import{S as Zl,i as Bl,s as Sl,e as a,k as d,w,t as r,M as Wl,c as s,d as t,m as c,a as i,x as v,h as l,b as y,G as e,g as b,y as I,q as $,o as T,B as P,v as Vl,L as le}from"../../../chunks/vendor-hf-doc-builder.js";import{T as dr}from"../../../chunks/Tip-hf-doc-builder.js";import{D as ie}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as te}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Un}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as re}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Cl(j){let p,_,f,m,g;return m=new te({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(){p=a("p"),_=r("If you get the error message below, you need to finetune the weights for your downstream task:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"If you get the error message below, you need to finetune the weights for your downstream task:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Dl(j){let p,_,f,m,g,n,h,C;return{c(){p=a("p"),_=r("To use private or "),f=a("a"),m=r("gated"),g=r(` models, log-in with
`),n=a("code"),h=r("huggingface-cli login"),C=r("."),this.h()},l(E){p=s(E,"P",{});var x=i(p);_=l(x,"To use private or "),f=s(x,"A",{href:!0,rel:!0});var Y=i(f);m=l(Y,"gated"),Y.forEach(t),g=l(x,` models, log-in with
`),n=s(x,"CODE",{});var z=i(n);h=l(z,"huggingface-cli login"),z.forEach(t),C=l(x,"."),x.forEach(t),this.h()},h(){y(f,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),y(f,"rel","nofollow")},m(E,x){b(E,p,x),e(p,_),e(p,f),e(f,m),e(p,g),e(p,n),e(n,h),e(p,C)},d(E){E&&t(p)}}}function Rl(j){let p,_,f,m,g;return m=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHRUb0ltYWdlJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JUZXh0VG9JbWFnZS5mcm9tX3ByZXRyYWluZWQoJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIpJTBBcHJpbnQocGlwZWxpbmUuX19jbGFzc19fKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForTextToImage

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = AutoPipelineForTextToImage.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-built_in">print</span>(pipeline.__class__)`}}),{c(){p=a("p"),_=r("Examples:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"Examples:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Nl(j){let p,_;return p=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHRUb0ltYWdlJTJDJTIwQXV0b1BpcGVsaW5lRm9ySW1hZ2VUb0ltYWdlJTBBJTBBcGlwZV9pMmklMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JJbWFnZTJJbWFnZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMkMlMjByZXF1aXJlc19zYWZldHlfY2hlY2tlciUzREZhbHNlJTBBKSUwQSUwQXBpcGVfdDJpJTIwJTNEJTIwQXV0b1BpcGVsaW5lRm9yVGV4dFRvSW1hZ2UuZnJvbV9waXBlKHBpcGVfdDJpKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForTextToImage, AutoPipelineForImageToImage

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, requires_safety_checker=<span class="hljs-literal">False</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_t2i = AutoPipelineForTextToImage.from_pipe(pipe_t2i)`}}),{c(){w(p.$$.fragment)},l(f){v(p.$$.fragment,f)},m(f,m){I(p,f,m),_=!0},p:le,i(f){_||($(p.$$.fragment,f),_=!0)},o(f){T(p.$$.fragment,f),_=!1},d(f){P(p,f)}}}function Xl(j){let p,_,f,m,g;return m=new te({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(){p=a("p"),_=r("If you get the error message below, you need to finetune the weights for your downstream task:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"If you get the error message below, you need to finetune the weights for your downstream task:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Yl(j){let p,_,f,m,g,n,h,C;return{c(){p=a("p"),_=r("To use private or "),f=a("a"),m=r("gated"),g=r(` models, log-in with
`),n=a("code"),h=r("huggingface-cli login"),C=r("."),this.h()},l(E){p=s(E,"P",{});var x=i(p);_=l(x,"To use private or "),f=s(x,"A",{href:!0,rel:!0});var Y=i(f);m=l(Y,"gated"),Y.forEach(t),g=l(x,` models, log-in with
`),n=s(x,"CODE",{});var z=i(n);h=l(z,"huggingface-cli login"),z.forEach(t),C=l(x,"."),x.forEach(t),this.h()},h(){y(f,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),y(f,"rel","nofollow")},m(E,x){b(E,p,x),e(p,_),e(p,f),e(f,m),e(p,g),e(p,n),e(n,h),e(p,C)},d(E){E&&t(p)}}}function zl(j){let p,_,f,m,g;return m=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHRUb0ltYWdlJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JJbWFnZVRvSW1hZ2UuZnJvbV9wcmV0cmFpbmVkKCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyKSUwQXByaW50KHBpcGVsaW5lLl9fY2xhc3NfXyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForTextToImage

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = AutoPipelineForImageToImage.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-built_in">print</span>(pipeline.__class__)`}}),{c(){p=a("p"),_=r("Examples:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"Examples:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Ll(j){let p,_,f,m,g;return m=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHRUb0ltYWdlJTJDJTIwQXV0b1BpcGVsaW5lRm9ySW1hZ2VUb0ltYWdlJTBBJTBBcGlwZV90MmklMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JUZXh0MkltYWdlLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUyQyUyMHJlcXVpcmVzX3NhZmV0eV9jaGVja2VyJTNERmFsc2UlMEEpJTBBJTBBcGlwZV9pMmklMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JJbWFnZVRvSW1hZ2UuZnJvbV9waXBlKHBpcGVfdDJpKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForTextToImage, AutoPipelineForImageToImage

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_t2i = AutoPipelineForText2Image.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, requires_safety_checker=<span class="hljs-literal">False</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_i2i = AutoPipelineForImageToImage.from_pipe(pipe_t2i)`}}),{c(){p=a("p"),_=r("Examples:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"Examples:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Hl(j){let p,_,f,m,g;return m=new te({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(){p=a("p"),_=r("If you get the error message below, you need to finetune the weights for your downstream task:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"If you get the error message below, you need to finetune the weights for your downstream task:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Ql(j){let p,_,f,m,g,n,h,C;return{c(){p=a("p"),_=r("To use private or "),f=a("a"),m=r("gated"),g=r(` models, log-in with
`),n=a("code"),h=r("huggingface-cli login"),C=r("."),this.h()},l(E){p=s(E,"P",{});var x=i(p);_=l(x,"To use private or "),f=s(x,"A",{href:!0,rel:!0});var Y=i(f);m=l(Y,"gated"),Y.forEach(t),g=l(x,` models, log-in with
`),n=s(x,"CODE",{});var z=i(n);h=l(z,"huggingface-cli login"),z.forEach(t),C=l(x,"."),x.forEach(t),this.h()},h(){y(f,"href","https://huggingface.co/docs/hub/models-gated#gated-models"),y(f,"rel","nofollow")},m(E,x){b(E,p,x),e(p,_),e(p,f),e(f,m),e(p,g),e(p,n),e(n,h),e(p,C)},d(E){E&&t(p)}}}function ql(j){let p,_,f,m,g;return m=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHRUb0ltYWdlJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JJbWFnZVRvSW1hZ2UuZnJvbV9wcmV0cmFpbmVkKCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyKSUwQXByaW50KHBpcGVsaW5lLl9fY2xhc3NfXyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForTextToImage

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = AutoPipelineForImageToImage.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-built_in">print</span>(pipeline.__class__)`}}),{c(){p=a("p"),_=r("Examples:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"Examples:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Ol(j){let p,_,f,m,g;return m=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9QaXBlbGluZUZvclRleHRUb0ltYWdlJTJDJTIwQXV0b1BpcGVsaW5lRm9ySW5wYWludGluZyUwQSUwQXBpcGVfdDJpJTIwJTNEJTIwQXV0b1BpcGVsaW5lRm9yVGV4dDJJbWFnZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyRGVlcEZsb3lkJTJGSUYtSS1YTC12MS4wJTIyJTJDJTIwcmVxdWlyZXNfc2FmZXR5X2NoZWNrZXIlM0RGYWxzZSUwQSklMEElMEFwaXBlX2lucGFpbnQlMjAlM0QlMjBBdXRvUGlwZWxpbmVGb3JJbnBhaW50aW5nLmZyb21fcGlwZShwaXBlX3QyaSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForTextToImage, AutoPipelineForInpainting

