Buckets:
hf-doc-build/doc / diffusers /main /ko /_app /pages /tutorials /basic_training.mdx-hf-doc-builder.js
| import{S as bU,i as VU,s as QU,e as n,k as y,w as o,t,M as dU,c as p,d as s,m as j,x as i,a as J,h as M,b as c,N as cM,G as a,g as U,y as T,q as m,o as r,B as w,v as gU}from"../../chunks/vendor-hf-doc-builder.js";import{T as AU}from"../../chunks/Tip-hf-doc-builder.js";import{I as uU}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as h}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as EU}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function RU(vs){let C,N,I,F,b;return{c(){C=n("p"),N=t("\u{1F4A1} \uC774 \uD559\uC2B5 \uD29C\uD1A0\uB9AC\uC5BC\uC740 "),I=n("a"),F=t("Training with \u{1F9E8} Diffusers"),b=t(" \uB178\uD2B8\uBD81 \uAE30\uBC18\uC73C\uB85C \uD569\uB2C8\uB2E4. Diffusion \uBAA8\uB378\uC758 \uC791\uB3D9 \uBC29\uC2DD \uBC0F \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 \uB178\uD2B8\uBD81\uC744 \uD655\uC778\uD558\uC138\uC694!"),this.h()},l(f){C=p(f,"P",{});var V=J(C);N=M(V,"\u{1F4A1} \uC774 \uD559\uC2B5 \uD29C\uD1A0\uB9AC\uC5BC\uC740 "),I=p(V,"A",{href:!0,rel:!0});var g=J(I);F=M(g,"Training with \u{1F9E8} Diffusers"),g.forEach(s),b=M(V," \uB178\uD2B8\uBD81 \uAE30\uBC18\uC73C\uB85C \uD569\uB2C8\uB2E4. Diffusion \uBAA8\uB378\uC758 \uC791\uB3D9 \uBC29\uC2DD \uBC0F \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 \uB178\uD2B8\uBD81\uC744 \uD655\uC778\uD558\uC138\uC694!"),V.forEach(s),this.h()},h(){c(I,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb"),c(I,"rel","nofollow")},m(f,V){U(f,C,V),a(C,N),a(C,I),a(I,F),a(C,b)},d(f){f&&s(C)}}}function BU(vs){let C,N,I,F,b,f,V,g,te,os,Me,Ss,D,Ue,P,ne,pe,Ws,A,Je,ll,ye,je,is,ce,oe,zs,X,Ys,E,ie,sl,Te,me,al,re,we,Os,el,$s,v,he,tl,Ce,Ie,Hs,Ml,xs,Nl,fe,qs,Ul,Ks,S,ue,nl,be,Ve,Ls,pl,Ps,Jl,Qe,la,W,de,Ts,ge,Ae,sa,yl,aa,jl,Ee,ea,z,Re,cl,Be,_e,ta,ol,Ma,u,ke,il,Ze,Ge,Tl,ms,Ne,Fe,rs,De,Xe,ws,ve,Se,Ua,R,We,hs,ze,Ye,ml,Cs,Oe,$e,na,rl,pa,Fl,Is,oM,Ja,Dl,He,ya,B,Y,fs,xe,qe,us,Ke,Le,Pe,Xl,bs,lt,st,at,vl,Vs,et,tt,ja,wl,ca,_,Mt,Qs,Ut,nt,ds,pt,Jt,oa,hl,ia,O,yt,Cl,jt,ct,Ta,Il,ma,G,$,gs,fl,ot,As,it,ra,H,Tt,Es,mt,rt,wa,ul,ha,Sl,wt,Ca,bl,Ia,Wl,ht,fa,Vl,Ct,ua,k,It,Rs,ft,ut,Bs,bt,Vt,ba,d,_s,Qt,dt,ks,gt,At,Zs,Et,Rt,Va,Ql,Qa,zl,Gs,iM,da,Yl,Bt,ga,dl,Aa,gl,_t,Ea,Ol,kt,Ra,$l,Zt,Ba,Al,_a,x,Gt,Ns,Nt,Ft,ka,El,Za,Hl,Dt,Ga,xl,Xt,Na,Rl,Fa,q,vt,Fs,St,Wt,Da,Bl,Xa,ql,zt,va,_l,Sa,Kl,Ds,TM,Wa,kl,Yt,za,K,Ot,Ll,$t,Ht,Ya,Q,Pl,ls,xt,qt,Kt,ss,as,Lt,Pt,lM,es,ts,sM,aM,eM,Ms,Us,tM,MM,Oa;return I=new EU({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/basic_training.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/basic_training.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/basic_training.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/basic_training.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/basic_training.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/basic_training.ipynb"}]}}),g=new uU({}),X=new AU({props:{$$slots:{default:[RU]},$$scope:{ctx:vs}}}),el=new h({props:{code:"IXBpcCUyMGluc3RhbGwlMjBkaWZmdXNlcnMlNUJ0cmFpbmluZyU1RA==",highlighted:"!pip install diffusers[training]"}}),Ml=new h({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login | |
| <span class="hljs-meta">>>> </span>notebook_login()`}}),Ul=new h({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login"}}),pl=new h({props:{code:"IXN1ZG8lMjBhcHQlMjAtcXElMjBpbnN0YWxsJTIwZ2l0LWxmcyUwQSFnaXQlMjBjb25maWclMjAtLWdsb2JhbCUyMGNyZWRlbnRpYWwuaGVscGVyJTIwc3RvcmU=",highlighted:`!sudo apt -qq install git-lfs | |
| !git config --global credential.helper store`}}),yl=new h({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass | |
| <span class="hljs-meta">>>> </span>@dataclass | |
| <span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">TrainingConfig</span>: | |
| <span class="hljs-meta">... </span> image_size = <span class="hljs-number">128</span> <span class="hljs-comment"># \uC0DD\uC131\uB418\uB294 \uC774\uBBF8\uC9C0 \uD574\uC0C1\uB3C4</span> | |
| <span class="hljs-meta">... </span> train_batch_size = <span class="hljs-number">16</span> | |
| <span class="hljs-meta">... </span> eval_batch_size = <span class="hljs-number">16</span> <span class="hljs-comment"># \uD3C9\uAC00 \uB3D9\uC548\uC5D0 \uC0D8\uD50C\uB9C1\uD560 \uC774\uBBF8\uC9C0 \uC218</span> | |
| <span class="hljs-meta">... </span> num_epochs = <span class="hljs-number">50</span> | |
| <span class="hljs-meta">... </span> gradient_accumulation_steps = <span class="hljs-number">1</span> | |
| <span class="hljs-meta">... </span> learning_rate = <span class="hljs-number">1e-4</span> | |
| <span class="hljs-meta">... </span> lr_warmup_steps = <span class="hljs-number">500</span> | |
| <span class="hljs-meta">... </span> save_image_epochs = <span class="hljs-number">10</span> | |
| <span class="hljs-meta">... </span> save_model_epochs = <span class="hljs-number">30</span> | |
