Buckets:
| import{s as E2,o as P2,n as K}from"../chunks/scheduler.53228c21.js";import{S as q2,i as A2,e as o,s as t,c as l,h as Y2,a as s,d as n,b as r,f as g,g as d,j as u,k as _,l as a,m as x,n as f,t as p,o as c,p as m}from"../chunks/index.100fac89.js";import{C as z2}from"../chunks/CopyLLMTxtMenu.05adabe5.js";import{D as b}from"../chunks/Docstring.2192d6da.js";import{C as ee}from"../chunks/CodeBlock.d30a6509.js";import{E as O}from"../chunks/ExampleCodeBlock.400bff15.js";import{H as A,E as Q2}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.6cf479e3.js";function O2(T){let h,w="Example:",$,L,y;return L=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function K2(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function eL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function aL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function tL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function rL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function oL(T){let h,w;return h=new ee({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span>pipe.load_lora_weights(path_1, adapter_name=<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.load_lora_weights(path_2, adapter_name=<span class="hljs-string">"adapter-2"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>image_1 = pipe(**kwargs) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># switch to adapter-2, offload adapter-1</span> | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-1"</span>], device=<span class="hljs-string">"cpu"</span>) | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-2"</span>], device=<span class="hljs-string">"cuda:0"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-2"</span>) | |
| <span class="hljs-meta">>>> </span>image_2 = pipe(**kwargs) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># switch back to adapter-1, offload adapter-2</span> | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-2"</span>], device=<span class="hljs-string">"cpu"</span>) | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-1"</span>], device=<span class="hljs-string">"cuda:0"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>...`,wrap:!1}}),{c(){l(h.$$.fragment)},l($){d(h.$$.fragment,$)},m($,L){f(h,$,L),w=!0},p:K,i($){w||(p(h.$$.fragment,$),w=!0)},o($){c(h.$$.fragment,$),w=!1},d($){m(h,$)}}}function sL(T){let h,w="Examples:",$,L,y;return L=new ee({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-kvfsh7"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function nL(T){let h,w="Examples:",$,L,y;return L=new ee({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-kvfsh7"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function iL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function lL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function dL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function fL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function pL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function cL(T){let h,w="Example:",$,L,y;return L=new ee({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">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-11lpom8"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function mL(T){let h,w;return h=new ee({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span>pipe.load_lora_weights(path_1, adapter_name=<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.load_lora_weights(path_2, adapter_name=<span class="hljs-string">"adapter-2"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>image_1 = pipe(**kwargs) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># switch to adapter-2, offload adapter-1</span> | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-1"</span>], device=<span class="hljs-string">"cpu"</span>) | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-2"</span>], device=<span class="hljs-string">"cuda:0"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-2"</span>) | |
| <span class="hljs-meta">>>> </span>image_2 = pipe(**kwargs) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># switch back to adapter-1, offload adapter-2</span> | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-2"</span>], device=<span class="hljs-string">"cpu"</span>) | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-1"</span>], device=<span class="hljs-string">"cuda:0"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>...`,wrap:!1}}),{c(){l(h.$$.fragment)},l($){d(h.$$.fragment,$)},m($,L){f(h,$,L),w=!0},p:K,i($){w||(p(h.$$.fragment,$),w=!0)},o($){c(h.$$.fragment,$),w=!1},d($){m(h,$)}}}function uL(T){let h,w="Examples:",$,L,y;return L=new ee({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){h=o("p"),h.textContent=w,$=t(),l(L.$$.fragment)},l(i){h=s(i,"P",{"data-svelte-h":!0}),u(h)!=="svelte-kvfsh7"&&(h.textContent=w),$=r(i),d(L.$$.fragment,i)},m(i,M){x(i,h,M),x(i,$,M),f(L,i,M),y=!0},p:K,i(i){y||(p(L.$$.fragment,i),y=!0)},o(i){c(L.$$.fragment,i),y=!1},d(i){i&&(n(h),n($)),m(L,i)}}}function _L(T){let h,w,$,L,y,i,M,kd,lr,Bh='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_12229/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_12229/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Id,dr,Nh='<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>SkyReelsV2LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2" rel="nofollow">SkyReels-V2</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_12229/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>QwenImageLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen" rel="nofollow">Qwen Image</a></li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',Vd,Ye,Eh='<p>To learn more about how to load LoRA weights, see the <a href="../../using-diffusers/loading_adapters#lora">LoRA</a> loading guide.