import{S as ui,i as gi,s as _i,e as n,k as c,w as b,t as i,M as vi,c as a,d as t,m as f,a as s,x,h as l,b as p,G as e,g as v,y,q as w,o as M,B as $,v as bi,L as Xo}from"../../chunks/vendor-hf-doc-builder.js";import{T as ds}from"../../chunks/Tip-hf-doc-builder.js";import{D as U}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Qo}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ko}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ho}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function xi(E){let d,T,u,m,_,r,h,F,A,G,C;return{c(){d=n("p"),T=i("\u{1F9EA} The "),u=n("code"),m=i("LoaderMixins"),_=i(" are highly experimental and prone to future changes. To use private or "),r=n("a"),h=i("gated"),F=i(" models, log-in with "),A=n("code"),G=i("huggingface-cli login"),C=i("."),this.h()},l(Z){d=a(Z,"P",{});var L=s(d);T=l(L,"\u{1F9EA} The "),u=a(L,"CODE",{});var re=s(u);m=l(re,"LoaderMixins"),re.forEach(t),_=l(L," are highly experimental and prone to future changes. To use private or "),r=a(L,"A",{href:!0,rel:!0});var ne=s(r);h=l(ne,"gated"),ne.forEach(t),F=l(L," models, log-in with "),A=a(L,"CODE",{});var O=s(A);G=l(O,"huggingface-cli login"),O.forEach(t),C=l(L,"."),L.forEach(t),this.h()},h(){p(r,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),p(r,"rel","nofollow")},m(Z,L){v(Z,d,L),e(d,T),e(d,u),e(u,m),e(d,_),e(d,r),e(r,h),e(d,F),e(d,A),e(A,G),e(d,C)},d(Z){Z&&t(d)}}}function yi(E){let d,T,u,m,_;return m=new Qo({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> torch

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.load_textual_inversion(<span class="hljs-string">&quot;sd-concepts-library/cat-toy&quot;</span>)

prompt = <span class="hljs-string">&quot;A &lt;cat-toy&gt; backpack&quot;</span>

image = pipe(prompt, num_inference_steps=<span class="hljs-number">50</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;cat-backpack.png&quot;</span>)`}}),{c(){d=n("p"),T=i("To load a textual inversion embedding vector in \u{1F917} Diffusers format:"),u=c(),b(m.$$.fragment)},l(r){d=a(r,"P",{});var h=s(d);T=l(h,"To load a textual inversion embedding vector in \u{1F917} Diffusers format:"),h.forEach(t),u=f(r),x(m.$$.fragment,r)},m(r,h){v(r,d,h),e(d,T),v(r,u,h),y(m,r,h),_=!0},p:Xo,i(r){_||(w(m.$$.fragment,r),_=!0)},o(r){M(m.$$.fragment,r),_=!1},d(r){r&&t(d),r&&t(u),$(m,r)}}}function wi(E){let d,T,u,m,_;return m=new Qo({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
<span class="hljs-keyword">import</span> torch

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipe.load_textual_inversion(<span class="hljs-string">&quot;./charturnerv2.pt&quot;</span>, token=<span class="hljs-string">&quot;charturnerv2&quot;</span>)

prompt = <span class="hljs-string">&quot;charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details.&quot;</span>

image = pipe(prompt, num_inference_steps=<span class="hljs-number">50</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;character.png&quot;</span>)`}}),{c(){d=n("p"),T=i("locally:"),u=c(),b(m.$$.fragment)},l(r){d=a(r,"P",{});var h=s(d);T=l(h,"locally:"),h.forEach(t),u=f(r),x(m.$$.fragment,r)},m(r,h){v(r,d,h),e(d,T),v(r,u,h),y(m,r,h),_=!0},p:Xo,i(r){_||(w(m.$$.fragment,r),_=!0)},o(r){M(m.$$.fragment,r),_=!1},d(r){r&&t(d),r&&t(u),$(m,r)}}}function Mi(E){let d,T,u,m,_;return{c(){d=n("p"),T=i("We support loading A1111 formatted LoRA checkpoints in a limited capacity."),u=c(),m=n("p"),_=i("This function is experimental and might change in the future.")},l(r){d=a(r,"P",{});var h=s(d);T=l(h,"We support loading A1111 formatted LoRA checkpoints in a limited capacity."),h.forEach(t),u=f(r),m=a(r,"P",{});var F=s(m);_=l(F,"This function is experimental and might change in the future."),F.forEach(t)},m(r,h){v(r,d,h),e(d,T),v(r,u,h),v(r,m,h),e(m,_)},d(r){r&&t(d),r&&t(u),r&&t(m)}}}function $i(E){let d,T,u,m,_;return m=new Qo({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>...'}}),{c(){d=n("p"),T=i("Examples:"),u=c(),b(m.$$.fragment)},l(r){d=a(r,"P",{});var h=s(d);T=l(h,"Examples:"),h.forEach(t),u=f(r),x(m.$$.fragment,r)},m(r,h){v(r,d,h),e(d,T),v(r,u,h),y(m,r,h),_=!0},p:Xo,i(r){_||(w(m.$$.fragment,r),_=!0)},o(r){M(m.$$.fragment,r),_=!1},d(r){r&&t(d),r&&t(u),$(m,r)}}}function Ti(E){let d,T,u,m,_;return m=new Qo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download pipeline from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionPipeline.from_single_file(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download pipeline from local file</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># file is downloaded under ./v1-5-pruned-emaonly.ckpt</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionPipeline.from_single_file(<span class="hljs-string">&quot;./v1-5-pruned-emaonly&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Enable float16 and move to GPU</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionPipeline.from_single_file(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt&quot;</span>,
