import{S as eo,i as ao,s as oo,e as l,k as p,w as T,t as o,M as so,c as r,d as a,m as u,a as n,x as J,h as s,b as h,G as e,g as d,y as j,q as E,o as $,B as k,v as lo}from"../../chunks/vendor-hf-doc-builder.js";import{T as to}from"../../chunks/Tip-hf-doc-builder.js";import{I as Me}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Yt}from"../../chunks/CodeBlock-hf-doc-builder.js";function ro(nt){let f,w,c,y,g;return{c(){f=l("p"),w=o("\u{1F4A1} Learn more about how to create an image dataset for training in the "),c=l("a"),y=o("Create an image dataset"),g=o(" guide."),this.h()},l(m){f=r(m,"P",{});var M=n(f);w=s(M,"\u{1F4A1} Learn more about how to create an image dataset for training in the "),c=r(M,"A",{href:!0,rel:!0});var b=n(c);y=s(b,"Create an image dataset"),b.forEach(a),g=s(M," guide."),M.forEach(a),this.h()},h(){h(c,"href","https://huggingface.co/docs/datasets/image_dataset"),h(c,"rel","nofollow")},m(m,M){d(m,f,M),e(f,w),e(f,c),e(c,y),e(f,g)},d(m){m&&a(f)}}}function no(nt){let f,w,c,y,g;return{c(){f=l("p"),w=o("\u{1F4A1} For more details and context about creating and uploading a dataset to the Hub, take a look at the "),c=l("a"),y=o("Image search with \u{1F917} Datasets"),g=o(" post."),this.h()},l(m){f=r(m,"P",{});var M=n(f);w=s(M,"\u{1F4A1} For more details and context about creating and uploading a dataset to the Hub, take a look at the "),c=r(M,"A",{href:!0,rel:!0});var b=n(c);y=s(b,"Image search with \u{1F917} Datasets"),b.forEach(a),g=s(M," post."),M.forEach(a),this.h()},h(){h(c,"href","https://huggingface.co/blog/image-search-datasets"),h(c,"rel","nofollow")},m(m,M){d(m,f,M),e(f,w),e(f,c),e(c,y),e(f,g)},d(m){m&&a(f)}}}function io(nt){let f,w,c,y,g,m,M,b,ye,Ct,Z,_e,D,ge,we,it,ve,be,Ft,dt,Te,At,x,S,Je,Mt,je,Ee,$e,Q,ke,yt,Ze,Ie,Wt,Y,Nt,X,C,_t,V,Ue,gt,Ge,zt,F,Xe,P,wt,Be,Re,Ht,O,qt,A,xe,vt,Ye,Ce,Dt,L,St,B,W,bt,K,Fe,Tt,Ae,Qt,N,Vt,I,We,tt,Jt,Ne,ze,jt,He,qe,Pt,_,De,Et,Se,Qe,$t,Ve,Pe,kt,Oe,Le,Zt,Ke,ta,It,ea,aa,Ot,et,Lt,z,oa,at,sa,la,Kt,ot,te,H,ra,Ut,na,ia,ee,st,ae,R,q,Gt,lt,da,Xt,ha,oe,U,fa,Bt,pa,ua,Rt,ca,ma,se,G,Ma,ht,ya,_a,ft,ga,wa,le;return m=new Me({}),Y=new to({props:{$$slots:{default:[ro]},$$scope:{ctx:nt}}}),V=new Me({}),O=new Yt({props:{code:"ZGF0YV9kaXIlMkZ4eHgucG5nJTBBZGF0YV9kaXIlMkZ4eHkucG5nJTBBZGF0YV9kaXIlMkYlNUIuLi4lNUQlMkZ4eHoucG5n",highlighted:`data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png`}}),L=new Yt({props:{code:"YWNjZWxlcmF0ZSUyMGxhdW5jaCUyMHRyYWluX3VuY29uZGl0aW9uYWwucHklMjAlNUMlMEElMjAlMjAlMjAlMjAtLXRyYWluX2RhdGFfZGlyJTIwJTNDcGF0aC10by10cmFpbi1kaXJlY3RvcnklM0UlMjAlNUMlMEElMjAlMjAlMjAlMjAlM0NvdGhlci1hcmd1bWVudHMlM0U=",highlighted:`accelerate launch train_unconditional.py \\
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    &lt;other-arguments&gt;`}}),K=new Me({}),N=new to({props:{$$slots:{default:[no]},$$scope:{ctx:nt}}}),et=new Yt({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-comment"># example 1: local folder</span>
dataset = load_dataset(<span class="hljs-string">&quot;imagefolder&quot;</span>, data_dir=<span class="hljs-string">&quot;path_to_your_folder&quot;</span>)

