Buckets:
| import{s as wx,o as yx,n as te}from"../chunks/scheduler.53228c21.js";import{S as Tx,i as kx,e as o,s as r,c as l,h as Sx,a as s,d as n,b as t,f as g,g as i,j as u,k as _,l as a,m as L,n as d,t as f,o as p,p as c}from"../chunks/index.100fac89.js";import{C as Cx}from"../chunks/CopyLLMTxtMenu.1b50ff75.js";import{D as h}from"../chunks/Docstring.2d4c264a.js";import{C as oe}from"../chunks/CodeBlock.d30a6509.js";import{E as re}from"../chunks/ExampleCodeBlock.1e70ba81.js";import{H,E as Dx}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.f94e0d6c.js";function Ix(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHQySW1hZ2UlMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwQXV0b1BpcGVsaW5lRm9yVGV4dDJJbWFnZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUwQSUyMCUyMCUyMCUyMCUyMmpiaWxja2UtaGYlMkZzZHhsLWNpbmVtYXRpYy0xJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJweXRvcmNoX2xvcmFfd2VpZ2h0cy5zYWZldGVuc29ycyUyMiUyQyUyMGFkYXB0ZXJfbmFtZXMlM0QlMjJjaW5lbWF0aWMlMjIlMEEpJTBBcGlwZWxpbmUuZGVsZXRlX2FkYXB0ZXJzKCUyMmNpbmVtYXRpYyUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Vx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Hx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Ux(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Jx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Rx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Nx(T){let b,y;return b=new oe({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(b.$$.fragment)},l(x){i(b.$$.fragment,x)},m(x,$){d(b,x,$),y=!0},p:te,i(x){y||(f(b.$$.fragment,x),y=!0)},o(x){p(b.$$.fragment,x),y=!1},d(x){c(b,x)}}}function Zx(T){let b,y="Examples:",x,$,M;return $=new oe({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-kvfsh7"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Xx(T){let b,y="Examples:",x,$,M;return $=new oe({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-kvfsh7"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function jx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Fx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Gx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Wx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Ex(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Bx(T){let b,y="Example:",x,$,M;return $=new oe({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-11lpom8"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Px(T){let b,y;return b=new oe({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(b.$$.fragment)},l(x){i(b.$$.fragment,x)},m(x,$){d(b,x,$),y=!0},p:te,i(x){y||(f(b.$$.fragment,x),y=!0)},o(x){p(b.$$.fragment,x),y=!1},d(x){c(b,x)}}}function qx(T){let b,y="Examples:",x,$,M;return $=new oe({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){b=o("p"),b.textContent=y,x=r(),l($.$$.fragment)},l(m){b=s(m,"P",{"data-svelte-h":!0}),u(b)!=="svelte-kvfsh7"&&(b.textContent=y),x=t(m),i($.$$.fragment,m)},m(m,w){L(m,b,w),L(m,x,w),d($,m,w),M=!0},p:te,i(m){M||(f($.$$.fragment,m),M=!0)},o(m){p($.$$.fragment,m),M=!1},d(m){m&&(n(b),n(x)),c($,m)}}}function Ax(T){let b,y,x,$,M,m,w,cp,Xt,Q2='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_12652/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_12652/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',mp,jt,K2='<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_12652/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>LTX2LoraLoaderMixin</code> provides similar functions for <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx2" 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>',up,aa,O2='<p>To learn more about how to load LoRA weights, see the <a href="../../tutorials/using_peft_for_inference">LoRA</a> loading guide.</p>',_p,Ft,gp,k,Gt,Mu,Jl,e$="Utility class for handling LoRAs.",wu,De,Wt,yu,Rl,a$="Delete an adapter’s LoRA layers from the pipeline.",Tu,ra,ku,Ie,Et,Su,Nl,r$="Disables the active LoRA layers of the pipeline.",Cu,ta,Du,Ve,Bt,Iu,Zl,t$="Enables the active LoRA layers of the pipeline.",Vu,oa,Hu,sa,Pt,Uu,Xl,o$=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,Ju,Me,qt,Ru,jl,s$="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Nu,At,n$="<p>> This is an experimental API.</p>",Zu,na,Xu,He,Yt,ju,Fl,l$="Gets the list of the current active adapters.",Fu,la,Gu,ia,zt,Wu,Gl,i$="Gets the current list of all available adapters in the pipeline.",Eu,Ue,Qt,Bu,Wl,d$="Set the currently active adapters for use in the pipeline.",Pu,da,qu,we,Kt,Au,El,f$=`Moves the LoRAs listed in <code>adapter_names</code> to a target device. Useful for offloading the LoRA to the CPU in case | |
| you want to load multiple adapters and free some GPU memory.`,Yu,Bl,p$=`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.`,zu,fa,Qu,Je,Ot,Ku,Pl,c$=`Reverses the effect of | |
| <a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Ou,eo,m$="<p>> This is an experimental API.</p>",e_,Re,ao,a_,ql,u$="Unloads the LoRA parameters.",r_,pa,t_,ca,ro,o_,Al,_$="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",hp,to,vp,O,oo,s_,Yl,g$=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_12652/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a> and | |
| <a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,n_,ma,so,l_,zl,h$="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",i_,ua,no,d_,Ql,v$="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",f_,se,lo,p_,Kl,b$=`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>.`,c_,Ol,$$="All kwargs are forwarded to <code>self.lora_state_dict</code>.",m_,ei,L$=`See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is | |