<span class="hljs-meta">... </span>    torch_dtype=torch.float16,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)`}}),{c(){d=n("p"),T=i("Examples:"),u=c(),b(m.$$.fragment)},l(r){d=a(r,"P",{});var h=s(d);T=l(h,"Examples:"),h.forEach(t),u=f(r),x(m.$$.fragment,r)},m(r,h){v(r,d,h),e(d,T),v(r,u,h),y(m,r,h),_=!0},p:Xo,i(r){_||(w(m.$$.fragment,r),_=!0)},o(r){M(m.$$.fragment,r),_=!1},d(r){r&&t(d),r&&t(u),$(m,r)}}}function Li(E){let d,T,u,m,_;return m=new Qo({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionControlnetPipeline, ControlNetModel

url = <span class="hljs-string">&quot;https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth&quot;</span>  <span class="hljs-comment"># can also be a local path</span>
model = ControlNetModel.from_single_file(url)

url = <span class="hljs-string">&quot;https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors&quot;</span>  <span class="hljs-comment"># can also be a local path</span>
pipe = StableDiffusionControlnetPipeline.from_single_file(url, controlnet=controlnet)`}}),{c(){d=n("p"),T=i("Examples:"),u=c(),b(m.$$.fragment)},l(r){d=a(r,"P",{});var h=s(d);T=l(h,"Examples:"),h.forEach(t),u=f(r),x(m.$$.fragment,r)},m(r,h){v(r,d,h),e(d,T),v(r,u,h),y(m,r,h),_=!0},p:Xo,i(r){_||(w(m.$$.fragment,r),_=!0)},o(r){M(m.$$.fragment,r),_=!1},d(r){r&&t(d),r&&t(u),$(m,r)}}}function ki(E){let d,T,u,m,_,r,h,F,A,G,C;return{c(){d=n("p"),T=i("Make sure to pass both "),u=n("code"),m=i("image_size"),_=i(" and "),r=n("code"),h=i("scaling_factor"),F=i(" to "),A=n("code"),G=i("from_single_file()"),C=i(` if you want to load
a VAE that does accompany a stable diffusion model of v2 or higher or SDXL.`)},l(Z){d=a(Z,"P",{});var L=s(d);T=l(L,"Make sure to pass both "),u=a(L,"CODE",{});var re=s(u);m=l(re,"image_size"),re.forEach(t),_=l(L," and "),r=a(L,"CODE",{});var ne=s(r);h=l(ne,"scaling_factor"),ne.forEach(t),F=l(L," to "),A=a(L,"CODE",{});var O=s(A);G=l(O,"from_single_file()"),O.forEach(t),C=l(L,` if you want to load
a VAE that does accompany a stable diffusion model of v2 or higher or SDXL.`),L.forEach(t)},m(Z,L){v(Z,d,L),e(d,T),e(d,u),e(u,m),e(d,_),e(d,r),e(r,h),e(d,F),e(d,A),e(A,G),e(d,C)},d(Z){Z&&t(d)}}}function Ei(E){let d,T,u,m,_;return m=new Qo({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9lbmNvZGVyS0wlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGc3RhYmlsaXR5YWklMkZzZC12YWUtZnQtbXNlLW9yaWdpbmFsJTJGYmxvYiUyRm1haW4lMkZ2YWUtZnQtbXNlLTg0MDAwMC1lbWEtcHJ1bmVkLnNhZmV0ZW5zb3JzJTIyJTIwJTIwJTIzJTIwY2FuJTIwYWxzbyUyMGJlJTIwbG9jYWwlMjBmaWxlJTBBbW9kZWwlMjAlM0QlMjBBdXRvZW5jb2RlcktMLmZyb21fc2luZ2xlX2ZpbGUodXJsKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoencoderKL

url = <span class="hljs-string">&quot;https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors&quot;</span>  <span class="hljs-comment"># can also be local file</span>
model = AutoencoderKL.from_single_file(url)`}}),{c(){d=n("p"),T=i("Examples:"),u=c(),b(m.$$.fragment)},l(r){d=a(r,"P",{});var h=s(d);T=l(h,"Examples:"),h.forEach(t),u=f(r),x(m.$$.fragment,r)},m(r,h){v(r,d,h),e(d,T),v(r,u,h),y(m,r,h),_=!0},p:Xo,i(r){_||(w(m.$$.fragment,r),_=!0)},o(r){M(m.$$.fragment,r),_=!1},d(r){r&&t(d),r&&t(u),$(m,r)}}}function Ui(E){let d,T,u,m,_,r,h,F,A,G,C,Z,L,re,ne,O,ve,Yt,ae,be,Ko,Ve,$r,et,Tr,zt,B,Be,Lr,xe,qe,kr,q,Er,Eo,Ur,Fr,Ye,ot,Cr,Ir,tt,Jr,Dr,Ar,ye,ze,Zr,Oe,Nr,Uo,Sr,Wr,Ot,se,we,rt,He,jr,nt,Gr,Ht,N,Xe,Pr,at,Rr,Vr,I,Qe,Br,Ke,qr,Fo,Yr,zr,Or,st,Hr,Xr,Me,Qr,eo,Kr,oo,en,on,tn,$e,rn,Te,to,nn,it,an,Xt,ie,Le,lt,ro,sn,dt,ln,Qt,k,no,dn,le,cn,Co,fn,mn,ao,ct,pn,hn,un,ke,so,gn,Ee,_n,ft,vn,bn,mt,xn,yn,Ue,io,wn,de,Mn,pt,$n,Tn,ht,Ln,kn,En,J,lo,Un,Y,Fn,ut,Cn,In,gt,Jn,Dn,_t,An,Zn,Nn,co,Sn,vt,Wn,jn,Gn,fo,Pn,Io,Rn,Vn,Bn,ce,qn,Jo,Yn,zn,bt,On,Hn,Xn,fe,Qn,Do,Kn,ea,xt,oa,ta,ra,H,mo,na,yt,aa,sa,Fe,ia,Ce,po,la,wt,da,ca,X,ho,fa,Mt,ma,pa,Ie,Kt,me,Je,$t,uo,ha,Tt,ua,er,z,go,ga,pe,_a,Lt,va,ba,Ao,xa,ya,wa,Q,_o,Ma,S,$a,Zo,Ta,La,kt,ka,Ea,Et,Ua,Fa,Ut,Ca,Ia,Ja,De,or,he,Ae,Ft,vo,Da,Ct,Aa,tr,ue,bo,Za,K,xo,Na,W,Sa,No,Wa,ja,It,Ga,Pa,Jt,Ra,Va,Dt,Ba,qa,Ya,Ze,rr,ge,Ne,At,yo,za,Zt,Oa,nr,_e,wo,Ha,P,Mo,Xa,j,Qa,So,Ka,es,Nt,os,ts,St,rs,ns,Wt,as,ss,is,Se,ls,We,ar;return r=new ko({}),ve=new ds({props:{warning:!0,$$slots:{default:[xi]},$$scope:{ctx:E}}}),Ve=new ko({}),Be=new U({props:{name:"class diffusers.loaders.UNet2DConditionLoadersMixin",anchor:"diffusers.loaders.UNet2DConditionLoadersMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L182"}}),qe=new U({props:{name:"load_attn_procs",anchor:"diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs",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.UNet2DConditionLoadersMixin.load_attn_procs.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 model id (for example <code>google/ddpm-celebahq-256</code>) of a pretrained model hosted on