</p>',Jd,fr,Rd,D,pr,fc,Tn,Ph="Utility class for handling LoRAs.",pc,ye,cr,cc,Dn,qh="Delete an adapter’s LoRA layers from the pipeline.",mc,ze,uc,Me,mr,_c,Sn,Ah="Disables the active LoRA layers of the pipeline.",gc,Qe,hc,we,ur,bc,Cn,Yh="Enables the active LoRA layers of the pipeline.",vc,Oe,Lc,Ke,_r,$c,Un,zh=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,xc,ge,gr,yc,kn,Qh="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Mc,hr,Oh="<p>> This is an experimental API.</p>",wc,ea,Tc,Te,br,Dc,In,Kh="Gets the list of the current active adapters.",Sc,aa,Cc,ta,vr,Uc,Vn,eb="Gets the current list of all available adapters in the pipeline.",kc,De,Lr,Ic,Jn,ab="Set the currently active adapters for use in the pipeline.",Vc,ra,Jc,he,$r,Rc,Rn,tb=`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.`,Hc,Hn,rb=`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.`,Zc,oa,jc,Se,xr,Xc,Zn,ob=`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>.`,Gc,yr,sb="<p>> This is an experimental API.</p>",Wc,Ce,Mr,Fc,jn,nb="Unloads the LoRA parameters.",Bc,sa,Nc,na,wr,Ec,Xn,ib="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",Hd,Tr,Zd,Y,Dr,Pc,Gn,lb=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_12229/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>.`,qc,ia,Sr,Ac,Wn,db="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Yc,la,Cr,zc,Fn,fb="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Qc,ae,Ur,Oc,Bn,pb=`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>.`,Kc,Nn,cb="All kwargs are forwarded to <code>self.lora_state_dict</code>.",em,En,mb=`See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is | |
| loaded.`,am,Pn,ub=`See <a href="/docs/diffusers/pr_12229/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>.`,tm,qn,_b=`See <a href="/docs/diffusers/pr_12229/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>.`,rm,Ue,kr,om,An,gb="Return state dict for lora weights and the network alphas.",sm,Ir,hb=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,nm,da,Vr,im,Yn,bb="Save the LoRA parameters corresponding to the UNet and text encoder.",jd,Jr,Xd,V,Rr,lm,zn,vb=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_12229/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>.`,dm,fa,Hr,fm,Qn,Lb="See <code>fuse_lora()</code> for more details.",pm,pa,Zr,cm,On,$b="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",mm,ca,jr,um,Kn,xb="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",_m,ma,Xr,gm,ei,yb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',hm,ke,Gr,bm,ai,Mb="Return state dict for lora weights and the network alphas.",vm,Wr,wb=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,Lm,ua,Fr,$m,ti,Tb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',xm,_a,Br,ym,ri,Db="See <code>unfuse_lora()</code> for more details.",Gd,Nr,Wd,I,Er,Mm,oi,Sb=`Load LoRA layers into <a href="/docs/diffusers/pr_12229/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>.`,wm,si,Cb='Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Tm,ga,Pr,Dm,ni,Ub="See <code>fuse_lora()</code> for more details.",Sm,ha,qr,Cm,ii,kb="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Um,ba,Ar,km,li,Ib='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Im,va,Yr,Vm,di,Vb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Jm,La,zr,Rm,fi,Jb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Hm,$a,Qr,Zm,pi,Rb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',jm,xa,Or,Xm,ci,Hb="See <code>unfuse_lora()</code> for more details.",Fd,Kr,Bd,k,eo,Gm,mi,Zb=`Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>, | |
| <a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,Wm,ui,jb='Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Fm,ya,ao,Bm,_i,Xb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Nm,Ma,to,Em,gi,Gb="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Pm,wa,ro,qm,hi,Wb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Am,_e,oo,Ym,bi,Fb=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and | |
| <code>self.text_encoder</code>.`,zm,vi,Bb="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Qm,Li,Nb=`See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is | |
| loaded.`,Om,$i,Eb=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state | |
| dict is loaded into <code>self.transformer</code>.`,Km,Ta,so,eu,xi,Pb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',au,Da,no,tu,yi,qb="Save the LoRA parameters corresponding to the UNet and text encoder.",ru,Ie,io,ou,Mi,Ab=`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>.`,su,lo,Yb="<p>> This is an experimental API.</p>",nu,Ve,fo,iu,wi,zb="Unloads the LoRA parameters.",lu,Sa,Nd,po,Ed,R,co,du,Ti,Qb='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',fu,Ca,mo,pu,Di,Ob="See <code>fuse_lora()</code> for more details.",cu,Ua,uo,mu,Si,Kb='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',uu,ka,_o,_u,Ci,ev='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',gu,Ia,go,hu,Ui,av='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',bu,Va,ho,vu,ki,tv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Lu,Ja,bo,$u,Ii,rv="See <code>unfuse_lora()</code> for more details.",Pd,vo,qd,H,Lo,xu,Vi,ov='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',yu,Ra,$o,Mu,Ji,sv="See <code>fuse_lora()</code> for more details.",wu,Ha,xo,Tu,Ri,nv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Du,Za,yo,Su,Hi,iv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Cu,ja,Mo,Uu,Zi,lv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',ku,Xa,wo,Iu,ji,dv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Vu,Ga,To,Ju,Xi,fv="See <code>unfuse_lora()</code> for more details.",Ad,Do,Yd,Z,So,Ru,Gi,pv='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',Hu,Wa,Co,Zu,Wi,cv="See <code>fuse_lora()</code> for more details.",ju,Fa,Uo,Xu,Fi,mv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Gu,Ba,ko,Wu,Bi,uv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Fu,Na,Io,Bu,Ni,_v='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Nu,Ea,Vo,Eu,Ei,gv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Pu,Pa,Jo,qu,Pi,hv="See <code>unfuse_lora()</code> for more