chunks/scheduler.8c3d61f6.js";import{S as Kh,i as eg,g as n,s as a,r as m,A as og,h as s,f as d,c as r,j as w,u as p,x as f,k as v,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 A}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.36ab7a66.js";import{C as xo}from"../chunks/CodeBlock.a9c4becf.js";import{E as Lo}from"../chunks/ExampleCodeBlock.a18c7ba7.js";import{H as Z,E as tg}from"../chunks/getInferenceSnippets.33773fa8.js";function ag(T){let t,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(){t=n("p"),t.innerHTML=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-1fw6lx1"&&(t.innerHTML=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function rg(T){let t,b="Example:",l,c,M;return c=new xo({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.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;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_names=<span class="hljs-string">&quot;cinematic&quot;</span>
)
pipeline.delete_adapters(<span class="hljs-string">&quot;cinematic&quot;</span>)`,wrap:!1}}),{c(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-11lpom8"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function ng(T){let t,b="Example:",l,c,M;return c=new xo({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.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;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;cinematic&quot;</span>
)
pipeline.disable_lora()`,wrap:!1}}),{c(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-11lpom8"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function sg(T){let t,b="Example:",l,c,M;return c=new xo({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.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;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;cinematic&quot;</span>
)
pipeline.enable_lora()`,wrap:!1}}),{c(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-11lpom8"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function ig(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function dg(T){let t,b="Example:",l,c,M;return c=new xo({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(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-11lpom8"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function lg(T){let t,b="Example:",l,c,M;return c=new xo({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(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-11lpom8"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function cg(T){let t,b="Example:",l,c,M;return c=new xo({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHQySW1hZ2UlMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwQXV0b1BpcGVsaW5lRm9yVGV4dDJJbWFnZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUwQSUyMCUyMCUyMCUyMCUyMmpiaWxja2UtaGYlMkZzZHhsLWNpbmVtYXRpYy0xJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJweXRvcmNoX2xvcmFfd2VpZ2h0cy5zYWZldGVuc29ycyUyMiUyQyUyMGFkYXB0ZXJfbmFtZSUzRCUyMmNpbmVtYXRpYyUyMiUwQSklMEFwaXBlbGluZS5sb2FkX2xvcmFfd2VpZ2h0cyglMjJuZXJpanMlMkZwaXhlbC1hcnQteGwlMjIlMkMlMjB3ZWlnaHRfbmFtZSUzRCUyMnBpeGVsLWFydC14bC5zYWZldGVuc29ycyUyMiUyQyUyMGFkYXB0ZXJfbmFtZSUzRCUyMnBpeGVsJTIyKSUwQXBpcGVsaW5lLnNldF9hZGFwdGVycyglNUIlMjJjaW5lbWF0aWMlMjIlMkMlMjAlMjJwaXhlbCUyMiU1RCUyQyUyMGFkYXB0ZXJfd2VpZ2h0cyUzRCU1QjAuNSUyQyUyMDAuNSU1RCk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.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;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;cinematic&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.set_adapters([<span class="hljs-string">&quot;cinematic&quot;</span>, <span class="hljs-string">&quot;pixel&quot;</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-11lpom8"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function fg(T){let t,b;return t=new xo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.load_lora_weights(path_1, adapter_name=<span class="hljs-string">&quot;adapter-1&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.load_lora_weights(path_2, adapter_name=<span class="hljs-string">&quot;adapter-2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.set_adapters(<span class="hljs-string">&quot;adapter-1&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image_1 = pipe(**kwargs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># switch to adapter-2, offload adapter-1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">&quot;adapter-1&quot;</span>], device=<span class="hljs-string">&quot;cpu&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">&quot;adapter-2&quot;</span>], device=<span class="hljs-string">&quot;cuda:0&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.set_adapters(<span class="hljs-string">&quot;adapter-2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image_2 = pipe(**kwargs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># switch back to adapter-1, offload adapter-2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">&quot;adapter-2&quot;</span>], device=<span class="hljs-string">&quot;cpu&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">&quot;adapter-1&quot;</span>], device=<span class="hljs-string">&quot;cuda:0&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.set_adapters(<span class="hljs-string">&quot;adapter-1&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>...`,wrap:!1}}),{c(){m(t.$$.fragment)},l(l){p(t.$$.fragment,l)},m(l,c){_(t,l,c),b=!0},p:D,i(l){b||(u(t.$$.fragment,l),b=!0)},o(l){h(t.