the Hub.</li>
<li>A path to a directory (for example <code>./my_model_directory</code>) containing the model weights saved
with <a href="/docs/diffusers/v0.20.0/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.UNet2DConditionLoadersMixin.load_attn_procs.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.UNet2DConditionLoadersMixin.load_attn_procs.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.UNet2DConditionLoadersMixin.load_attn_procs.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to resume downloading the model weights and configuration files. If set to <code>False</code>, any
incompletely downloaded files are deleted.`,name:"resume_download"},{anchor:"diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs.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.UNet2DConditionLoadersMixin.load_attn_procs.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.UNet2DConditionLoadersMixin.load_attn_procs.use_auth_token",description:`<strong>use_auth_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:"use_auth_token"},{anchor:"diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs.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.UNet2DConditionLoadersMixin.load_attn_procs.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.UNet2DConditionLoadersMixin.load_attn_procs.mirror",description:`<strong>mirror</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Mirror source to resolve accessibility issues if you&#x2019;re downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.`,name:"mirror"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L186"}}),ze=new U({props:{name:"save_attn_procs",anchor:"diffusers.loaders.UNet2DConditionLoadersMixin.save_attn_procs",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{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:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.UNet2DConditionLoadersMixin.save_attn_procs.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory to save an attention processor to. Will be created if it doesn&#x2019;t exist.`,name:"save_directory"},{anchor:"diffusers.loaders.UNet2DConditionLoadersMixin.save_attn_procs.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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.UNet2DConditionLoadersMixin.save_attn_procs.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) &#x2014;
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.UNet2DConditionLoadersMixin.save_attn_procs.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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/v0.20.0/src/diffusers/loaders.py#L494"}}),He=new ko({}),Xe=new U({props:{name:"class diffusers.loaders.TextualInversionLoaderMixin",anchor:"diffusers.loaders.TextualInversionLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L572"}}),Qe=new U({props:{name:"load_textual_inversion",anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, typing.List[str], typing.Dict[str, torch.Tensor], typing.List[typing.Dict[str, torch.Tensor]]]"},{name:"token",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code> or <code>List[str or os.PathLike]</code> or <code>Dict</code> or <code>List[Dict]</code>) &#x2014;
Can be either one of the following or a list of them:</p>
<ul>
<li>A string, the <em>model id</em> (for example <code>sd-concepts-library/low-poly-hd-logos-icons</code>) of a
pretrained model hosted on the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_text_inversion_directory/</code>) containing the textual
inversion weights.</li>
<li>A path to a <em>file</em> (for example <code>./my_text_inversions.pt</code>) containing textual inversion 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"},{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.token",description:`<strong>token</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
Override the token to use for the textual inversion weights. If <code>pretrained_model_name_or_path</code> is a
list, then <code>token</code> must also be a list of equal length.`,name:"token"},{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.weight_name",description:`<strong>weight_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Name of a custom weight file. This should be used when:</p>
<ul>
<li>The saved textual inversion file is in &#x1F917; Diffusers format, but was saved under a specific weight
name such as <code>text_inv.bin</code>.</li>
<li>The saved textual inversion file is in the Automatic1111 format.</li>