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_t2i = AutoPipelineForText2Image.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;DeepFloyd/IF-I-XL-v1.0&quot;</span>, requires_safety_checker=<span class="hljs-literal">False</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)`}}),{c(){p=a("p"),_=r("Examples:"),f=d(),w(m.$$.fragment)},l(n){p=s(n,"P",{});var h=i(p);_=l(h,"Examples:"),h.forEach(t),f=c(n),v(m.$$.fragment,n)},m(n,h){b(n,p,h),e(p,_),b(n,f,h),I(m,n,h),g=!0},p:le,i(n){g||($(m.$$.fragment,n),g=!0)},o(n){T(m.$$.fragment,n),g=!1},d(n){n&&t(p),n&&t(f),P(m,n)}}}function Kl(j){let p,_,f,m,g,n,h,C,E,x,Y,z,qo,L,Gn,st,En,Jn,it,Zn,Bn,rt,Sn,Wn,Oo,lt,Vn,Ko,Fe,en,pt,Cn,tn,Me,on,dt,Dn,nn,ct,Rn,an,D,Mt,mt,Nn,Xn,At,ft,Yn,zn,kt,ht,Ln,Hn,Ut,ut,Qn,qn,Gt,gt,On,Kn,Et,_t,ea,sn,oe,pe,Jt,Ae,ta,Zt,oa,rn,F,ke,na,Bt,aa,sa,yt,bt,ia,ra,la,Ue,pa,wt,da,ca,ma,Ge,fa,St,ha,ua,ga,Wt,_a,ya,Vt,de,Ct,ba,wa,Dt,va,Ia,$a,k,Ee,Ta,Rt,Pa,xa,Nt,ja,Fa,Je,Xt,Ma,Aa,Yt,ka,Ua,ne,Ga,zt,Ea,Ja,vt,Za,Ba,Sa,Ze,Wa,Lt,Va,Ca,Da,ce,Ra,me,Na,fe,Xa,R,Be,Ya,Ht,za,La,Qt,Ha,Qa,qt,qa,Oa,Se,Ka,Ot,es,ts,os,he,ln,ae,ue,Kt,We,ns,eo,as,pn,M,Ve,ss,to,is,rs,It,$t,ls,ps,ds,K,cs,Tt,ms,fs,oo,hs,us,no,gs,_s,ys,Ce,bs,ao,ws,vs,Is,so,$s,Ts,io,ge,ro,Ps,xs,lo,js,Fs,Ms,U,De,As,po,ks,Us,co,Gs,Es,Re,mo,Js,Zs,fo,Bs,Ss,Ne,Ws,ho,Vs,Cs,Ds,Xe,Rs,uo,Ns,Xs,Ys,_e,zs,ye,Ls,be,Hs,N,Ye,Qs,go,qs,Os,_o,Ks,ei,yo,ti,oi,ze,ni,bo,ai,si,ii,we,dn,se,ve,wo,Le,ri,vo,li,cn,A,He,pi,Io,di,ci,Pt,xt,mi,fi,hi,Qe,ui,jt,gi,_i,yi,qe,bi,$o,wi,vi,Ii,To,$i,Ti,Po,Ie,xo,Pi,xi,jo,ji,Fi,Mi,G,Oe,Ai,Fo,ki,Ui,Mo,Gi,Ei,Ke,Ao,Ji,Zi,ko,Bi,Si,et,Wi,Uo,Vi,Ci,Di,tt,Ri,Go,Ni,Xi,Yi,$e,zi,Te,Li,Pe,Hi,X,ot,Qi,Eo,qi,Oi,Jo,Ki,er,Zo,tr,or,nt,nr,Bo,ar,sr,ir,xe,mn;return n=new Un({}),Fe=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFBpcGVsaW5lRm9ySW1hZ2VUb0ltYWdlJTBBJTBBcGlwZV9pMmklMjAlM0QlMjBQaXBlbGluZUZvckltYWdlb0ltYWdlLmZyb21fcHJldHJhaW5lZCglMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> PipelineForImageToImage

pipe_i2i = PipelineForImageoImage.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>)`}}),Me=new te({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFBpcGVsaW5lRm9ySW5wYWludGluZyUwQSUwQXBpcGVfaW5wYWludCUyMCUzRCUyMEF1dG9QaXBlbGluZUZvcklucGFpbnRpbmcuZnJvbV9waXBlKHBpcGVfaTJpKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> PipelineForInpainting

pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_i2i)`}}),Ae=new Un({}),ke=new ie({props:{name:"class diffusers.AutoPipelineForText2Image",anchor:"diffusers.AutoPipelineForText2Image",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/auto_pipeline.py#L158"}}),Ee=new ie({props:{name:"from_pretrained",anchor:"diffusers.AutoPipelineForText2Image.from_pretrained",parameters:[{name:"pretrained_model_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.from_pretrained.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.AutoPipelineForText2Image.from_pretrained.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.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.AutoPipelineForText2Image.from_pretrained.kwargs",description:`<strong>kwargs</strong> (remaining dictionary of keyword arguments, <em>optional</em>) &#x2014;
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
class). The overwritten components are passed directly to the pipelines <code>__init__</code> method. See example
below for more information.`,name:"kwargs"},{anchor:"diffusers.AutoPipelineForText2Image.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
loading <code>from_flax</code>.`,name:"variant"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/auto_pipeline.py#L187"}}),ce=new re({props:{anchor:"diffusers.AutoPipelineForText2Image.from_pretrained.example",$$slots:{default:[Cl]},$$scope:{ctx:j}}}),me=new dr({props:{$$slots:{default:[Dl]},$$scope:{ctx:j}}}),fe=new re({props:{anchor:"diffusers.AutoPipelineForText2Image.from_pretrained.example-2",$$slots:{default:[Rl]},$$scope:{ctx:j}}}),Be=new ie({props:{name:"from_pipe",anchor:"diffusers.AutoPipelineForText2Image.from_pipe",parameters:[{name:"pipeline",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.AutoPipelineForText2Image.from_pipe.pipeline",description:`<strong>pipeline</strong> (<code>DiffusionPipeline</code>) &#x2014;
an instantiated <code>DiffusionPipeline</code> object`,name:"pipeline"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/auto_pipeline.py#L313"}}),he=new re({props:{anchor:"diffusers.AutoPipelineForText2Image.from_pipe.example",$$slots:{default:[Nl]},$$scope:{ctx:j}}}),We=new Un({}),Ve=new ie({props:{name:"class diffusers.AutoPipelineForImage2Image",anchor:"diffusers.AutoPipelineForImage2Image",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/auto_pipeline.py#L401"}}),De=new ie({props:{name:"from_pretrained",anchor:"diffusers.AutoPipelineForImage2Image.from_pretrained",parameters:[{name:"pretrained_model_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.from_pretrained.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.AutoPipelineForImage2Image.from_pretrained.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.from_pretrained.max_memory",description:`<strong>max_memory</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
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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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.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.AutoPipelineForImage2Image.from_pretrained.kwargs",description:`<strong>kwargs</strong> (remaining dictionary of keyword arguments, <em>optional</em>) &#x2014;
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
class). The overwritten components are passed directly to the pipelines <code>__init__</code> method. See example
below for more information.`,name:"kwargs"},{anchor:"diffusers.AutoPipelineForImage2Image.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
loading <code>from_flax</code>.`,name:"variant"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/auto_pipeline.py#L430"}}),_e=new re({props:{anchor:"diffusers.AutoPipelineForImage2Image.from_pretrained.example",$$slots:{default:[Xl]},$$scope:{ctx:j}}}),ye=new dr({props:{$$slots:{default:[Yl]},$$scope:{ctx:j}}}),be=new re({props:{anchor:"diffusers.AutoPipelineForImage2Image.from_pretrained.example-2",$$slots:{default:[zl]},$$scope:{ctx:j}}}),Ye=new ie({props:{name:"from_pipe",anchor:"diffusers.AutoPipelineForImage2Image.from_pipe",parameters:[{name:"pipeline",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.AutoPipelineForImage2Image.from_pipe.pipeline",description:`<strong>pipeline</strong> (<code>DiffusionPipeline</code>) &#x2014;
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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.AutoPipelineForInpainting.from_pretrained.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.AutoPipelineForInpainting.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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cached versions if they exist.`,name:"force_download"},{anchor:"diffusers.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
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allowed by Git.`,name:"revision"},{anchor:"diffusers.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.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.AutoPipelineForInpainting.from_pretrained.kwargs",description:`<strong>kwargs</strong> (remaining dictionary of keyword arguments, <em>optional</em>) &#x2014;
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
class). The overwritten components are passed directly to the pipelines <code>__init__</code> method. See example
below for more information.`,name:"kwargs"},{anchor:"diffusers.AutoPipelineForInpainting.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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