| loaded.`,u_,ai,x$=`See <a href="/docs/diffusers/pr_12652/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>.`,__,ri,M$=`See <a href="/docs/diffusers/pr_12652/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>.`,g_,Ne,io,h_,ti,w$="Return state dict for lora weights and the network alphas.",v_,fo,y$=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,b_,_a,po,$_,oi,T$="Save the LoRA parameters corresponding to the UNet and text encoder.",bp,co,$p,U,mo,L_,si,k$=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_12652/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>.`,x_,ga,uo,M_,ni,S$="See <code>fuse_lora()</code> for more details.",w_,ha,_o,y_,li,C$="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",T_,va,go,k_,ii,D$="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",S_,ba,ho,C_,di,I$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',D_,Ze,vo,I_,fi,V$="Return state dict for lora weights and the network alphas.",V_,bo,H$=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,H_,$a,$o,U_,pi,U$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',J_,La,Lo,R_,ci,J$="See <code>unfuse_lora()</code> for more details.",Lp,xo,xp,V,Mo,N_,mi,R$=`Load LoRA layers into <a href="/docs/diffusers/pr_12652/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>.`,Z_,ui,N$='Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',X_,xa,wo,j_,_i,Z$="See <code>fuse_lora()</code> for more details.",F_,Ma,yo,G_,gi,X$="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",W_,wa,To,E_,hi,j$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',B_,ya,ko,P_,vi,F$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',q_,Ta,So,A_,bi,G$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Y_,ka,Co,z_,$i,W$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Q_,Sa,Do,K_,Li,E$="See <code>unfuse_lora()</code> for more details.",Mp,Io,wp,I,Vo,O_,xi,B$=`Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>, | |
| <a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow"><code>CLIPTextModel</code></a>.`,eg,Mi,P$='Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/flux#diffusers.FluxPipeline">FluxPipeline</a>.',ag,Ca,Ho,rg,wi,q$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',tg,Da,Uo,og,yi,A$="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",sg,Ia,Jo,ng,Ti,Y$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',lg,Va,Ro,ig,ki,z$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',dg,Ha,No,fg,Si,Q$='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',pg,Ua,Zo,cg,Ci,K$="Save the LoRA parameters corresponding to the UNet and text encoder.",mg,Xe,Xo,ug,Di,O$=`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>.`,_g,jo,eL="<p>> This is an experimental API.</p>",gg,je,Fo,hg,Ii,aL="Unloads the LoRA parameters.",vg,Ja,yp,Go,Tp,R,Wo,bg,Vi,rL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/flux2_transformer#diffusers.Flux2Transformer2DModel">Flux2Transformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/flux2#diffusers.Flux2Pipeline">Flux2Pipeline</a>.',$g,Ra,Eo,Lg,Hi,tL="See <code>fuse_lora()</code> for more details.",xg,Na,Bo,Mg,Ui,oL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',wg,Za,Po,yg,Ji,sL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Tg,Xa,qo,kg,Ri,nL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Sg,ja,Ao,Cg,Ni,lL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Dg,Fa,Yo,Ig,Zi,iL="See <code>unfuse_lora()</code> for more details.",kp,zo,Sp,N,Qo,Vg,Xi,dL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/ltx2_video_transformer3d#diffusers.LTX2VideoTransformer3DModel">LTX2VideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/ltx2#diffusers.LTX2Pipeline">LTX2Pipeline</a>.',Hg,Ga,Ko,Ug,ji,fL="See <code>fuse_lora()</code> for more details.",Jg,Wa,Oo,Rg,Fi,pL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Ng,Ea,es,Zg,Gi,cL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Xg,Ba,as,jg,Wi,mL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Fg,Pa,rs,Gg,Ei,uL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Wg,qa,ts,Eg,Bi,_L="See <code>unfuse_lora()</code> for more details.",Cp,os,Dp,Z,ss,Bg,Pi,gL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/cogvideox_transformer3d#diffusers.CogVideoXTransformer3DModel">CogVideoXTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/cogvideox#diffusers.CogVideoXPipeline">CogVideoXPipeline</a>.',Pg,Aa,ns,qg,qi,hL="See <code>fuse_lora()</code> for more details.",Ag,Ya,ls,Yg,Ai,vL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',zg,za,is,Qg,Yi,bL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Kg,Qa,ds,Og,zi,$L='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',eh,Ka,fs,ah,Qi,LL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',rh,Oa,ps,th,Ki,xL="See <code>unfuse_lora()</code> for more details.",Ip,cs,Vp,X,ms,oh,Oi,ML='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/mochi_transformer3d#diffusers.MochiTransformer3DModel">MochiTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/mochi#diffusers.MochiPipeline">MochiPipeline</a>.',sh,er,us,nh,ed,wL="See <code>fuse_lora()</code> for more details.",lh,ar,_s,ih,ad,yL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',dh,rr,gs,fh,rd,TL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',ph,tr,hs,ch,td,kL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',mh,or,vs,uh,od,SL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',_h,sr,bs,gh,sd,CL="See <code>unfuse_lora()</code> for more details.",Hp,$s,Up,j,Ls,hh,nd,DL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',vh,nr,xs,bh,ld,IL="See <code>fuse_lora()</code> for more details.",$h,lr,Ms,Lh,id,VL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',xh,ir,ws,Mh,dd,HL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',wh,dr,ys,yh,fd,UL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Th,fr,Ts,kh,pd,JL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Sh,pr,ks,Ch,cd,RL="See <code>unfuse_lora()</code> for more details.",Jp,Ss,Rp,F,Cs,Dh,md,NL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/ltx_video_transformer3d#diffusers.LTXVideoTransformer3DModel">LTXVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/ltx_video#diffusers.LTXPipeline">LTXPipeline</a>.',Ih,cr,Ds,Vh,ud,ZL="See <code>fuse_lora()</code> for more