import{S as Na,i as Va,s as Ca,e as s,k as f,w,t as l,M as Xa,c as a,d as o,m,a as r,x as $,h as d,b as h,G as e,g as P,y as x,q as M,o as j,B as T,v as Sa,L as yo}from"../../../chunks/vendor-hf-doc-builder.js";import{T as ct}from"../../../chunks/Tip-hf-doc-builder.js";import{D as F}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as vo}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Os}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as _o}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Ra(E){let i,b,c,p,g,t,u,k,Z,U,I,W;return{c(){i=s("p"),b=l("Pipelines do not offer any training functionality. You\u2019ll notice PyTorch\u2019s autograd is disabled by decorating the "),c=s("code"),p=l("__call__()"),g=l(" method with a "),t=s("a"),u=s("code"),k=l("torch.no_grad"),Z=l(" decorator because pipelines should not be used for training. If you\u2019re interested in training, please take a look at the "),U=s("a"),I=l("Training"),W=l(" guides instead!"),this.h()},l(G){i=a(G,"P",{});var J=r(i);b=d(J,"Pipelines do not offer any training functionality. You\u2019ll notice PyTorch\u2019s autograd is disabled by decorating the "),c=a(J,"CODE",{});var A=r(c);p=d(A,"__call__()"),A.forEach(o),g=d(J," method with a "),t=a(J,"A",{href:!0,rel:!0});var C=r(t);u=a(C,"CODE",{});var ao=r(u);k=d(ao,"torch.no_grad"),ao.forEach(o),C.forEach(o),Z=d(J," decorator because pipelines should not be used for training. If you\u2019re interested in training, please take a look at the "),U=a(J,"A",{href:!0});var io=r(U);I=d(io,"Training"),io.forEach(o),W=d(J," guides instead!"),J.forEach(o),this.h()},h(){h(t,"href","https://pytorch.org/docs/stable/generated/torch.no_grad.html"),h(t,"rel","nofollow"),h(U,"href","../traininig/overview")},m(G,J){P(G,i,J),e(i,b),e(i,c),e(c,p),e(i,g),e(i,t),e(t,u),e(u,k),e(i,Z),e(i,U),e(U,I),e(i,W)},d(G){G&&o(i)}}}function Ya(E){let i,b,c,p,g;return p=new vo({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> (
<span class="hljs-meta">... </span>    StableDiffusionPipeline,
<span class="hljs-meta">... </span>    StableDiffusionImg2ImgPipeline,
<span class="hljs-meta">... </span>    StableDiffusionInpaintPipeline,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text2img = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
<span class="hljs-meta">&gt;&gt;&gt; </span>inpaint = StableDiffusionInpaintPipeline(**text2img.components)`}}),{c(){i=s("p"),b=l("Examples:"),c=f(),w(p.$$.fragment)},l(t){i=a(t,"P",{});var u=r(i);b=d(u,"Examples:"),u.forEach(o),c=m(t),$(p.$$.fragment,t)},m(t,u){P(t,i,u),e(i,b),P(t,c,u),x(p,t,u),g=!0},p:yo,i(t){g||(M(p.$$.fragment,t),g=!0)},o(t){j(p.$$.fragment,t),g=!1},d(t){t&&o(i),t&&o(c),T(p,t)}}}function za(E){let i,b,c,p,g,t,u,k;return{c(){i=s("p"),b=l("To use private or "),c=s("a"),p=l("gated models"),g=l(`, log-in with
`),t=s("code"),u=l("huggingface-cli login"),k=l("."),this.h()},l(Z){i=a(Z,"P",{});var U=r(i);b=d(U,"To use private or "),c=a(U,"A",{href:!0,rel:!0});var I=r(c);p=d(I,"gated models"),I.forEach(o),g=d(U,`, log-in with
`),t=a(U,"CODE",{});var W=r(t);u=d(W,"huggingface-cli login"),W.forEach(o),k=d(U,"."),U.forEach(o),this.h()},h(){h(c,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),h(c,"rel","nofollow")},m(Z,U){P(Z,i,U),e(i,b),e(i,c),e(c,p),e(i,g),e(i,t),e(t,u),e(i,k)},d(Z){Z&&o(i)}}}function Aa(E){let i,b;return{c(){i=s("p"),b=l(`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`)},l(c){i=a(c,"P",{});var p=r(i);b=d(p,`\u26A0\uFE0F When memory efficient attention and sliced attention are both enabled, memory efficient attention takes
precedent.`),p.forEach(o)},m(c,p){P(c,i,p),e(i,b)},d(c){c&&o(i)}}}function Ha(E){let i,b,c,p,g;return p=new vo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> xformers.ops <span class="hljs-keyword">import</span> MemoryEfficientAttentionFlashAttentionOp