details.",zd,Ro,Qd,j,Ho,Au,qi,bv='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',Yu,qa,Zo,zu,Ai,vv="See <code>fuse_lora()</code> for more details.",Qu,Aa,jo,Ou,Yi,Lv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Ku,Ya,Xo,e_,zi,$v='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',a_,za,Go,t_,Qi,xv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',r_,Qa,Wo,o_,Oi,yv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',s_,Oa,Fo,n_,Ki,Mv="See <code>unfuse_lora()</code> for more details.",Od,Bo,Kd,X,No,i_,el,wv='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',l_,Ka,Eo,d_,al,Tv="See <code>fuse_lora()</code> for more details.",f_,et,Po,p_,tl,Dv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',c_,at,qo,m_,rl,Sv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',u_,tt,Ao,__,ol,Cv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',g_,rt,Yo,h_,sl,Uv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',b_,ot,zo,v_,nl,kv="See <code>unfuse_lora()</code> for more details.",ef,Qo,af,G,Oo,L_,il,Iv='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',$_,st,Ko,x_,ll,Vv="See <code>fuse_lora()</code> for more details.",y_,nt,es,M_,dl,Jv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',w_,it,as,T_,fl,Rv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',D_,lt,ts,S_,pl,Hv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',C_,dt,rs,U_,cl,Zv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',k_,ft,os,I_,ml,jv="See <code>unfuse_lora()</code> for more details.",tf,ss,rf,W,ns,V_,ul,Xv='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',J_,pt,is,R_,_l,Gv="See <code>fuse_lora()</code> for more details.",H_,ct,ls,Z_,gl,Wv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',j_,mt,ds,X_,hl,Fv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',G_,ut,fs,W_,bl,Bv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',F_,_t,ps,B_,vl,Nv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',N_,gt,cs,E_,Ll,Ev="See <code>unfuse_lora()</code> for more details.",of,ms,sf,F,us,P_,$l,Pv='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/cogview4#diffusers.CogView4Pipeline">CogView4Pipeline</a>.',q_,ht,_s,A_,xl,qv="See <code>fuse_lora()</code> for more details.",Y_,bt,gs,z_,yl,Av='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Q_,vt,hs,O_,Ml,Yv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',K_,Lt,bs,eg,wl,zv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',ag,$t,vs,tg,Tl,Qv='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',rg,xt,Ls,og,Dl,Ov="See <code>unfuse_lora()</code> for more details.",nf,$s,lf,B,xs,sg,Sl,Kv='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',ng,yt,ys,ig,Cl,e2="See <code>fuse_lora()</code> for more details.",lg,Mt,Ms,dg,Ul,a2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',fg,wt,ws,pg,kl,t2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',cg,Tt,Ts,mg,Il,r2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',ug,Dt,Ds,_g,Vl,o2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',gg,St,Ss,hg,Jl,s2="See <code>unfuse_lora()</code> for more details.",df,Cs,ff,N,Us,bg,Rl,n2='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/skyreels_v2_transformer_3d#diffusers.SkyReelsV2Transformer3DModel">SkyReelsV2Transformer3DModel</a>.',vg,Ct,ks,Lg,Hl,i2="See <code>fuse_lora()</code> for more details.",$g,Ut,Is,xg,Zl,l2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',yg,kt,Vs,Mg,jl,d2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',wg,It,Js,Tg,Xl,f2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Dg,Vt,Rs,Sg,Gl,p2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Cg,Jt,Hs,Ug,Wl,c2="See <code>unfuse_lora()</code> for more details.",pf,Zs,cf,Le,js,kg,Rt,Xs,Ig,Fl,m2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Vg,Ht,Gs,Jg,Bl,u2="Save the LoRA parameters corresponding to the UNet and text encoder.",mf,Ws,uf,E,Fs,Rg,Nl,_2='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel">HiDreamImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/hidream#diffusers.HiDreamImagePipeline">HiDreamImagePipeline</a>.',Hg,Zt,Bs,Zg,El,g2="See <code>fuse_lora()</code> for more details.",jg,jt,Ns,Xg,Pl,h2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Gg,Xt,Es,Wg,ql,b2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Fg,Gt,Ps,Bg,Al,v2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Ng,Wt,qs,Eg,Yl,L2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Pg,Ft,As,qg,zl,$2="See <code>unfuse_lora()</code> for more details.",_f,Ys,gf,P,zs,Ag,Ql,x2='Load LoRA layers into <a href="/docs/diffusers/pr_12229/en/api/models/qwenimage_transformer2d#diffusers.QwenImageTransformer2DModel">QwenImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12229/en/api/pipelines/qwenimage#diffusers.QwenImagePipeline">QwenImagePipeline</a>.',Yg,Bt,Qs,zg,Ol,y2="See <code>fuse_lora()</code> for more details.",Qg,Nt,Os,Og,Kl,M2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Kg,Et,Ks,eh,ed,w2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',ah,Pt,en,th,ad,T2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',rh,qt,an,oh,td,D2='See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',sh,At,tn,nh,rd,S2="See <code>unfuse_lora()</code> for more details.",hf,rn,bf,S,on,ih,od,C2="Utility class for handling LoRAs.",lh,Je,sn,dh,sd,U2="Delete an adapter’s LoRA layers from the pipeline.",fh,Yt,ph,Re,nn,ch,nd,k2="Disables the active LoRA layers of the pipeline.",mh,zt,uh,He,ln,_h,id,I2="Enables the active LoRA layers of the pipeline.",gh,Qt,hh,Ot,dn,bh,ld,V2=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,vh,be,fn,Lh,dd,J2="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",$h,pn,R2="<p>> This is an experimental API.</p>",xh,Kt,yh,Ze,cn,Mh,fd,H2="Gets the list of the current active adapters.",wh,er,Th,ar,mn,Dh,pd,Z2="Gets the current list of all available adapters in the pipeline.",Sh,je,un,Ch,cd,j2="Set the currently active adapters for use in the pipeline.",Uh,tr,kh,ve,_n,Ih,md,X2=`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.`,Vh,ud,G2=`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.`,Jh,rr,Rh,Xe,gn,Hh,_d,W2=`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>.`,Zh,hn,F2="<p>> This is an experimental API.</p>",jh,Ge,bn,Xh,gd,B2="Unloads the LoRA parameters.",Gh,or,Wh,sr,vn,Fh,hd,N2="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",vf,Ln,Lf,Ud,$f;return y=new z2({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),M=new A({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),fr=new A({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),pr=new b({props:{name:"class diffusers.loaders.lora_base.LoraBaseMixin",anchor:"diffusers.loaders.lora_base.LoraBaseMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L478"}}),cr=new b({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>) — | |