details.",Hh,mr,Is,Uh,_d,XL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Jh,ur,Vs,Rh,gd,jL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Nh,_r,Hs,Zh,hd,FL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Xh,gr,Us,jh,vd,GL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Fh,hr,Js,Gh,bd,WL="See <code>unfuse_lora()</code> for more details.",Np,Rs,Zp,G,Ns,Wh,$d,EL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/sana_transformer2d#diffusers.SanaTransformer2DModel">SanaTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/sana#diffusers.SanaPipeline">SanaPipeline</a>.',Eh,vr,Zs,Bh,Ld,BL="See <code>fuse_lora()</code> for more details.",Ph,br,Xs,qh,xd,PL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Ah,$r,js,Yh,Md,qL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',zh,Lr,Fs,Qh,wd,AL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Kh,xr,Gs,Oh,yd,YL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',ev,Mr,Ws,av,Td,zL="See <code>unfuse_lora()</code> for more details.",Xp,Es,jp,W,Bs,rv,kd,QL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/hunyuan_video#diffusers.HunyuanVideoPipeline">HunyuanVideoPipeline</a>.',tv,wr,Ps,ov,Sd,KL="See <code>fuse_lora()</code> for more details.",sv,yr,qs,nv,Cd,OL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',lv,Tr,As,iv,Dd,e1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',dv,kr,Ys,fv,Id,a1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',pv,Sr,zs,cv,Vd,r1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',mv,Cr,Qs,uv,Hd,t1="See <code>unfuse_lora()</code> for more details.",Fp,Ks,Gp,E,Os,_v,Ud,o1='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',gv,Dr,en,hv,Jd,s1="See <code>fuse_lora()</code> for more details.",vv,Ir,an,bv,Rd,n1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',$v,Vr,rn,Lv,Nd,l1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',xv,Hr,tn,Mv,Zd,i1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',wv,Ur,on,yv,Xd,d1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Tv,Jr,sn,kv,jd,f1="See <code>unfuse_lora()</code> for more details.",Wp,nn,Ep,B,ln,Sv,Fd,p1='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/cogview4#diffusers.CogView4Pipeline">CogView4Pipeline</a>.',Cv,Rr,dn,Dv,Gd,c1="See <code>fuse_lora()</code> for more details.",Iv,Nr,fn,Vv,Wd,m1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Hv,Zr,pn,Uv,Ed,u1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Jv,Xr,cn,Rv,Bd,_1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Nv,jr,mn,Zv,Pd,g1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Xv,Fr,un,jv,qd,h1="See <code>unfuse_lora()</code> for more details.",Bp,_n,Pp,P,gn,Fv,Ad,v1='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> and <code>[WanImageToVideoPipeline</code>].',Gv,Gr,hn,Wv,Yd,b1="See <code>fuse_lora()</code> for more details.",Ev,Wr,vn,Bv,zd,$1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Pv,Er,bn,qv,Qd,L1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Av,Br,$n,Yv,Kd,x1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',zv,Pr,Ln,Qv,Od,M1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Kv,qr,xn,Ov,ef,w1="See <code>unfuse_lora()</code> for more details.",qp,Mn,Ap,q,wn,eb,af,y1='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/skyreels_v2_transformer_3d#diffusers.SkyReelsV2Transformer3DModel">SkyReelsV2Transformer3DModel</a>.',ab,Ar,yn,rb,rf,T1="See <code>fuse_lora()</code> for more details.",tb,Yr,Tn,ob,tf,k1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',sb,zr,kn,nb,of,S1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',lb,Qr,Sn,ib,sf,C1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',db,Kr,Cn,fb,nf,D1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',pb,Or,Dn,cb,lf,I1="See <code>unfuse_lora()</code> for more details.",Yp,In,zp,ke,Vn,mb,et,Hn,ub,df,V1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',_b,at,Un,gb,ff,H1="Save the LoRA parameters corresponding to the UNet and text encoder.",Qp,Jn,Kp,A,Rn,hb,pf,U1='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/hidream_image_transformer#diffusers.HiDreamImageTransformer2DModel">HiDreamImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/hidream#diffusers.HiDreamImagePipeline">HiDreamImagePipeline</a>.',vb,rt,Nn,bb,cf,J1="See <code>fuse_lora()</code> for more details.",$b,tt,Zn,Lb,mf,R1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',xb,ot,Xn,Mb,uf,N1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',wb,st,jn,yb,_f,Z1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Tb,nt,Fn,kb,gf,X1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Sb,lt,Gn,Cb,hf,j1="See <code>unfuse_lora()</code> for more details.",Op,Wn,ec,Y,En,Db,vf,F1='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/qwenimage_transformer2d#diffusers.QwenImageTransformer2DModel">QwenImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/qwenimage#diffusers.QwenImagePipeline">QwenImagePipeline</a>.',Ib,it,Bn,Vb,bf,G1="See <code>fuse_lora()</code> for more details.",Hb,dt,Pn,Ub,$f,W1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Jb,ft,qn,Rb,Lf,E1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Nb,pt,An,Zb,xf,B1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Xb,ct,Yn,jb,Mf,P1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Fb,mt,zn,Gb,wf,q1="See <code>unfuse_lora()</code> for more details.",ac,Qn,rc,z,Kn,Wb,yf,A1='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/z_image_transformer2d#diffusers.ZImageTransformer2DModel">ZImageTransformer2DModel</a>. Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/z_image#diffusers.ZImagePipeline">ZImagePipeline</a>.',Eb,ut,On,Bb,Tf,Y1="See <code>fuse_lora()</code> for more details.",Pb,_t,el,qb,kf,z1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Ab,gt,al,Yb,Sf,Q1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',zb,ht,rl,Qb,Cf,K1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Kb,vt,tl,Ob,Df,O1='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',e2,bt,ol,a2,If,ex="See <code>unfuse_lora()</code> for more details.",tc,sl,oc,Q,nl,r2,Vf,ax="Load LoRA layers into <code>Kandinsky5Transformer3DModel</code>,",t2,$t,ll,o2,Hf,rx="See <code>fuse_lora()</code> for more details.",s2,Lt,il,n2,Uf,tx='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',l2,xt,dl,i2,Jf,ox='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',d2,Mt,fl,f2,Rf,sx='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',p2,wt,pl,c2,Nf,nx='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',m2,yt,cl,u2,Zf,lx="See <code>unfuse_lora()</code> for more details.",sc,ml,nc,S,ul,_2,Xf,ix="Utility class for handling LoRAs.",g2,Fe,_l,h2,jf,dx="Delete an adapter’s LoRA layers from the pipeline.",v2,Tt,b2,Ge,gl,$2,Ff,fx="Disables the active LoRA layers of the pipeline.",L2,kt,x2,We,hl,M2,Gf,px="Enables the active LoRA layers of the pipeline.",w2,St,y2,Ct,vl,T2,Wf,cx=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,k2,ye,bl,S2,Ef,mx="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",C2,$l,ux="<p>> This is an experimental API.</p>",D2,Dt,I2,Ee,Ll,V2,Bf,_x="Gets the list of the current active adapters.",H2,It,U2,Vt,xl,J2,Pf,gx="Gets the current list of all available adapters in the pipeline.",R2,Be,Ml,N2,qf,hx="Set the currently active adapters for use in the pipeline.",Z2,Ht,X2,Te,wl,j2,Af,vx=`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.`,F2,Yf,bx=`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.`,G2,Ut,W2,Pe,yl,E2,zf,$x=`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>.`,B2,Tl,Lx="<p>> This is an experimental API.</p>",P2,qe,kl,q2,Qf,xx="Unloads the LoRA parameters.",A2,Jt,Y2,Rt,Sl,z2,Kf,Mx="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",lc,Cl,ic,pp,dc;return M=new Cx({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new H({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),Ft=new H({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),Gt=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_12652/src/diffusers/loaders/lora_base.py#L479"}}),Wt=new h({props:{name:"delete_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters",parameters:[{name:"adapter_names",val:": list[str] | str"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>list[str, str]</code>) — | |
| The names of the adapters to delete.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L839"}}),ra=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.example",$$slots:{default:[Ix]},$$scope:{ctx:T}}}),Et=new h({props:{name:"disable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L779"}}),ta=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora.example",$$slots:{default:[Vx]},$$scope:{ctx:T}}}),Bt=new h({props:{name:"enable_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L809"}}),oa=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora.example",$$slots:{default:[Hx]},$$scope:{ctx:T}}}),Pt=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_12652/src/diffusers/loaders/lora_base.py#L986"}}),qt=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora",parameters:[{name:"components",val:": list[str] = []"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = 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_12652/src/diffusers/loaders/lora_base.py#L537"}}),na=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[Ux]},$$scope:{ctx:T}}}),Yt=new h({props:{name:"get_active_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L877"}}),la=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[Jx]},$$scope:{ctx:T}}}),zt=new h({props:{name:"get_list_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L910"}}),Qt=new h({props:{name:"set_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters",parameters:[{name:"adapter_names",val:": list[str] | str"},{name:"adapter_weights",val:": float | dict | list[float] | list[dict] | None = None"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>list[str]</code> or <code>str</code>) — | |
| The names of the adapters to use.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_weights",description:`<strong>adapter_weights</strong> (<code>list[float, float]</code>, <em>optional</em>) — | |
| The adapter(s) weights to use with the UNet. If <code>None</code>, the weights are set to <code>1.0</code> for all the | |
| adapters.`,name:"adapter_weights"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L676"}}),da=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.example",$$slots:{default:[Rx]},$$scope:{ctx:T}}}),Kt=new h({props:{name:"set_lora_device",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device",parameters:[{name:"adapter_names",val:": list[str]"},{name:"device",val:": torch.device | str | int"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.adapter_names",description:`<strong>adapter_names</strong> (<code>list[str]</code>) — | |
| list of adapters to send device to.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.device",description:`<strong>device</strong> (<code>torch.device | str | int</code>) — | |
| Device to send the adapters to. Can be either a torch device, a str or an integer.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L932"}}),fa=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.example",$$slots:{default:[Nx]},$$scope:{ctx:T}}}),Ot=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora",parameters:[{name:"components",val:": list[str] = []"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>list[str]</code>) — list of LoRA-injectable components to unfuse LoRA from.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_unet",description:"<strong>unfuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) — Whether to unfuse the UNet LoRA parameters.",name:"unfuse_unet"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_text_encoder",description:`<strong>unfuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) — | |
| Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the | |
