Buckets:
| import{s as N2,o as E2,n as O}from"../chunks/scheduler.53228c21.js";import{S as P2,i as q2,e as o,s as t,c as l,h as A2,a as s,d as n,b as r,f as h,g as d,j as u,k as _,l as a,m as x,n as f,t as p,o as c,p as m}from"../chunks/index.100fac89.js";import{C as Y2}from"../chunks/CopyLLMTxtMenu.d379e2c2.js";import{D as v}from"../chunks/Docstring.38f1c7dc.js";import{C as ee}from"../chunks/CodeBlock.d30a6509.js";import{E as K}from"../chunks/ExampleCodeBlock.5b0c77bb.js";import{H as A,E as z2}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.7ff5b7b1.js";function Q2(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function K2(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function O2(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function e$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEFwaXBlbGluZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJzdGFiaWxpdHlhaSUyRnN0YWJsZS1kaWZmdXNpb24teGwtYmFzZS0xLjAlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMEEpLnRvKCUyMmN1ZGElMjIpJTBBcGlwZWxpbmUubG9hZF9sb3JhX3dlaWdodHMoJTIybmVyaWpzJTJGcGl4ZWwtYXJ0LXhsJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJwaXhlbC1hcnQteGwuc2FmZXRlbnNvcnMlMjIlMkMlMjBhZGFwdGVyX25hbWUlM0QlMjJwaXhlbCUyMiklMEFwaXBlbGluZS5mdXNlX2xvcmEobG9yYV9zY2FsZSUzRDAuNyk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function a$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function t$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function r$(T){let g,M;return g=new ee({props:{code:"cGlwZS5sb2FkX2xvcmFfd2VpZ2h0cyhwYXRoXzElMkMlMjBhZGFwdGVyX25hbWUlM0QlMjJhZGFwdGVyLTElMjIpJTBBcGlwZS5sb2FkX2xvcmFfd2VpZ2h0cyhwYXRoXzIlMkMlMjBhZGFwdGVyX25hbWUlM0QlMjJhZGFwdGVyLTIlMjIpJTBBcGlwZS5zZXRfYWRhcHRlcnMoJTIyYWRhcHRlci0xJTIyKSUwQWltYWdlXzElMjAlM0QlMjBwaXBlKCoqa3dhcmdzKSUwQSUyMyUyMHN3aXRjaCUyMHRvJTIwYWRhcHRlci0yJTJDJTIwb2ZmbG9hZCUyMGFkYXB0ZXItMSUwQXBpcGVsaW5lLnNldF9sb3JhX2RldmljZShhZGFwdGVyX25hbWVzJTNEJTVCJTIyYWRhcHRlci0xJTIyJTVEJTJDJTIwZGV2aWNlJTNEJTIyY3B1JTIyKSUwQXBpcGVsaW5lLnNldF9sb3JhX2RldmljZShhZGFwdGVyX25hbWVzJTNEJTVCJTIyYWRhcHRlci0yJTIyJTVEJTJDJTIwZGV2aWNlJTNEJTIyY3VkYSUzQTAlMjIpJTBBcGlwZS5zZXRfYWRhcHRlcnMoJTIyYWRhcHRlci0yJTIyKSUwQWltYWdlXzIlMjAlM0QlMjBwaXBlKCoqa3dhcmdzKSUwQSUyMyUyMHN3aXRjaCUyMGJhY2slMjB0byUyMGFkYXB0ZXItMSUyQyUyMG9mZmxvYWQlMjBhZGFwdGVyLTIlMEFwaXBlbGluZS5zZXRfbG9yYV9kZXZpY2UoYWRhcHRlcl9uYW1lcyUzRCU1QiUyMmFkYXB0ZXItMiUyMiU1RCUyQyUyMGRldmljZSUzRCUyMmNwdSUyMiklMEFwaXBlbGluZS5zZXRfbG9yYV9kZXZpY2UoYWRhcHRlcl9uYW1lcyUzRCU1QiUyMmFkYXB0ZXItMSUyMiU1RCUyQyUyMGRldmljZSUzRCUyMmN1ZGElM0EwJTIyKSUwQXBpcGUuc2V0X2FkYXB0ZXJzKCUyMmFkYXB0ZXItMSUyMiklMEEuLi4=",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(g.$$.fragment)},l($){d(g.$$.fragment,$)},m($,b){f(g,$,b),M=!0},p:O,i($){M||(p(g.$$.fragment,$),M=!0)},o($){c(g.$$.fragment,$),M=!1},d($){m(g,$)}}}function o$(T){let g,M="Examples:",$,b,y;return b=new ee({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-kvfsh7"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function s$(T){let g,M="Examples:",$,b,y;return b=new ee({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-kvfsh7"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function n$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEthbmRpbnNreTVUMlZQaXBlbGluZSUwQSUwQXBpcGVsaW5lJTIwJTNEJTIwS2FuZGluc2t5NVQyVlBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJhaS1mb3JldmVyJTJGS2FuZGluc2t5LTUuMC1UMlYlMjIpJTBBcGlwZWxpbmUubG9hZF9sb3JhX3dlaWdodHMoJTIycGF0aCUyRnRvJTJGbG9yYS5zYWZldGVuc29ycyUyMiklMEFwaXBlbGluZS5mdXNlX2xvcmEobG9yYV9zY2FsZSUzRDAuNyk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> Kandinsky5T2VPipeline | |
| pipeline = Kandinsky5T2VPipeline.from_pretrained(<span class="hljs-string">"ai-forever/Kandinsky-5.0-T2V"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"path/to/lora.safetensors"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function i$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_names=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.delete_adapters(<span class="hljs-string">"cinematic"</span>)`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function l$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.disable_lora()`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function d$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.enable_lora()`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function f$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function p$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIyJTJDJTBBKS50byglMjJjdWRhJTIyKSUwQXBpcGVsaW5lLmxvYWRfbG9yYV93ZWlnaHRzKCUyMkNpcm9OMjAyMiUyRnRveS1mYWNlJTIyJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjJ0b3lfZmFjZV9zZHhsLnNhZmV0ZW5zb3JzJTIyJTJDJTIwYWRhcHRlcl9uYW1lJTNEJTIydG95JTIyKSUwQXBpcGVsaW5lLmdldF9hY3RpdmVfYWRhcHRlcnMoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights(<span class="hljs-string">"CiroN2022/toy-face"</span>, weight_name=<span class="hljs-string">"toy_face_sdxl.safetensors"</span>, adapter_name=<span class="hljs-string">"toy"</span>) | |
| pipeline.get_active_adapters()`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function c$(T){let g,M="Example:",$,b,y;return b=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = AutoPipelineForText2Image.from_pretrained( | |
| <span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>, torch_dtype=torch.float16 | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| pipeline.load_lora_weights( | |
| <span class="hljs-string">"jbilcke-hf/sdxl-cinematic-1"</span>, weight_name=<span class="hljs-string">"pytorch_lora_weights.safetensors"</span>, adapter_name=<span class="hljs-string">"cinematic"</span> | |
| ) | |
| pipeline.load_lora_weights(<span class="hljs-string">"nerijs/pixel-art-xl"</span>, weight_name=<span class="hljs-string">"pixel-art-xl.safetensors"</span>, adapter_name=<span class="hljs-string">"pixel"</span>) | |
| pipeline.set_adapters([<span class="hljs-string">"cinematic"</span>, <span class="hljs-string">"pixel"</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-11lpom8"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function m$(T){let g,M;return g=new ee({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span>pipe.load_lora_weights(path_1, adapter_name=<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.load_lora_weights(path_2, adapter_name=<span class="hljs-string">"adapter-2"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>image_1 = pipe(**kwargs) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># switch to adapter-2, offload adapter-1</span> | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-1"</span>], device=<span class="hljs-string">"cpu"</span>) | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-2"</span>], device=<span class="hljs-string">"cuda:0"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-2"</span>) | |
