Buckets:
| import{s as _L,o as hL,n as ee}from"../chunks/scheduler.53228c21.js";import{S as vL,i as bL,e as o,s as t,c as l,h as $L,a as s,d as n,b as r,f as _,g as d,j as u,k as g,l as a,m as L,n as f,t as p,o as c,p as m}from"../chunks/index.100fac89.js";import{C as LL}from"../chunks/CopyLLMTxtMenu.831459b9.js";import{D as h}from"../chunks/Docstring.eb4126c9.js";import{C as ae}from"../chunks/CodeBlock.d30a6509.js";import{E as O}from"../chunks/ExampleCodeBlock.11c2f474.js";import{H as V,E as xL}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.377b30e8.js";function yL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function ML(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function wL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function TL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function DL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function SL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function kL(T){let v,w;return v=new ae({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(v.$$.fragment)},l(x){d(v.$$.fragment,x)},m(x,$){f(v,x,$),w=!0},p:ee,i(x){w||(p(v.$$.fragment,x),w=!0)},o(x){c(v.$$.fragment,x),w=!1},d(x){m(v,x)}}}function UL(T){let v,w="Examples:",x,$,y;return $=new ae({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-kvfsh7"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function CL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEthbmRpbnNreTVUMlZQaXBlbGluZSUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwS2FuZGluc2t5NVQyVlBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJhaS1mb3JldmVyJTJGS2FuZGluc2t5LTUuMC1UMlYlMjIpJTBBcGlwZWxpbmUubG9hZF9sb3JhX3dlaWdodHMoJTIycGF0aCUyRnRvJTJGbG9yYS5zYWZldGVuc29ycyUyMiklMEFwaXBlbGluZS5mdXNlX2xvcmEobG9yYV9zY2FsZSUzRDAuNyk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> Kandinsky5T2VPipeline | |
| pipeline = Kandinsky5T2VPipeline.from_pretrained(<span class="hljs-string">"ai-forever/Kandinsky-5.0-T2V"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"path/to/lora.safetensors"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function IL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function VL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function JL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function RL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function HL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function ZL(T){let v,w="Example:",x,$,y;return $=new ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-11lpom8"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function XL(T){let v,w;return v=new ae({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(v.$$.fragment)},l(x){d(v.$$.fragment,x)},m(x,$){f(v,x,$),w=!0},p:ee,i(x){w||(p(v.$$.fragment,x),w=!0)},o(x){c(v.$$.fragment,x),w=!1},d(x){m(v,x)}}}function jL(T){let v,w="Examples:",x,$,y;return $=new ae({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){v=o("p"),v.textContent=w,x=t(),l($.$$.fragment)},l(i){v=s(i,"P",{"data-svelte-h":!0}),u(v)!=="svelte-kvfsh7"&&(v.textContent=w),x=r(i),d($.$$.fragment,i)},m(i,M){L(i,v,M),L(i,x,M),f($,i,M),y=!0},p:ee,i(i){y||(p($.$$.fragment,i),y=!0)},o(i){c($.$$.fragment,i),y=!1},d(i){i&&(n(v),n(x)),m($,i)}}}function GL(T){let v,w,x,$,y,i,M,uf,Lr,nb='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_12820/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_12820/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',gf,xr,ib='<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_12820/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>ZImageLoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/zimage" rel="nofollow">Z-Image</a>.</li> <li><code>Flux2LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux2" rel="nofollow">Flux2</a>.</li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',_f,Qe,lb='<p>To learn more about how to load LoRA weights, see the <a href="../../tutorials/using_peft_for_inference">LoRA</a> loading guide.</p>',hf,yr,vf,D,Mr,fm,ai,db="Utility class for handling LoRAs.",pm,Te,wr,cm,ti,fb="Delete an adapter’s LoRA layers from the pipeline.",mm,ze,um,De,Tr,gm,ri,pb="Disables the active LoRA layers of the pipeline.",_m,Ke,hm,Se,Dr,vm,oi,cb="Enables the active LoRA layers of the pipeline.",bm,Oe,$m,ea,Sr,Lm,si,mb=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,xm,be,kr,ym,ni,ub="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Mm,Ur,gb="<p>> This is an experimental API.</p>",wm,aa,Tm,ke,Cr,Dm,ii,_b="Gets the list of the current active adapters.",Sm,ta,km,ra,Ir,Um,li,hb="Gets the current list of all available adapters in the pipeline.",Cm,Ue,Vr,Im,di,vb="Set the currently active adapters for use in the pipeline.",Vm,oa,Jm,$e,Jr,Rm,fi,bb=`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.`,Hm,pi,$b=`After offloading the LoRA adapters to CPU, as long as the rest of the model is still on GPU, the LoRA adapters | |
| can no longer be used for inference, as that would cause a device mismatch. Remember to set the device back to | |
| GPU before using those LoRA adapters for inference.`,Zm,sa,Xm,Ce,Rr,jm,ci,Lb=`Reverses the effect of | |