| LoRA parameters then it won’t have any effect.`,name:"unfuse_text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L623"}}),ao=new h({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L514"}}),pa=new re({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[Zx]},$$scope:{ctx:T}}}),ro=new h({props:{name:"write_lora_layers",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.write_lora_layers",parameters:[{name:"state_dict",val:": dict[str, torch.Tensor]"},{name:"save_directory",val:": str"},{name:"is_main_process",val:": bool"},{name:"weight_name",val:": str"},{name:"save_function",val:": Callable"},{name:"safe_serialization",val:": bool"},{name:"lora_adapter_metadata",val:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L1009"}}),to=new H({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),oo=new h({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L132"}}),so=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_12652/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_12652/src/diffusers/loaders/lora_pipeline.py#L415"}}),no=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_12652/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_12652/src/diffusers/loaders/lora_pipeline.py#L354"}}),lo=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = 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_12652/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_12652/src/diffusers/loaders/lora_pipeline.py#L142"}}),io=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | 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_12652/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>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_12652/src/diffusers/loaders/lora_pipeline.py#L243"}}),po=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"unet_lora_layers",val:": dict = None"},{name:"text_encoder_lora_layers",val:": dict = 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_12652/src/diffusers/loaders/lora_pipeline.py#L473"}}),co=new H({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),mo=new h({props:{name:"class diffusers.loaders.StableDiffusionXLLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L596"}}),uo=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['unet', 'text_encoder', 'text_encoder_2']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L962"}}),_o=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_12652/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_12652/src/diffusers/loaders/lora_pipeline.py#L855"}}),go=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_12652/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_12652/src/diffusers/loaders/lora_pipeline.py#L793"}}),ho=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L607"}}),vo=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | 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_12652/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>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_12652/src/diffusers/loaders/lora_pipeline.py#L681"}}),$o=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"unet_lora_layers",val:": dict = None"},{name:"text_encoder_lora_layers",val:": dict = None"},{name:"text_encoder_2_lora_layers",val:": dict = 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_12652/src/diffusers/loaders/lora_pipeline.py#L914"}}),Lo=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['unet', 'text_encoder', 'text_encoder_2']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L981"}}),xo=new H({props:{title:"SD3LoraLoaderMixin",local:"diffusers.loaders.SD3LoraLoaderMixin",headingTag:"h2"}}),Mo=new h({props:{name:"class diffusers.loaders.SD3LoraLoaderMixin",anchor:"diffusers.loaders.SD3LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L988"}}),wo=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.SD3LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer', 'text_encoder', 'text_encoder_2']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1260"}}),yo=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_12652/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_12652/src/diffusers/loaders/lora_pipeline.py#L1151"}}),To=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_12652/src/diffusers/loaders/lora_pipeline.py#L1120"}}),ko=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | 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_12652/src/diffusers/loaders/lora_pipeline.py#L1055"}}),So=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1001"}}),Co=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = None"},{name:"text_encoder_lora_layers",val:": dict = None"},{name:"text_encoder_2_lora_layers",val:": dict = 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_12652/src/diffusers/loaders/lora_pipeline.py#L1210"}}),Do=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SD3LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer', 'text_encoder', 'text_encoder_2']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1280"}}),Io=new H({props:{title:"FluxLoraLoaderMixin",local:"diffusers.loaders.FluxLoraLoaderMixin",headingTag:"h2"}}),Vo=new h({props:{name:"class diffusers.loaders.FluxLoraLoaderMixin",anchor:"diffusers.loaders.FluxLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1487"}}),Ho=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.FluxLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1936"}}),Uo=new h({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"text_encoder",val:""},{name:"prefix",val:" = None"},{name:"lora_scale",val:" = 1.0"},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) — | |
| A standard state dict containing the lora layer parameters. The key should be prefixed with an | |
| additional <code>text_encoder</code> to distinguish between unet lora layers.`,name:"state_dict"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>dict[str, float]</code>) — | |
| The value of the network alpha used for stable learning and preventing underflow. This value has the | |
| same meaning as the <code>--network_alpha</code> option in the kohya-ss trainer script. Refer to <a href="https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning" rel="nofollow">this | |
| link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) — | |
