diffusers/main/en/_app/immutable/nodes/10.c8dd2789.js

import{s as Pt,o as Xt,n as Ze}from"../chunks/scheduler.8c3d61f6.js";import{S as It,i as Wt,g as o,s as r,r as y,A as Vt,h as i,f as d,c as n,j as T,u as b,x as g,k as J,y as a,a as x,v as $,d as M,t as w,w as _}from"../chunks/index.da70eac4.js";import{T as Ft}from"../chunks/Tip.1d9b8c37.js";import{D as F}from"../chunks/Docstring.ee4b6913.js";import{C as Ve}from"../chunks/CodeBlock.00a903b3.js";import{E as We}from"../chunks/ExampleCodeBlock.f7bd2c1f.js";import{H as Gt,E as Rt}from"../chunks/EditOnGithub.1e64e623.js";function Et(U){let s,v='Refer to the <a href="../../tutorials/using_peft_for_inference.md">Inference with PEFT</a> tutorial for an overview of how to use PEFT in Diffusers for inference.';return{c(){s=o("p"),s.innerHTML=v},l(p){s=i(p,"P",{"data-svelte-h":!0}),g(s)!=="svelte-1yhqcl5"&&(s.innerHTML=v)},m(p,l){x(p,s,l)},p:Ze,d(p){p&&d(s)}}}function kt(U){let s,v="Example:",p,l,u;return l=new Ve({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(
    <span class="hljs-string">&quot;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_names=<span class="hljs-string">&quot;cinematic&quot;</span>
)
pipeline.delete_adapters(<span class="hljs-string">&quot;cinematic&quot;</span>)`,wrap:!1}}),{c(){s=o("p"),s.textContent=v,p=r(),y(l.$$.fragment)},l(t){s=i(t,"P",{"data-svelte-h":!0}),g(s)!=="svelte-11lpom8"&&(s.textContent=v),p=n(t),b(l.$$.fragment,t)},m(t,h){x(t,s,h),x(t,p,h),$(l,t,h),u=!0},p:Ze,i(t){u||(M(l.$$.fragment,t),u=!0)},o(t){w(l.$$.fragment,t),u=!1},d(t){t&&(d(s),d(p)),_(l,t)}}}function Bt(U){let s,v="Example:",p,l,u;return l=new Ve({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(
    <span class="hljs-string">&quot;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;cinematic&quot;</span>
)
pipeline.disable_lora()`,wrap:!1}}),{c(){s=o("p"),s.textContent=v,p=r(),y(l.$$.fragment)},l(t){s=i(t,"P",{"data-svelte-h":!0}),g(s)!=="svelte-11lpom8"&&(s.textContent=v),p=n(t),b(l.$$.fragment,t)},m(t,h){x(t,s,h),x(t,p,h),$(l,t,h),u=!0},p:Ze,i(t){u||(M(l.$$.fragment,t),u=!0)},o(t){w(l.$$.fragment,t),u=!1},d(t){t&&(d(s),d(p)),_(l,t)}}}function Lt(U){let s,v="Example:",p,l,u;return l=new Ve({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(
    <span class="hljs-string">&quot;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;cinematic&quot;</span>
)
pipeline.enable_lora()`,wrap:!1}}),{c(){s=o("p"),s.textContent=v,p=r(),y(l.$$.fragment)},l(t){s=i(t,"P",{"data-svelte-h":!0}),g(s)!=="svelte-11lpom8"&&(s.textContent=v),p=n(t),b(l.$$.fragment,t)},m(t,h){x(t,s,h),x(t,p,h),$(l,t,h),u=!0},p:Ze,i(t){u||(M(l.$$.fragment,t),u=!0)},o(t){w(l.$$.fragment,t),u=!1},d(t){t&&(d(s),d(p)),_(l,t)}}}function Yt(U){let s,v="Example:",p,l,u;return l=new Ve({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(
    <span class="hljs-string">&quot;jbilcke-hf/sdxl-cinematic-1&quot;</span>, weight_name=<span class="hljs-string">&quot;pytorch_lora_weights.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;cinematic&quot;</span>
)
pipeline.load_lora_weights(<span class="hljs-string">&quot;nerijs/pixel-art-xl&quot;</span>, weight_name=<span class="hljs-string">&quot;pixel-art-xl.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;pixel&quot;</span>)
pipeline.set_adapters([<span class="hljs-string">&quot;cinematic&quot;</span>, <span class="hljs-string">&quot;pixel&quot;</span>], adapter_weights=[<span class="hljs-number">0.5</span>, <span class="hljs-number">0.5</span>])`,wrap:!1}}),{c(){s=o("p"),s.textContent=v,p=r(),y(l.$$.fragment)},l(t){s=i(t,"P",{"data-svelte-h":!0}),g(s)!=="svelte-11lpom8"&&(s.textContent=v),p=n(t),b(l.$$.fragment,t)},m(t,h){x(t,s,h),x(t,p,h),$(l,t,h),u=!0},p:Ze,i(t){u||(M(l.$$.fragment,t),u=!0)},o(t){w(l.$$.fragment,t),u=!1},d(t){t&&(d(s),d(p)),_(l,t)}}}function qt(U){let s,v,p,l,u,t,h,ct='Diffusers supports loading adapters such as <a href="../../using-diffusers/loading_adapters">LoRA</a> with the <a href="https://huggingface.co/docs/peft/index" rel="nofollow">PEFT</a> library with the <a href="/docs/diffusers/main/en/api/loaders/peft#diffusers.loaders.PeftAdapterMixin">PeftAdapterMixin</a> class. This allows modeling classes in Diffusers like <a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, <a href="/docs/diffusers/main/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a> to operate with an adapter.',Ce,L,je,A,Ge,f,D,Fe,ne,ut=`A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them in a base model, check out the PEFT
<a href="https://huggingface.co/docs/peft/index" rel="nofollow">documentation</a>.`,Re,le,ht="Install the latest version of PEFT, and use this mixin to:",Ee,oe,gt="<li>Attach new adapters in the model.</li> <li>Attach multiple adapters and iteratively activate/deactivate them.</li> <li>Activate/deactivate all adapters from the model.</li> <li>Get a list of the active adapters.</li>",ke,Z,H,Be,ie,yt="Gets the current list of active adapters of the model.",Le,de,bt=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
<a href="https://huggingface.co/docs/peft" rel="nofollow">documentation</a>.`,Ye,C,z,qe,pe,$t=`Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned
to the adapter to follow the convention of the PEFT library.`,Qe,fe,Mt=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT
<a href="https://huggingface.co/docs/peft" rel="nofollow">documentation</a>.`,Se,j,N,Ae,me,wt="Delete an adapter’s LoRA layers from the underlying model.",De,Y,He,G,K,ze,ce,_t="Disable all adapters attached to the model and fallback to inference with the base model only.",Ne,ue,xt=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
<a href="https://huggingface.co/docs/peft" rel="nofollow">documentation</a>.`,Ke,P,O,Oe,he,vt="Disables the active LoRA layers of the underlying model.",et,q,tt,X,ee,at,ge,Ut=`Enable adapters that are attached to the model. The model uses <code>self.active_adapters()</code> to retrieve the list of
adapters to enable.`,st,ye,Tt=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
<a href="https://huggingface.co/docs/peft" rel="nofollow">documentation</a>.`,rt,I,te,nt,be,Jt="Enables the active LoRA layers of the underlying model.",lt,Q,ot,W,ae,it,$e,Zt="Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.",dt,Me,Ct=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
<a href="https://huggingface.co/docs/peft" rel="nofollow">documentation</a>.`,pt,V,se,ft,we,jt="Set the currently active adapters for use in the UNet.",mt,S,Pe,re,Xe,Je,Ie;return u=new Gt({props:{title:"PEFT",local:"peft",headingTag:"h1"}}),L=new Ft({props:{$$slots:{default:[Et]},$$scope:{ctx:U}}}),A=new Gt({props:{title:"PeftAdapterMixin",local:"diffusers.loaders.PeftAdapterMixin",headingTag:"h2"}}),D=new F({props:{name:"class diffusers.loaders.PeftAdapterMixin",anchor:"diffusers.loaders.PeftAdapterMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L39"}}),H=new F({props:{name:"active_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L243"}}),z=new F({props:{name:"add_adapter",anchor:"diffusers.loaders.PeftAdapterMixin.add_adapter",parameters:[{name:"adapter_config",val:""},{name:"adapter_name",val:": str = 'default'"}],parametersDescription:[{anchor:"diffusers.loaders.PeftAdapterMixin.add_adapter.adapter_config",description:`<strong>adapter_config</strong> (<code>[~peft.PeftConfig]</code>) &#x2014;
The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt
methods.`,name:"adapter_config"},{anchor:"diffusers.loaders.PeftAdapterMixin.add_adapter.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;default&quot;</code>) &#x2014;
The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.`,name:"adapter_name"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L111"}}),N=new F({props:{name:"delete_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.delete_adapters",parameters:[{name:"adapter_names",val:": Union"}],parametersDescription:[{anchor:"diffusers.loaders.PeftAdapterMixin.delete_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>Union[List[str], str]</code>) &#x2014;
The names (single string or list of strings) of the adapter to delete.`,name:"adapter_names"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L361"}}),Y=new We({props:{anchor:"diffusers.loaders.PeftAdapterMixin.delete_adapters.example",$$slots:{default:[kt]},$$scope:{ctx:U}}}),K=new F({props:{name:"disable_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.disable_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L198"}}),O=new F({props:{name:"disable_lora",anchor:"diffusers.loaders.PeftAdapterMixin.disable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L315"}}),q=new We({props:{anchor:"diffusers.loaders.PeftAdapterMixin.disable_lora.example",$$slots:{default:[Bt]},$$scope:{ctx:U}}}),ee=new F({props:{name:"enable_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.enable_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L220"}}),te=new F({props:{name:"enable_lora",anchor:"diffusers.loaders.PeftAdapterMixin.enable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L338"}}),Q=new We({props:{anchor:"diffusers.loaders.PeftAdapterMixin.enable_lora.example",$$slots:{default:[Lt]},$$scope:{ctx:U}}}),ae=new F({props:{name:"set_adapter",anchor:"diffusers.loaders.PeftAdapterMixin.set_adapter",parameters:[{name:"adapter_name",val:": Union"}],parametersDescription:[{anchor:"diffusers.loaders.PeftAdapterMixin.set_adapter.adapter_name",description:`<strong>adapter_name</strong> (Union[str, List[str]])) &#x2014;
The list of adapters to set or the adapter name in the case of a single adapter.`,name:"adapter_name"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/peft.py#L149"}}),se=new F({props:{name:"set_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.set_adapters",parameters:[{name:"adapter_names",val:": Union"},{name:"weights",val:": Union = None"}],parametersDescription:[{anchor:"diffusers.loaders.PeftAdapterMixin.set_adapters.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code> or <code>str</code>) &#x2014;
The names of the adapters to use.`,name:"adapter_names"},{anchor:"diffusers.loaders.PeftAdapterMixin.set_adapters.adapter_weights",description:`<strong>adapter_weights</strong> (<code>Union[List[float], float]</code>, <em>optional</em>) &#x2014;
The adapter(s) weights to use with the UNet. If <code>None</code>, the weights are set to <code>1.0</code> for all the
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