</ul>`,name:"weight_name"},{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.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.TextualInversionLoaderMixin.load_textual_inversion.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.TextualInversionLoaderMixin.load_textual_inversion.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to resume downloading the model weights and configuration files. If set to <code>False</code>, any
incompletely downloaded files are deleted.`,name:"resume_download"},{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.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.TextualInversionLoaderMixin.load_textual_inversion.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.TextualInversionLoaderMixin.load_textual_inversion.use_auth_token",description:`<strong>use_auth_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:"use_auth_token"},{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.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.TextualInversionLoaderMixin.load_textual_inversion.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.TextualInversionLoaderMixin.load_textual_inversion.mirror",description:`<strong>mirror</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Mirror source to resolve accessibility issues if you&#x2019;re downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.`,name:"mirror"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L634"}}),Me=new Ho({props:{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.example",$$slots:{default:[yi]},$$scope:{ctx:E}}}),$e=new Ho({props:{anchor:"diffusers.loaders.TextualInversionLoaderMixin.load_textual_inversion.example-2",$$slots:{default:[wi]},$$scope:{ctx:E}}}),to=new U({props:{name:"maybe_convert_prompt",anchor:"diffusers.loaders.TextualInversionLoaderMixin.maybe_convert_prompt",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]]"},{name:"tokenizer",val:": PreTrainedTokenizer"}],parametersDescription:[{anchor:"diffusers.loaders.TextualInversionLoaderMixin.maybe_convert_prompt.prompt",description:`<strong>prompt</strong> (<code>str</code> or list of <code>str</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.loaders.TextualInversionLoaderMixin.maybe_convert_prompt.tokenizer",description:`<strong>tokenizer</strong> (<code>PreTrainedTokenizer</code>) &#x2014;
The tokenizer responsible for encoding the prompt into input tokens.`,name:"tokenizer"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L577",returnDescription:`
<p>The converted prompt</p>
`,returnType:`
<p><code>str</code> or list of <code>str</code></p>
`}}),ro=new ko({}),no=new U({props:{name:"class diffusers.loaders.LoraLoaderMixin",anchor:"diffusers.loaders.LoraLoaderMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L898"}}),so=new U({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"}],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"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L1212"}}),io=new U({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:""}],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;
See <code>LoRALinearLayer</code> for more details.`,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"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L1170"}}),lo=new U({props:{name:"load_lora_weights",anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_weights",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.LoraLoaderMixin.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>) &#x2014;
See <a href="/docs/diffusers/v0.20.0/en/api/loaders#diffusers.loaders.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"pretrained_model_name_or_path_or_dict"},{anchor:"diffusers.loaders.LoraLoaderMixin.load_lora_weights.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
See <a href="/docs/diffusers/v0.20.0/en/api/loaders#diffusers.loaders.LoraLoaderMixin.lora_state_dict">lora_state_dict()</a>.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L906"}}),mo=new U({props:{name:"lora_state_dict",anchor:"diffusers.loaders.LoraLoaderMixin.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.LoraLoaderMixin.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>) &#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/v0.20.0/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.LoraLoaderMixin.lora_state_dict.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.LoraLoaderMixin.lora_state_dict.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.LoraLoaderMixin.lora_state_dict.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to resume downloading the model weights and configuration files. If set to <code>False</code>, any