| The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) — | |
| Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) — | |
| How much to scale the output of the lora linear layer before it is added with the output of the regular | |
| lora layer.`,name:"lora_scale"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| Adapter name to be used for referencing the loaded adapter model. If not specified, it will use | |
| <code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| Speed up model loading by only loading the pretrained LoRA weights and not initializing the random | |
| weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_text_encoder.metadata",description:`<strong>metadata</strong> (<code>dict</code>) — | |
| Optional LoRA adapter metadata. When supplied, the <code>LoraConfig</code> arguments of <code>peft</code> won’t be derived | |
| from the state dict.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1813"}}),Jo=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"metadata",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1727"}}),Ro=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1625"}}),No=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.FluxLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"return_alphas",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1500"}}),Zo=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = None"},{name:"text_encoder_lora_layers",val:": dict = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — | |
| Directory to save LoRA parameters to. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.transformer_lora_layers",description:`<strong>transformer_lora_layers</strong> (<code>dict[str, torch.nn.Module]</code> or <code>dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>transformer</code>.`,name:"transformer_lora_layers"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>dict[str, torch.nn.Module]</code> or <code>dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text | |
| encoder LoRA state dict because it comes from 🤗 Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether the process calling this is the main process or not. Useful during distributed training and you | |
| need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main | |
| process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) — | |
| The function to use to save the state dictionary. Useful during distributed training when you need to | |
| replace <code>torch.save</code> with another method. Can be configured with the environment variable | |
| <code>DIFFUSERS_SAVE_MODE</code>.`,name:"save_function"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1872"}}),Xo=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer', 'text_encoder']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>list[str]</code>) — list of LoRA-injectable components to unfuse LoRA from.",name:"components"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1968"}}),Fo=new h({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights",parameters:[{name:"reset_to_overwritten_params",val:" = False"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.reset_to_overwritten_params",description:`<strong>reset_to_overwritten_params</strong> (<code>bool</code>, defaults to <code>False</code>) — Whether to reset the LoRA-loaded modules | |
| to their original params. Refer to the <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux" rel="nofollow">Flux | |
| documentation</a> to learn more.`,name:"reset_to_overwritten_params"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1985"}}),Ja=new re({props:{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.example",$$slots:{default:[Xx]},$$scope:{ctx:T}}}),Go=new H({props:{title:"Flux2LoraLoaderMixin",local:"diffusers.loaders.Flux2LoraLoaderMixin",headingTag:"h2"}}),Wo=new h({props:{name:"class diffusers.loaders.Flux2LoraLoaderMixin",anchor:"diffusers.loaders.Flux2LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5416"}}),Eo=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5592"}}),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_12652/src/diffusers/loaders/lora_pipeline.py#L5524"}}),Po=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5483"}}),qo=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5424"}}),Ao=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5556"}}),Yo=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.Flux2LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5612"}}),zo=new H({props:{title:"LTX2LoraLoaderMixin",local:"diffusers.loaders.LTX2LoraLoaderMixin",headingTag:"h2"}}),Qo=new h({props:{name:"class diffusers.loaders.LTX2LoraLoaderMixin",anchor:"diffusers.loaders.LTX2LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3016"}}),Ko=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.LTX2LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3216"}}),Oo=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.LTX2LoraLoaderMixin.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"},{name:"prefix",val:": str = 'transformer'"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3147"}}),es=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.LTX2LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3089"}}),as=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.LTX2LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3025"}}),rs=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.LTX2LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3180"}}),ts=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.LTX2LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3236"}}),os=new H({props:{title:"CogVideoXLoraLoaderMixin",local:"diffusers.loaders.CogVideoXLoraLoaderMixin",headingTag:"h2"}}),ss=new h({props:{name:"class