| <a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Gm,Hr,xb="<p>> This is an experimental API.</p>",Wm,Ie,Zr,Nm,mi,yb="Unloads the LoRA parameters.",Fm,na,Bm,ia,Xr,Em,ui,Mb="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",bf,jr,$f,Q,Gr,Pm,gi,wb=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_12820/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>.`,qm,la,Wr,Am,_i,Tb="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Ym,da,Nr,Qm,hi,Db="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",zm,te,Fr,Km,vi,Sb=`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>.`,Om,bi,kb="All kwargs are forwarded to <code>self.lora_state_dict</code>.",eu,$i,Ub=`See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is | |
| loaded.`,au,Li,Cb=`See <a href="/docs/diffusers/pr_12820/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>.`,tu,xi,Ib=`See <a href="/docs/diffusers/pr_12820/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>.`,ru,Ve,Br,ou,yi,Vb="Return state dict for lora weights and the network alphas.",su,Er,Jb=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,nu,fa,Pr,iu,Mi,Rb="Save the LoRA parameters corresponding to the UNet and text encoder.",Lf,qr,xf,I,Ar,lu,wi,Hb=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_12820/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>.`,du,pa,Yr,fu,Ti,Zb="See <code>fuse_lora()</code> for more details.",pu,ca,Qr,cu,Di,Xb="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",mu,ma,zr,uu,Si,jb="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",gu,ua,Kr,_u,ki,Gb='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',hu,Je,Or,vu,Ui,Wb="Return state dict for lora weights and the network alphas.",bu,eo,Nb=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,$u,ga,ao,Lu,Ci,Fb='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',xu,_a,to,yu,Ii,Bb="See <code>unfuse_lora()</code> for more details.",yf,ro,Mf,C,oo,Mu,Vi,Eb=`Load LoRA layers into <a href="/docs/diffusers/pr_12820/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>.`,wu,Ji,Pb='Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Tu,ha,so,Du,Ri,qb="See <code>fuse_lora()</code> for more details.",Su,va,no,ku,Hi,Ab="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Uu,ba,io,Cu,Zi,Yb='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Iu,$a,lo,Vu,Xi,Qb='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Ju,La,fo,Ru,ji,zb='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Hu,xa,po,Zu,Gi,Kb='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Xu,ya,co,ju,Wi,Ob="See <code>unfuse_lora()</code> for more details.",wf,mo,Tf,uo,go,Df,_o,Sf,J,ho,Gu,Ni,e2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/flux2_transformer#diffusers.Flux2Transformer2DModel">Flux2Transformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/flux2#diffusers.Flux2Pipeline">Flux2Pipeline</a>.',Wu,Ma,vo,Nu,Fi,a2="See <code>fuse_lora()</code> for more details.",Fu,wa,bo,Bu,Bi,t2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Eu,Ta,$o,Pu,Ei,r2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',qu,Da,Lo,Au,Pi,o2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Yu,Sa,xo,Qu,qi,s2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',zu,ka,yo,Ku,Ai,n2="See <code>unfuse_lora()</code> for more details.",kf,Mo,Uf,R,wo,Ou,Yi,i2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',eg,Ua,To,ag,Qi,l2="See <code>fuse_lora()</code> for more details.",tg,Ca,Do,rg,zi,d2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',og,Ia,So,sg,Ki,f2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',ng,Va,ko,ig,Oi,p2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',lg,Ja,Uo,dg,el,c2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',fg,Ra,Co,pg,al,m2="See <code>unfuse_lora()</code> for more details.",Cf,Io,If,H,Vo,cg,tl,u2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',mg,Ha,Jo,ug,rl,g2="See <code>fuse_lora()</code> for more details.",gg,Za,Ro,_g,ol,_2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',hg,Xa,Ho,vg,sl,h2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',bg,ja,Zo,$g,nl,v2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Lg,Ga,Xo,xg,il,b2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',yg,Wa,jo,Mg,ll,$2="See <code>unfuse_lora()</code> for more details.",Vf,Go,Jf,Z,Wo,wg,dl,L2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',Tg,Na,No,Dg,fl,x2="See <code>fuse_lora()</code> for more details.",Sg,Fa,Fo,kg,pl,y2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Ug,Ba,Bo,Cg,cl,M2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Ig,Ea,Eo,Vg,ml,w2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Jg,Pa,Po,Rg,ul,T2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Hg,qa,qo,Zg,gl,D2="See <code>unfuse_lora()</code> for more details.",Rf,Ao,Hf,X,Yo,Xg,_l,S2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',jg,Aa,Qo,Gg,hl,k2="See <code>fuse_lora()</code> for more details.",Wg,Ya,zo,Ng,vl,U2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Fg,Qa,Ko,Bg,bl,C2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Eg,za,Oo,Pg,$l,I2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',qg,Ka,es,Ag,Ll,V2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Yg,Oa,as,Qg,xl,J2="See <code>unfuse_lora()</code> for more details.",Zf,ts,Xf,j,rs,zg,yl,R2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',Kg,et,os,Og,Ml,H2="See <code>fuse_lora()</code> for more