$$.fragment,l),b=!1},d(l){g(t,l)}}}function mg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function pg(T){let t,b="Examples:",l,c,M;return c=new xo({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(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function _g(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function ug(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function hg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function gg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Lg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function xg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function bg(T){let t,b="Examples:",l,c,M;return c=new xo({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(){t=n("p"),t.textContent=b,l=a(),m(c.$$.fragment)},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=b),l=r(i),p(c.$$.fragment,i)},m(i,y){x(i,t,y),x(i,l,y),_(c,i,y),M=!0},p:D,i(i){M||(u(c.$$.fragment,i),M=!0)},o(i){h(c.$$.fragment,i),M=!1},d(i){i&&(d(t),d(l)),g(c,i)}}}function wg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function vg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function $g(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function yg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Mg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function Tg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Dg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function Cg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Sg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function Ag(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function kg(T){let t,b="We support loading original format HunyuanVideo LoRA checkpoints.",l,c,M="This function is experimental and might change in the future.";return{c(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-gyrs6h"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function Rg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Ig(T){let t,b="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,M="This function is experimental and might change in the 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t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Ug(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function Hg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Fg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function Xg(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function Eg(T){let t,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(){t=n("p"),t.textContent=b,l=a(),c=n("p"),c.textContent=M},l(i){t=s(i,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15l1sdn"&&(t.textContent=b),l=r(i),c=s(i,"P",{"data-svelte-h":!0}),f(c)!=="svelte-3fufvn"&&(c.textContent=M)},m(i,y){x(i,t,y),x(i,l,y),x(i,c,y)},p:D,d(i){i&&(d(t),d(l),d(c))}}}function Ng(T){let t,b="This is an experimental API.";return{c(){t=n("p"),t.textContent=b},l(l){t=s(l,"P",{"data-svelte-h":!0}),f(t)!=="svelte-8w79b9"&&(t.textContent=b)},m(l,c){x(l,t,c)},p:D,d(l){l&&d(t)}}}function zg(T){let t,b,l,c,M,i,y,R_='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/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/main/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Md,ta,I_='<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/main/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>',Td,bo,Dd,aa,Cd,C,ra,Jl,Hn,V_="Utility class for handling LoRAs.",jl,we,na,Gl,Fn,W_="Delete an adapter’s LoRA layers from the pipeline.",Zl,wo,Bl,ve,sa,Yl,Xn,P_="Disables the active LoRA layers of the pipeline.",Ql,vo,Ol,$e,ia,Kl,En,U_="Enables the active LoRA layers of the pipeline.",ec,$o,oc,yo,da,tc,Nn,H_=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are
different.`,ac,he,la,rc,zn,F_="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",nc,Mo,sc,To,ic,ye,ca,dc,qn,X_="Gets the list of the current active adapters.",lc,Do,cc,Co,fa,fc,Jn,E_="Gets the current list of all available adapters in the pipeline.",mc,Me,ma,pc,jn,N_="Set the currently active adapters for use in the pipeline.",_c,So,uc,ge,pa,hc,Gn,z_=`Moves the LoRAs listed in <code>adapter_names</code> to a target device. Useful for offloading the LoRA to the CPU in case
you want to load multiple adapters and free some GPU memory.`,gc,Zn,q_=`After offloading the LoRA adapters to CPU, as long as the rest of the model is still on GPU, the LoRA adapters
can no longer be used for inference, as that would cause a device mismatch. Remember to set the device back to
GPU before using those LoRA adapters for inference.`,Lc,Ao,xc,Te,_a,bc,Bn,J_=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,wc,ko,vc,De,ua,$c,Yn,j_="Unloads the LoRA parameters.",yc,Ro,Mc,Io,ha,Tc,Qn,G_="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",Sd,ga,Ad,W,La,Dc,On,Z_=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/main/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>.`,Cc,Vo,xa,Sc,Kn,B_="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ac,Wo,ba,kc,es,Y_="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Rc,Q,wa,Ic,os,Q_=`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>.`,Vc,ts,O_="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Wc,as,K_=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Pc,rs,eu=`See <a href="/docs/diffusers/main/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>.`,Uc,ns,ou=`See <a href="/docs/diffusers/main/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>.`,Hc,Ce,va,Fc,ss,tu="Return state dict for lora weights and the network alphas.",Xc,Po,Ec,Uo,$a,Nc,is,au="Save the LoRA parameters corresponding to the UNet and text encoder.",kd,ya,Rd,P,Ma,zc,ds,ru=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/main/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>.