incompletely downloaded files are deleted.`,name:"resume_download"},{anchor:"diffusers.loaders.LoraLoaderMixin.lora_state_dict.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.LoraLoaderMixin.lora_state_dict.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.LoraLoaderMixin.lora_state_dict.use_auth_token",description:`<strong>use_auth_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:"use_auth_token"},{anchor:"diffusers.loaders.LoraLoaderMixin.lora_state_dict.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.LoraLoaderMixin.lora_state_dict.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.LoraLoaderMixin.lora_state_dict.mirror",description:`<strong>mirror</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Mirror source to resolve accessibility issues if you&#x2019;re downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.`,name:"mirror"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L936"}}),Fe=new ds({props:{warning:!0,$$slots:{default:[Mi]},$$scope:{ctx:E}}}),po=new U({props:{name:"save_lora_weights",anchor:"diffusers.loaders.LoraLoaderMixin.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"}],parametersDescription:[{anchor:"diffusers.loaders.LoraLoaderMixin.save_lora_weights.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
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State dict of the LoRA layers corresponding to the <code>unet</code>.`,name:"unet_lora_layers"},{anchor:"diffusers.loaders.LoraLoaderMixin.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>) &#x2014;
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 &#x1F917; Transformers.`,name:"text_encoder_lora_layers"},{anchor:"diffusers.loaders.LoraLoaderMixin.save_lora_weights.is_main_process",description:`<strong>is_main_process</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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.LoraLoaderMixin.save_lora_weights.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) &#x2014;
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.LoraLoaderMixin.save_lora_weights.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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/v0.20.0/src/diffusers/loaders.py#L1410"}}),ho=new U({props:{name:"unload_lora_weights",anchor:"diffusers.loaders.LoraLoaderMixin.unload_lora_weights",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L1667"}}),Ie=new Ho({props:{anchor:"diffusers.loaders.LoraLoaderMixin.unload_lora_weights.example",$$slots:{default:[$i]},$$scope:{ctx:E}}}),uo=new ko({}),go=new U({props:{name:"class diffusers.loaders.FromSingleFileMixin",anchor:"diffusers.loaders.FromSingleFileMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L1714"}}),_o=new U({props:{name:"from_single_file",anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file",parameters:[{name:"pretrained_model_link_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.pretrained_model_link_or_path",description:`<strong>pretrained_model_link_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Can be either:<ul>
<li>A link to the <code>.ckpt</code> file (for example
<code>&quot;https://huggingface.co/&lt;repo_id&gt;/blob/main/&lt;path_to_file&gt;.ckpt&quot;</code>) on the Hub.</li>
<li>A path to a <em>file</em> containing all pipeline weights.</li>
</ul>`,name:"pretrained_model_link_or_path"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.torch_dtype",description:`<strong>torch_dtype</strong> (<code>str</code> or <code>torch.dtype</code>, <em>optional</em>) &#x2014;
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dtype is automatically derived from the model&#x2019;s weights.`,name:"torch_dtype"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.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.FromSingleFileMixin.from_single_file.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.FromSingleFileMixin.from_single_file.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to resume downloading the model weights and configuration files. If set to <code>False</code>, any
incompletely downloaded files are deleted.`,name:"resume_download"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.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.FromSingleFileMixin.from_single_file.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.FromSingleFileMixin.from_single_file.use_auth_token",description:`<strong>use_auth_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:"use_auth_token"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.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.FromSingleFileMixin.from_single_file.use_safetensors",description:`<strong>use_safetensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
If set to <code>None</code>, the safetensors weights are downloaded if they&#x2019;re available <strong>and</strong> if the
safetensors library is installed. If set to <code>True</code>, the model is forcibly loaded from safetensors