| <span class="hljs-meta">... </span> mixed_precision = <span class="hljs-string">"fp16"</span> <span class="hljs-comment"># \`no\`\uB294 float32, \uC790\uB3D9 \uD63C\uD569 \uC815\uBC00\uB3C4\uB97C \uC704\uD55C \`fp16\`</span> | |
| <span class="hljs-meta">... </span> output_dir = <span class="hljs-string">"ddpm-butterflies-128"</span> <span class="hljs-comment"># \uB85C\uCEEC \uBC0F HF Hub\uC5D0 \uC800\uC7A5\uB418\uB294 \uBAA8\uB378\uBA85</span> | |
| <span class="hljs-meta">... </span> push_to_hub = <span class="hljs-literal">True</span> <span class="hljs-comment"># \uC800\uC7A5\uB41C \uBAA8\uB378\uC744 HF Hub\uC5D0 \uC5C5\uB85C\uB4DC\uD560\uC9C0 \uC5EC\uBD80</span> | |
| <span class="hljs-meta">... </span> hub_private_repo = <span class="hljs-literal">False</span> | |
| <span class="hljs-meta">... </span> overwrite_output_dir = <span class="hljs-literal">True</span> <span class="hljs-comment"># \uB178\uD2B8\uBD81\uC744 \uB2E4\uC2DC \uC2E4\uD589\uD560 \uB54C \uC774\uC804 \uBAA8\uB378\uC5D0 \uB36E\uC5B4\uC50C\uC6B8\uC9C0</span> | |
| <span class="hljs-meta">... </span> seed = <span class="hljs-number">0</span> | |
| <span class="hljs-meta">>>> </span>config = TrainingConfig()`}}),ol=new h({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBY29uZmlnLmRhdGFzZXRfbmFtZSUyMCUzRCUyMCUyMmh1Z2dhbiUyRnNtaXRoc29uaWFuX2J1dHRlcmZsaWVzX3N1YnNldCUyMiUwQWRhdGFzZXQlMjAlM0QlMjBsb2FkX2RhdGFzZXQoY29uZmlnLmRhdGFzZXRfbmFtZSUyQyUyMHNwbGl0JTNEJTIydHJhaW4lMjIp",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset | |
| <span class="hljs-meta">>>> </span>config.dataset_name = <span class="hljs-string">"huggan/smithsonian_butterflies_subset"</span> | |
| <span class="hljs-meta">>>> </span>dataset = load_dataset(config.dataset_name, split=<span class="hljs-string">"train"</span>)`}}),rl=new h({props:{code:"aW1wb3J0JTIwbWF0cGxvdGxpYi5weXBsb3QlMjBhcyUyMHBsdCUwQSUwQWZpZyUyQyUyMGF4cyUyMCUzRCUyMHBsdC5zdWJwbG90cygxJTJDJTIwNCUyQyUyMGZpZ3NpemUlM0QoMTYlMkMlMjA0KSklMEFmb3IlMjBpJTJDJTIwaW1hZ2UlMjBpbiUyMGVudW1lcmF0ZShkYXRhc2V0JTVCJTNBNCU1RCU1QiUyMmltYWdlJTIyJTVEKSUzQSUwQSUyMCUyMCUyMCUyMGF4cyU1QmklNUQuaW1zaG93KGltYWdlKSUwQSUyMCUyMCUyMCUyMGF4cyU1QmklNUQuc2V0X2F4aXNfb2ZmKCklMEFmaWcuc2hvdygp",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt | |
| <span class="hljs-meta">>>> </span>fig, axs = plt.subplots(<span class="hljs-number">1</span>, <span class="hljs-number">4</span>, figsize=(<span class="hljs-number">16</span>, <span class="hljs-number">4</span>)) | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(dataset[:<span class="hljs-number">4</span>][<span class="hljs-string">"image"</span>]): | |
| <span class="hljs-meta">... </span> axs[i].imshow(image) | |
| <span class="hljs-meta">... </span> axs[i].set_axis_off() | |
| <span class="hljs-meta">>>> </span>fig.show()`}}),wl=new h({props:{code:"ZnJvbSUyMHRvcmNodmlzaW9uJTIwaW1wb3J0JTIwdHJhbnNmb3JtcyUwQSUwQXByZXByb2Nlc3MlMjAlM0QlMjB0cmFuc2Zvcm1zLkNvbXBvc2UoJTBBJTIwJTIwJTIwJTIwJTVCJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwdHJhbnNmb3Jtcy5SZXNpemUoKGNvbmZpZy5pbWFnZV9zaXplJTJDJTIwY29uZmlnLmltYWdlX3NpemUpKSUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHRyYW5zZm9ybXMuUmFuZG9tSG9yaXpvbnRhbEZsaXAoKSUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHRyYW5zZm9ybXMuVG9UZW5zb3IoKSUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHRyYW5zZm9ybXMuTm9ybWFsaXplKCU1QjAuNSU1RCUyQyUyMCU1QjAuNSU1RCklMkMlMEElMjAlMjAlMjAlMjAlNUQlMEEp",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torchvision <span class="hljs-keyword">import</span> transforms | |
| <span class="hljs-meta">>>> </span>preprocess = transforms.Compose( | |
| <span class="hljs-meta">... </span> [ | |
| <span class="hljs-meta">... </span> transforms.Resize((config.image_size, config.image_size)), | |
| <span class="hljs-meta">... </span> transforms.RandomHorizontalFlip(), | |
| <span class="hljs-meta">... </span> transforms.ToTensor(), | |
| <span class="hljs-meta">... </span> transforms.Normalize([<span class="hljs-number">0.5</span>], [<span class="hljs-number">0.5</span>]), | |
| <span class="hljs-meta">... </span> ] | |
| <span class="hljs-meta">... </span>)`}}),hl=new h({props:{code:"ZGVmJTIwdHJhbnNmb3JtKGV4YW1wbGVzKSUzQSUwQSUyMCUyMCUyMCUyMGltYWdlcyUyMCUzRCUyMCU1QnByZXByb2Nlc3MoaW1hZ2UuY29udmVydCglMjJSR0IlMjIpKSUyMGZvciUyMGltYWdlJTIwaW4lMjBleGFtcGxlcyU1QiUyMmltYWdlJTIyJTVEJTVEJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwJTdCJTIyaW1hZ2VzJTIyJTNBJTIwaW1hZ2VzJTdEJTBBJTBBJTBBZGF0YXNldC5zZXRfdHJhbnNmb3JtKHRyYW5zZm9ybSk=",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">transform</span>(<span class="hljs-params">examples</span>): | |
| <span class="hljs-meta">... </span> images = [preprocess(image.convert(<span class="hljs-string">"RGB"</span>)) <span class="hljs-keyword">for</span> image <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"image"</span>]] | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> {<span class="hljs-string">"images"</span>: images} | |