`,qc,Ho,Ta,Jc,ls,nu="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",jc,Fo,Da,Gc,cs,su="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Zc,O,Ca,Bc,fs,iu=`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>.`,Yc,ms,du="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Qc,ps,lu=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Oc,_s,cu=`See <a href="/docs/diffusers/main/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>.`,Kc,us,fu=`See <a href="/docs/diffusers/main/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>.`,ef,Se,Sa,of,hs,mu="Return state dict for lora weights and the network alphas.",tf,Xo,af,Eo,Aa,rf,gs,pu="Save the LoRA parameters corresponding to the UNet and text encoder.",Id,ka,Vd,R,Ra,nf,Ls,_u=`Load LoRA layers into <a href="/docs/diffusers/main/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>.`,sf,xs,uu='Specific to <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',df,No,Ia,lf,bs,hu="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",cf,zo,Va,ff,ws,gu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",mf,oe,Wa,pf,vs,Lu=`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>.`,_f,$s,xu="All kwargs are forwarded to <code>self.lora_state_dict</code>.",uf,ys,bu=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,hf,Ms,wu=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,gf,Ae,Pa,Lf,Ts,vu="Return state dict for lora weights and the network alphas.",xf,qo,bf,Jo,Ua,wf,Ds,$u="Save the LoRA parameters corresponding to the UNet and text encoder.",vf,ke,Ha,$f,Cs,yu=`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>.`,yf,jo,Wd,Fa,Pd,k,Xa,Mf,Ss,Mu=`Load LoRA layers into <a href="/docs/diffusers/main/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>.`,Tf,As,Tu='Specific to <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Df,Go,Ea,Cf,ks,Du="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Sf,Zo,Na,Af,Rs,Cu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",kf,te,za,Rf,Is,Su=`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>.`,If,Vs,Au="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Vf,Ws,ku=`See <a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Wf,Ps,Ru=`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,Re,qa,Uf,Us,Iu="Return state dict for lora weights and the network alphas.",Hf,Bo,Ff,Yo,Ja,Xf,Hs,Vu="Save the LoRA parameters corresponding to the UNet and text encoder.",Ef,Ie,ja,Nf,Fs,Wu=`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>.`,zf,Qo,qf,Ve,Ga,Jf,Xs,Pu="Unloads the LoRA parameters.",jf,Oo,Ud,Za,Hd,U,Ba,Gf,Es,Uu='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',Zf,Ko,Ya,Bf,Ns,Hu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Yf,et,Qa,Qf,zs,Fu=`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/main/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>.`,Of,We,Oa,Kf,qs,Xu="Return state dict for lora weights and the network alphas.",em,ot,om,tt,Ka,tm,Js,Eu="Save the LoRA parameters corresponding to the transformer.",am,Pe,er,rm,js,Nu=`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>.`,nm,at,Fd,or,Xd,H,tr,sm,Gs,zu='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',im,rt,ar,dm,Zs,qu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",lm,nt,rr,cm,Bs,Ju=`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/main/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>.`,fm,Ue,nr,mm,Ys,ju="Return state dict for lora weights and the network alphas.",pm,st,_m,it,sr,um,Qs,Gu="Save the LoRA parameters corresponding to the transformer.",hm,He,ir,gm,Os,Zu=`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>.`,Lm,dt,Ed,dr,Nd,F,lr,xm,Ks,Bu='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/main/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',bm,lt,cr,wm,ei,Yu="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",vm,ct,fr,$m,oi,Qu=`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/main/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>.`,ym,Fe,mr,Mm,ti,Ou="Return state dict for lora weights and the network alphas.",Tm,ft,Dm,mt,pr,Cm,ai,Ku="Save the LoRA parameters corresponding to the transformer.",Sm,Xe,_r,Am,ri,eh=`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>.`,km,pt,zd,ur,qd,X,hr,Rm,ni,oh='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',Im,_t,gr,Vm,si,th="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Wm,ut,Lr,Pm,ii,ah=`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/main/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>.`,Um,Ee,xr,Hm,di,rh="Return state dict for lora weights and the network alphas.",Fm,ht,Xm,gt,br,Em,li,nh="Save the LoRA parameters corresponding to the transformer.",Nm,Ne,wr,zm,ci,sh=`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>.`,qm,Lt,Jd,vr,jd,E,$r,Jm,fi,ih='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',jm,xt,yr,Gm,mi,dh="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Zm,bt,Mr,Bm,pi,lh=`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/main/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>.`,Ym,ze,Tr,Qm,_i,ch="Return state dict for lora weights and the network alphas.",Om,wt,Km,vt,Dr,ep,ui,fh="Save the LoRA parameters corresponding to the transformer.",op,qe,Cr,tp,hi,mh=`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,$t,Gd,Sr,Zd,N,Ar,rp,gi,ph='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',np,yt,kr,sp,Li,_h="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",ip,Mt,Rr,dp,xi,uh=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and
<code>self.text_encoder</code>. All kwargs are forwarded to <code>self.lora_state_dict</code>. See
<a href="/docs/diffusers/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.