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Workaround for not accepting attention shape using VAE for Flash Attention</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.vae.enable_xformers_memory_efficient_attention(attention_op=<span class="hljs-literal">None</span>)`}}),{c(){i=s("p"),b=l("Examples:"),c=f(),w(p.$$.fragment)},l(t){i=a(t,"P",{});var u=r(i);b=d(u,"Examples:"),u.forEach(o),c=m(t),$(p.$$.fragment,t)},m(t,u){P(t,i,u),e(i,b),P(t,c,u),x(p,t,u),g=!0},p:yo,i(t){g||(M(p.$$.fragment,t),g=!0)},o(t){j(p.$$.fragment,t),g=!1},d(t){t&&o(i),t&&o(c),T(p,t)}}}function Qa(E){let i,b,c,p,g;return p=new vo({props:{code:"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",highlighted:`<span class="hljs-comment">Some</span> <span class="hljs-comment">weights</span> <span class="hljs-comment">of</span> <span class="hljs-comment">UNet2DConditionModel were not initialized from the model checkpoint at</span> <span class="hljs-comment">runwayml/stable</span><span class="hljs-literal">-</span><span class="hljs-comment">diffusion</span><span class="hljs-literal">-</span><span class="hljs-comment">v1</span><span class="hljs-literal">-</span><span class="hljs-comment">5 and are newly initialized because the shapes did</span> <span class="hljs-comment">not</span> <span class="hljs-comment">match:</span>
<span class="hljs-literal">-</span> <span class="hljs-comment">conv_in</span><span class="hljs-string">.</span><span class="hljs-comment">weight:</span> <span class="hljs-comment">found</span> <span class="hljs-comment">shape</span> <span class="hljs-comment">torch</span><span class="hljs-string">.</span><span class="hljs-comment">Size(</span><span class="hljs-title">[</span><span class="hljs-comment">320</span><span class="hljs-string">,</span> <span class="hljs-comment">4</span><span class="hljs-string">,</span> <span class="hljs-comment">3</span><span class="hljs-string">,</span> <span class="hljs-comment">3</span><span class="hljs-title">]</span><span class="hljs-comment">) in the checkpoint and</span> <span class="hljs-comment">torch</span><span class="hljs-string">.</span><span class="hljs-comment">Size(</span><span class="hljs-title">[</span><span class="hljs-comment">320</span><span class="hljs-string">,</span> <span class="hljs-comment">9</span><span class="hljs-string">,</span> <span class="hljs-comment">3</span><span class="hljs-string">,</span> <span class="hljs-comment">3</span><span class="hljs-title">]</span><span class="hljs-comment">) in the model instantiated</span>
<span class="hljs-comment">You</span> <span class="hljs-comment">should</span> <span class="hljs-comment">probably</span> <span class="hljs-comment">TRAIN this model on a down-stream task to be able to use it for predictions and inference</span><span class="hljs-string">.</span>`}}),{c(){i=s("p"),b=l("If you get the error message below, you need to finetune the weights for your downstream task:"),c=f(),w(p.$$.fragment)},l(t){i=a(t,"P",{});var u=r(i);b=d(u,"If you get the error message below, you need to finetune the weights for your downstream task:"),u.forEach(o),c=m(t),$(p.$$.fragment,t)},m(t,u){P(t,i,u),e(i,b),P(t,c,u),x(p,t,u),g=!0},p:yo,i(t){g||(M(p.$$.fragment,t),g=!0)},o(t){j(p.$$.fragment,t),g=!1},d(t){t&&o(i),t&&o(c),T(p,t)}}}function La(E){let i,b,c,p,g,t,u,k;return{c(){i=s("p"),b=l("To use private or "),c=s("a"),p=l("gated"),g=l(` models, log-in with
`),t=s("code"),u=l("huggingface-cli login"),k=l("."),this.h()},l(Z){i=a(Z,"P",{});var U=r(i);b=d(U,"To use private or "),c=a(U,"A",{href:!0,rel:!0});var I=r(c);p=d(I,"gated"),I.forEach(o),g=d(U,` models, log-in with
`),t=a(U,"CODE",{});var W=r(t);u=d(W,"huggingface-cli login"),W.forEach(o),k=d(U,"."),U.forEach(o),this.h()},h(){h(c,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),h(c,"rel","nofollow")},m(Z,U){P(Z,i,U),e(i,b),e(i,c),e(c,p),e(i,g),e(i,t),e(t,u),e(i,k)},d(Z){Z&&o(i)}}}function qa(E){let i,b,c,p,g;return p=new vo({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> DiffusionPipeline