<span class="hljs-comment"># example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)</span>
dataset = load_dataset(<span class="hljs-string">&quot;imagefolder&quot;</span>, data_files=<span class="hljs-string">&quot;path_to_zip_file&quot;</span>)

<span class="hljs-comment"># example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)</span>
dataset = load_dataset(
    <span class="hljs-string">&quot;imagefolder&quot;</span>,
    data_files=<span class="hljs-string">&quot;https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip&quot;</span>,
)

<span class="hljs-comment"># example 4: providing several splits</span>
dataset = load_dataset(
    <span class="hljs-string">&quot;imagefolder&quot;</span>, data_files={<span class="hljs-string">&quot;train&quot;</span>: [<span class="hljs-string">&quot;path/to/file1&quot;</span>, <span class="hljs-string">&quot;path/to/file2&quot;</span>], <span class="hljs-string">&quot;test&quot;</span>: [<span class="hljs-string">&quot;path/to/file3&quot;</span>, <span class="hljs-string">&quot;path/to/file4&quot;</span>]}
)`}}),ot=new Yt({props:{code:"JTIzJTIwYXNzdW1pbmclMjB5b3UlMjBoYXZlJTIwcmFuJTIwdGhlJTIwaHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4lMjBjb21tYW5kJTIwaW4lMjBhJTIwdGVybWluYWwlMEFkYXRhc2V0LnB1c2hfdG9faHViKCUyMm5hbWVfb2ZfeW91cl9kYXRhc2V0JTIyKSUwQSUwQSUyMyUyMGlmJTIweW91JTIwd2FudCUyMHRvJTIwcHVzaCUyMHRvJTIwYSUyMHByaXZhdGUlMjByZXBvJTJDJTIwc2ltcGx5JTIwcGFzcyUyMHByaXZhdGUlM0RUcnVlJTNBJTBBZGF0YXNldC5wdXNoX3RvX2h1YiglMjJuYW1lX29mX3lvdXJfZGF0YXNldCUyMiUyQyUyMHByaXZhdGUlM0RUcnVlKQ==",highlighted:`<span class="hljs-comment"># assuming you have ran the huggingface-cli login command in a terminal</span>
dataset.push_to_hub(<span class="hljs-string">&quot;name_of_your_dataset&quot;</span>)

<span class="hljs-comment"># if you want to push to a private repo, simply pass private=True:</span>
dataset.push_to_hub(<span class="hljs-string">&quot;name_of_your_dataset&quot;</span>, private=<span class="hljs-literal">True</span>)`}}),st=new Yt({props:{code:"YWNjZWxlcmF0ZSUyMGxhdW5jaCUyMC0tbWl4ZWRfcHJlY2lzaW9uJTNEJTIyZnAxNiUyMiUyMCUyMHRyYWluX3RleHRfdG9faW1hZ2UucHklMjAlNUMlMEElMjAlMjAtLXByZXRyYWluZWRfbW9kZWxfbmFtZV9vcl9wYXRoJTNEJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMjAlNUMlMEElMjAlMjAtLWRhdGFzZXRfbmFtZSUzRCUyMm5hbWVfb2ZfeW91cl9kYXRhc2V0JTIyJTIwJTVDJTBBJTIwJTIwJTNDb3RoZXItYXJndW1lbnRzJTNF",highlighted:`accelerate launch --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span>  train_text_to_image.py \\
  --pretrained_model_name_or_path=<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span> \\
  --dataset_name=<span class="hljs-string">&quot;name_of_your_dataset&quot;</span> \\
  &lt;other-arguments&gt;`}}),lt=new Me({}),{c(){f=l("meta"),w=p(),c=l("h1"),y=l("a"),g=l("span"),T(m.$$.fragment),M=p(),b=l("span"),ye=o("Create a dataset for training"),Ct=p(),Z=l("p"),_e=o("There are many datasets on the "),D=l("a"),ge=o("Hub"),we=o(" to train a model on, but if you can\u2019t find one you\u2019re interested in or want to use your own, you can create a dataset with the \u{1F917} "),it=l("a"),ve=o("Datasets"),be=o(" library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. 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