| <span class="hljs-meta">>>> </span>image_2 = pipe(**kwargs) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># switch back to adapter-1, offload adapter-2</span> | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-2"</span>], device=<span class="hljs-string">"cpu"</span>) | |
| <span class="hljs-meta">>>> </span>pipeline.set_lora_device(adapter_names=[<span class="hljs-string">"adapter-1"</span>], device=<span class="hljs-string">"cuda:0"</span>) | |
| <span class="hljs-meta">>>> </span>pipe.set_adapters(<span class="hljs-string">"adapter-1"</span>) | |
| <span class="hljs-meta">>>> </span>...`,wrap:!1}}),{c(){l(g.$$.fragment)},l($){d(g.$$.fragment,$)},m($,b){f(g,$,b),M=!0},p:O,i($){M||(p(g.$$.fragment,$),M=!0)},o($){c(g.$$.fragment,$),M=!1},d($){m(g,$)}}}function u$(T){let g,M="Examples:",$,b,y;return b=new ee({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">>>> </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">>>> </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">>>> </span>...',wrap:!1}}),{c(){g=o("p"),g.textContent=M,$=t(),l(b.$$.fragment)},l(i){g=s(i,"P",{"data-svelte-h":!0}),u(g)!=="svelte-kvfsh7"&&(g.textContent=M),$=r(i),d(b.$$.fragment,i)},m(i,w){x(i,g,w),x(i,$,w),f(b,i,w),y=!0},p:O,i(i){y||(p(b.$$.fragment,i),y=!0)},o(i){c(b.$$.fragment,i),y=!1},d(i){i&&(n(g),n($)),m(b,i)}}}function _$(T){let g,M,$,b,y,i,w,tf,vr,Jv='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:',rf,br,Hv='<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>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',of,Oe,Zv='<p>To learn more about how to load LoRA weights, see the <a href="../../tutorials/using_peft_for_inference">LoRA</a> loading guide.</p>',sf,Lr,nf,k,$r,Fc,Nn,jv="Utility class for handling LoRAs.",Bc,Me,xr,Nc,En,Xv="Delete an adapter’s LoRA layers from the pipeline.",Ec,ea,Pc,Te,yr,qc,Pn,Gv="Disables the active LoRA layers of the pipeline.",Ac,aa,Yc,ke,wr,zc,qn,Wv="Enables the active LoRA layers of the pipeline.",Qc,ta,Kc,ra,Mr,Oc,An,Fv=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,em,ve,Tr,am,Yn,Bv="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",tm,kr,Nv="<p>> This is an experimental API.</p>",rm,oa,om,De,Dr,sm,zn,Ev="Gets the list of the current active adapters.",nm,sa,im,na,Sr,lm,Qn,Pv="Gets the current list of all available adapters in the pipeline.",dm,Se,Cr,fm,Kn,qv="Set the currently active adapters for use in the pipeline.",pm,ia,cm,be,Ur,mm,On,Av=`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.`,um,ei,Yv=`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.`,_m,la,gm,Ce,Ir,hm,ai,zv=`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>.`,vm,Vr,Qv="<p>> This is an experimental API.</p>",bm,Ue,Rr,Lm,ti,Kv="Unloads the LoRA parameters.",$m,da,xm,fa,Jr,ym,ri,Ov="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",lf,Hr,df,z,Zr,wm,oi,eb=`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>.`,Mm,pa,jr,Tm,si,ab="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",km,ca,Xr,Dm,ni,tb="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Sm,te,Gr,Cm,ii,rb=`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>.`,Um,li,ob="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Im,di,sb=`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.`,Vm,fi,nb=`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>.`,Rm,pi,ib=`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>.`,Jm,Ie,Wr,Hm,ci,lb="Return state dict for lora weights and the network alphas.",Zm,Fr,db=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,jm,ma,Br,Xm,mi,fb="Save the LoRA parameters corresponding to the UNet and text encoder.",ff,Nr,pf,V,Er,Gm,ui,pb=`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>.`,Wm,ua,Pr,Fm,_i,cb="See <code>fuse_lora()</code> for more details.",Bm,_a,qr,Nm,gi,mb="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Em,ga,Ar,Pm,hi,ub="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",qm,ha,Yr,Am,vi,_b='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Ym,Ve,zr,zm,bi,gb="Return state dict for lora weights and the network alphas.",Qm,Qr,hb=`<p>> We support loading A1111 formatted LoRA checkpoints in a limited capacity. > > This function is | |
| experimental and might change in the future.</p>`,Km,va,Kr,Om,Li,vb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',eu,ba,Or,au,$i,bb="See <code>unfuse_lora()</code> for more details.",cf,eo,mf,I,ao,tu,xi,Lb=`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>.`,ru,yi,$b='Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',ou,La,to,su,wi,xb="See <code>fuse_lora()</code> for more details.",nu,$a,ro,iu,Mi,yb="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",lu,xa,oo,du,Ti,wb='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.',fu,ya,so,pu,ki,Mb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',cu,wa,no,mu,Di,Tb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',uu,Ma,io,_u,Si,kb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',gu,Ta,lo,hu,Ci,Db="See <code>unfuse_lora()</code> for more details.",uf,fo,_f,U,po,vu,Ui,Sb=`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>.`,bu,Ii,Cb='Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',Lu,ka,co,$u,Vi,Ub='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',xu,Da,mo,yu,Ri,Ib="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",wu,Sa,uo,Mu,Ji,Vb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',Tu,he,_o,ku,Hi,Rb=`Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code> and | |
| <code>self.text_encoder</code>.`,Du,Zi,Jb="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Su,ji,Hb=`See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is | |
| loaded.`,Cu,Xi,Zb=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state | |
| dict is loaded into <code>self.transformer</code>.`,Uu,Ca,go,Iu,Gi,jb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Vu,Ua,ho,Ru,Wi,Xb="Save the LoRA parameters corresponding to the UNet and text encoder.",Ju,Re,vo,Hu,Fi,Gb=`Reverses the effect of | |