weights. If set to <code>False</code>, safetensors weights are not loaded.`,name:"use_safetensors"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.extract_ema",description:`<strong>extract_ema</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to extract the EMA weights or not. Pass <code>True</code> to extract the EMA weights which usually yield
higher quality images for inference. Non-EMA weights are usually better for continuing finetuning.`,name:"extract_ema"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.upcast_attention",description:`<strong>upcast_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Whether the attention computation should always be upcasted.`,name:"upcast_attention"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable
Diffusion v2 base model. Use 768 for Stable Diffusion v2.`,name:"image_size"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.prediction_type",description:`<strong>prediction_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The prediction type the model was trained on. Use <code>&apos;epsilon&apos;</code> for all Stable Diffusion v1 models and
the Stable Diffusion v2 base model. Use <code>&apos;v_prediction&apos;</code> for Stable Diffusion v2.`,name:"prediction_type"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.num_in_channels",description:`<strong>num_in_channels</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of input channels. If <code>None</code>, it is automatically inferred.`,name:"num_in_channels"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.scheduler_type",description:`<strong>scheduler_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pndm&quot;</code>) &#x2014;
Type of scheduler to use. Should be one of <code>[&quot;pndm&quot;, &quot;lms&quot;, &quot;heun&quot;, &quot;euler&quot;, &quot;euler-ancestral&quot;, &quot;dpm&quot;, &quot;ddim&quot;]</code>.`,name:"scheduler_type"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.load_safety_checker",description:`<strong>load_safety_checker</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to load the safety checker or not.`,name:"load_safety_checker"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.text_encoder",description:`<strong>text_encoder</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIPTextModel</a>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
An instance of <code>CLIPTextModel</code> to use, specifically the
<a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant. If this
parameter is <code>None</code>, the function loads a new instance of <code>CLIPTextModel</code> by itself if needed.`,name:"text_encoder"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.vae",description:`<strong>vae</strong> (<code>AutoencoderKL</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If
this parameter is <code>None</code>, the function will load a new instance of [CLIP] by itself, if needed.`,name:"vae"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.tokenizer",description:`<strong>tokenizer</strong> (<a href="https://huggingface.co/docs/transformers/v4.31.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
An instance of <code>CLIPTokenizer</code> to use. If this parameter is <code>None</code>, the function loads a new instance
of <code>CLIPTokenizer</code> by itself if needed.`,name:"tokenizer"},{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.kwargs",description:`<strong>kwargs</strong> (remaining dictionary of keyword arguments, <em>optional</em>) &#x2014;
Can be used to overwrite load and saveable variables (for example the pipeline components of the
specific pipeline class). The overwritten components are directly passed to the pipelines <code>__init__</code>
method. See example below for more information.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L1725"}}),De=new Ho({props:{anchor:"diffusers.loaders.FromSingleFileMixin.from_single_file.example",$$slots:{default:[Ti]},$$scope:{ctx:E}}}),vo=new ko({}),bo=new U({props:{name:"class diffusers.loaders.FromOriginalControlnetMixin",anchor:"diffusers.loaders.FromOriginalControlnetMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L2140"}}),xo=new U({props:{name:"from_single_file",anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file",parameters:[{name:"pretrained_model_link_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.pretrained_model_link_or_path",description:`<strong>pretrained_model_link_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Can be either:<ul>
<li>A link to the <code>.ckpt</code> file (for example
<code>&quot;https://huggingface.co/&lt;repo_id&gt;/blob/main/&lt;path_to_file&gt;.ckpt&quot;</code>) on the Hub.</li>
<li>A path to a <em>file</em> containing all pipeline weights.</li>
</ul>`,name:"pretrained_model_link_or_path"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.torch_dtype",description:`<strong>torch_dtype</strong> (<code>str</code> or <code>torch.dtype</code>, <em>optional</em>) &#x2014;