See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,lp,Je,Ir,cp,bi,hh="Return state dict for lora weights and the network alphas.",fp,Tt,mp,Dt,Vr,pp,wi,gh="Save the LoRA parameters corresponding to the transformer.",_p,je,Wr,up,vi,Lh=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,hp,Ct,Bd,Pr,Yd,z,Ur,gp,$i,xh='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',Lp,St,Hr,xp,yi,bh="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",bp,At,Fr,wp,Mi,wh=`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/main/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>.`,vp,Ge,Xr,$p,Ti,vh="Return state dict for lora weights and the network alphas.",yp,kt,Mp,Rt,Er,Tp,Di,$h="Save the LoRA parameters corresponding to the transformer.",Dp,Ze,Nr,Cp,Ci,yh=`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>.`,Sp,It,Qd,zr,Od,q,qr,Ap,Si,Mh='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/cogview4#diffusers.CogView4Pipeline">CogView4Pipeline</a>.',kp,Vt,Jr,Rp,Ai,Th="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Ip,Wt,jr,Vp,ki,Dh=`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/main/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>.`,Wp,Be,Gr,Pp,Ri,Ch="Return state dict for lora weights and the network alphas.",Up,Pt,Hp,Ut,Zr,Fp,Ii,Sh="Save the LoRA parameters corresponding to the transformer.",Xp,Ye,Br,Ep,Vi,Ah=`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>.`,Np,Ht,Kd,Yr,el,J,Qr,zp,Wi,kh='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',qp,Ft,Or,Jp,Pi,Rh="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",jp,Xt,Kr,Gp,Ui,Ih=`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/main/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>.`,Zp,Qe,en,Bp,Hi,Vh="Return state dict for lora weights and the network alphas.",Yp,Et,Qp,Nt,on,Op,Fi,Wh="Save the LoRA parameters corresponding to the transformer.",Kp,Oe,tn,e_,Xi,Ph=`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>.`,o_,zt,ol,an,tl,Le,rn,t_,qt,nn,a_,Ei,Uh="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",r_,Jt,sn,n_,Ni,Hh="Save the LoRA parameters corresponding to the UNet and text encoder.",al,dn,rl,j,ln,s_,zi,Fh='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel">HiDreamImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/hidream#diffusers.HiDreamImagePipeline">HiDreamImagePipeline</a>.',i_,jt,cn,d_,qi,Xh="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",l_,Gt,fn,c_,Ji,Eh=`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/main/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>.`,f_,Ke,mn,m_,ji,Nh="Return state dict for lora weights and the network alphas.",p_,Zt,__,Bt,pn,u_,Gi,zh="Save the LoRA parameters corresponding to the transformer.",h_,eo,_n,g_,Zi,qh=`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_,Yt,nl,un,sl,G,hn,x_,Bi,Jh='Load LoRA layers into <a href="/docs/diffusers/main/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',b_,Qt,gn,w_,Yi,jh="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",v_,Ot,Ln,$_,Qi,Gh=`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/main/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>.`,y_,oo,xn,M_,Oi,Zh="Return state dict for lora weights and the network alphas.",T_,Kt,D_,ea,bn,C_,Ki,Bh="Save the LoRA parameters corresponding to the transformer.",S_,to,wn,A_,ed,Yh=`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>.`,k_,oa,il,vn,dl,yd,ll;return M=new Z({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),bo=new A({props:{$$slots:{default:[ag]},$$scope:{ctx:T}}}),aa=new Z({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),ra=new $({props:{name:"class diffusers.loaders.lora_base.LoraBaseMixin",anchor:"diffusers.loaders.lora_base.LoraBaseMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L475"}}),na=new $({props:{name:"delete_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>Union[List[str], str]</code>) &#x2014;
The names of the adapters to delete.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L839"}}),wo=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.example",$$slots:{default:[rg]},$$scope:{ctx:T}}}),sa=new $({props:{name:"disable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L779"}}),vo=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora.example",$$slots:{default:[ng]},$$scope:{ctx:T}}}),ia=new $({props:{name:"enable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L809"}}),$o=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora.example",$$slots:{default:[sg]},$$scope:{ctx:T}}}),da=new $({props:{name:"enable_lora_hotswap",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora_hotswap",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora_hotswap.target_rank",description:`<strong>target_rank</strong> (<code>int</code>) &#x2014;
The highest rank among all the adapters that will be loaded.`,name:"target_rank"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora_hotswap.check_compiled",description:`<strong>check_compiled</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;error&quot;</code>) &#x2014;
How to handle a model that is already compiled. The check can return the following messages:<ul>
<li>&#x201C;error&#x201D; (default): raise an error</li>
<li>&#x201C;warn&#x201D;: issue a warning</li>
<li>&#x201C;ignore&#x201D;: do nothing</li>
</ul>`,name:"check_compiled"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L986"}}),la=new $({props:{name:"fuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.components",description:"<strong>components</strong> &#x2014; (<code>List[str]</code>): List of LoRA-injectable components to fuse the LoRAs into.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, defaults to 1.0) &#x2014;