details.",e_,at,ss,a_,wl,Z2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',t_,tt,ns,r_,Tl,X2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',o_,rt,is,s_,Dl,j2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',n_,ot,ls,i_,Sl,G2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',l_,st,ds,d_,kl,W2="See <code>unfuse_lora()</code> for more details.",jf,fs,Gf,G,ps,f_,Ul,N2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',p_,nt,cs,c_,Cl,F2="See <code>fuse_lora()</code> for more details.",m_,it,ms,u_,Il,B2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',g_,lt,us,__,Vl,E2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',h_,dt,gs,v_,Jl,P2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',b_,ft,_s,$_,Rl,q2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',L_,pt,hs,x_,Hl,A2="See <code>unfuse_lora()</code> for more details.",Wf,vs,Nf,W,bs,y_,Zl,Y2='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',M_,ct,$s,w_,Xl,Q2="See <code>fuse_lora()</code> for more details.",T_,mt,Ls,D_,jl,z2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',S_,ut,xs,k_,Gl,K2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',U_,gt,ys,C_,Wl,O2='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',I_,_t,Ms,V_,Nl,e$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',J_,ht,ws,R_,Fl,a$="See <code>unfuse_lora()</code> for more details.",Ff,Ts,Bf,N,Ds,H_,Bl,t$='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/cogview4#diffusers.CogView4Pipeline">CogView4Pipeline</a>.',Z_,vt,Ss,X_,El,r$="See <code>fuse_lora()</code> for more details.",j_,bt,ks,G_,Pl,o$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',W_,$t,Us,N_,ql,s$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',F_,Lt,Cs,B_,Al,n$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',E_,xt,Is,P_,Yl,i$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',q_,yt,Vs,A_,Ql,l$="See <code>unfuse_lora()</code> for more details.",Ef,Js,Pf,F,Rs,Y_,zl,d$='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',Q_,Mt,Hs,z_,Kl,f$="See <code>fuse_lora()</code> for more details.",K_,wt,Zs,O_,Ol,p$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',eh,Tt,Xs,ah,ed,c$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',th,Dt,js,rh,ad,m$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',oh,St,Gs,sh,td,u$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',nh,kt,Ws,ih,rd,g$="See <code>unfuse_lora()</code> for more details.",qf,Ns,Af,B,Fs,lh,od,_$='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/skyreels_v2_transformer_3d#diffusers.SkyReelsV2Transformer3DModel">SkyReelsV2Transformer3DModel</a>.',dh,Ut,Bs,fh,sd,h$="See <code>fuse_lora()</code> for more details.",ph,Ct,Es,ch,nd,v$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',mh,It,Ps,uh,id,b$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',gh,Vt,qs,_h,ld,$$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',hh,Jt,As,vh,dd,L$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',bh,Rt,Ys,$h,fd,x$="See <code>unfuse_lora()</code> for more details.",Yf,Qs,Qf,ye,zs,Lh,Ht,Ks,xh,pd,y$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',yh,Zt,Os,Mh,cd,M$="Save the LoRA parameters corresponding to the UNet and text encoder.",zf,en,Kf,E,an,wh,md,w$='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel">HiDreamImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/hidream#diffusers.HiDreamImagePipeline">HiDreamImagePipeline</a>.',Th,Xt,tn,Dh,ud,T$="See <code>fuse_lora()</code> for more details.",Sh,jt,rn,kh,gd,D$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Uh,Gt,on,Ch,_d,S$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Ih,Wt,sn,Vh,hd,k$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Jh,Nt,nn,Rh,vd,U$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Hh,Ft,ln,Zh,bd,C$="See <code>unfuse_lora()</code> for more details.",Of,dn,ep,P,fn,Xh,$d,I$='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/qwenimage_transformer2d#diffusers.QwenImageTransformer2DModel">QwenImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/qwenimage#diffusers.QwenImagePipeline">QwenImagePipeline</a>.',jh,Bt,pn,Gh,Ld,V$="See <code>fuse_lora()</code> for more details.",Wh,Et,cn,Nh,xd,J$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Fh,Pt,mn,Bh,yd,R$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Eh,qt,un,Ph,Md,H$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',qh,At,gn,Ah,wd,Z$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Yh,Yt,_n,Qh,Td,X$="See <code>unfuse_lora()</code> for more details.",ap,hn,tp,q,vn,zh,Dd,j$='Load LoRA layers into <a href="/docs/diffusers/pr_12820/en/api/models/z_image_transformer2d#diffusers.ZImageTransformer2DModel">ZImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12820/en/api/pipelines/z_image#diffusers.ZImagePipeline">ZImagePipeline</a>.',Kh,Qt,bn,Oh,Sd,G$="See <code>fuse_lora()</code> for more details.",ev,zt,$n,av,kd,W$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',tv,Kt,Ln,rv,Ud,N$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',ov,Ot,xn,sv,Cd,F$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',nv,er,yn,iv,Id,B$='See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',lv,ar,Mn,dv,Vd,E$="See <code>unfuse_lora()</code> for more details.",rp,wn,op,A,Tn,fv,Jd,P$="Load LoRA layers into <code>Kandinsky5Transformer3DModel</code>,",pv,Re,Dn,cv,Rd,q$="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",mv,tr,uv,rr,Sn,gv,Hd,A$="Load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",_v,or,kn,hv,Zd,Y$="Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code>",vv,sr,Un,bv,Xd,Q$="Return