diffusers/pr_9875/en/_app/immutable/nodes/10.31ebf36e.js

import{s as Ft,o as At,n as je}from"../chunks/scheduler.8c3d61f6.js";import{S as Et,i as Lt,g as l,s,r as _,A as qt,h as i,f as d,c as o,j as T,u as b,x as u,k as U,y as a,a as M,v as y,d as $,t as x,w}from"../chunks/index.da70eac4.js";import{T as Bt}from"../chunks/Tip.1d9b8c37.js";import{D as C}from"../chunks/Docstring.6b390b9a.js";import{C as Ae}from"../chunks/CodeBlock.00a903b3.js";import{E as Fe}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as Vt,E as Yt}from"../chunks/EditOnGithub.1e64e623.js";function Qt(J){let r,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(){r=l("p"),r.innerHTML=v},l(f){r=i(f,"P",{"data-svelte-h":!0}),u(r)!=="svelte-1yhqcl5"&&(r.innerHTML=v)},m(f,n){M(f,r,n)},p:je,d(f){f&&d(r)}}}function St(J){let r,v="Example:",f,n,h;return n=new Ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&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(){r=l("p"),r.textContent=v,f=s(),_(n.$$.fragment)},l(t){r=i(t,"P",{"data-svelte-h":!0}),u(r)!=="svelte-11lpom8"&&(r.textContent=v),f=o(t),b(n.$$.fragment,t)},m(t,g){M(t,r,g),M(t,f,g),y(n,t,g),h=!0},p:je,i(t){h||($(n.$$.fragment,t),h=!0)},o(t){x(n.$$.fragment,t),h=!1},d(t){t&&(d(r),d(f)),w(n,t)}}}function Dt(J){let r,v="Example:",f,n,h;return n=new Ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&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(){r=l("p"),r.textContent=v,f=s(),_(n.$$.fragment)},l(t){r=i(t,"P",{"data-svelte-h":!0}),u(r)!=="svelte-11lpom8"&&(r.textContent=v),f=o(t),b(n.$$.fragment,t)},m(t,g){M(t,r,g),M(t,f,g),y(n,t,g),h=!0},p:je,i(t){h||($(n.$$.fragment,t),h=!0)},o(t){x(n.$$.fragment,t),h=!1},d(t){t&&(d(r),d(f)),w(n,t)}}}function Ht(J){let r,v="Example:",f,n,h;return n=new Ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&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(){r=l("p"),r.textContent=v,f=s(),_(n.$$.fragment)},l(t){r=i(t,"P",{"data-svelte-h":!0}),u(r)!=="svelte-11lpom8"&&(r.textContent=v),f=o(t),b(n.$$.fragment,t)},m(t,g){M(t,r,g),M(t,f,g),y(n,t,g),h=!0},p:je,i(t){h||($(n.$$.fragment,t),h=!0)},o(t){x(n.$$.fragment,t),h=!1},d(t){t&&(d(r),d(f)),w(n,t)}}}function zt(J){let r,v="Example:",f,n,h;return n=new Ae({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&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(){r=l("p"),r.textContent=v,f=s(),_(n.$$.fragment)},l(t){r=i(t,"P",{"data-svelte-h":!0}),u(r)!=="svelte-11lpom8"&&(r.textContent=v),f=o(t),b(n.$$.fragment,t)},m(t,g){M(t,r,g),M(t,f,g),y(n,t,g),h=!0},p:je,i(t){h||($(n.$$.fragment,t),h=!0)},o(t){x(n.$$.fragment,t),h=!1},d(t){t&&(d(r),d(f)),w(n,t)}}}function Nt(J){let r,v,f,n,h,t,g,yt='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/pr_9875/en/api/loaders/peft#diffusers.loaders.PeftAdapterMixin">PeftAdapterMixin</a> class. This allows modeling classes in Diffusers like <a href="/docs/diffusers/pr_9875/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, <a href="/docs/diffusers/pr_9875/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a> to operate with an adapter.',Ge,L,Ie,D,Xe,p,H,Ee,le,$t=`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>.`,Le,ie,xt="Install the latest version of PEFT, and use this mixin to:",qe,de,wt="<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>",Be,Z,z,Ye,pe,Mt="Gets the current list of active adapters of the model.",Qe,fe,vt=`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>.`,Se,P,N,De,ce,Tt=`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.`,He,me,Ut=`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>.`,ze,j,K,Ne,he,Jt="Delete an adapter’s LoRA layers from the underlying model.",Ke,q,Oe,G,O,et,ue,Ct="Disable all adapters attached to the model and fallback to inference with the base model only.",tt,ge,Zt=`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>.`,at,I,ee,rt,_e,Pt="Disables the active LoRA layers of the underlying model.",st,B,ot,X,te,nt,be,jt=`Enable adapters that are attached to the model. The model uses <code>self.active_adapters()</code> to retrieve the list of