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Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.`,name:"safe_fusing"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L533"}}),Mo=new A({props:{warning:!0,$$slots:{default:[ig]},$$scope:{ctx:T}}}),To=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[dg]},$$scope:{ctx:T}}}),ca=new $({props:{name:"get_active_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L877"}}),Do=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[lg]},$$scope:{ctx:T}}}),fa=new $({props:{name:"get_list_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L910"}}),ma=new $({props:{name:"set_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"},{name:"adapter_weights",val:": typing.Union[float, typing.Dict, typing.List[float], typing.List[typing.Dict], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code> or <code>str</code>) &#x2014;
The names of the adapters to use.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_weights",description:`<strong>adapter_weights</strong> (<code>Union[List[float], float]</code>, <em>optional</em>) &#x2014;
The adapter(s) weights to use with the UNet. If <code>None</code>, the weights are set to <code>1.0</code> for all the
adapters.`,name:"adapter_weights"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L680"}}),So=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.example",$$slots:{default:[cg]},$$scope:{ctx:T}}}),pa=new $({props:{name:"set_lora_device",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device",parameters:[{name:"adapter_names",val:": typing.List[str]"},{name:"device",val:": typing.Union[torch.device, str, int]"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>) &#x2014;
List of adapters to send device to.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.device",description:`<strong>device</strong> (<code>Union[torch.device, str, int]</code>) &#x2014;
Device to send the adapters to. Can be either a torch device, a str or an integer.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L932"}}),Ao=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.example",$$slots:{default:[fg]},$$scope:{ctx:T}}}),_a=new $({props:{name:"unfuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) &#x2014; List of LoRA-injectable components to unfuse LoRA from.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_unet",description:"<strong>unfuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to unfuse the UNet LoRA parameters.",name:"unfuse_unet"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_text_encoder",description:`<strong>unfuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn&#x2019;t monkey-patched with the
LoRA parameters then it won&#x2019;t have any effect.`,name:"unfuse_text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L623"}}),ko=new A({props:{warning:!0,$$slots:{default:[mg]},$$scope:{ctx:T}}}),ua=new $({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L510"}}),Ro=new Lo({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[pg]},$$scope:{ctx:T}}}),ha=new $({props:{name:"write_lora_layers",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.write_lora_layers",parameters:[{name:"state_dict",val:": typing.Dict[str, torch.Tensor]"},{name:"save_directory",val:": str"},{name:"is_main_process",val:": bool"},{name:"weight_name",val:": str"},{name:"save_function",val:": typing.Callable"},{name:"safe_serialization",val:": bool"},{name:"lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_base.py#L1009"}}),ga=new Z({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),La=new $({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L127"}}),xa=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"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional <code>text_encoder</code> to distinguish between unet lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
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
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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/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L415"}}),ba=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"},{name:"metadata",val:" = None"}],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
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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/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet.metadata",description:`<strong>metadata</strong> (<code>dict</code>) &#x2014;
Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py#L354"}}),wa=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/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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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/main/src/diffusers/loaders/lora_pipeline.py#L137"}}),va=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/main/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
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Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
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Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won&#x2019;t be derived
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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/main/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
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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/main/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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Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
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The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from
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The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
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State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text
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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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<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
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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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