| The names of the adapters to delete.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L838"}}),ze=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.example",$$slots:{default:[O2]},$$scope:{ctx:T}}}),mr=new b({props:{name:"disable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L778"}}),Qe=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora.example",$$slots:{default:[K2]},$$scope:{ctx:T}}}),ur=new b({props:{name:"enable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L808"}}),Oe=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora.example",$$slots:{default:[eL]},$$scope:{ctx:T}}}),_r=new b({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>) — | |
| 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>"error"</code>) — | |
| How to handle a model that is already compiled. The check can return the following messages:<ul> | |
| <li>“error” (default): raise an error</li> | |
| <li>“warn”: issue a warning</li> | |
| <li>“ignore”: do nothing</li> | |
| </ul>`,name:"check_compiled"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L985"}}),gr=new b({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> — (<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) — | |
| Controls how much to influence the outputs with the LoRA parameters.`,name:"lora_scale"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.safe_fusing",description:`<strong>safe_fusing</strong> (<code>bool</code>, defaults to <code>False</code>) — | |
| 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>) — | |
| 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/vr_12229/src/diffusers/loaders/lora_base.py#L536"}}),ea=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[aL]},$$scope:{ctx:T}}}),br=new b({props:{name:"get_active_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L876"}}),aa=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[tL]},$$scope:{ctx:T}}}),vr=new b({props:{name:"get_list_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L909"}}),Lr=new b({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>) — | |
| 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>) — | |
| 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/vr_12229/src/diffusers/loaders/lora_base.py#L675"}}),ra=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.example",$$slots:{default:[rL]},$$scope:{ctx:T}}}),$r=new b({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>) — | |
| 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>) — | |
| 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/vr_12229/src/diffusers/loaders/lora_base.py#L931"}}),oa=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.example",$$slots:{default:[oL]},$$scope:{ctx:T}}}),xr=new b({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>) — 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>) — 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>) — | |
| Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the | |
| LoRA parameters then it won’t have any effect.`,name:"unfuse_text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L622"}}),Mr=new b({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_base.py#L513"}}),sa=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[sL]},$$scope:{ctx:T}}}),wr=new b({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/vr_12229/src/diffusers/loaders/lora_base.py#L1008"}}),Tr=new A({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Dr=new b({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L128"}}),Sr=new b({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>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| Adapter name to be used for referencing the loaded adapter model. If not specified, it will use | |
| <code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| 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>) — | |
| See <a href="/docs/diffusers/pr_12229/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>) — | |
| Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won’t be derived | |
| from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L411"}}),Cr=new b({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>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| See <a href="/docs/diffusers/pr_12229/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>) — | |
| Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won’t be derived | |
| from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L350"}}),Ur=new b({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>) — | |
| See <a href="/docs/diffusers/pr_12229/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>) — | |
| 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>) — | |
| 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>) — | |
| 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_12229/src/diffusers/loaders/lora_pipeline.py#L138"}}),kr=new b({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>) — | |
| 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_12229/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>) — | |
| 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>) — | |
| 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>) — | |