<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 = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/ldm-text2im-large-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download pipeline that requires an authorization token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># For more information on access tokens, please refer to this section</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># of the documentation](https://huggingface.co/docs/hub/security-tokens)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Use a different scheduler</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LMSDiscreteScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.scheduler = scheduler`}}),{c(){i=s("p"),b=l("Examples:"),c=f(),w(p.$$.fragment)},l(t){i=a(t,"P",{});var u=r(i);b=d(u,"Examples:"),u.forEach(o),c=m(t),$(p.$$.fragment,t)},m(t,u){P(t,i,u),e(i,b),P(t,c,u),x(p,t,u),g=!0},p:yo,i(t){g||(M(p.$$.fragment,t),g=!0)},o(t){j(p.$$.fragment,t),g=!1},d(t){t&&o(i),t&&o(c),T(p,t)}}}function Oa(E){let i,b,c,p,g;return p=new vo({props:{code:"U29tZSUyMHdlaWdodHMlMjBvZiUyMEZsYXhVTmV0MkRDb25kaXRpb25Nb2RlbCUyMHdlcmUlMjBub3QlMjBpbml0aWFsaXplZCUyMGZyb20lMjB0aGUlMjBtb2RlbCUyMGNoZWNrcG9pbnQlMjBhdCUyMHJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIwYW5kJTIwYXJlJTIwbmV3bHklMjBpbml0aWFsaXplZCUyMGJlY2F1c2UlMjB0aGUlMjBzaGFwZXMlMjBkaWQlMjBub3QlMjBtYXRjaCUzQQ==",highlighted:'<span class="hljs-comment">Some</span> <span class="hljs-comment">weights</span> <span class="hljs-comment">of</span> <span class="hljs-comment">FlaxUNet2DConditionModel were not initialized from the model checkpoint at</span> <span class="hljs-comment">runwayml/stable</span><span class="hljs-literal">-</span><span class="hljs-comment">diffusion</span><span class="hljs-literal">-</span><span class="hljs-comment">v1</span><span class="hljs-literal">-</span><span class="hljs-comment">5 and are newly initialized because the shapes did</span> <span class="hljs-comment">not</span> <span class="hljs-comment">match:</span>'}}),{c(){i=s("p"),b=l("If you get the error message below, you need to finetune the weights for your downstream task:"),c=f(),w(p.$$.fragment)},l(t){i=a(t,"P",{});var u=r(i);b=d(u,"If you get the error message below, you need to finetune the weights for your downstream task:"),u.forEach(o),c=m(t),$(p.$$.fragment,t)},m(t,u){P(t,i,u),e(i,b),P(t,c,u),x(p,t,u),g=!0},p:yo,i(t){g||(M(p.$$.fragment,t),g=!0)},o(t){j(p.$$.fragment,t),g=!1},d(t){t&&o(i),t&&o(c),T(p,t)}}}function Ka(E){let i,b,c,p,g,t,u,k,Z,U,I;return{c(){i=s("p"),b=l("To use private or "),c=s("a"),p=l("gated models"),g=l(`, log-in with
`),t=s("code"),u=l("huggingface-cli login"),k=l(`. You can also activate the special
`),Z=s("a"),U=l("\u201Coffline-mode\u201D"),I=l(` to use this method in a
firewalled environment.`),this.h()},l(W){i=a(W,"P",{});var G=r(i);b=d(G,"To use private or "),c=a(G,"A",{href:!0,rel:!0});var J=r(c);p=d(J,"gated models"),J.forEach(o),g=d(G,`, log-in with
`),t=a(G,"CODE",{});var A=r(t);u=d(A,"huggingface-cli login"),A.forEach(o),k=d(G,`. You can also activate the special
`),Z=a(G,"A",{href:!0,rel:!0});var C=r(Z);U=d(C,"\u201Coffline-mode\u201D"),C.forEach(o),I=d(G,` to use this method in a
firewalled environment.`),G.forEach(o),this.h()},h(){h(c,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),h(c,"rel","nofollow"),h(Z,"href","https://huggingface.co/diffusers/installation.html#offline-mode"),h(Z,"rel","nofollow")},m(W,G){P(W,i,G),e(i,b),e(i,c),e(c,p),e(i,g),e(i,t),e(t,u),e(i,k),e(i,Z),e(Z,U),e(i,I)},d(W){W&&o(i)}}}function ei(E){let i,b,c,p,g;return p=new vo({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> FlaxDiffusionPipeline

<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><span class="hljs-comment"># Requires to be logged in to Hugging Face hub,</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># see more in [the documentation](https://huggingface.co/docs/hub/security-tokens)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline, params = FlaxDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>,
<span class="hljs-meta">... </span>    revision=<span class="hljs-string">&quot;bf16&quot;</span>,
<span class="hljs-meta">... </span>    dtype=jnp.bfloat16,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download pipeline, but use a different scheduler</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FlaxDPMSolverMultistepScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>dpmpp, dpmpp_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
<span class="hljs-meta">... </span>    model_id,