| <a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,Zu,bo,Wb="<p>> This is an experimental API.</p>",ju,Je,Lo,Xu,Bi,Fb="Unloads the LoRA parameters.",Gu,Ia,gf,$o,hf,J,xo,Wu,Ni,Bb='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>.',Fu,Va,yo,Bu,Ei,Nb="See <code>fuse_lora()</code> for more details.",Nu,Ra,wo,Eu,Pi,Eb='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.',Pu,Ja,Mo,qu,qi,Pb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Au,Ha,To,Yu,Ai,qb='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',zu,Za,ko,Qu,Yi,Ab='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Ku,ja,Do,Ou,zi,Yb="See <code>unfuse_lora()</code> for more details.",vf,So,bf,H,Co,e_,Qi,zb='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>.',a_,Xa,Uo,t_,Ki,Qb="See <code>fuse_lora()</code> for more details.",r_,Ga,Io,o_,Oi,Kb='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.',s_,Wa,Vo,n_,el,Ob='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',i_,Fa,Ro,l_,al,eL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',d_,Ba,Jo,f_,tl,aL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',p_,Na,Ho,c_,rl,tL="See <code>unfuse_lora()</code> for more details.",Lf,Zo,$f,Z,jo,m_,ol,rL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/aura_flow_transformer2d#diffusers.AuraFlowTransformer2DModel">AuraFlowTransformer2DModel</a> Specific to <a href="/docs/diffusers/pr_12652/en/api/pipelines/aura_flow#diffusers.AuraFlowPipeline">AuraFlowPipeline</a>.',u_,Ea,Xo,__,sl,oL="See <code>fuse_lora()</code> for more details.",g_,Pa,Go,h_,nl,sL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details.',v_,qa,Wo,b_,il,nL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',L_,Aa,Fo,$_,ll,iL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',x_,Ya,Bo,y_,dl,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.',w_,za,No,M_,fl,dL="See <code>unfuse_lora()</code> for more details.",xf,Eo,yf,j,Po,T_,pl,fL='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>.',k_,Qa,qo,D_,cl,pL="See <code>fuse_lora()</code> for more details.",S_,Ka,Ao,C_,ml,cL='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.',U_,Oa,Yo,I_,ul,mL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',V_,et,zo,R_,_l,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.',J_,at,Qo,H_,gl,_L='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Z_,tt,Ko,j_,hl,gL="See <code>unfuse_lora()</code> for more details.",wf,Oo,Mf,X,es,X_,vl,hL='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>.',G_,rt,as,W_,bl,vL="See <code>fuse_lora()</code> for more details.",F_,ot,ts,B_,Ll,bL='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.',N_,st,rs,E_,$l,LL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',P_,nt,os,q_,xl,$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.',A_,it,ss,Y_,yl,xL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',z_,lt,ns,Q_,wl,yL="See <code>unfuse_lora()</code> for more details.",Tf,is,kf,G,ls,K_,Ml,wL='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>.',O_,dt,ds,eg,Tl,ML="See <code>fuse_lora()</code> for more details.",ag,ft,fs,tg,kl,TL='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.',rg,pt,ps,og,Dl,kL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',sg,ct,cs,ng,Sl,DL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',ig,mt,ms,lg,Cl,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.',dg,ut,us,fg,Ul,CL="See <code>unfuse_lora()</code> for more details.",Df,_s,Sf,W,gs,pg,Il,UL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/lumina2_transformer2d#diffusers.Lumina2Transformer2DModel">Lumina2Transformer2DModel</a>. Specific to <code>Lumina2Text2ImgPipeline</code>.',cg,_t,hs,mg,Vl,IL="See <code>fuse_lora()</code> for more details.",ug,gt,vs,_g,Rl,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.',gg,ht,bs,hg,Jl,RL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',vg,vt,Ls,bg,Hl,JL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Lg,bt,$s,$g,Zl,HL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',xg,Lt,xs,yg,jl,ZL="See <code>unfuse_lora()</code> for more details.",Cf,ys,Uf,F,ws,wg,Xl,jL='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>.',Mg,$t,Ms,Tg,Gl,XL="See <code>fuse_lora()</code> for more details.",kg,xt,Ts,Dg,Wl,GL='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.',Sg,yt,ks,Cg,Fl,WL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Ug,wt,Ds,Ig,Bl,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.',Vg,Mt,Ss,Rg,Nl,BL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Jg,Tt,Cs,Hg,El,NL="See <code>unfuse_lora()</code> for more details.",If,Us,Vf,B,Is,Zg,Pl,EL='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>].',jg,kt,Vs,Xg,ql,PL="See <code>fuse_lora()</code> for more details.",Gg,Dt,Rs,Wg,Al,qL='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.',Fg,St,Js,Bg,Yl,AL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Ng,Ct,Hs,Eg,zl,YL='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,Ut,Zs,qg,Ql,zL='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Ag,It,js,Yg,Kl,QL="See <code>unfuse_lora()</code> for more details.",Rf,Xs,Jf,N,Gs,zg,Ol,KL='Load LoRA layers into <a href="/docs/diffusers/pr_12652/en/api/models/skyreels_v2_transformer_3d#diffusers.SkyReelsV2Transformer3DModel">SkyReelsV2Transformer3DModel</a>.',Qg,Vt,Ws,Kg,ed,OL="See <code>fuse_lora()</code> for more details.",Og,Rt,Fs,eh,ad,e2='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,Jt,Bs,th,td,a2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',rh,Ht,Ns,oh,rd,t2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',sh,Zt,Es,nh,od,r2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',ih,jt,Ps,lh,sd,o2="See <code>unfuse_lora()</code> for more details.",Hf,qs,Zf,xe,As,dh,Xt,Ys,fh,nd,s2='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.',ph,Gt,zs,ch,id,n2="Save the LoRA parameters corresponding to the UNet and text encoder.",jf,Qs,Xf,E,Ks,mh,ld,i2='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>.',uh,Wt,Os,_h,dd,l2="See <code>fuse_lora()</code> for more details.",gh,Ft,en,hh,fd,d2='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.',vh,Bt,an,bh,pd,f2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Lh,Nt,tn,$h,cd,p2='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,Et,rn,yh,md,c2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',wh,Pt,on,Mh,ud,m2="See <code>unfuse_lora()</code> for more details.",Gf,sn,Wf,P,nn,Th,_d,u2='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>.',kh,qt,ln,Dh,gd,_2="See <code>fuse_lora()</code> for more details.",Sh,At,dn,Ch,hd,g2='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.',Uh,Yt,fn,Ih,vd,h2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a> for more details.',Vh,zt,pn,Rh,bd,v2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details.',Jh,Qt,cn,Hh,Ld,b2='See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights">save_lora_weights()</a> for more information.',Zh,Kt,mn,jh,$d,L2="See <code>unfuse_lora()</code> for more details.",Ff,un,Bf,q,_n,Xh,xd,$2="Load LoRA layers into <code>Kandinsky5Transformer3DModel</code>,",Gh,He,gn,Wh,yd,x2="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Fh,Ot,Bh,er,hn,Nh,wd,y2="Load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Eh,ar,vn,Ph,Md,w2="Load LoRA weights specified in <code>pretrained_model_name_or_path_or_dict</code> into <code>self.transformer</code>",qh,tr,bn,Ah,Td,M2="Return state dict for lora weights and the network alphas.",Yh,rr,Ln,zh,kd,T2="Save the LoRA parameters corresponding to the transformer and text encoders.",Qh,or,$n,Kh,Dd,k2="Reverses the effect of <code>pipe.fuse_lora()</code>.",Nf,xn,Ef,D,yn,Oh,Sd,D2="Utility class for handling LoRAs.",ev,Ze,wn,av,Cd,S2="Delete an adapter’s LoRA layers from the pipeline.",tv,sr,rv,je,Mn,ov,Ud,C2="Disables the active LoRA layers of the pipeline.",sv,nr,nv,Xe,Tn,iv,Id,U2="Enables the active LoRA layers of the pipeline.",lv,ir,dv,lr,kn,fv,Vd,I2=`Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are | |
