import{S as Lo,i as Qo,s as Do,e as r,k as d,w as b,t as l,M as Po,c as s,d as a,m as w,a as n,x as T,h as o,b as c,G as t,g as h,y as J,q as U,o as v,B as $,v as Ho,L as Da}from"../../chunks/vendor-hf-doc-builder.js";import{T as Vo}from"../../chunks/Tip-hf-doc-builder.js";import{I as et}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as ve}from"../../chunks/CodeBlock-hf-doc-builder.js";import{F as Yo,M as Pa}from"../../chunks/Markdown-hf-doc-builder.js";function zo(X){let p,_;return{c(){p=r("p"),_=l("The text-to-image fine-tuning script is experimental. It\u2019s easy to overfit and run into issues like catastrophic forgetting. We recommend you explore different hyperparameters to get the best results on your dataset.")},l(i){p=s(i,"P",{});var g=n(p);_=o(g,"The text-to-image fine-tuning script is experimental. It\u2019s easy to overfit and run into issues like catastrophic forgetting. We recommend you explore different hyperparameters to get the best results on your dataset."),g.forEach(a)},m(i,g){h(i,p,g),t(p,_)},d(i){i&&a(p)}}}function qo(X){let p,_,i,g,W,Z,ue,ge,ye,R,D,N,V,Me,F,re,se,be,O,ne,Te,j,me,C,$e,ie,P,B,Ee,E,Re,pe,Je,I,K,ee,Ie,te,A,ae,de,x,he,le,H,z,k,S,we,u,y,Ue,Y,oe,je,Ce,_e,fe,Ae,L,Ne,q,xe,G,Xe;return ne=new ve({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
<span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">&quot;lambdalabs/pokemon-blip-captions&quot;</span>

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-variable">$MODEL_NAME</span> \\
  --dataset_name=<span class="hljs-variable">$dataset_name</span> \\
  --use_ema \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --gradient_accumulation_steps=4 \\
  --gradient_checkpointing \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --lr_scheduler=<span class="hljs-string">&quot;constant&quot;</span> --lr_warmup_steps=0 \\
  --output_dir=<span class="hljs-string">&quot;sd-pokemon-model&quot;</span> \\
  --push_to_hub`}}),he=new ve({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
<span class="hljs-built_in">export</span> TRAIN_DIR=<span class="hljs-string">&quot;path_to_your_dataset&quot;</span>
<span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">&quot;path_to_save_model&quot;</span>

accelerate launch train_text_to_image.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --train_data_dir=<span class="hljs-variable">$TRAIN_DIR</span> \\
  --use_ema \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --gradient_accumulation_steps=4 \\
  --gradient_checkpointing \\
  --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --lr_scheduler=<span class="hljs-string">&quot;constant&quot;</span> 
  --lr_warmup_steps=0 \\
  --output_dir=<span class="hljs-variable">\${OUTPUT_DIR}</span> \\
  --push_to_hub`}}),S=new et({}),G=new ve({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
<span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">&quot;lambdalabs/pokemon-blip-captions&quot;</span>