state dict for lora weights and the network alphas.",$v,nr,Cn,Lv,jd,z$="Save the LoRA parameters corresponding to the transformer and text encoders.",xv,ir,In,yv,Gd,K$="Reverses the effect of <code>pipe.fuse_lora()</code>.",sp,Vn,np,S,Jn,Mv,Wd,O$="Utility class for handling LoRAs.",wv,He,Rn,Tv,Nd,eL="Delete an adapter’s LoRA layers from the pipeline.",Dv,lr,Sv,Ze,Hn,kv,Fd,aL="Disables the active LoRA layers of the pipeline.",Uv,dr,Cv,Xe,Zn,Iv,Bd,tL="Enables the active LoRA layers of the pipeline.",Vv,fr,Jv,pr,Xn,Rv,Ed,rL=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,Hv,Le,jn,Zv,Pd,oL="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Xv,Gn,sL="<p>> This is an experimental API.</p>",jv,cr,Gv,je,Wn,Wv,qd,nL="Gets the list of the current active adapters.",Nv,mr,Fv,ur,Nn,Bv,Ad,iL="Gets the current list of all available adapters in the pipeline.",Ev,Ge,Fn,Pv,Yd,lL="Set the currently active adapters for use in the pipeline.",qv,gr,Av,xe,Bn,Yv,Qd,dL=`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.`,Qv,zd,fL=`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.`,zv,_r,Kv,We,En,Ov,Kd,pL=`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>.`,eb,Pn,cL="<p>> This is an experimental API.</p>",ab,Ne,qn,tb,Od,mL="Unloads the LoRA parameters.",rb,hr,ob,vr,An,sb,ef,uL="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",ip,Yn,lp,mf,dp;return y=new LL({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),M=new V({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),yr=new V({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),Mr=new h({props:{name:"class diffusers.loaders.lora_base.LoraBaseMixin",anchor:"diffusers.loaders.lora_base.LoraBaseMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L544"}}),wr=new h({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_12820/src/diffusers/loaders/lora_base.py#L904"}}),ze=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.example",$$slots:{default:[yL]},$$scope:{ctx:T}}}),Tr=new h({props:{name:"disable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L844"}}),Ke=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora.example",$$slots:{default:[ML]},$$scope:{ctx:T}}}),Dr=new h({props:{name:"enable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L874"}}),Oe=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora.example",$$slots:{default:[wL]},$$scope:{ctx:T}}}),Sr=new h({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_12820/src/diffusers/loaders/lora_base.py#L1051"}}),kr=new h({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_12820/src/diffusers/loaders/lora_base.py#L602"}}),aa=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[TL]},$$scope:{ctx:T}}}),Cr=new h({props:{name:"get_active_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L942"}}),ta=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[DL]},$$scope:{ctx:T}}}),Ir=new h({props:{name:"get_list_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L975"}}),Vr=new h({props:{name:"set_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"},{name:"adapter_weights",val:": typing.Union[float, typing.Dict, typing.List[float], typing.List[typing.Dict], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code> or <code>str</code>) — | |
| The names of the adapters to use.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_weights",description:`<strong>adapter_weights</strong> (<code>Union[List[float], float]</code>, <em>optional</em>) — | |
| The adapter(s) weights to use with the UNet. If <code>None</code>, the weights are set to <code>1.0</code> for all the | |
| adapters.`,name:"adapter_weights"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L741"}}),oa=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.example",$$slots:{default:[SL]},$$scope:{ctx:T}}}),Jr=new h({props:{name:"set_lora_device",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device",parameters:[{name:"adapter_names",val:": typing.List[str]"},{name:"device",val:": typing.Union[torch.device, str, int]"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>) — | |
| List of adapters to send device to.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.device",description:`<strong>device</strong> (<code>Union[torch.device, str, int]</code>) — | |
| Device to send the adapters to. Can be either a torch device, a str or an integer.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L997"}}),sa=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.example",$$slots:{default:[kL]},$$scope:{ctx:T}}}),Rr=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) — List of LoRA-injectable components to unfuse LoRA from.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_unet",description:"<strong>unfuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) — Whether to unfuse the UNet LoRA parameters.",name:"unfuse_unet"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_text_encoder",description:`<strong>unfuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) — | |
| Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the | |