| different.`,pv,Le,Dn,cv,Rd,V2="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",mv,Sn,R2="<p>> This is an experimental API.</p>",uv,dr,_v,Ge,Cn,gv,Jd,J2="Gets the list of the current active adapters.",hv,fr,vv,pr,Un,bv,Hd,H2="Gets the current list of all available adapters in the pipeline.",Lv,We,In,$v,Zd,Z2="Set the currently active adapters for use in the pipeline.",xv,cr,yv,$e,Vn,wv,jd,j2=`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.`,Mv,Xd,X2=`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.`,Tv,mr,kv,Fe,Rn,Dv,Gd,G2=`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>.`,Sv,Jn,W2="<p>> This is an experimental API.</p>",Cv,Be,Hn,Uv,Wd,F2="Unloads the LoRA parameters.",Iv,ur,Vv,_r,Zn,Rv,Fd,B2="Writes the state dict of the LoRA layers (optionally with metadata) to disk.",Pf,jn,qf,af,Af;return y=new Y2({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new A({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),Lr=new A({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),$r=new v({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#L478"}}),xr=new v({props:{name:"delete_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>Union[List[str], str]</code>) — | |
| The names of the adapters to delete.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L838"}}),ea=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.example",$$slots:{default:[Q2]},$$scope:{ctx:T}}}),yr=new v({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#L778"}}),aa=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora.example",$$slots:{default:[K2]},$$scope:{ctx:T}}}),wr=new v({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#L808"}}),ta=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora.example",$$slots:{default:[O2]},$$scope:{ctx:T}}}),Mr=new v({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#L985"}}),Tr=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.components",description:"<strong>components</strong> — (<code>List[str]</code>): List of LoRA-injectable components to fuse the LoRAs into.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, defaults to 1.0) — | |
| Controls how much to influence the outputs with the LoRA parameters.`,name:"lora_scale"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.safe_fusing",description:`<strong>safe_fusing</strong> (<code>bool</code>, defaults to <code>False</code>) — | |
| Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.`,name:"safe_fusing"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) — | |
| Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L536"}}),oa=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[e$]},$$scope:{ctx:T}}}),Dr=new v({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#L876"}}),sa=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[a$]},$$scope:{ctx:T}}}),Sr=new v({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#L909"}}),Cr=new v({props:{name:"set_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"},{name:"adapter_weights",val:": typing.Union[float, typing.Dict, typing.List[float], typing.List[typing.Dict], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code> or <code>str</code>) — | |
| The names of the adapters to use.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_weights",description:`<strong>adapter_weights</strong> (<code>Union[List[float], float]</code>, <em>optional</em>) — | |
| The adapter(s) weights to use with the UNet. If <code>None</code>, the weights are set to <code>1.0</code> for all the | |
| adapters.`,name:"adapter_weights"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L675"}}),ia=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.example",$$slots:{default:[t$]},$$scope:{ctx:T}}}),Ur=new v({props:{name:"set_lora_device",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device",parameters:[{name:"adapter_names",val:": typing.List[str]"},{name:"device",val:": typing.Union[torch.device, str, int]"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>) — | |
| List of adapters to send device to.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.device",description:`<strong>device</strong> (<code>Union[torch.device, str, int]</code>) — | |
| Device to send the adapters to. Can be either a torch device, a str or an integer.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L931"}}),la=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.example",$$slots:{default:[r$]},$$scope:{ctx:T}}}),Ir=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) — List of LoRA-injectable components to unfuse LoRA from.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_unet",description:"<strong>unfuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) — Whether to unfuse the UNet LoRA parameters.",name:"unfuse_unet"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_text_encoder",description:`<strong>unfuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) — | |
| Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the | |
| LoRA parameters then it won’t have any effect.`,name:"unfuse_text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L622"}}),Rr=new v({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#L513"}}),da=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[o$]},$$scope:{ctx:T}}}),Jr=new v({props:{name:"write_lora_layers",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.write_lora_layers",parameters:[{name:"state_dict",val:": typing.Dict[str, torch.Tensor]"},{name:"save_directory",val:": str"},{name:"is_main_process",val:": bool"},{name:"weight_name",val:": str"},{name:"save_function",val:": typing.Callable"},{name:"safe_serialization",val:": bool"},{name:"lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L1008"}}),Hr=new A({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),Zr=new v({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#L128"}}),jr=new v({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#L411"}}),Xr=new v({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#L350"}}),Gr=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) — | |
| See <a href="/docs/diffusers/pr_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#L138"}}),Wr=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) — | |
| Can be either:</p> | |
| <ul> | |
| <li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on | |
| the Hub.</li> | |
| <li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved | |
| with <a href="/docs/diffusers/pr_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>Union[str, os.PathLike]</code>, <em>optional</em>) — | |
| Path to a directory where a downloaded pretrained model configuration is cached if the standard cache | |
| is not used.`,name:"cache_dir"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether or not to force the (re-)download of the model weights and configuration files, overriding the | |