<span class="hljs-meta">... </span>    subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>dpm_pipe, dpm_params = FlaxStableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    model_id, revision=<span class="hljs-string">&quot;bf16&quot;</span>, dtype=jnp.bfloat16, scheduler=dpmpp
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dpm_params[<span class="hljs-string">&quot;scheduler&quot;</span>] = dpmpp_state`}}),{c(){i=s("p"),b=l("Examples:"),c=f(),w(p.$$.fragment)},l(t){i=a(t,"P",{});var u=r(i);b=d(u,"Examples:"),u.forEach(o),c=m(t),$(p.$$.fragment,t)},m(t,u){P(t,i,u),e(i,b),P(t,c,u),x(p,t,u),g=!0},p:yo,i(t){g||(M(p.$$.fragment,t),g=!0)},o(t){j(p.$$.fragment,t),g=!1},d(t){t&&o(i),t&&o(c),T(p,t)}}}function oi(E){let i,b,c,p,g,t,u,k,Z,U,I,W,G,J,A,C,ao,io,ft,q,mt,H,O,wo,Me,Ft,$o,Nt,ut,_,je,Vt,xo,Ct,Xt,ro,lo,St,Rt,Yt,Te,Mo,zt,At,jo,Ht,Qt,To,Lt,qt,Ue,K,Uo,Ot,Kt,Po,en,on,tn,ee,Zo,nn,sn,Eo,an,rn,ln,oe,Pe,dn,Do,pn,cn,po,Ze,fn,co,Ee,mn,X,De,un,Je,hn,Jo,gn,bn,_n,ke,yn,ko,vn,wn,$n,te,xn,ne,Ge,Mn,Ie,jn,Go,Tn,Un,Pn,se,We,Zn,Be,En,Fe,Dn,Jn,kn,z,Ne,Gn,Io,In,Wn,ae,Bn,ie,Ve,Fn,Wo,Nn,Vn,re,Ce,Cn,Q,Xn,Bo,Sn,Rn,Fo,Yn,zn,An,S,Xe,Hn,Se,Qn,Re,Ln,qn,On,le,Kn,de,es,N,Ye,os,No,ts,ns,ze,ss,Vo,as,is,rs,pe,ls,ce,ds,fe,ps,me,Ae,cs,Co,fs,ms,ue,He,us,Qe,hs,fo,gs,bs,ht,L,he,Xo,Le,_s,So,ys,gt,D,qe,vs,Ro,ws,$s,mo,uo,xs,Ms,js,Yo,zo,Ts,Us,Ao,Ps,Zs,Ho,ge,Qo,Es,Ds,Lo,Js,ks,Gs,V,Oe,Is,qo,Ws,Bs,Oo,Fs,Ns,be,Vs,_e,Cs,ye,Xs,ve,Ke,Ss,Ko,Rs,Ys,we,eo,zs,oo,As,ho,Hs,Qs,bt;return t=new Os({}),q=new ct({props:{warning:!0,$$slots:{default:[Ra]},$$scope:{ctx:E}}}),Me=new Os({}),je=new F({props:{name:"class diffusers.DiffusionPipeline",anchor:"diffusers.DiffusionPipeline",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L476"}}),Pe=new F({props:{name:"__call__",anchor:"diffusers.DiffusionPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}]}}),Ze=new F({props:{name:"device",anchor:"diffusers.DiffusionPipeline.device",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L720",returnDescription:`
<p>The torch device on which the pipeline is located.</p>
`,returnType:`
<p><code>torch.device</code></p>
`}}),Ee=new F({props:{name:"to",anchor:"diffusers.DiffusionPipeline.to",parameters:[{name:"torch_device",val:": typing.Union[str, torch.device, NoneType] = None"},{name:"torch_dtype",val:": typing.Optional[torch.dtype] = None"},{name:"silence_dtype_warnings",val:": bool = False"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L645"}}),De=new F({props:{name:"components",anchor:"diffusers.DiffusionPipeline.components",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1547"}}),te=new _o({props:{anchor:"diffusers.DiffusionPipeline.components.example",$$slots:{default:[Ya]},$$scope:{ctx:E}}}),Ge=new F({props:{name:"disable_attention_slicing",anchor:"diffusers.DiffusionPipeline.disable_attention_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1684"}}),We=new F({props:{name:"disable_xformers_memory_efficient_attention",anchor:"diffusers.DiffusionPipeline.disable_xformers_memory_efficient_attention",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1643"}}),Ne=new F({props:{name:"download",anchor:"diffusers.DiffusionPipeline.download",parameters:[{name:"pretrained_model_name",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DiffusionPipeline.download.pretrained_model_name",description:`<strong>pretrained_model_name</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
A string, the <em>repository id</em> (for example <code>CompVis/ldm-text2im-large-256</code>) of a pretrained pipeline
hosted on the Hub.`,name:"pretrained_model_name"},{anchor:"diffusers.DiffusionPipeline.download.custom_pipeline",description:`<strong>custom_pipeline</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Can be either:</p>
<ul>
<li>
<p>A string, the <em>repository id</em> (for example <code>CompVis/ldm-text2im-large-256</code>) of a pretrained
pipeline hosted on the Hub. The repository must contain a file called <code>pipeline.py</code> that defines
the custom pipeline.</p>
</li>
<li>
<p>A string, the <em>file name</em> of a community pipeline hosted on GitHub under
<a href="https://github.com/huggingface/diffusers/tree/main/examples/community" rel="nofollow">Community</a>. Valid file