Override the default <code>torch.dtype</code> and load the model with another dtype. If <code>&quot;auto&quot;</code> is passed, the
dtype is automatically derived from the model&#x2019;s weights.`,name:"torch_dtype"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.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.FromOriginalControlnetMixin.from_single_file.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.FromOriginalControlnetMixin.from_single_file.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to resume downloading the model weights and configuration files. If set to <code>False</code>, any
incompletely downloaded files are deleted.`,name:"resume_download"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.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.FromOriginalControlnetMixin.from_single_file.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 True, the model
won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.use_auth_token",description:`<strong>use_auth_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:"use_auth_token"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.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.FromOriginalControlnetMixin.from_single_file.use_safetensors",description:`<strong>use_safetensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
If set to <code>None</code>, the safetensors weights are downloaded if they&#x2019;re available <strong>and</strong> if the
safetensors library is installed. If set to <code>True</code>, the model is forcibly loaded from safetensors
weights. If set to <code>False</code>, safetensors weights are not loaded.`,name:"use_safetensors"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable
Diffusion v2 base model. Use 768 for Stable Diffusion v2.`,name:"image_size"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.upcast_attention",description:`<strong>upcast_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Whether the attention computation should always be upcasted.`,name:"upcast_attention"},{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.kwargs",description:`<strong>kwargs</strong> (remaining dictionary of keyword arguments, <em>optional</em>) &#x2014;
Can be used to overwrite load and saveable variables (for example the pipeline components of the
specific pipeline class). The overwritten components are directly passed to the pipelines <code>__init__</code>
method. See example below for more information.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L2141"}}),Ze=new Ho({props:{anchor:"diffusers.loaders.FromOriginalControlnetMixin.from_single_file.example",$$slots:{default:[Li]},$$scope:{ctx:E}}}),yo=new ko({}),wo=new U({props:{name:"class diffusers.loaders.FromOriginalVAEMixin",anchor:"diffusers.loaders.FromOriginalVAEMixin",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.20.0/src/diffusers/loaders.py#L1947"}}),Mo=new U({props:{name:"from_single_file",anchor:"diffusers.loaders.FromOriginalVAEMixin.from_single_file",parameters:[{name:"pretrained_model_link_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.loaders.FromOriginalVAEMixin.from_single_file.pretrained_model_link_or_path",description:`<strong>pretrained_model_link_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Can be either:<ul>
<li>A link to the <code>.ckpt</code> file (for example
<code>&quot;https://huggingface.co/&lt;repo_id&gt;/blob/main/&lt;path_to_file&gt;.ckpt&quot;</code>) on the Hub.</li>
<li>A path to a <em>file</em> containing all pipeline weights.</li>
</ul>`,name:"pretrained_model_link_or_path"},{anchor:"diffusers.loaders.FromOriginalVAEMixin.from_single_file.torch_dtype",description:`<strong>torch_dtype</strong> (<code>str</code> or <code>torch.dtype</code>, <em>optional</em>) &#x2014;
Override the default <code>torch.dtype</code> and load the model with another dtype. If <code>&quot;auto&quot;</code> is passed, the
dtype is automatically derived from the model&#x2019;s weights.`,name:"torch_dtype"},{anchor:"diffusers.loaders.FromOriginalVAEMixin.from_single_file.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.FromOriginalVAEMixin.from_single_file.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.FromOriginalVAEMixin.from_single_file.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to resume downloading the model weights and configuration files. If set to <code>False</code>, any
incompletely downloaded files are deleted.`,name:"resume_download"},{anchor:"diffusers.loaders.FromOriginalVAEMixin.from_single_file.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.FromOriginalVAEMixin.from_single_file.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 True, the model
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