| <span class="hljs-meta">>>> </span>dataset.set_transform(transform)`}}),Il=new h({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEF0cmFpbl9kYXRhbG9hZGVyJTIwJTNEJTIwdG9yY2gudXRpbHMuZGF0YS5EYXRhTG9hZGVyKGRhdGFzZXQlMkMlMjBiYXRjaF9zaXplJTNEY29uZmlnLnRyYWluX2JhdGNoX3NpemUlMkMlMjBzaHVmZmxlJTNEVHJ1ZSk=",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch | |
| <span class="hljs-meta">>>> </span>train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=<span class="hljs-literal">True</span>)`}}),fl=new uU({}),ul=new h({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DModel | |
| <span class="hljs-meta">>>> </span>model = UNet2DModel( | |
| <span class="hljs-meta">... </span> sample_size=config.image_size, <span class="hljs-comment"># \uD0C0\uAC9F \uC774\uBBF8\uC9C0 \uD574\uC0C1\uB3C4</span> | |
| <span class="hljs-meta">... </span> in_channels=<span class="hljs-number">3</span>, <span class="hljs-comment"># \uC785\uB825 \uCC44\uB110 \uC218, RGB \uC774\uBBF8\uC9C0\uC5D0\uC11C 3</span> | |
| <span class="hljs-meta">... </span> out_channels=<span class="hljs-number">3</span>, <span class="hljs-comment"># \uCD9C\uB825 \uCC44\uB110 \uC218</span> | |
| <span class="hljs-meta">... </span> layers_per_block=<span class="hljs-number">2</span>, <span class="hljs-comment"># UNet \uBE14\uB7ED\uB2F9 \uBA87 \uAC1C\uC758 ResNet \uB808\uC774\uC5B4\uAC00 \uC0AC\uC6A9\uB418\uB294\uC9C0</span> | |
| <span class="hljs-meta">... </span> block_out_channels=(<span class="hljs-number">128</span>, <span class="hljs-number">128</span>, <span class="hljs-number">256</span>, <span class="hljs-number">256</span>, <span class="hljs-number">512</span>, <span class="hljs-number">512</span>), <span class="hljs-comment"># \uAC01 UNet \uBE14\uB7ED\uC744 \uC704\uD55C \uCD9C\uB825 \uCC44\uB110 \uC218</span> | |
| <span class="hljs-meta">... </span> down_block_types=( | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"DownBlock2D"</span>, <span class="hljs-comment"># \uC77C\uBC18\uC801\uC778 ResNet \uB2E4\uC6B4\uC0D8\uD50C\uB9C1 \uBE14\uB7ED</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"DownBlock2D"</span>, | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"DownBlock2D"</span>, | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"DownBlock2D"</span>, | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"AttnDownBlock2D"</span>, <span class="hljs-comment"># spatial self-attention\uC774 \uD3EC\uD568\uB41C \uC77C\uBC18\uC801\uC778 ResNet \uB2E4\uC6B4\uC0D8\uD50C\uB9C1 \uBE14\uB7ED</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"DownBlock2D"</span>, | |
| <span class="hljs-meta">... </span> ), | |
| <span class="hljs-meta">... </span> up_block_types=( | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"UpBlock2D"</span>, <span class="hljs-comment"># \uC77C\uBC18\uC801\uC778 ResNet \uC5C5\uC0D8\uD50C\uB9C1 \uBE14\uB7ED</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"AttnUpBlock2D"</span>, <span class="hljs-comment"># spatial self-attention\uC774 \uD3EC\uD568\uB41C \uC77C\uBC18\uC801\uC778 ResNet \uC5C5\uC0D8\uD50C\uB9C1 \uBE14\uB7ED</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"UpBlock2D"</span>, | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"UpBlock2D"</span>, | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"UpBlock2D"</span>, | |
| <span class="hljs-meta">... </span> <span class="hljs-string">"UpBlock2D"</span>, | |
| <span class="hljs-meta">... </span> ), | |
| <span class="hljs-meta">... </span>)`}}),bl=new h({props:{code:"c2FtcGxlX2ltYWdlJTIwJTNEJTIwZGF0YXNldCU1QjAlNUQlNUIlMjJpbWFnZXMlMjIlNUQudW5zcXVlZXplKDApJTBBcHJpbnQoJTIySW5wdXQlMjBzaGFwZSUzQSUyMiUyQyUyMHNhbXBsZV9pbWFnZS5zaGFwZSklMEElMEFwcmludCglMjJPdXRwdXQlMjBzaGFwZSUzQSUyMiUyQyUyMG1vZGVsKHNhbXBsZV9pbWFnZSUyQyUyMHRpbWVzdGVwJTNEMCkuc2FtcGxlLnNoYXBlKQ==",highlighted:`<span class="hljs-meta">>>> </span>sample_image = dataset[<span class="hljs-number">0</span>][<span class="hljs-string">"images"</span>].unsqueeze(<span class="hljs-number">0</span>) | |
| <span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(<span class="hljs-string">"Input shape:"</span>, sample_image.shape) | |
| Input shape: torch.Size([<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">128</span>, <span class="hljs-number">128</span>]) | |
| <span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(<span class="hljs-string">"Output shape:"</span>, model(sample_image, timestep=<span class="hljs-number">0</span>).sample.shape) | |
| Output shape: torch.Size([<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">128</span>, <span class="hljs-number">128</span>])`}}),Ql=new h({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMScheduler | |
| <span class="hljs-meta">>>> </span>noise_scheduler = DDPMScheduler(num_train_timesteps=<span class="hljs-number">1000</span>) | |
| <span class="hljs-meta">>>> </span>noise = torch.randn(sample_image.shape) | |
| <span class="hljs-meta">>>> </span>timesteps = torch.LongTensor([<span class="hljs-number">50</span>]) | |
| <span class="hljs-meta">>>> </span>noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps) | |
| <span class="hljs-meta">>>> </span>Image.fromarray(((noisy_image.permute(<span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>) + <span class="hljs-number">1.0</span>) * <span class="hljs-number">127.5</span>).<span class="hljs-built_in">type</span>(torch.uint8).numpy()[<span class="hljs-number">0</span>])`}}),dl=new h({props:{code:"aW1wb3J0JTIwdG9yY2gubm4uZnVuY3Rpb25hbCUyMGFzJTIwRiUwQSUwQW5vaXNlX3ByZWQlMjAlM0QlMjBtb2RlbChub2lzeV9pbWFnZSUyQyUyMHRpbWVzdGVwcykuc2FtcGxlJTBBbG9zcyUyMCUzRCUyMEYubXNlX2xvc3Mobm9pc2VfcHJlZCUyQyUyMG5vaXNlKQ==",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch.nn.functional <span class="hljs-keyword">as</span> F | |
| <span class="hljs-meta">>>> </span>noise_pred = model(noisy_image, timesteps).sample | |
| <span class="hljs-meta">>>> </span>loss = F.mse_loss(noise_pred, noise)`}}),Al=new h({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers.optimization <span class="hljs-keyword">import</span> get_cosine_schedule_with_warmup | |
| <span class="hljs-meta">>>> </span>optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate) | |
| <span class="hljs-meta">>>> </span>lr_scheduler = get_cosine_schedule_with_warmup( | |
| <span class="hljs-meta">... </span> optimizer=optimizer, | |
| <span class="hljs-meta">... </span> num_warmup_steps=config.lr_warmup_steps, | |
| <span class="hljs-meta">... </span> num_training_steps=(<span class="hljs-built_in">len</span>(train_dataloader) * config.num_epochs), | |
| <span class="hljs-meta">... </span>)`}}),El=new h({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMPipeline | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> math | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> os | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">make_grid</span>(<span class="hljs-params">images, rows, cols</span>): | |
| <span class="hljs-meta">... </span> w, h = images[<span class="hljs-number">0</span>].size | |
| <span class="hljs-meta">... </span> grid = Image.new(<span class="hljs-string">"RGB"</span>, size=(cols * w, rows * h)) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> i, image <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images): | |
| <span class="hljs-meta">... </span> grid.paste(image, box=(i % cols * w, i // cols * h)) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> grid | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">evaluate</span>(<span class="hljs-params">config, epoch, pipeline</span>): | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uB79C\uB364\uD55C \uB178\uC774\uC988\uB85C \uBD80\uD130 \uC774\uBBF8\uC9C0\uB97C \uCD94\uCD9C\uD569\uB2C8\uB2E4.(\uC774\uB294 \uC5ED\uC804\uD30C diffusion \uACFC\uC815\uC785\uB2C8\uB2E4.)</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uAE30\uBCF8 \uD30C\uC774\uD504\uB77C\uC778 \uCD9C\uB825 \uD615\uD0DC\uB294 \`List[PIL.Image]\` \uC785\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> images = pipeline( | |
| <span class="hljs-meta">... </span> batch_size=config.eval_batch_size, | |
| <span class="hljs-meta">... </span> generator=torch.manual_seed(config.seed), | |
| <span class="hljs-meta">... </span> ).images | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uC774\uBBF8\uC9C0\uB4E4\uC744 \uADF8\uB9AC\uB4DC\uB85C \uB9CC\uB4E4\uC5B4\uC90D\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> image_grid = make_grid(images, rows=<span class="hljs-number">4</span>, cols=<span class="hljs-number">4</span>) | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uC774\uBBF8\uC9C0\uB4E4\uC744 \uC800\uC7A5\uD569\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> test_dir = os.path.join(config.output_dir, <span class="hljs-string">"samples"</span>) | |
| <span class="hljs-meta">... </span> os.makedirs(test_dir, exist_ok=<span class="hljs-literal">True</span>) | |