| A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}</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>) — | |
| Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model | |
| won’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>) — | |
| The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from | |
| <code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) — | |
| The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier | |
| allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>""</code>) — | |
| The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>, defaults to None) — | |
| Name of the serialized state dict file.`,name:"weight_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) — | |
| When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L239"}}),Vr=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"unet_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"unet_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — | |
| Directory to save LoRA parameters to. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.unet_lora_layers",description:`<strong>unet_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text | |
| encoder LoRA state dict because it comes from 🤗 Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether the process calling this is the main process or not. Useful during distributed training and you | |
| need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main | |
| process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) — | |
| The function to use to save the state dictionary. Useful during distributed training when you need to | |
| replace <code>torch.save</code> with another method. Can be configured with the environment variable | |
| <code>DIFFUSERS_SAVE_MODE</code>.`,name:"save_function"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.unet_lora_adapter_metadata",description:`<strong>unet_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the unet to be serialized with the state dict.`,name:"unet_lora_adapter_metadata"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L469"}}),Jr=new A({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),Rr=new b({props:{name:"class diffusers.loaders.StableDiffusionXLLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L592"}}),Hr=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['unet', 'text_encoder', 'text_encoder_2']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L958"}}),Zr=new b({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.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.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) — | |
| The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) — | |
| 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>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| 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>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) — | |
| Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won’t be derived | |
| from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L851"}}),jr=new b({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"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) — | |
| The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| 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_unet.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| Speed up model loading only loading the pretrained LoRA weights and not initializing the random | |
| weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_into_unet.metadata",description:`<strong>metadata</strong> (<code>dict</code>) — | |
| Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won’t be derived | |
| from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L789"}}),Xr=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L603"}}),Gr=new b({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.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.StableDiffusionXLLoraLoaderMixin.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>) — | |
| 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_12229/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.StableDiffusionXLLoraLoaderMixin.lora_state_dict.cache_dir",description:`<strong>cache_dir</strong> (<code>Union[str, os.PathLike]</code>, <em>optional</em>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.lora_state_dict.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| 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.StableDiffusionXLLoraLoaderMixin.lora_state_dict.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) — | |
| A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}</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>) — | |
| Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model | |
| won’t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) — | |
| The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from | |
| <code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) — | |
| The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier | |
| allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>""</code>) — | |
| The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>, defaults to None) — | |
| Name of the serialized state dict file.`,name:"weight_name"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) — | |
| When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L677"}}),Fr=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"unet_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_2_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"unet_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"},{name:"text_encoder_2_lora_adapter_metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L910"}}),Br=new b({props:{name:"unfuse_lora",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['unet', 'text_encoder', 'text_encoder_2']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L977"}}),Nr=new A({props:{title:"SD3LoraLoaderMixin",local:"diffusers.loaders.SD3LoraLoaderMixin",headingTag:"h2"}}),Er=new b({props:{name:"class diffusers.loaders.SD3LoraLoaderMixin",anchor:"diffusers.loaders.SD3LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L984"}}),Pr=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.SD3LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder', 'text_encoder_2']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1256"}}),qr=new b({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.SD3LoraLoaderMixin.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.SD3LoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) — | |
