import{s as nr,o as sr,n as Se}from"../chunks/scheduler.8c3d61f6.js";import{S as dr,i as ir,g as n,s as o,r as m,A as lr,h as s,f as i,c as r,j as y,u as h,x as u,k as T,y as t,a as b,v as g,d as x,t as L,w as v}from"../chunks/index.589a98e8.js";import{T as Dt}from"../chunks/Tip.42aa8582.js";import{D as C}from"../chunks/Docstring.27406313.js";import{C as ko}from"../chunks/CodeBlock.36627b28.js";import{E as Co}from"../chunks/ExampleCodeBlock.3dc467a7.js";import{H as Do,E as cr}from"../chunks/EditOnGithub.e5a8d9cb.js";function fr(D){let a,$='To learn more about how to load LoRA weights, see the <a href="../../using-diffusers/loading_adapters#lora">LoRA</a> loading guide.';return{c(){a=n("p"),a.innerHTML=$},l(l){a=s(l,"P",{"data-svelte-h":!0}),u(a)!=="svelte-1fw6lx1"&&(a.innerHTML=$)},m(l,c){b(l,a,c)},p:Se,d(l){l&&i(a)}}}function pr(D){let a,$="This is an experimental API.";return{c(){a=n("p"),a.textContent=$},l(l){a=s(l,"P",{"data-svelte-h":!0}),u(a)!=="svelte-8w79b9"&&(a.textContent=$)},m(l,c){b(l,a,c)},p:Se,d(l){l&&i(a)}}}function _r(D){let a,$="Example:",l,c,w;return c=new ko({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">&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;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.fuse_lora(lora_scale=<span class="hljs-number">0.7</span>)`,wrap:!1}}),{c(){a=n("p"),a.textContent=$,l=o(),m(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),u(a)!=="svelte-11lpom8"&&(a.textContent=$),l=r(d),h(c.$$.fragment,d)},m(d,M){b(d,a,M),b(d,l,M),g(c,d,M),w=!0},p:Se,i(d){w||(x(c.$$.fragment,d),w=!0)},o(d){L(c.$$.fragment,d),w=!1},d(d){d&&(i(a),i(l)),v(c,d)}}}function ur(D){let a,$="Example:",l,c,w;return c=new ko({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">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline.load_lora_weights(<span class="hljs-string">&quot;CiroN2022/toy-face&quot;</span>, weight_name=<span class="hljs-string">&quot;toy_face_sdxl.safetensors&quot;</span>, adapter_name=<span class="hljs-string">&quot;toy&quot;</span>)
pipeline.get_active_adapters()`,wrap:!1}}),{c(){a=n("p"),a.textContent=$,l=o(),m(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),u(a)!=="svelte-11lpom8"&&(a.textContent=$),l=r(d),h(c.$$.fragment,d)},m(d,M){b(d,a,M),b(d,l,M),g(c,d,M),w=!0},p:Se,i(d){w||(x(c.$$.fragment,d),w=!0)},o(d){L(c.$$.fragment,d),w=!1},d(d){d&&(i(a),i(l)),v(c,d)}}}function mr(D){let a,$="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",l,c,w="This function is experimental and might change in the future.";return{c(){a=n("p"),a.textContent=$,l=o(),c=n("p"),c.textContent=w},l(d){a=s(d,"P",{"data-svelte-h":!0}),u(a)!=="svelte-15l1sdn"&&(a.textContent=$),l=r(d),c=s(d,"P",{"data-svelte-h":!0}),u(c)!=="svelte-3fufvn"&&(c.textContent=w)},m(d,M){b(d,a,M),b(d,l,M),b(d,c,M)},p:Se,d(d){d&&(i(a),i(l),i(c))}}}function hr(D){let a,$="This is an experimental API.";return{c(){a=n("p"),a.textContent=$},l(l){a=s(l,"P",{"data-svelte-h":!0}),u(a)!=="svelte-8w79b9"&&(a.textContent=$)},m(l,c){b(l,a,c)},p:Se,d(l){l&&i(a)}}}function gr(D){let a,$="Examples:",l,c,w;return c=new ko({props:{code:"JTIzJTIwQXNzdW1pbmclMjAlNjBwaXBlbGluZSU2MCUyMGlzJTIwYWxyZWFkeSUyMGxvYWRlZCUyMHdpdGglMjB0aGUlMjBMb1JBJTIwcGFyYW1ldGVycy4lMEFwaXBlbGluZS51bmxvYWRfbG9yYV93ZWlnaHRzKCklMEEuLi4=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Assuming `pipeline` is already loaded with the LoRA parameters.</span>\n<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.unload_lora_weights()\n<span class="hljs-meta">&gt;&gt;&gt; </span>...',wrap:!1}}),{c(){a=n("p"),a.textContent=$,l=o(),m(c.$$.fragment)},l(d){a=s(d,"P",{"data-svelte-h":!0}),u(a)!=="svelte-kvfsh7"&&(a.textContent=$),l=r(d),h(c.$$.fragment,d)},m(d,M){b(d,a,M),b(d,l,M),g(c,d,M),w=!0},p:Se,i(d){w||(x(c.$$.fragment,d),w=!0)},o(d){L(c.$$.fragment,d),w=!1},d(d){d&&(i(a),i(l)),v(c,d)}}}function xr(D){let a,$,l,c,w,d,M,Ro="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 UNet, text encoder or both. There are two classes for loading LoRA weights:",pt,ne,Ao='<li><code>LoraLoaderMixin</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>LoraLoaderMixin</code> class for loading and saving LoRA weights. It can only be used with the SDXL model.</li>',_t,j,ut,se,mt,f,de,kt,Ie,So=`Load LoRA layers into <a href="/docs/diffusers/pr_8492/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>.`,Rt,Ue,ie,At,Z,le,St,Pe,Io="Disables the LoRA layers for the text encoder.",It,X,ce,Ut,Ne,Uo="Enables the LoRA layers for the text encoder.",Pt,S,fe,Nt,Ee,Po="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",Et,G,Ht,V,Jt,N,pe,Wt,He,No="Gets the list of the current active adapters.",jt,z,Zt,q,_e,Xt,Je,Eo="Gets the current list of all available adapters in the pipeline.",Gt,B,ue,Vt,We,Ho="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",zt,F,me,qt,je,Jo="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",Bt,Y,he,Ft,Ze,Wo="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Yt,k,ge,Qt,Xe,jo=`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>.`,Ot,Ge,Zo="All kwargs are forwarded to <code>self.lora_state_dict</code>.",Kt,Ve,Xo='See <a href="/docs/diffusers/pr_8492/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.',eo,ze,Go=`See <a href="/docs/diffusers/pr_8492/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is loaded into