| <span class="hljs-meta">... </span> image_grid.save(<span class="hljs-string">f"<span class="hljs-subst">{test_dir}</span>/<span class="hljs-subst">{epoch:04d}</span>.png"</span>)`}}),Rl=new h({props:{code:"from%20accelerate%20import%20Accelerator%0Afrom%20huggingface_hub%20import%20HfFolder%2C%20Repository%2C%20whoami%0Afrom%20tqdm.auto%20import%20tqdm%0Afrom%20pathlib%20import%20Path%0Aimport%20os%0A%0A%0Adef%20get_full_repo_name(model_id%3A%20str%2C%20organization%3A%20str%20%3D%20None%2C%20token%3A%20str%20%3D%20None)%3A%0A%20%20%20%20if%20token%20is%20None%3A%0A%20%20%20%20%20%20%20%20token%20%3D%20HfFolder.get_token()%0A%20%20%20%20if%20organization%20is%20None%3A%0A%20%20%20%20%20%20%20%20username%20%3D%20whoami(token)%5B%22name%22%5D%0A%20%20%20%20%20%20%20%20return%20f%22%7Busername%7D%2F%7Bmodel_id%7D%22%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20return%20f%22%7Borganization%7D%2F%7Bmodel_id%7D%22%0A%0A%0Adef%20train_loop(config%2C%20model%2C%20noise_scheduler%2C%20optimizer%2C%20train_dataloader%2C%20lr_scheduler)%3A%0A%20%20%20%20%23%20accelerator%EC%99%80%20tensorboard%20%EB%A1%9C%EA%B9%85%20%EC%B4%88%EA%B8%B0%ED%99%94%0A%20%20%20%20accelerator%20%3D%20Accelerator(%0A%20%20%20%20%20%20%20%20mixed_precision%3Dconfig.mixed_precision%2C%0A%20%20%20%20%20%20%20%20gradient_accumulation_steps%3Dconfig.gradient_accumulation_steps%2C%0A%20%20%20%20%20%20%20%20log_with%3D%22tensorboard%22%2C%0A%20%20%20%20%20%20%20%20logging_dir%3Dos.path.join(config.output_dir%2C%20%22logs%22)%2C%0A%20%20%20%20)%0A%20%20%20%20if%20accelerator.is_main_process%3A%0A%20%20%20%20%20%20%20%20if%20config.push_to_hub%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20repo_name%20%3D%20get_full_repo_name(Path(config.output_dir).name)%0A%20%20%20%20%20%20%20%20%20%20%20%20repo%20%3D%20Repository(config.output_dir%2C%20clone_from%3Drepo_name)%0A%20%20%20%20%20%20%20%20elif%20config.output_dir%20is%20not%20None%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20os.makedirs(config.output_dir%2C%20exist_ok%3DTrue)%0A%20%20%20%20%20%20%20%20accelerator.init_trackers(%22train_example%22)%0A%0A%20%20%20%20%23%20%EB%AA%A8%EB%93%A0%20%EA%B2%83%EC%9D%B4%20%EC%A4%80%EB%B9%84%EB%90%98%EC%97%88%EC%8A%B5%EB%8B%88%EB%8B%A4.%0A%20%20%20%20%23%20%EA%B8%B0%EC%96%B5%ED%95%B4%EC%95%BC%20%ED%95%A0%20%ED%8A%B9%EC%A0%95%ED%95%9C%20%EC%88%9C%EC%84%9C%EB%8A%94%20%EC%97%86%EC%9C%BC%EB%A9%B0%20%EC%A4%80%EB%B9%84%ED%95%9C%20%EB%B0%A9%EB%B2%95%EC%97%90%20%EC%A0%9C%EA%B3%B5%ED%95%9C%20%EA%B2%83%EA%B3%BC%20%EB%8F%99%EC%9D%BC%ED%95%9C%20%EC%88%9C%EC%84%9C%EB%A1%9C%20%EA%B0%9D%EC%B2%B4%EC%9D%98%20%EC%95%95%EC%B6%95%EC%9D%84%20%ED%92%80%EB%A9%B4%20%EB%90%A9%EB%8B%88%EB%8B%A4.%0A%20%20%20%20model%2C%20optimizer%2C%20train_dataloader%2C%20lr_scheduler%20%3D%20accelerator.prepare(%0A%20%20%20%20%20%20%20%20model%2C%20optimizer%2C%20train_dataloader%2C%20lr_scheduler%0A%20%20%20%20)%0A%0A%20%20%20%20global_step%20%3D%200%0A%0A%20%20%20%20%23%20%EC%9D%B4%EC%A0%9C%20%EB%AA%A8%EB%8D%B8%EC%9D%84%20%ED%95%99%EC%8A%B5%ED%95%A9%EB%8B%88%EB%8B%A4.%0A%20%20%20%20for%20epoch%20in%20range(config.num_epochs)%3A%0A%20%20%20%20%20%20%20%20progress_bar%20%3D%20tqdm(total%3Dlen(train_dataloader)%2C%20disable%3Dnot%20accelerator.is_local_main_process)%0A%20%20%20%20%20%20%20%20progress_bar.set_description(f%22Epoch%20%7Bepoch%7D%22)%0A%0A%20%20%20%20%20%20%20%20for%20step%2C%20batch%20in%20enumerate(train_dataloader)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20clean_images%20%3D%20batch%5B%22images%22%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20%EC%9D%B4%EB%AF%B8%EC%A7%80%EC%97%90%20%EB%8D%94%ED%95%A0%20%EB%85%B8%EC%9D%B4%EC%A6%88%EB%A5%BC%20%EC%83%98%ED%94%8C%EB%A7%81%ED%95%A9%EB%8B%88%EB%8B%A4.%0A%20%20%20%20%20%20%20%20%20%20%20%20noise%20%3D%20torch.randn(clean_images.shape).to(clean_images.device)%0A%20%20%20%20%20%20%20%20%20%20%20%20bs%20%3D%20clean_images.shape%5B0%5D%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20%EA%B0%81%20%EC%9D%B4%EB%AF%B8%EC%A7%80%EB%A5%BC%20%EC%9C%84%ED%95%9C%20%EB%9E%9C%EB%8D%A4%ED%95%9C%20%ED%83%80%EC%9E%84%EC%8A%A4%ED%85%9D(timestep)%EC%9D%84%20%EC%83%98%ED%94%8C%EB%A7%81%ED%95%A9%EB%8B%88%EB%8B%A4.%0A%20%20%20%20%20%20%20%20%20%20%20%20timesteps%20%3D%20torch.randint(%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%200%2C%20noise_scheduler.config.num_train_timesteps%2C%20(bs%2C)%2C%20device%3Dclean_images.device%0A%20%20%20%20%20%20%20%20%20%20%20%20).long()%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20%EA%B0%81%20%ED%83%80%EC%9E%84%EC%8A%A4%ED%85%9D%EC%9D%98%20%EB%85%B8%EC%9D%B4%EC%A6%88%20%ED%81%AC%EA%B8%B0%EC%97%90%20%EB%94%B0%EB%9D%BC%20%EA%B9%A8%EB%81%97%ED%95%9C%20%EC%9D%B4%EB%AF%B8%EC%A7%80%EC%97%90%20%EB%85%B8%EC%9D%B4%EC%A6%88%EB%A5%BC%20%EC%B6%94%EA%B0%80%ED%95%A9%EB%8B%88%EB%8B%A4.%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20(%EC%9D%B4%EB%8A%94%20foward%20diffusion%20%EA%B3%BC%EC%A0%95%EC%9E%85%EB%8B%88%EB%8B%A4.)%0A%20%20%20%20%20%20%20%20%20%20%20%20noisy_images%20%3D%20noise_scheduler.add_noise(clean_images%2C%20noise%2C%20timesteps)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20with%20accelerator.accumulate(model)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20%EB%85%B8%EC%9D%B4%EC%A6%88%EB%A5%BC%20%EB%B0%98%EB%B3%B5%EC%A0%81%EC%9C%BC%EB%A1%9C%20%EC%98%88%EC%B8%A1%ED%95%A9%EB%8B%88%EB%8B%A4.