| cached versions if they exist.`,name:"force_download"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) — | |
| A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model | |
| won’t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) — | |
| The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from | |
| <code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) — | |
| The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier | |
| allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>""</code>) — | |
| The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>, defaults to None) — | |
| Name of the serialized state dict file.`,name:"weight_name"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) — | |
| When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L239"}}),Br=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"unet_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"unet_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — | |
| Directory to save LoRA parameters to. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.unet_lora_layers",description:`<strong>unet_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text | |
| encoder LoRA state dict because it comes from 🤗 Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether the process calling this is the main process or not. Useful during distributed training and you | |
| need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main | |
| process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) — | |
| The function to use to save the state dictionary. Useful during distributed training when you need to | |
| replace <code>torch.save</code> with another method. Can be configured with the environment variable | |
| <code>DIFFUSERS_SAVE_MODE</code>.`,name:"save_function"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.unet_lora_adapter_metadata",description:`<strong>unet_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the unet to be serialized with the state dict.`,name:"unet_lora_adapter_metadata"},{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L469"}}),Nr=new A({props:{title:"StableDiffusionXLLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin",headingTag:"h2"}}),Er=new v({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#L592"}}),Pr=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['unet', 'text_encoder', 'text_encoder_2']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L958"}}),qr=new v({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#L851"}}),Ar=new v({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#L789"}}),Yr=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L603"}}),zr=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) — | |
| Can be either:</p> | |
| <ul> | |
| <li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on | |
| the Hub.</li> | |
| <li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved | |
| with <a href="/docs/diffusers/pr_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>Union[str, os.PathLike]</code>, <em>optional</em>) — | |
| Path to a directory where a downloaded pretrained model configuration is cached if the standard cache | |
| is not used.`,name:"cache_dir"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether or not to force the (re-)download of the model weights and configuration files, overriding the | |
| cached versions if they exist.`,name:"force_download"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) — | |
| A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model | |
| won’t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) — | |
| The token to use as HTTP bearer authorization for remote files. If <code>True</code>, the token generated from | |
| <code>diffusers-cli login</code> (stored in <code>~/.huggingface</code>) is used.`,name:"token"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) — | |
| The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier | |
| allowed by Git.`,name:"revision"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>""</code>) — | |
| The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>, defaults to None) — | |
| Name of the serialized state dict file.`,name:"weight_name"},{anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) — | |
| When enabled, additionally return the LoRA adapter metadata, typically found in the state dict.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L677"}}),Kr=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"unet_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_2_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"unet_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"},{name:"text_encoder_2_lora_adapter_metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L910"}}),Or=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.StableDiffusionXLLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['unet', 'text_encoder', 'text_encoder_2']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L977"}}),eo=new A({props:{title:"SD3LoraLoaderMixin",local:"diffusers.loaders.SD3LoraLoaderMixin",headingTag:"h2"}}),ao=new v({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#L984"}}),to=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.SD3LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder', 'text_encoder_2']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1256"}}),ro=new v({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#L1147"}}),oo=new v({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#L1116"}}),so=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:" = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1051"}}),no=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SD3LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L997"}}),io=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SD3LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_2_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"},{name:"text_encoder_2_lora_adapter_metadata",val:" = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1206"}}),lo=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SD3LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder', 'text_encoder_2']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1276"}}),fo=new A({props:{title:"FluxLoraLoaderMixin",local:"diffusers.loaders.FluxLoraLoaderMixin",headingTag:"h2"}}),po=new v({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#L1483"}}),co=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.FluxLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1955"}}),mo=new v({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#L1832"}}),uo=new v({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#L1746"}}),_o=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) — | |