adapters to enable.`,lt,ye,Gt=`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>.`,it,k,ae,dt,$e,It="Enables the active LoRA layers of the underlying model.",pt,Y,ft,Q,re,ct,xe,Xt="Loads a LoRA adapter into the underlying model.",mt,W,se,ht,we,kt="Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.",ut,Me,Wt=`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>.`,gt,R,oe,_t,ve,Rt="Set the currently active adapters for use in the UNet.",bt,S,ke,ne,We,Pe,Re;return h=new Vt({props:{title:"PEFT",local:"peft",headingTag:"h1"}}),L=new Bt({props:{$$slots:{default:[Qt]},$$scope:{ctx:J}}}),D=new Vt({props:{title:"PeftAdapterMixin",local:"diffusers.loaders.PeftAdapterMixin",headingTag:"h2"}}),H=new C({props:{name:"class diffusers.loaders.PeftAdapterMixin",anchor:"diffusers.loaders.PeftAdapterMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_9875/src/diffusers/loaders/peft.py#L54"}}),z=new C({props:{name:"active_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_9875/src/diffusers/loaders/peft.py#L467"}}),N=new C({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/vr_9875/src/diffusers/loaders/peft.py#L335"}}),K=new C({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/vr_9875/src/diffusers/loaders/peft.py#L585"}}),q=new Fe({props:{anchor:"diffusers.loaders.PeftAdapterMixin.delete_adapters.example",$$slots:{default:[St]},$$scope:{ctx:J}}}),O=new C({props:{name:"disable_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.disable_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_9875/src/diffusers/loaders/peft.py#L422"}}),ee=new C({props:{name:"disable_lora",anchor:"diffusers.loaders.PeftAdapterMixin.disable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_9875/src/diffusers/loaders/peft.py#L539"}}),B=new Fe({props:{anchor:"diffusers.loaders.PeftAdapterMixin.disable_lora.example",$$slots:{default:[Dt]},$$scope:{ctx:J}}}),te=new C({props:{name:"enable_adapters",anchor:"diffusers.loaders.PeftAdapterMixin.enable_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_9875/src/diffusers/loaders/peft.py#L444"}}),ae=new C({props:{name:"enable_lora",anchor:"diffusers.loaders.PeftAdapterMixin.enable_lora",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_9875/src/diffusers/loaders/peft.py#L562"}}),Y=new Fe({props:{anchor:"diffusers.loaders.PeftAdapterMixin.enable_lora.example",$$slots:{default:[Ht]},$$scope:{ctx:J}}}),re=new C({props:{name:"load_lora_adapter",anchor:"diffusers.loaders.PeftAdapterMixin.load_lora_adapter",parameters:[{name:"pretrained_model_name_or_path_or_dict",val:""},{name:"prefix",val:" = 'transformer'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.PeftAdapterMixin.load_lora_adapter.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>) &#x2014;
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_9875/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.PeftAdapterMixin.load_lora_adapter.prefix",description:"<strong>prefix</strong> (<code>str</code>, <em>optional</em>) &#x2014; Prefix to filter the state dict.",name:"prefix"},{anchor:"diffusers.loaders.PeftAdapterMixin.load_lora_adapter.cache_dir",description:`<strong>cache_dir</strong> (<code>Union[str, os.PathLike]</code>, <em>optional</em>) &#x2014;
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.PeftAdapterMixin.load_lora_adapter.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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.PeftAdapterMixin.load_lora_adapter.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, for example, <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"diffusers.loaders.PeftAdapterMixin.load_lora_adapter.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to only load local model weights and configuration files or not. If set to <code>True</code>, the model
won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.PeftAdapterMixin.load_lora_adapter.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
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.PeftAdapterMixin.load_lora_adapter.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
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.PeftAdapterMixin.load_lora_adapter.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&quot;</code>) &#x2014;
The subfolder location of a model file within a larger model repository on the Hub or locally.`,name:"subfolder"},{anchor:"diffusers.loaders.PeftAdapterMixin.load_lora_adapter.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
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
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