%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20noise_pred%20%3D%20model(noisy_images%2C%20timesteps%2C%20return_dict%3DFalse)%5B0%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20loss%20%3D%20F.mse_loss(noise_pred%2C%20noise)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20accelerator.backward(loss)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20accelerator.clip_grad_norm_(model.parameters()%2C%201.0)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20optimizer.step()%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20lr_scheduler.step()%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20optimizer.zero_grad()%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20progress_bar.update(1)%0A%20%20%20%20%20%20%20%20%20%20%20%20logs%20%3D%20%7B%22loss%22%3A%20loss.detach().item()%2C%20%22lr%22%3A%20lr_scheduler.get_last_lr()%5B0%5D%2C%20%22step%22%3A%20global_step%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20progress_bar.set_postfix(**logs)%0A%20%20%20%20%20%20%20%20%20%20%20%20accelerator.log(logs%2C%20step%3Dglobal_step)%0A%20%20%20%20%20%20%20%20%20%20%20%20global_step%20%2B%3D%201%0A%0A%20%20%20%20%20%20%20%20%23%20%EA%B0%81%20%EC%97%90%ED%8F%AC%ED%81%AC%EA%B0%80%20%EB%81%9D%EB%82%9C%20%ED%9B%84%20evaluate()%EC%99%80%20%EB%AA%87%20%EA%B0%80%EC%A7%80%20%EB%8D%B0%EB%AA%A8%20%EC%9D%B4%EB%AF%B8%EC%A7%80%EB%A5%BC%20%EC%84%A0%ED%83%9D%EC%A0%81%EC%9C%BC%EB%A1%9C%20%EC%83%98%ED%94%8C%EB%A7%81%ED%95%98%EA%B3%A0%20%EB%AA%A8%EB%8D%B8%EC%9D%84%20%EC%A0%80%EC%9E%A5%ED%95%A9%EB%8B%88%EB%8B%A4.%0A%20%20%20%20%20%20%20%20if%20accelerator.is_main_process%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20pipeline%20%3D%20DDPMPipeline(unet%3Daccelerator.unwrap_model(model)%2C%20scheduler%3Dnoise_scheduler)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20(epoch%20%2B%201)%20%25%20config.save_image_epochs%20%3D%3D%200%20or%20epoch%20%3D%3D%20config.num_epochs%20-%201%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20evaluate(config%2C%20epoch%2C%20pipeline)%0A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20(epoch%20%2B%201)%20%25%20config.save_model_epochs%20%3D%3D%200%20or%20epoch%20%3D%3D%20config.num_epochs%20-%201%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20config.push_to_hub%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20repo.push_to_hub(commit_message%3Df%22Epoch%20%7Bepoch%7D%22%2C%20blocking%3DTrue)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20pipeline.save_pretrained(config.output_dir)",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> Accelerator | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> HfFolder, Repository, whoami | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> os | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">get_full_repo_name</span>(<span class="hljs-params">model_id: <span class="hljs-built_in">str</span>, organization: <span class="hljs-built_in">str</span> = <span class="hljs-literal">None</span>, token: <span class="hljs-built_in">str</span> = <span class="hljs-literal">None</span></span>): | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> token <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>: | |
| <span class="hljs-meta">... </span> token = HfFolder.get_token() | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> organization <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>: | |
| <span class="hljs-meta">... </span> username = whoami(token)[<span class="hljs-string">"name"</span>] | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> <span class="hljs-string">f"<span class="hljs-subst">{username}</span>/<span class="hljs-subst">{model_id}</span>"</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>: | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> <span class="hljs-string">f"<span class="hljs-subst">{organization}</span>/<span class="hljs-subst">{model_id}</span>"</span> | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">train_loop</span>(<span class="hljs-params">config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler</span>): | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># accelerator\uC640 tensorboard \uB85C\uAE45 \uCD08\uAE30\uD654</span> | |
| <span class="hljs-meta">... </span> accelerator = Accelerator( | |
| <span class="hljs-meta">... </span> mixed_precision=config.mixed_precision, | |
| <span class="hljs-meta">... </span> gradient_accumulation_steps=config.gradient_accumulation_steps, | |
| <span class="hljs-meta">... </span> log_with=<span class="hljs-string">"tensorboard"</span>, | |
| <span class="hljs-meta">... </span> logging_dir=os.path.join(config.output_dir, <span class="hljs-string">"logs"</span>), | |
| <span class="hljs-meta">... </span> ) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> accelerator.is_main_process: | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> config.push_to_hub: | |
| <span class="hljs-meta">... </span> repo_name = get_full_repo_name(Path(config.output_dir).name) | |