names must match the file name and not the pipeline script (<code>clip_guided_stable_diffusion</code>
instead of <code>clip_guided_stable_diffusion.py</code>). Community pipelines are always loaded from the
current <code>main</code> branch of GitHub.</p>
</li>
<li>
<p>A path to a <em>directory</em> (<code>./my_pipeline_directory/</code>) containing a custom pipeline. The directory
must contain a file called <code>pipeline.py</code> that defines the custom pipeline.</p>
</li>
</ul>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						
<p>&#x1F9EA; This is an experimental feature and may change in the future.</p>

					</div>
<p>For more information on how to load and create custom pipelines, take a look at <a href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/contribute_pipeline" rel="nofollow">How to contribute a
community pipeline</a>.`,name:"custom_pipeline"},{anchor:"diffusers.DiffusionPipeline.download.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.DiffusionPipeline.download.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.DiffusionPipeline.download.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.DiffusionPipeline.download.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"diffusers.DiffusionPipeline.download.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.DiffusionPipeline.download.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.DiffusionPipeline.download.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.DiffusionPipeline.download.custom_revision",description:`<strong>custom_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, or a commit id similar to
<code>revision</code> when loading a custom pipeline from the Hub. It can be a &#x1F917; Diffusers version when loading a
custom pipeline from GitHub, otherwise it defaults to <code>&quot;main&quot;</code> when loading from the Hub.`,name:"custom_revision"},{anchor:"diffusers.DiffusionPipeline.download.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"},{anchor:"diffusers.DiffusionPipeline.download.variant",description:`<strong>variant</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Load weights from a specified variant filename such as <code>&quot;fp16&quot;</code> or <code>&quot;ema&quot;</code>. This is ignored when
loading <code>from_flax</code>.`,name:"variant"},{anchor:"diffusers.DiffusionPipeline.download.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.DiffusionPipeline.download.use_onnx",description:`<strong>use_onnx</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If set to <code>True</code>, ONNX weights will always be downloaded if present. If set to <code>False</code>, ONNX weights
will never be downloaded. By default <code>use_onnx</code> defaults to the <code>_is_onnx</code> class attribute which is
<code>False</code> for non-ONNX pipelines and <code>True</code> for ONNX pipelines. ONNX weights include both files ending
with <code>.onnx</code> and <code>.pb</code>.`,name:"use_onnx"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1212",returnDescription:`
<p>A path to the downloaded pipeline.</p>
`,returnType:`
<p><code>os.PathLike</code></p>
`}}),ae=new ct({props:{$$slots:{default:[za]},$$scope:{ctx:E}}}),Ve=new F({props:{name:"enable_attention_slicing",anchor:"diffusers.DiffusionPipeline.enable_attention_slicing",parameters:[{name:"slice_size",val:": typing.Union[str, int, NoneType] = 'auto'"}],parametersDescription:[{anchor:"diffusers.DiffusionPipeline.enable_attention_slicing.slice_size",description:`<strong>slice_size</strong> (<code>str</code> or <code>int</code>, <em>optional</em>, defaults to <code>&quot;auto&quot;</code>) &#x2014;
When <code>&quot;auto&quot;</code>, halves the input to the attention heads, so attention will be computed in two steps. If
<code>&quot;max&quot;</code>, maximum amount of memory will be saved by running only one slice at a time. If a number is
provided, uses as many slices as <code>attention_head_dim // slice_size</code>. In this case, <code>attention_head_dim</code>
