import{s as Da,o as Ta,n as Y}from"../chunks/scheduler.8c3d61f6.js";import{S as Sa,i as ka,g as a,s as t,r as _,A as Aa,h as n,f as d,c as r,j as $,u as m,x as p,k as M,y as o,a as b,v as u,d as h,t as g,w as x}from"../chunks/index.da70eac4.js";import{T as Yo}from"../chunks/Tip.1d9b8c37.js";import{D as y}from"../chunks/Docstring.6b390b9a.js";import{C as Nr}from"../chunks/CodeBlock.00a903b3.js";import{E as Er}from"../chunks/ExampleCodeBlock.db12be95.js";import{H as Qo,E as Ca}from"../chunks/EditOnGithub.1e64e623.js";function Ra(S){let s,L='To learn more about how to load LoRA weights, see the <a href="../../using-diffusers/loading_adapters#lora">LoRA</a> loading guide.';return{c(){s=a("p"),s.innerHTML=L},l(f){s=n(f,"P",{"data-svelte-h":!0}),p(s)!=="svelte-1fw6lx1"&&(s.innerHTML=L)},m(f,l){b(f,s,l)},p:Y,d(f){f&&d(s)}}}function Pa(S){let s,L="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",f,l,v="This function is experimental and might change in the future.";return{c(){s=a("p"),s.textContent=L,f=t(),l=a("p"),l.textContent=v},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=L),f=r(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=v)},m(i,w){b(i,s,w),b(i,f,w),b(i,l,w)},p:Y,d(i){i&&(d(s),d(f),d(l))}}}function Ia(S){let s,L="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",f,l,v="This function is experimental and might change in the future.";return{c(){s=a("p"),s.textContent=L,f=t(),l=a("p"),l.textContent=v},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=L),f=r(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=v)},m(i,w){b(i,s,w),b(i,f,w),b(i,l,w)},p:Y,d(i){i&&(d(s),d(f),d(l))}}}function Ha(S){let s,L="We support loading A1111 formatted LoRA checkpoints in a limited capacity.",f,l,v="This function is experimental and might change in the future.";return{c(){s=a("p"),s.textContent=L,f=t(),l=a("p"),l.textContent=v},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-15l1sdn"&&(s.textContent=L),f=r(i),l=n(i,"P",{"data-svelte-h":!0}),p(l)!=="svelte-3fufvn"&&(l.textContent=v)},m(i,w){b(i,s,w),b(i,f,w),b(i,l,w)},p:Y,d(i){i&&(d(s),d(f),d(l))}}}function Ua(S){let s,L="This is an experimental API.";return{c(){s=a("p"),s.textContent=L},l(f){s=n(f,"P",{"data-svelte-h":!0}),p(s)!=="svelte-8w79b9"&&(s.textContent=L)},m(f,l){b(f,s,l)},p:Y,d(f){f&&d(s)}}}function Ea(S){let s,L="Example:",f,l,v;return l=new Nr({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(){s=a("p"),s.textContent=L,f=t(),_(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-11lpom8"&&(s.textContent=L),f=r(i),m(l.$$.fragment,i)},m(i,w){b(i,s,w),b(i,f,w),u(l,i,w),v=!0},p:Y,i(i){v||(h(l.$$.fragment,i),v=!0)},o(i){g(l.$$.fragment,i),v=!1},d(i){i&&(d(s),d(f)),x(l,i)}}}function Na(S){let s,L="Example:",f,l,v;return l=new Nr({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(){s=a("p"),s.textContent=L,f=t(),_(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-11lpom8"&&(s.textContent=L),f=r(i),m(l.$$.fragment,i)},m(i,w){b(i,s,w),b(i,f,w),u(l,i,w),v=!0},p:Y,i(i){v||(h(l.$$.fragment,i),v=!0)},o(i){g(l.$$.fragment,i),v=!1},d(i){i&&(d(s),d(f)),x(l,i)}}}function Xa(S){let s,L="This is an experimental API.";return{c(){s=a("p"),s.textContent=L},l(f){s=n(f,"P",{"data-svelte-h":!0}),p(s)!=="svelte-8w79b9"&&(s.textContent=L)},m(f,l){b(f,s,l)},p:Y,d(f){f&&d(s)}}}function Wa(S){let s,L="Examples:",f,l,v;return l=new Nr({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(){s=a("p"),s.textContent=L,f=t(),_(l.$$.fragment)},l(i){s=n(i,"P",{"data-svelte-h":!0}),p(s)!=="svelte-kvfsh7"&&(s.textContent=L),f=r(i),m(l.$$.fragment,i)},m(i,w){b(i,s,w),b(i,f,w),u(l,i,w),v=!0},p:Y,i(i){v||(h(l.$$.fragment,i),v=!0)},o(i){g(l.$$.fragment,i),v=!1},d(i){i&&(d(s),d(f)),x(l,i)}}}function Ba(S){let s,L,f,l,v,i,w,Xr='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_10037/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>, for example) or a Transformer (<a href="/docs/diffusers/pr_10037/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>, for example). There are several classes for loading LoRA weights:',Oo,be,Wr='<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>AmusedLoraLoaderMixin</code> is for the <a href="/docs/diffusers/pr_10037/en/api/pipelines/amused#diffusers.AmusedPipeline">AmusedPipeline</a>.</li> <li><code>LoraBaseMixin</code> provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.</li>',et,Q,ot,Le,tt,k,ve,wt,no,Br=`Load LoRA layers into Stable Diffusion <a href="/docs/diffusers/pr_10037/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>.`,$t,K,we,Mt,so,Jr="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",yt,O,$e,Dt,io,jr="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",Tt,I,Me,St,lo,qr=`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>.`,kt,co,Vr="All kwargs are forwarded to <code>self.lora_state_dict</code>.",At,fo,zr=`See <a href="/docs/diffusers/pr_10037/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Ct,po,Zr=`See <a href="/docs/diffusers/pr_10037/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>.`,Rt,_o,Gr=`See <a href="/docs/diffusers/pr_10037/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>.