| <span class="hljs-meta">... </span> repo = Repository(config.output_dir, clone_from=repo_name) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">elif</span> config.output_dir <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">None</span>: | |
| <span class="hljs-meta">... </span> os.makedirs(config.output_dir, exist_ok=<span class="hljs-literal">True</span>) | |
| <span class="hljs-meta">... </span> accelerator.init_trackers(<span class="hljs-string">"train_example"</span>) | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uBAA8\uB4E0 \uAC83\uC774 \uC900\uBE44\uB418\uC5C8\uC2B5\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uAE30\uC5B5\uD574\uC57C \uD560 \uD2B9\uC815\uD55C \uC21C\uC11C\uB294 \uC5C6\uC73C\uBA70 \uC900\uBE44\uD55C \uBC29\uBC95\uC5D0 \uC81C\uACF5\uD55C \uAC83\uACFC \uB3D9\uC77C\uD55C \uC21C\uC11C\uB85C \uAC1D\uCCB4\uC758 \uC555\uCD95\uC744 \uD480\uBA74 \uB429\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
| <span class="hljs-meta">... </span> model, optimizer, train_dataloader, lr_scheduler | |
| <span class="hljs-meta">... </span> ) | |
| <span class="hljs-meta">... </span> global_step = <span class="hljs-number">0</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uC774\uC81C \uBAA8\uB378\uC744 \uD559\uC2B5\uD569\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(config.num_epochs): | |
| <span class="hljs-meta">... </span> progress_bar = tqdm(total=<span class="hljs-built_in">len</span>(train_dataloader), disable=<span class="hljs-keyword">not</span> accelerator.is_local_main_process) | |
| <span class="hljs-meta">... </span> progress_bar.set_description(<span class="hljs-string">f"Epoch <span class="hljs-subst">{epoch}</span>"</span>) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> step, batch <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(train_dataloader): | |
| <span class="hljs-meta">... </span> clean_images = batch[<span class="hljs-string">"images"</span>] | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uC774\uBBF8\uC9C0\uC5D0 \uB354\uD560 \uB178\uC774\uC988\uB97C \uC0D8\uD50C\uB9C1\uD569\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> noise = torch.randn(clean_images.shape).to(clean_images.device) | |
| <span class="hljs-meta">... </span> bs = clean_images.shape[<span class="hljs-number">0</span>] | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uAC01 \uC774\uBBF8\uC9C0\uB97C \uC704\uD55C \uB79C\uB364\uD55C \uD0C0\uC784\uC2A4\uD15D(timestep)\uC744 \uC0D8\uD50C\uB9C1\uD569\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> timesteps = torch.randint( | |
| <span class="hljs-meta">... </span> <span class="hljs-number">0</span>, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device | |
| <span class="hljs-meta">... </span> ).long() | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uAC01 \uD0C0\uC784\uC2A4\uD15D\uC758 \uB178\uC774\uC988 \uD06C\uAE30\uC5D0 \uB530\uB77C \uAE68\uB057\uD55C \uC774\uBBF8\uC9C0\uC5D0 \uB178\uC774\uC988\uB97C \uCD94\uAC00\uD569\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># (\uC774\uB294 foward diffusion \uACFC\uC815\uC785\uB2C8\uB2E4.)</span> | |
| <span class="hljs-meta">... </span> noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> accelerator.accumulate(model): | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uB178\uC774\uC988\uB97C \uBC18\uBCF5\uC801\uC73C\uB85C \uC608\uCE21\uD569\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> noise_pred = model(noisy_images, timesteps, return_dict=<span class="hljs-literal">False</span>)[<span class="hljs-number">0</span>] | |
| <span class="hljs-meta">... </span> loss = F.mse_loss(noise_pred, noise) | |
| <span class="hljs-meta">... </span> accelerator.backward(loss) | |
| <span class="hljs-meta">... </span> accelerator.clip_grad_norm_(model.parameters(), <span class="hljs-number">1.0</span>) | |
| <span class="hljs-meta">... </span> optimizer.step() | |
| <span class="hljs-meta">... </span> lr_scheduler.step() | |
| <span class="hljs-meta">... </span> optimizer.zero_grad() | |
| <span class="hljs-meta">... </span> progress_bar.update(<span class="hljs-number">1</span>) | |
| <span class="hljs-meta">... </span> logs = {<span class="hljs-string">"loss"</span>: loss.detach().item(), <span class="hljs-string">"lr"</span>: lr_scheduler.get_last_lr()[<span class="hljs-number">0</span>], <span class="hljs-string">"step"</span>: global_step} | |
| <span class="hljs-meta">... </span> progress_bar.set_postfix(**logs) | |
| <span class="hljs-meta">... </span> accelerator.log(logs, step=global_step) | |
| <span class="hljs-meta">... </span> global_step += <span class="hljs-number">1</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-comment"># \uAC01 \uC5D0\uD3EC\uD06C\uAC00 \uB05D\uB09C \uD6C4 evaluate()\uC640 \uBA87 \uAC00\uC9C0 \uB370\uBAA8 \uC774\uBBF8\uC9C0\uB97C \uC120\uD0DD\uC801\uC73C\uB85C \uC0D8\uD50C\uB9C1\uD558\uACE0 \uBAA8\uB378\uC744 \uC800\uC7A5\uD569\uB2C8\uB2E4.</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> accelerator.is_main_process: | |