<code>self.unet</code>.`,to,qe,Vo=`See <a href="/docs/diffusers/pr_8492/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.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>.`,oo,E,xe,ro,Be,zo="Return state dict for lora weights and the network alphas.",ao,Q,no,O,Le,so,Fe,qo="Save the LoRA parameters corresponding to the UNet and text encoder.",io,K,ve,lo,Ye,Bo="Sets the adapter layers for the text encoder.",co,ee,be,fo,Qe,Fo=`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.`,po,H,$e,_o,Oe,Yo=`Reverses the effect of
<a href="https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora" rel="nofollow"><code>pipe.fuse_lora()</code></a>.`,uo,te,mo,J,we,ho,Ke,Qo="Unloads the LoRA parameters.",go,oe,ht,Me,gt,A,ye,xo,et,Oo="This class overrides <code>LoraLoaderMixin</code> with LoRA loading/saving code that’s specific to SDXL",Lo,R,Te,vo,tt,Ko=`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>.`,bo,ot,er="All kwargs are forwarded to <code>self.lora_state_dict</code>.",$o,rt,tr='See <a href="/docs/diffusers/pr_8492/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is loaded.',wo,at,or=`See <a href="/docs/diffusers/pr_8492/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.load_lora_into_unet">load_lora_into_unet()</a> for more details on how the state dict is loaded into
<code>self.unet</code>.`,Mo,nt,rr=`See <a href="/docs/diffusers/pr_8492/en/api/loaders/lora#diffusers.loaders.LoraLoaderMixin.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>.`,yo,re,Ce,To,st,ar="Save the LoRA parameters corresponding to the UNet and text encoder.",xt,De,Lt,ct,vt;return w=new Do({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),j=new Dt({props:{$$slots:{default:[fr]},$$scope:{ctx:D}}}),se=new Do({props:{title:"LoraLoaderMixin",local:"diffusers.loaders.LoraLoaderMixin",headingTag:"h2"}}),de=new C({props:{name:"class diffusers.loaders.LoraLoaderMixin",anchor:"diffusers.loaders.LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L68"}}),ie=new C({props:{name:"delete_adapters",anchor:"diffusers.loaders.LoraLoaderMixin.delete_adapters",parameters:[{name:"adapter_names",val:": Union"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.delete_adapters.Deletes",description:`<strong>Deletes</strong> the LoRA layers of <code>adapter_name</code> for the unet and text-encoder(s). &#x2014;
adapter_names (<code>Union[List[str], str]</code>):
The names of the adapter to delete. Can be a single string or a list of strings`,name:"Deletes"}],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L1048"}}),le=new C({props:{name:"disable_lora_for_text_encoder",anchor:"diffusers.loaders.LoraLoaderMixin.disable_lora_for_text_encoder",parameters:[{name:"text_encoder",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.disable_lora_for_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>torch.nn.Module</code>, <em>optional</em>) &#x2014;
The text encoder module to disable the LoRA layers for. If <code>None</code>, it will try to get the
<code>text_encoder</code> attribute.`,name:"text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L912"}}),ce=new C({props:{name:"enable_lora_for_text_encoder",anchor:"diffusers.loaders.LoraLoaderMixin.enable_lora_for_text_encoder",parameters:[{name:"text_encoder",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.enable_lora_for_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>torch.nn.Module</code>, <em>optional</em>) &#x2014;
The text encoder module to enable the LoRA layers for. If <code>None</code>, it will try to get the <code>text_encoder</code>
attribute.`,name:"text_encoder"}],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L929"}}),fe=new C({props:{name:"fuse_lora",anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora",parameters:[{name:"fuse_unet",val:": bool = True"},{name:"fuse_text_encoder",val:": bool = True"},{name:"lora_scale",val:": float = 1.0"},{name:"safe_fusing",val:": bool = False"},{name:"adapter_names",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.fuse_unet",description:"<strong>fuse_unet</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to fuse the UNet LoRA parameters.",name:"fuse_unet"},{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.fuse_text_encoder",description:`<strong>fuse_text_encoder</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to fuse the text encoder LoRA parameters. If the text encoder wasn&#x2019;t monkey-patched with the