| LoRA parameters then it won’t have any effect.`,name:"unfuse_text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L688"}}),Zr=new h({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L579"}}),na=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[UL]},$$scope:{ctx:T}}}),Xr=new h({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_12820/src/diffusers/loaders/lora_base.py#L1074"}}),jr=new V({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Gr=new h({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L58"}}),Wr=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L341"}}),Nr=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L280"}}),Fr=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L68"}}),Br=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L169"}}),Pr=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L399"}}),qr=new V({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),Ar=new h({props:{name:"class diffusers.loaders.StableDiffusionXLLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L522"}}),Yr=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L888"}}),Qr=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L781"}}),zr=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L719"}}),Kr=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L533"}}),Or=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L607"}}),ao=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L840"}}),to=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L907"}}),ro=new V({props:{title:"SD3LoraLoaderMixin",local:"diffusers.loaders.SD3LoraLoaderMixin",headingTag:"h2"}}),oo=new h({props:{name:"class diffusers.loaders.SD3LoraLoaderMixin",anchor:"diffusers.loaders.SD3LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L914"}}),so=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L1186"}}),no=new h({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_12820/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_12820/src/diffusers/loaders/lora_pipeline.py#L1077"}}),io=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L1046"}}),lo=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L981"}}),fo=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L927"}}),po=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L1136"}}),co=new h({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_12820/src/diffusers/loaders/lora_pipeline.py#L1206"}}),mo=new V({props:{title:"FluxLoraLoaderMixin",local:"diffusers.loaders.FluxLoraLoaderMixin",headingTag:"h2"}}),go=new h({props:{name:"class diffusers.loaders.FluxLoraLoaderMixin",anchor:"diffusers.loaders.FluxLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L1413"}}),_o=new V({props:{title:"Flux2LoraLoaderMixin",local:"diffusers.loaders.Flux2LoraLoaderMixin",headingTag:"h2"}}),ho=new h({props:{name:"class diffusers.loaders.Flux2LoraLoaderMixin",anchor:"diffusers.loaders.Flux2LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L4436"}}),vo=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4612"}}),bo=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4544"}}),$o=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4503"}}),Lo=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4444"}}),xo=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4576"}}),yo=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L4632"}}),Mo=new V({props:{title:"CogVideoXLoraLoaderMixin",local:"diffusers.loaders.CogVideoXLoraLoaderMixin",headingTag:"h2"}}),wo=new h({props:{name:"class diffusers.loaders.CogVideoXLoraLoaderMixin",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L1579"}}),To=new 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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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| replace <code>torch.save</code> with another method. Can be configured with the environment variable | |
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h({props:{name:"fuse_lora",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4203"}}),cn=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4135"}}),mn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4094"}}),un=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4032"}}),gn=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4167"}}),_n=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L4223"}}),hn=new V({props:{title:"ZImageLoraLoaderMixin",local:"diffusers.loaders.ZImageLoraLoaderMixin",headingTag:"h2"}}),vn=new h({props:{name:"class diffusers.loaders.ZImageLoraLoaderMixin",anchor:"diffusers.loaders.ZImageLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L4230"}}),bn=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4409"}}),$n=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4341"}}),Ln=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4300"}}),xn=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4238"}}),yn=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L4373"}}),Mn=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L4429"}}),wn=new V({props:{title:"KandinskyLoraLoaderMixin",local:"diffusers.loaders.KandinskyLoraLoaderMixin",headingTag:"h2"}}),Tn=new h({props:{name:"class diffusers.loaders.KandinskyLoraLoaderMixin",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L2785"}}),Dn=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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:""}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.KandinskyLoraLoaderMixin.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.KandinskyLoraLoaderMixin.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.`,name:"safe_fusing"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) — | |