| <span class="hljs-meta">... </span> pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> (epoch + <span class="hljs-number">1</span>) % config.save_image_epochs == <span class="hljs-number">0</span> <span class="hljs-keyword">or</span> epoch == config.num_epochs - <span class="hljs-number">1</span>: | |
| <span class="hljs-meta">... </span> evaluate(config, epoch, pipeline) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> (epoch + <span class="hljs-number">1</span>) % config.save_model_epochs == <span class="hljs-number">0</span> <span class="hljs-keyword">or</span> epoch == config.num_epochs - <span class="hljs-number">1</span>: | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> config.push_to_hub: | |
| <span class="hljs-meta">... </span> repo.push_to_hub(commit_message=<span class="hljs-string">f"Epoch <span class="hljs-subst">{epoch}</span>"</span>, blocking=<span class="hljs-literal">True</span>) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>: | |
| <span class="hljs-meta">... </span> pipeline.save_pretrained(config.output_dir)`}}),Bl=new h({props:{code:"ZnJvbSUyMGFjY2VsZXJhdGUlMjBpbXBvcnQlMjBub3RlYm9va19sYXVuY2hlciUwQSUwQWFyZ3MlMjAlM0QlMjAoY29uZmlnJTJDJTIwbW9kZWwlMkMlMjBub2lzZV9zY2hlZHVsZXIlMkMlMjBvcHRpbWl6ZXIlMkMlMjB0cmFpbl9kYXRhbG9hZGVyJTJDJTIwbHJfc2NoZWR1bGVyKSUwQSUwQW5vdGVib29rX2xhdW5jaGVyKHRyYWluX2xvb3AlMkMlMjBhcmdzJTJDJTIwbnVtX3Byb2Nlc3NlcyUzRDEp",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> notebook_launcher | |
| <span class="hljs-meta">>>> </span>args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler) | |
| <span class="hljs-meta">>>> </span>notebook_launcher(train_loop, args, num_processes=<span class="hljs-number">1</span>)`}}),_l=new h({props:{code:"aW1wb3J0JTIwZ2xvYiUwQSUwQXNhbXBsZV9pbWFnZXMlMjAlM0QlMjBzb3J0ZWQoZ2xvYi5nbG9iKGYlMjIlN0Jjb25maWcub3V0cHV0X2RpciU3RCUyRnNhbXBsZXMlMkYqLnBuZyUyMikpJTBBSW1hZ2Uub3BlbihzYW1wbGVfaW1hZ2VzJTVCLTElNUQp",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> glob | |
| <span class="hljs-meta">>>> </span>sample_images = <span class="hljs-built_in">sorted</span>(glob.glob(<span class="hljs-string">f"<span class="hljs-subst">{config.output_dir}</span>/samples/*.png"</span>)) | |
| <span class="hljs-meta">>>> </span>Image.<span class="hljs-built_in">open</span>(sample_images[-<span class="hljs-number">1</span>])`}}),{c(){C=n("meta"),N=y(),o(I.$$.fragment),F=y(),b=n("h1"),f=n("a"),V=n("span"),o(g.$$.fragment),te=y(),os=n("span"),Me=t("Diffusion \uBAA8\uB378\uC744 \uD559\uC2B5\uD558\uAE30"),Ss=y(),D=n("p"),Ue=t("Unconditional \uC774\uBBF8\uC9C0 \uC0DD\uC131\uC740 \uD559\uC2B5\uC5D0 \uC0AC\uC6A9\uB41C \uB370\uC774\uD130\uC14B\uACFC \uC720\uC0AC\uD55C \uC774\uBBF8\uC9C0\uB97C \uC0DD\uC131\uD558\uB294 diffusion \uBAA8\uB378\uC5D0\uC11C \uC778\uAE30 \uC788\uB294 \uC5B4\uD50C\uB9AC\uCF00\uC774\uC158\uC785\uB2C8\uB2E4. \uC77C\uBC18\uC801\uC73C\uB85C, \uAC00\uC7A5 \uC88B\uC740 \uACB0\uACFC\uB294 \uD2B9\uC815 \uB370\uC774\uD130\uC14B\uC5D0 \uC0AC\uC804 \uD6C8\uB828\uB41C \uBAA8\uB378\uC744 \uD30C\uC778\uD29C\uB2DD\uD558\uB294 \uAC83\uC73C\uB85C \uC5BB\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC774 "),P=n("a"),ne=t("\uD5C8\uBE0C"),pe=t("\uC5D0\uC11C \uC774\uB7EC\uD55C \uB9CE\uC740 \uCCB4\uD06C\uD3EC\uC778\uD2B8\uB97C \uCC3E\uC744 \uC218 \uC788\uC9C0\uB9CC, \uB9CC\uC57D \uB9C8\uC74C\uC5D0 \uB4DC\uB294 \uCCB4\uD06C\uD3EC\uC778\uD2B8\uB97C \uCC3E\uC9C0 \uBABB\uD588\uB2E4\uBA74, \uC5B8\uC81C\uB4E0\uC9C0 \uC2A4\uC2A4\uB85C \uD559\uC2B5\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4!"),Ws=y(),A=n("p"),Je=t("\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC740 \uB098\uB9CC\uC758 \u{1F98B} \uB098\uBE44 \u{1F98B}\uB97C \uC0DD\uC131\uD558\uAE30 \uC704\uD574 "),ll=n("a"),ye=t("Smithsonian Butterflies"),je=t(" \uB370\uC774\uD130\uC14B\uC758 \uD558\uC704 \uC9D1\uD569\uC5D0\uC11C "),is=n("code"),ce=t("UNet2DModel"),oe=t(" \uBAA8\uB378\uC744 \uD559\uC2B5\uD558\uB294 \uBC29\uBC95\uC744 \uAC00\uB974\uCCD0\uC904 \uAC83\uC785\uB2C8\uB2E4."),zs=y(),o(X.$$.fragment),Ys=y(),E=n("p"),ie=t("\uC2DC\uC791 \uC804\uC5D0, \u{1F917} Datasets\uC744 \uBD88\uB7EC\uC624\uACE0 \uC804\uCC98\uB9AC\uD558\uAE30 \uC704\uD574 \uB370\uC774\uD130\uC14B\uC774 \uC124\uCE58\uB418\uC5B4 \uC788\uB294\uC9C0 \uB2E4\uC218 GPU\uC5D0\uC11C \uD559\uC2B5\uC744 \uAC04\uC18C\uD654\uD558\uAE30 \uC704\uD574 \u{1F917} Accelerate \uAC00 \uC124\uCE58\uB418\uC5B4 \uC788\uB294\uC9C0 \uD655\uC778\uD558\uC138\uC694. \uADF8 \uD6C4 \uD559\uC2B5 \uBA54\uD2B8\uB9AD\uC744 \uC2DC\uAC01\uD654\uD558\uAE30 \uC704\uD574 "),sl=n("a"),Te=t("TensorBoard"),me=t("\uB97C \uB610\uD55C \uC124\uCE58\uD558\uC138\uC694. 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