LoRA parameters then it won&#x2019;t have any effect.`,name:"fuse_text_encoder"},{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>, defaults to 1.0) &#x2014;
Controls how much to influence the outputs with the LoRA parameters.`,name:"lora_scale"},{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.safe_fusing",description:`<strong>safe_fusing</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.`,name:"safe_fusing"},{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.adapter_names",description:`<strong>adapter_names</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
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_8492/src/diffusers/loaders/lora.py#L745"}}),G=new Dt({props:{warning:!0,$$slots:{default:[pr]},$$scope:{ctx:D}}}),V=new Co({props:{anchor:"diffusers.loaders.LoraLoaderMixin.fuse_lora.example",$$slots:{default:[_r]},$$scope:{ctx:D}}}),pe=new C({props:{name:"get_active_adapters",anchor:"diffusers.loaders.LoraLoaderMixin.get_active_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L1072"}}),z=new Co({props:{anchor:"diffusers.loaders.LoraLoaderMixin.get_active_adapters.example",$$slots:{default:[ur]},$$scope:{ctx:D}}}),_e=new C({props:{name:"get_list_adapters",anchor:"diffusers.loaders.LoraLoaderMixin.get_list_adapters",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L1104"}}),ue=new C({props:{name:"load_lora_into_text_encoder",anchor:"diffusers.loaders.LoraLoaderMixin.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"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_text_encoder.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
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.LoraLoaderMixin.load_lora_into_text_encoder.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
See <code>LoRALinearLayer</code> for more details.`,name:"network_alphas"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_text_encoder.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
The text encoder model to load the LoRA layers into.`,name:"text_encoder"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_text_encoder.prefix",description:`<strong>prefix</strong> (<code>str</code>) &#x2014;
Expected prefix of the <code>text_encoder</code> in the <code>state_dict</code>.`,name:"prefix"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_text_encoder.lora_scale",description:`<strong>lora_scale</strong> (<code>float</code>) &#x2014;
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.LoraLoaderMixin.load_lora_into_text_encoder.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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"}],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L406"}}),me=new C({props:{name:"load_lora_into_transformer",anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_transformer",parameters:[{name:"state_dict",val:""},{name:"network_alphas",val:""},{name:"transformer",val:""},{name:"adapter_name",val:" = None"},{name:"_pipeline",val:" = None"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_transformer.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
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.LoraLoaderMixin.load_lora_into_transformer.network_alphas",description:`<strong>network_alphas</strong> (<code>Dict[str, float]</code>) &#x2014;
See <code>LoRALinearLayer</code> for more details.`,name:"network_alphas"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_transformer.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_transformer.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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"}],source:"https://github.com/huggingface/diffusers/blob/vr_8492/src/diffusers/loaders/lora.py#L523"}}),he=new C({props:{name:"load_lora_into_unet",anchor:"diffusers.loaders.LoraLoaderMixin.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"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_unet.state_dict",description:`<strong>state_dict</strong> (<code>dict</code>) &#x2014;
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.LoraLoaderMixin.load_lora_into_unet.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
link</a>.`,name:"network_alphas"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model to load the LoRA layers into.`,name:"unet"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_into_unet.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
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<li>A string, the <em>model id</em> (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
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<li>A path to a <em>directory</em> (for example <code>./my_model_directory</code>) containing the model weights saved
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<li>A <a href="https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict" rel="nofollow">torch state
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