| See <a href="/docs/diffusers/pr_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.FluxLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| Adapter name to be used for referencing the loaded adapter model. If not specified, it will use | |
| <code>default_{i}</code> where i is the total number of adapters being loaded.`,name:"adapter_name"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| \`Speed up model loading by only loading the pretrained LoRA weights and not initializing the random | |
| weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1621"}}),go=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.FluxLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"return_alphas",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1496"}}),ho=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:" = None"},{name:"text_encoder_lora_adapter_metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — | |
| Directory to save LoRA parameters to. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.transformer_lora_layers",description:`<strong>transformer_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>transformer</code>.`,name:"transformer_lora_layers"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.text_encoder_lora_layers",description:`<strong>text_encoder_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>text_encoder</code>. Must explicitly pass the text | |
| encoder LoRA state dict because it comes from 🤗 Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether the process calling this is the main process or not. Useful during distributed training and you | |
| need to call this function on all processes. In this case, set <code>is_main_process=True</code> only on the main | |
| process to avoid race conditions.`,name:"is_main_process"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) — | |
| The function to use to save the state dictionary. Useful during distributed training when you need to | |
| replace <code>torch.save</code> with another method. Can be configured with the environment variable | |
| <code>DIFFUSERS_SAVE_MODE</code>.`,name:"save_function"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the transformer to be serialized with the state dict.`,name:"transformer_lora_adapter_metadata"},{anchor:"diffusers.loaders.FluxLoraLoaderMixin.save_lora_weights.text_encoder_lora_adapter_metadata",description:`<strong>text_encoder_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the text encoder to be serialized with the state dict.`,name:"text_encoder_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1891"}}),vo=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer', 'text_encoder']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) — List of LoRA-injectable components to unfuse LoRA from.",name:"components"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L1987"}}),Lo=new v({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#L2004"}}),Ia=new K({props:{anchor:"diffusers.loaders.FluxLoraLoaderMixin.unload_lora_weights.example",$$slots:{default:[s$]},$$scope:{ctx:T}}}),$o=new A({props:{title:"CogVideoXLoraLoaderMixin",local:"diffusers.loaders.CogVideoXLoraLoaderMixin",headingTag:"h2"}}),xo=new v({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#L2436"}}),yo=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2606"}}),wo=new v({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#L2540"}}),Mo=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2499"}}),To=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2444"}}),ko=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2572"}}),Do=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.CogVideoXLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2625"}}),So=new A({props:{title:"Mochi1LoraLoaderMixin",local:"diffusers.loaders.Mochi1LoraLoaderMixin",headingTag:"h2"}}),Co=new v({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#L2632"}}),Uo=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2805"}}),Io=new v({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#L2737"}}),Vo=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2696"}}),Ro=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L2640"}}),Jo=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.Mochi1LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, 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v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.WanLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4138"}}),js=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.WanLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4194"}}),Xs=new A({props:{title:"SkyReelsV2LoraLoaderMixin",local:"diffusers.loaders.SkyReelsV2LoraLoaderMixin",headingTag:"h2"}}),Gs=new v({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#L4201"}}),Ws=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4451"}}),Fs=new v({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#L4383"}}),Bs=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4318"}}),Ns=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4209"}}),Es=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4415"}}),Ps=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.SkyReelsV2LoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4471"}}),qs=new A({props:{title:"AmusedLoraLoaderMixin",local:"diffusers.loaders.AmusedLoraLoaderMixin",headingTag:"h2"}}),As=new v({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#L2284"}}),Ys=new v({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#L2289"}}),zs=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.AmusedLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"text_encoder_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"transformer_lora_layers",val:": typing.Dict[str, torch.nn.modules.module.Module] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"}],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#L2381"}}),Qs=new A({props:{title:"HiDreamImageLoraLoaderMixin",local:"diffusers.loaders.HiDreamImageLoraLoaderMixin",headingTag:"h2"}}),Ks=new v({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#L4678"}}),Os=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4854"}}),en=new v({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#L4786"}}),an=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4745"}}),tn=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4686"}}),rn=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4818"}}),on=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.HiDreamImageLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4874"}}),sn=new A({props:{title:"QwenImageLoraLoaderMixin",local:"diffusers.loaders.QwenImageLoraLoaderMixin",headingTag:"h2"}}),nn=new v({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#L4881"}}),ln=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5060"}}),dn=new v({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#L4992"}}),fn=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4951"}}),pn=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L4889"}}),cn=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:": typing.Optional[dict] = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5024"}}),mn=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.QwenImageLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L5080"}}),un=new A({props:{title:"KandinskyLoraLoaderMixin",local:"diffusers.loaders.KandinskyLoraLoaderMixin",headingTag:"h2"}}),_n=new v({props:{name:"class diffusers.loaders.KandinskyLoraLoaderMixin",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3642"}}),gn=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora.components",description:"<strong>components</strong> — (<code>List[str]</code>): List of LoRA-injectable components to fuse the LoRAs into.",name:"components"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, defaults to 1.0) — | |