`,Pt,B,ye,It,mo,Fr="Return state dict for lora weights and the network alphas.",Ht,ee,Ut,oe,De,Et,uo,Yr="Save the LoRA parameters corresponding to the UNet and text encoder.",rt,Te,at,A,Se,Nt,ho,Qr=`Load LoRA layers into Stable Diffusion XL <a href="/docs/diffusers/pr_10037/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>.`,Xt,te,ke,Wt,go,Kr="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",Bt,re,Ae,Jt,xo,Or="This will load the LoRA layers specified in <code>state_dict</code> into <code>unet</code>.",jt,H,Ce,qt,bo,ea=`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>.`,Vt,Lo,oa="All kwargs are forwarded to <code>self.lora_state_dict</code>.",zt,vo,ta=`See <a href="/docs/diffusers/pr_10037/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,Zt,wo,ra=`See <a href="/docs/diffusers/pr_10037/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>.`,Gt,$o,aa=`See <a href="/docs/diffusers/pr_10037/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>.`,Ft,J,Re,Yt,Mo,na="Return state dict for lora weights and the network alphas.",Qt,ae,Kt,ne,Pe,Ot,yo,sa="Save the LoRA parameters corresponding to the UNet and text encoder.",nt,Ie,st,T,He,er,Do,ia=`Load LoRA layers into <a href="/docs/diffusers/pr_10037/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>.`,or,To,da='Specific to <a href="/docs/diffusers/pr_10037/en/api/pipelines/stable_diffusion/stable_diffusion_3#diffusers.StableDiffusion3Pipeline">StableDiffusion3Pipeline</a>.',tr,se,Ue,rr,So,la="This will load the LoRA layers specified in <code>state_dict</code> into <code>text_encoder</code>",ar,ie,Ee,nr,ko,ca="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",sr,N,Ne,ir,Ao,fa=`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>.`,dr,Co,pa="All kwargs are forwarded to <code>self.lora_state_dict</code>.",lr,Ro,_a=`See <a href="/docs/diffusers/pr_10037/en/api/loaders/lora#diffusers.loaders.StableDiffusionLoraLoaderMixin.lora_state_dict">lora_state_dict()</a> for more details on how the state dict is
loaded.`,cr,Po,ma=`See <code>~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer</code> for more details on how the state
dict is loaded into <code>self.transformer</code>.`,fr,j,Xe,pr,Io,ua="Return state dict for lora weights and the network alphas.",_r,de,mr,le,We,ur,Ho,ha="Save the LoRA parameters corresponding to the UNet and text encoder.",it,Be,dt,G,Je,hr,ce,je,gr,Uo,ga="This will load the LoRA layers specified in <code>state_dict</code> into <code>transformer</code>.",lt,qe,ct,D,Ve,xr,Eo,xa="Utility class for handling LoRAs.",br,No,ze,Lr,X,Ze,vr,Xo,ba="Fuses the LoRA parameters into the original parameters of the corresponding blocks.",wr,fe,$r,pe,Mr,q,Ge,yr,Wo,La="Gets the list of the current active adapters.",Dr,_e,Tr,me,Fe,Sr,Bo,va="Gets the current list of all available adapters in the pipeline.",kr,ue,Ye,Ar,Jo,wa=`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.`,Cr,V,Qe,Rr,jo,$a=`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>.`,Pr,he,Ir,z,Ke,Hr,qo,Ma="Unloads the LoRA parameters.",Ur,ge,ft,Oe,pt,Ko,_t;return v=new Qo({props:{title:"LoRA",local:"lora",headingTag:"h1"}}),Q=new Yo({props:{$$slots:{default:[Ra]},$$scope:{ctx:S}}}),Le=new Qo({props:{title:"StableDiffusionLoraLoaderMixin",local:"diffusers.loaders.StableDiffusionLoraLoaderMixin",headingTag:"h2"}}),ve=new y({props:{name:"class diffusers.loaders.StableDiffusionLoraLoaderMixin",anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/loaders/lora_pipeline.py#L65"}}),we=new y({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"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.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.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder.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.StableDiffusionLoraLoaderMixin.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.StableDiffusionLoraLoaderMixin.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.StableDiffusionLoraLoaderMixin.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.StableDiffusionLoraLoaderMixin.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"},{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>) &#x2014;
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.`,name:"low_cpu_mem_usage"}],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/loaders/lora_pipeline.py#L310"}}),$e=new y({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"}],parametersDescription:[{anchor:"diffusers.loaders.StableDiffusionLoraLoaderMixin.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.StableDiffusionLoraLoaderMixin.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.StableDiffusionLoraLoaderMixin.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.StableDiffusionLoraLoaderMixin.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
<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>) &#x2014;
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
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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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<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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