accelerate launch --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> --multi_gpu  train_text_to_image.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --dataset_name=<span class="hljs-variable">$dataset_name</span> \\
  --use_ema \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --gradient_accumulation_steps=4 \\
  --gradient_checkpointing \\
  --max_train_steps=15000 \\ 
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --lr_scheduler=<span class="hljs-string">&quot;constant&quot;</span> \\
  --lr_warmup_steps=0 \\
  --output_dir=<span class="hljs-string">&quot;sd-pokemon-model&quot;</span> \\
  --push_to_hub`}}),{c(){p=r("p"),_=l("Launch the "),i=r("a"),g=l("PyTorch training script"),W=l(" for a fine-tuning run on the "),Z=r("a"),ue=l("Pok\xE9mon BLIP captions"),ge=l(" dataset like this."),ye=d(),R=r("p"),D=l("Specify the "),N=r("code"),V=l("MODEL_NAME"),Me=l(" environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the "),F=r("a"),re=r("code"),se=l("pretrained_model_name_or_path"),be=l(" argument."),O=d(),b(ne.$$.fragment),Te=d(),j=r("p"),me=l("To finetune on your own dataset, prepare the dataset according to the format required by \u{1F917} "),C=r("a"),$e=l("Datasets"),ie=l(". You can "),P=r("a"),B=l("upload your dataset to the Hub"),Ee=l(", or you can "),E=r("a"),Re=l("prepare a local folder with your files"),pe=l("."),Je=d(),I=r("p"),K=l("Modify the script if you want to use custom loading logic. We left pointers in the code in the appropriate places to help you. \u{1F917} The example script below shows how to finetune on a local dataset in "),ee=r("code"),Ie=l("TRAIN_DIR"),te=l(" and where to save the model to in "),A=r("code"),ae=l("OUTPUT_DIR"),de=l(":"),x=d(),b(he.$$.fragment),le=d(),H=r("h4"),z=r("a"),k=r("span"),b(S.$$.fragment),we=d(),u=r("span"),y=l("Training with multiple GPUs"),Ue=d(),Y=r("p"),oe=r("code"),je=l("accelerate"),Ce=l(" allows for seamless multi-GPU training. Follow the instructions "),_e=r("a"),fe=l("here"),Ae=l(`
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for running distributed training with `),L=s(ce,"CODE",{});var Fe=n(L);Ne=o(Fe,"accelerate"),Fe.forEach(a),q=o(ce,". Here is an example command:"),ce.forEach(a),xe=w(m),T(G.$$.fragment,m),this.h()},h(){c(i,"href","https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py"),c(i,"rel","nofollow"),c(Z,"href","https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions"),c(Z,"rel","nofollow"),c(F,"href","https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path"),c(F,"rel","nofollow"),c(C,"href","https://huggingface.co/docs/datasets/index"),c(C,"rel","nofollow"),c(P,"href","https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub"),c(P,"rel","nofollow"),c(E,"href","https://huggingface.co/docs/datasets/image_dataset#imagefolder"),c(E,"rel","nofollow"),c(z,"id","training-with-multiple-gpus"),c(z,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(z,"href","#training-with-multiple-gpus"),c(H,"class","relative group"),c(_e,"href","https://huggingface.co/docs/accelerate/basic_tutorials/launch"),c(_e,"rel","nofollow")},m(m,M){h(m,p,M),t(p,_),t(p,i),t(i,g),t(p,W),t(p,Z),t(Z,ue),t(p,ge),h(m,ye,M),h(m,R,M),t(R,D),t(R,N),t(N,V),t(R,Me),t(R,F),t(F,re),t(re,se),t(R,be),h(m,O,M),J(ne,m,M),h(m,Te,M),h(m,j,M),t(j,me),t(j,C),t(C,$e),t(j,ie),t(j,P),t(P,B),t(j,Ee),t(j,E),t(E,Re),t(j,pe),h(m,Je,M),h(m,I,M),t(I,K),t(I,ee),t(ee,Ie),t(I,te),t(I,A),t(A,ae),t(I,de),h(m,x,M),J(he,m,M),h(m,le,M),h(m,H,M),t(H,z),t(z,k),J(S,k,null),t(H,we),t(H,u),t(u,y),h(m,Ue,M),h(m,Y,M),t(Y,oe),t(oe,je),t(Y,Ce),t(Y,_e),t(_e,fe),t(Y,Ae),t(Y,L),t(L,Ne),t(Y,q),h(m,xe,M),J(G,m,M),Xe=!0},p:Da,i(m){Xe||(U(ne.$$.fragment,m),U(he.$$.fragment,m),U(S.$$.fragment,m),U(G.$$.fragment,m),Xe=!0)},o(m){v(ne.$$.fragment,m),v(he.$$.fragment,m),v(S.$$.fragment,m),v(G.$$.fragment,m),Xe=!1},d(m){m&&a(p),m&&a(ye),m&&a(R),m&&a(O),$(ne,m),m&&a(Te),m&&a(j),m&&a(Je),m&&a(I),m&&a(x),$(he,m),m&&a(le),m&&a(H),$(S),m&&a(Ue),m&&a(Y),m&&a(xe),$(G,m)}}}function Oo(X){let p,_;return p=new Pa({props:{$$slots:{default:[qo]},$$scope:{ctx:X}}}),{c(){b(p.$$.fragment)},l(i){T(p.$$.fragment,i)},m(i,g){J(p,i,g),_=!0},p(i,g){const W={};g&2&&(W.$$scope={dirty:g,ctx:i}),p.$set(W)},i(i){_||(U(p.$$.fragment,i),_=!0)},o(i){v(p.$$.fragment,i),_=!1},d(i){$(p,i)}}}function Ko(X){let p,_,i,g,W,Z,ue,ge,ye,R,D,N,V,Me,F,re,se,be,O,ne,Te,j,me,C,$e,ie,P,B,Ee,E,Re,pe,Je,I,K,ee,Ie,te,A,ae,de,x,he,le,H,z,k,S,we;return R=new ve({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1VJTIwLXIlMjByZXF1aXJlbWVudHNfZmxheC50eHQ=",highlighted:"pip install -U -r requirements_flax.txt"}}),B=new ve({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
<span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">&quot;lambdalabs/pokemon-blip-captions&quot;</span>