| Adapter names to be used for fusing.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L3023"}}),tr=new O({props:{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora.example",$$slots:{default:[CL]},$$scope:{ctx:T}}}),Sn=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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"}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) — | |
| A standard state dict containing the lora layer parameters.`,name:"state_dict"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>Kandinsky5Transformer3DModel</code>) — | |
| The transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| Adapter name to be used for referencing the loaded adapter model.`,name:"adapter_name"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.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.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) — | |
| Optional LoRA adapter metadata.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L2930"}}),kn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.KandinskyLoraLoaderMixin.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_12820/en/api/loaders/lora#diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"adapter_name"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place.`,name:"hotswap"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.KandinskyLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12820/en/api/loaders/lora#diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L2875"}}),Un=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.KandinskyLoraLoaderMixin.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> of a pretrained model hosted on the Hub.</li> | |
| <li>A path to a <em>directory</em> containing the model weights.</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.KandinskyLoraLoaderMixin.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.`,name:"cache_dir"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"force_download"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"proxies"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"local_files_only"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"token"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"revision"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"subfolder"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.KandinskyLoraLoaderMixin.lora_state_dict.use_safetensors",description:`<strong>use_safetensors</strong> (<code>bool</code>, <em>optional</em>) — | |
| Whether to use safetensors for loading.`,name:"use_safetensors"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L2793"}}),Cn=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — | |
| Directory to save LoRA parameters to.`,name:"save_directory"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.KandinskyLoraLoaderMixin.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.`,name:"is_main_process"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) — | |
| The function to use to save the state dictionary.`,name:"save_function"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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.`,name:"safe_serialization"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the transformer.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_pipeline.py#L2975"}}),In=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.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_12820/src/diffusers/loaders/lora_pipeline.py#L3060"}}),Vn=new V({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),Jn=new h({props:{name:"class diffusers.loaders.lora_base.LoraBaseMixin",anchor:"diffusers.loaders.lora_base.LoraBaseMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L544"}}),Rn=new h({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_12820/src/diffusers/loaders/lora_base.py#L904"}}),lr=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.example",$$slots:{default:[IL]},$$scope:{ctx:T}}}),Hn=new h({props:{name:"disable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L844"}}),dr=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora.example",$$slots:{default:[VL]},$$scope:{ctx:T}}}),Zn=new h({props:{name:"enable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12820/src/diffusers/loaders/lora_base.py#L874"}}),fr=new O({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora.example",$$slots:{default:[JL]},$$scope:{ctx:T}}}),Xn=new h({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_12820/src/diffusers/loaders/lora_base.py#L1051"}}),jn=new h({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) — | |
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| Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.`,name:"safe_fusing"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) — | |
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NL(T){return hL(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class QL extends vL{constructor(v){super(),bL(this,v,NL,GL,_L,{})}}export{QL as component}; | |
Xet Storage Details
- Size:
- 261 kB
- Xet hash:
- ce83b9a913d0fbff1e671464f135afa5729d5ea73ff4ff4c317a8704d340c619
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.