| 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.SD3LoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) — | |
| 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.SD3LoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) — | |
| The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) — | |
| 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>) — | |
| 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.SD3LoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| 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.SD3LoraLoaderMixin.load_lora_into_text_encoder.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| 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.SD3LoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) — | |
| Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won’t be derived | |
| from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1147"}}),Ar=new b({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"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1116"}}),Yr=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.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:" = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1051"}}),zr=new b({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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L997"}}),Qr=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_2_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"},{name:"text_encoder_2_lora_adapter_metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1206"}}),Or=new b({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SD3LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder', 'text_encoder_2']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1276"}}),Kr=new A({props:{title:"FluxLoraLoaderMixin",local:"diffusers.loaders.FluxLoraLoaderMixin",headingTag:"h2"}}),eo=new b({props:{name:"class diffusers.loaders.FluxLoraLoaderMixin",anchor:"diffusers.loaders.FluxLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1483"}}),ao=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.FluxLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1955"}}),to=new b({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) — | |
| 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.FluxLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) — | |
| 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.FluxLoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) — | |
| The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) — | |
| Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) — | |
| 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.FluxLoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| 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.FluxLoraLoaderMixin.load_lora_into_text_encoder.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| 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.FluxLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) — | |
| Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won’t be derived | |
| from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1832"}}),ro=new b({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"metadata",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1746"}}),oo=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.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.FluxLoraLoaderMixin.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>) — | |
| See <a href="/docs/diffusers/pr_12229/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.FluxLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| 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.FluxLoraLoaderMixin.load_lora_weights.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| \`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.FluxLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12229/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>) — | |
| See <a href="/docs/diffusers/pr_12229/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1621"}}),so=new b({props:{name:"lora_state_dict",anchor:"diffusers.loaders.FluxLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"return_alphas",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1496"}}),no=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — | |
| Directory to save LoRA parameters to. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.transformer_lora_layers",description:`<strong>transformer_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>transformer</code>.`,name:"transformer_lora_layers"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text | |
| encoder LoRA state dict because it comes from 🤗 Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether the process calling this is the main process or not. Useful during distributed training and you | |
| need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main | |
| process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) — | |
| The function to use to save the state dictionary. Useful during distributed training when you need to | |
| replace <code>torch.save</code> with another method. Can be configured with the environment variable | |