| Controls how much to influence the outputs with the LoRA parameters.`,name:"lora_scale"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora.safe_fusing",description:`<strong>safe_fusing</strong> (<code>bool</code>, defaults to <code>False</code>) — | |
| Whether to check fused weights for NaN values before fusing.`,name:"safe_fusing"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) — | |
| Adapter names to be used for fusing.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3880"}}),Ot=new K({props:{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.fuse_lora.example",$$slots:{default:[n$]},$$scope:{ctx:T}}}),hn=new v({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"},{name:"low_cpu_mem_usage",val:" = False"},{name:"hotswap",val:": bool = False"},{name:"metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) — | |
| A standard state dict containing the lora layer parameters.`,name:"state_dict"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.transformer",description:`<strong>transformer</strong> (<code>Kandinsky5Transformer3DModel</code>) — | |
| The transformer model to load the LoRA layers into.`,name:"transformer"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| Adapter name to be used for referencing the loaded adapter model.`,name:"adapter_name"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| Speed up model loading by only loading the pretrained LoRA weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights">load_lora_weights()</a>.`,name:"hotswap"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_into_transformer.metadata",description:`<strong>metadata</strong> (<code>dict</code>) — | |
| Optional LoRA adapter metadata.`,name:"metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3787"}}),vn=new v({props:{name:"load_lora_weights",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"adapter_name",val:": typing.Optional[str] = None"},{name:"hotswap",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) — | |
| See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| Adapter name to be used for referencing the loaded adapter model.`,name:"adapter_name"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights.hotswap",description:`<strong>hotswap</strong> (<code>bool</code>, <em>optional</em>) — | |
| Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place.`,name:"hotswap"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>) — | |
| Speed up model loading by only loading the pretrained LoRA weights and not initializing the random | |
| weights.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) — | |
| See <a href="/docs/diffusers/pr_12652/en/api/loaders/lora#diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3732"}}),bn=new v({props:{name:"lora_state_dict",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:": typing.Union[str, typing.Dict[str, torch.Tensor]]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.pretrained_model_name_or_path_or_dict",description:`<strong>pretrained_model_name_or_path_or_dict</strong> (<code>str</code> or <code>os.PathLike</code> or <code>dict</code>) — | |
| Can be either:</p> | |
| <ul> | |
| <li>A string, the <em>model id</em> of a pretrained model hosted on the Hub.</li> | |
| <li>A path to a <em>directory</em> containing the model weights.</li> | |
| <li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state | |
| dict</a>.</li> | |
| </ul>`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.cache_dir",description:`<strong>cache_dir</strong> (<code>Union[str, os.PathLike]</code>, <em>optional</em>) — | |
| Path to a directory where a downloaded pretrained model configuration is cached.`,name:"cache_dir"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether or not to force the (re-)download of the model weights.`,name:"force_download"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) — | |
| A dictionary of proxy servers to use by protocol or endpoint.`,name:"proxies"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether to only load local model weights and configuration files.`,name:"local_files_only"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) — | |
| The token to use as HTTP bearer authorization for remote files.`,name:"token"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) — | |
| The specific model version to use.`,name:"revision"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>""</code>) — | |
| The subfolder location of a model file within a larger model repository.`,name:"subfolder"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>, defaults to None) — | |
| Name of the serialized state dict file.`,name:"weight_name"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.use_safetensors",description:`<strong>use_safetensors</strong> (<code>bool</code>, <em>optional</em>) — | |
| Whether to use safetensors for loading.`,name:"use_safetensors"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.lora_state_dict.return_lora_metadata",description:`<strong>return_lora_metadata</strong> (<code>bool</code>, <em>optional</em>, defaults to False) — | |
| When enabled, additionally return the LoRA adapter metadata.`,name:"return_lora_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3650"}}),Ln=new v({props:{name:"save_lora_weights",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"transformer_lora_layers",val:": typing.Dict[str, typing.Union[torch.nn.modules.module.Module, torch.Tensor]] = None"},{name:"is_main_process",val:": bool = True"},{name:"weight_name",val:": str = None"},{name:"save_function",val:": typing.Callable = None"},{name:"safe_serialization",val:": bool = True"},{name:"transformer_lora_adapter_metadata",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — | |
| Directory to save LoRA parameters to.`,name:"save_directory"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.transformer_lora_layers",description:`<strong>transformer_lora_layers</strong> (<code>Dict[str, torch.nn.Module]</code> or <code>Dict[str, torch.Tensor]</code>) — | |