diffusers.loaders.CogVideoXLoraLoaderMixin",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2417"}}),ns=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2587"}}),ls=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2521"}}),is=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2480"}}),ds=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2425"}}),fs=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2553"}}),ps=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2606"}}),cs=new H({props:{title:"Mochi1LoraLoaderMixin",local:"diffusers.loaders.Mochi1LoraLoaderMixin",headingTag:"h2"}}),ms=new h({props:{name:"class diffusers.loaders.Mochi1LoraLoaderMixin",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2613"}}),us=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2786"}}),_s=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2718"}}),gs=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2677"}}),hs=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2621"}}),vs=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2750"}}),bs=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2806"}}),$s=new H({props:{title:"AuraFlowLoraLoaderMixin",local:"diffusers.loaders.AuraFlowLoraLoaderMixin",headingTag:"h2"}}),Ls=new h({props:{name:"class diffusers.loaders.AuraFlowLoraLoaderMixin",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1287"}}),xs=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1460"}}),Ms=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1392"}}),ws=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1351"}}),ys=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1295"}}),Ts=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1424"}}),ks=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.AuraFlowLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer', 'text_encoder']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1480"}}),Ss=new H({props:{title:"LTXVideoLoraLoaderMixin",local:"diffusers.loaders.LTXVideoLoraLoaderMixin",headingTag:"h2"}}),Cs=new h({props:{name:"class diffusers.loaders.LTXVideoLoraLoaderMixin",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2813"}}),Ds=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2989"}}),Is=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2921"}}),Vs=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2880"}}),Hs=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2821"}}),Us=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2953"}}),Js=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.LTXVideoLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3009"}}),Rs=new H({props:{title:"SanaLoraLoaderMixin",local:"diffusers.loaders.SanaLoraLoaderMixin",headingTag:"h2"}}),Ns=new h({props:{name:"class diffusers.loaders.SanaLoraLoaderMixin",anchor:"diffusers.loaders.SanaLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3243"}}),Zs=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.SanaLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3416"}}),Xs=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.SanaLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3348"}}),js=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SanaLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3307"}}),Fs=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SanaLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3251"}}),Gs=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SanaLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3380"}}),Ws=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SanaLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3436"}}),Es=new H({props:{title:"HunyuanVideoLoraLoaderMixin",local:"diffusers.loaders.HunyuanVideoLoraLoaderMixin",headingTag:"h2"}}),Bs=new h({props:{name:"class diffusers.loaders.HunyuanVideoLoraLoaderMixin",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3443"}}),Ps=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3619"}}),qs=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.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_12652/src/diffusers/loaders/lora_pipeline.py#L3551"}}),As=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3510"}}),Ys=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3451"}}),zs=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3583"}}),Qs=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.HunyuanVideoLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3639"}}),Ks=new H({props:{title:"Lumina2LoraLoaderMixin",local:"diffusers.loaders.Lumina2LoraLoaderMixin",headingTag:"h2"}}),Os=new h({props:{name:"class diffusers.loaders.Lumina2LoraLoaderMixin",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3646"}}),en=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3823"}}),an=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.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_12652/src/diffusers/loaders/lora_pipeline.py#L3755"}}),rn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3714"}}),tn=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3654"}}),on=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3787"}}),sn=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.Lumina2LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3843"}}),nn=new H({props:{title:"CogView4LoraLoaderMixin",local:"diffusers.loaders.CogView4LoraLoaderMixin",headingTag:"h2"}}),ln=new h({props:{name:"class diffusers.loaders.CogView4LoraLoaderMixin",anchor:"diffusers.loaders.CogView4LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4601"}}),dn=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4774"}}),fn=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.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_12652/src/diffusers/loaders/lora_pipeline.py#L4706"}}),pn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4665"}}),cn=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4609"}}),mn=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4738"}}),un=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.CogView4LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4794"}}),_n=new H({props:{title:"WanLoraLoaderMixin",local:"diffusers.loaders.WanLoraLoaderMixin",headingTag:"h2"}}),gn=new h({props:{name:"class diffusers.loaders.WanLoraLoaderMixin",anchor:"diffusers.loaders.WanLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4050"}}),hn=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.WanLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4297"}}),vn=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.WanLoraLoaderMixin.