must be a multiple of <code>slice_size</code>.`,name:"slice_size"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1669"}}),Ce=new F({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.DiffusionPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:": int = 0"},{name:"device",val:": typing.Union[torch.device, str] = 'cuda'"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1178"}}),Xe=new F({props:{name:"enable_xformers_memory_efficient_attention",anchor:"diffusers.DiffusionPipeline.enable_xformers_memory_efficient_attention",parameters:[{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.DiffusionPipeline.enable_xformers_memory_efficient_attention.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
Override the default <code>None</code> operator for use as <code>op</code> argument to the
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention" rel="nofollow"><code>memory_efficient_attention()</code></a>
function of xFormers.`,name:"attention_op"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L1608"}}),le=new ct({props:{warning:!0,$$slots:{default:[Aa]},$$scope:{ctx:E}}}),de=new _o({props:{anchor:"diffusers.DiffusionPipeline.enable_xformers_memory_efficient_attention.example",$$slots:{default:[Ha]},$$scope:{ctx:E}}}),Ye=new F({props:{name:"from_pretrained",anchor:"diffusers.DiffusionPipeline.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike, NoneType]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>repo id</em> (for example <code>CompVis/ldm-text2im-large-256</code>) of a pretrained pipeline
hosted on the Hub.</li>
<li>A path to a <em>directory</em> (for example <code>./my_pipeline_directory/</code>) containing pipeline weights
saved using
<a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline.save_pretrained">save_pretrained()</a>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.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 &#x201C;auto&#x201D; is passed, the
dtype is automatically derived from the model&#x2019;s weights.`,name:"torch_dtype"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.custom_pipeline",description:`<strong>custom_pipeline</strong> (<code>str</code>, <em>optional</em>) &#x2014;</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
						
<p>&#x1F9EA; This is an experimental feature and may change in the future.</p>

					</div>
<p>Can be either:</p>
<ul>
<li>A string, the <em>repo id</em> (for example <code>hf-internal-testing/diffusers-dummy-pipeline</code>) of a custom
pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines
the custom pipeline.</li>
<li>A string, the <em>file name</em> of a community pipeline hosted on GitHub under
<a href="https://github.com/huggingface/diffusers/tree/main/examples/community" rel="nofollow">Community</a>. Valid file
names must match the file name and not the pipeline script (<code>clip_guided_stable_diffusion</code>
instead of <code>clip_guided_stable_diffusion.py</code>). Community pipelines are always loaded from the
current main branch of GitHub.</li>
<li>A path to a directory (<code>./my_pipeline_directory/</code>) containing a custom pipeline. The directory
must contain a file called <code>pipeline.py</code> that defines the custom pipeline.</li>
</ul>
<p>For more information on how to load and create custom pipelines, please have a look at <a href="https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview" rel="nofollow">Loading and
Adding Custom
Pipelines</a>`,name:"custom_pipeline"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.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.DiffusionPipeline.from_pretrained.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.DiffusionPipeline.from_pretrained.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.DiffusionPipeline.from_pretrained.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.DiffusionPipeline.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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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.DiffusionPipeline.from_pretrained.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.DiffusionPipeline.from_pretrained.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.DiffusionPipeline.from_pretrained.custom_revision",description:`<strong>custom_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, or a commit id similar to