| <code>DIFFUSERS_SAVE_MODE</code>.`,name:"save_function"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1891"}}),io=new b({props:{name:"unfuse_lora",anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) — List of LoRA-injectable components to unfuse LoRA from.",name:"components"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1987"}}),fo=new b({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights",parameters:[{name:"reset_to_overwritten_params",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.reset_to_overwritten_params",description:`<strong>reset_to_overwritten_params</strong> (<code>bool</code>, defaults to <code>False</code>) — Whether to reset the LoRA-loaded modules | |
| to their original params. Refer to the <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux" rel="nofollow">Flux | |
| documentation</a> to learn more.`,name:"reset_to_overwritten_params"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2004"}}),Sa=new O({props:{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.example",$$slots:{default:[nL]},$$scope:{ctx:T}}}),po=new A({props:{title:"CogVideoXLoraLoaderMixin",local:"diffusers.loaders.CogVideoXLoraLoaderMixin",headingTag:"h2"}}),co=new b({props:{name:"class diffusers.loaders.CogVideoXLoraLoaderMixin",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2436"}}),mo=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2606"}}),uo=new b({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2540"}}),_o=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2499"}}),go=new b({props:{name:"lora_state_dict",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2444"}}),ho=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2572"}}),bo=new b({props:{name:"unfuse_lora",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2625"}}),vo=new A({props:{title:"Mochi1LoraLoaderMixin",local:"diffusers.loaders.Mochi1LoraLoaderMixin",headingTag:"h2"}}),Lo=new b({props:{name:"class diffusers.loaders.Mochi1LoraLoaderMixin",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2632"}}),$o=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2805"}}),xo=new b({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2737"}}),yo=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2696"}}),Mo=new b({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2640"}}),wo=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2769"}}),To=new b({props:{name:"unfuse_lora",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2825"}}),Do=new A({props:{title:"AuraFlowLoraLoaderMixin",local:"diffusers.loaders.AuraFlowLoraLoaderMixin",headingTag:"h2"}}),So=new b({props:{name:"class diffusers.loaders.AuraFlowLoraLoaderMixin",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1283"}}),Co=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1456"}}),Uo=new b({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1388"}}),ko=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1347"}}),Io=new b({props:{name:"lora_state_dict",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1291"}}),Vo=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1420"}}),Jo=new b({props:{name:"unfuse_lora",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L1476"}}),Ro=new A({props:{title:"LTXVideoLoraLoaderMixin",local:"diffusers.loaders.LTXVideoLoraLoaderMixin",headingTag:"h2"}}),Ho=new b({props:{name:"class diffusers.loaders.LTXVideoLoraLoaderMixin",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2832"}}),Zo=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L3008"}}),jo=new b({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2940"}}),Xo=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2899"}}),Go=new b({props:{name:"lora_state_dict",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2840"}}),Wo=new b({props:{name:"save_lora_weights",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L2972"}}),Fo=new b({props:{name:"unfuse_lora",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L3028"}}),Bo=new A({props:{title:"SanaLoraLoaderMixin",local:"diffusers.loaders.SanaLoraLoaderMixin",headingTag:"h2"}}),No=new b({props:{name:"class diffusers.loaders.SanaLoraLoaderMixin",anchor:"diffusers.loaders.SanaLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L3035"}}),Eo=new b({props:{name:"fuse_lora",anchor:"diffusers.loaders.SanaLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{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:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L3208"}}),Po=new b({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.SanaLoraLoaderMixin.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"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12229/src/diffusers/loaders/lora_pipeline.py#L3140"}}),qo=new b({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SanaLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": 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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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| Whether the process calling this is the main process or not. Useful during distributed training and you | |
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| The function to use to save the state dictionary. Useful during distributed training when you need to | |
| replace <code>torch.save</code> with another method. Can be configured with the environment variable | |
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| How to handle a model that is already compiled. The check can return the following messages:<ul> | |
| <li>“error” (default): raise an error</li> | |
| <li>“warn”: issue a warning</li> | |
| <li>“ignore”: do nothing</li> | |
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| 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 | |
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| Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the | |
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Xet Storage Details
- Size:
- 247 kB
- Xet hash:
- 5057731dd2b87517555535b7741f9f3345db7b251449f02ce89578a29fd1ba5b
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Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.