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_12652/src/diffusers/loaders/lora_pipeline.py#L4229"}}),bn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.WanLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4164"}}),$n=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.WanLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4058"}}),Ln=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.WanLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4261"}}),xn=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.WanLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4317"}}),Mn=new H({props:{title:"SkyReelsV2LoraLoaderMixin",local:"diffusers.loaders.SkyReelsV2LoraLoaderMixin",headingTag:"h2"}}),wn=new h({props:{name:"class diffusers.loaders.SkyReelsV2LoraLoaderMixin",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4324"}}),yn=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4574"}}),Tn=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.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_12652/src/diffusers/loaders/lora_pipeline.py#L4506"}}),kn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4441"}}),Sn=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4332"}}),Cn=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4538"}}),Dn=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4594"}}),In=new H({props:{title:"AmusedLoraLoaderMixin",local:"diffusers.loaders.AmusedLoraLoaderMixin",headingTag:"h2"}}),Vn=new h({props:{name:"class diffusers.loaders.AmusedLoraLoaderMixin",anchor:"diffusers.loaders.AmusedLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2265"}}),Hn=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.AmusedLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"metadata",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2270"}}),Un=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.AmusedLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"text_encoder_lora_layers",val:": dict = None"},{name:"transformer_lora_layers",val:": dict = 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"}],parametersDescription:[{anchor:"diffusers.loaders.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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.AmusedLoraLoaderMixin.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"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2362"}}),Jn=new H({props:{title:"HiDreamImageLoraLoaderMixin",local:"diffusers.loaders.HiDreamImageLoraLoaderMixin",headingTag:"h2"}}),Rn=new h({props:{name:"class diffusers.loaders.HiDreamImageLoraLoaderMixin",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4801"}}),Nn=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4977"}}),Zn=new h({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.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_12652/src/diffusers/loaders/lora_pipeline.py#L4909"}}),Xn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4868"}}),jn=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4809"}}),Fn=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4941"}}),Gn=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4997"}}),Wn=new H({props:{title:"QwenImageLoraLoaderMixin",local:"diffusers.loaders.QwenImageLoraLoaderMixin",headingTag:"h2"}}),En=new h({props:{name:"class diffusers.loaders.QwenImageLoraLoaderMixin",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5004"}}),Bn=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5183"}}),Pn=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_12652/src/diffusers/loaders/lora_pipeline.py#L5115"}}),qn=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5074"}}),An=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5012"}}),Yn=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5147"}}),zn=new h({props:{name:"unfuse_lora",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5203"}}),Qn=new H({props:{title:"ZImageLoraLoaderMixin",local:"diffusers.loaders.ZImageLoraLoaderMixin",headingTag:"h2"}}),Kn=new h({props:{name:"class diffusers.loaders.ZImageLoraLoaderMixin",anchor:"diffusers.loaders.ZImageLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5210"}}),On=new h({props:{name:"fuse_lora",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": list = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5389"}}),el=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_12652/src/diffusers/loaders/lora_pipeline.py#L5321"}}),al=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5280"}}),rl=new h({props:{name:"lora_state_dict",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5218"}}),tl=new h({props:{name:"save_lora_weights",anchor:"diffusers.loaders.ZImageLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"transformer_lora_layers",val:": dict = 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:": dict | None = 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1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4023"}}),il=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"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3955"}}),dl=new h({props:{name:"load_lora_weights",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": str | dict[str, torch.Tensor]"},{name:"adapter_name",val:": str | None = 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| <ul> | |
| <li>“error” (default): raise an error</li> | |
| <li>“warn”: issue a warning</li> | |
| <li>“ignore”: do nothing</li> | |
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Xet Storage Details
- Size:
- 271 kB
- Xet hash:
- 2c675a439ff9dc9731c64c87bfa0f5843133dafddd983c39789c46aaf94009cf
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.