<code>revision</code> when loading a custom pipeline from the Hub. It can be a &#x1F917; Diffusers version when loading a
custom pipeline from GitHub, otherwise it defaults to <code>&quot;main&quot;</code> when loading from the Hub.`,name:"custom_revision"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.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"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.device_map",description:`<strong>device_map</strong> (<code>str</code> or <code>Dict[str, Union[int, str, torch.device]]</code>, <em>optional</em>) &#x2014;
A map that specifies where each submodule should go. It doesn&#x2019;t need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device.</p>
<p>Set <code>device_map=&quot;auto&quot;</code> to have &#x1F917; Accelerate automatically compute the most optimized <code>device_map</code>. For
more information about each option see <a href="https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map" rel="nofollow">designing a device
map</a>.`,name:"device_map"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.max_memory",description:`<strong>max_memory</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.`,name:"max_memory"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.offload_folder",description:`<strong>offload_folder</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
The path to offload weights if device_map contains the value <code>&quot;disk&quot;</code>.`,name:"offload_folder"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.offload_state_dict",description:`<strong>offload_state_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If <code>True</code>, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to <code>True</code>
when there is some disk offload.`,name:"offload_state_dict"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.low_cpu_mem_usage",description:`<strong>low_cpu_mem_usage</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code> if torch version &gt;= 1.9.0 else <code>False</code>) &#x2014;
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch &gt;= 1.9.0. If you are using an older version of PyTorch, setting this
argument to <code>True</code> will raise an error.`,name:"low_cpu_mem_usage"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.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.DiffusionPipeline.from_pretrained.use_onnx",description:`<strong>use_onnx</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
If set to <code>True</code>, ONNX weights will always be downloaded if present. If set to <code>False</code>, ONNX weights
will never be downloaded. By default <code>use_onnx</code> defaults to the <code>_is_onnx</code> class attribute which is
<code>False</code> for non-ONNX pipelines and <code>True</code> for ONNX pipelines. ONNX weights include both files ending
with <code>.onnx</code> and <code>.pb</code>.`,name:"use_onnx"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.kwargs",description:`<strong>kwargs</strong> (remaining dictionary of keyword arguments, <em>optional</em>) &#x2014;
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class). The overwritten components are passed directly to the pipelines <code>__init__</code> method. See example
below for more information.`,name:"kwargs"},{anchor:"diffusers.DiffusionPipeline.from_pretrained.variant",description:`<strong>variant</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Load weights from a specified variant filename such as <code>&quot;fp16&quot;</code> or <code>&quot;ema&quot;</code>. This is ignored when
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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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won&#x2019;t be downloaded from the Hub.`,name:"local_files_only"},{anchor:"diffusers.FlaxDiffusionPipeline.from_pretrained.use_auth_token",description:`<strong>use_auth_token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
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Mirror source to resolve accessibility issues if you&#x2019;re downloading a model in China. We do not
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