| State dict of the LoRA layers corresponding to the <code>transformer</code>.`,name:"transformer_lora_layers"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether the process calling this is the main process.`,name:"is_main_process"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) — | |
| The function to use to save the state dictionary.`,name:"save_function"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) — | |
| Whether to save the model using <code>safetensors</code> or the traditional PyTorch way.`,name:"safe_serialization"},{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.save_lora_weights.transformer_lora_adapter_metadata",description:`<strong>transformer_lora_adapter_metadata</strong> — | |
| LoRA adapter metadata associated with the transformer.`,name:"transformer_lora_adapter_metadata"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3832"}}),$n=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = ['transformer']"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.KandinskyLoraLoaderMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) — List of LoRA-injectable components to unfuse LoRA from.",name:"components"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_pipeline.py#L3917"}}),xn=new A({props:{title:"LoraBaseMixin",local:"diffusers.loaders.lora_base.LoraBaseMixin",headingTag:"h2"}}),yn=new v({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#L478"}}),wn=new v({props:{name:"delete_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>Union[List[str], str]</code>) — | |
| The names of the adapters to delete.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L838"}}),sr=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.delete_adapters.example",$$slots:{default:[i$]},$$scope:{ctx:T}}}),Mn=new v({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#L778"}}),nr=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.disable_lora.example",$$slots:{default:[l$]},$$scope:{ctx:T}}}),Tn=new v({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#L808"}}),ir=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.enable_lora.example",$$slots:{default:[d$]},$$scope:{ctx:T}}}),kn=new v({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#L985"}}),Dn=new v({props:{name:"fuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.components",description:"<strong>components</strong> — (<code>List[str]</code>): List of LoRA-injectable components to fuse the LoRAs into.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, defaults to 1.0) — | |
| Controls how much to influence the outputs with the LoRA parameters.`,name:"lora_scale"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.safe_fusing",description:`<strong>safe_fusing</strong> (<code>bool</code>, defaults to <code>False</code>) — | |
| Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.`,name:"safe_fusing"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) — | |
| Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L536"}}),dr=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.fuse_lora.example",$$slots:{default:[f$]},$$scope:{ctx:T}}}),Cn=new v({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#L876"}}),fr=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.get_active_adapters.example",$$slots:{default:[p$]},$$scope:{ctx:T}}}),Un=new v({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#L909"}}),In=new v({props:{name:"set_adapters",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters",parameters:[{name:"adapter_names",val:": typing.Union[typing.List[str], str]"},{name:"adapter_weights",val:": typing.Union[float, typing.Dict, typing.List[float], typing.List[typing.Dict], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code> or <code>str</code>) — | |
| The names of the adapters to use.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.adapter_weights",description:`<strong>adapter_weights</strong> (<code>Union[List[float], float]</code>, <em>optional</em>) — | |
| The adapter(s) weights to use with the UNet. If <code>None</code>, the weights are set to <code>1.0</code> for all the | |
| adapters.`,name:"adapter_weights"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L675"}}),cr=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_adapters.example",$$slots:{default:[c$]},$$scope:{ctx:T}}}),Vn=new v({props:{name:"set_lora_device",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device",parameters:[{name:"adapter_names",val:": typing.List[str]"},{name:"device",val:": typing.Union[torch.device, str, int]"}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>) — | |
| List of adapters to send device to.`,name:"adapter_names"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.device",description:`<strong>device</strong> (<code>Union[torch.device, str, int]</code>) — | |
| Device to send the adapters to. Can be either a torch device, a str or an integer.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L931"}}),mr=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.set_lora_device.example",$$slots:{default:[m$]},$$scope:{ctx:T}}}),Rn=new v({props:{name:"unfuse_lora",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora",parameters:[{name:"components",val:": typing.List[str] = []"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.components",description:"<strong>components</strong> (<code>List[str]</code>) — List of LoRA-injectable components to unfuse LoRA from.",name:"components"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_unet",description:"<strong>unfuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) — Whether to unfuse the UNet LoRA parameters.",name:"unfuse_unet"},{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unfuse_lora.unfuse_text_encoder",description:`<strong>unfuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) — | |
| Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn’t monkey-patched with the | |
| LoRA parameters then it won’t have any effect.`,name:"unfuse_text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/loaders/lora_base.py#L622"}}),Hn=new v({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#L513"}}),ur=new K({props:{anchor:"diffusers.loaders.lora_base.LoraBaseMixin.unload_lora_weights.example",$$slots:{default:[u$]},$$scope:{ctx:T}}}),Zn=new v({props:{name:"write_lora_layers",anchor:"diffusers.loaders.lora_base.LoraBaseMixin.write_lora_layers",parameters:[{name:"state_dict",val:": typing.Dict[str, torch.Tensor]"},{name:"save_directory",val:": str"},{name:"is_main_process",val:": bool"},{name:"weight_name",val:": str"},{name:"save_function",val:": typing.Callable"},{name:"safe_serialization",val:": bool"},{name:"lora_adapter_metadata",val:": typing.Optional[dict] = 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Xet Storage Details
- Size:
- 266 kB
- Xet hash:
- 409ac1ebd6e4bc9cc0a98d4e8e24b1733f64cf34728c9120f9e8c91c5e945738
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.