python train_text_to_image_flax.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --dataset_name=<span class="hljs-variable">$dataset_name</span> \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --output_dir=<span class="hljs-string">&quot;sd-pokemon-model&quot;</span> \\
  --push_to_hub`}}),S=new ve({props:{code:"ZXhwb3J0JTIwTU9ERUxfTkFNRSUzRCUyMmR1b25nbmElMkZzdGFibGUtZGlmZnVzaW9uLXYxLTQtZmxheCUyMiUwQWV4cG9ydCUyMFRSQUlOX0RJUiUzRCUyMnBhdGhfdG9feW91cl9kYXRhc2V0JTIyJTBBJTBBcHl0aG9uJTIwdHJhaW5fdGV4dF90b19pbWFnZV9mbGF4LnB5JTIwJTVDJTBBJTIwJTIwLS1wcmV0cmFpbmVkX21vZGVsX25hbWVfb3JfcGF0aCUzRCUyNE1PREVMX05BTUUlMjAlNUMlMEElMjAlMjAtLXRyYWluX2RhdGFfZGlyJTNEJTI0VFJBSU5fRElSJTIwJTVDJTBBJTIwJTIwLS1yZXNvbHV0aW9uJTNENTEyJTIwLS1jZW50ZXJfY3JvcCUyMC0tcmFuZG9tX2ZsaXAlMjAlNUMlMEElMjAlMjAtLXRyYWluX2JhdGNoX3NpemUlM0QxJTIwJTVDJTBBJTIwJTIwLS1taXhlZF9wcmVjaXNpb24lM0QlMjJmcDE2JTIyJTIwJTVDJTBBJTIwJTIwLS1tYXhfdHJhaW5fc3RlcHMlM0QxNTAwMCUyMCU1QyUwQSUyMCUyMC0tbGVhcm5pbmdfcmF0ZSUzRDFlLTA1JTIwJTVDJTBBJTIwJTIwLS1tYXhfZ3JhZF9ub3JtJTNEMSUyMCU1QyUwQSUyMCUyMC0tb3V0cHV0X2RpciUzRCUyMnNkLXBva2Vtb24tbW9kZWwlMjIlMjAlNUMlMEElMjAlMjAtLXB1c2hfdG9faHVi",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;duongna/stable-diffusion-v1-4-flax&quot;</span>
<span class="hljs-built_in">export</span> TRAIN_DIR=<span class="hljs-string">&quot;path_to_your_dataset&quot;</span>

python train_text_to_image_flax.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --train_data_dir=<span class="hljs-variable">$TRAIN_DIR</span> \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --output_dir=<span class="hljs-string">&quot;sd-pokemon-model&quot;</span> \\
  --push_to_hub`}}),{c(){p=r("p"),_=l("With Flax, it\u2019s possible to train a Stable Diffusion model faster on TPUs and GPUs thanks to "),i=r("a"),g=l("@duongna211"),W=l(". This is very efficient on TPU hardware but works great on GPUs too. The Flax training script doesn\u2019t support features like gradient checkpointing or gradient accumulation yet, so you\u2019ll need a GPU with at least 30GB of memory or a TPU v3."),Z=d(),ue=r("p"),ge=l("Before running the script, make sure you have the requirements installed:"),ye=d(),b(R.$$.fragment),D=d(),N=r("p"),V=l("Specify the "),Me=r("code"),F=l("MODEL_NAME"),re=l(" environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the "),se=r("a"),be=r("code"),O=l("pretrained_model_name_or_path"),ne=l(" argument."),Te=d(),j=r("p"),me=l("Now you can launch the "),C=r("a"),$e=l("Flax training script"),ie=l(" like this:"),P=d(),b(B.$$.fragment),Ee=d(),E=r("p"),Re=l("To finetune on your own dataset, prepare the dataset according to the format required by \u{1F917} "),pe=r("a"),Je=l("Datasets"),I=l(". You can "),K=r("a"),ee=l("upload your dataset to the Hub"),Ie=l(", or you can "),te=r("a"),A=l("prepare a local folder with your files"),ae=l("."),de=d(),x=r("p"),he=l("Modify the script if you want to use custom loading logic. We left pointers in the code in the appropriate places to help you. \u{1F917} The example script below shows how to finetune on a local dataset in "),le=r("code"),H=l("TRAIN_DIR"),z=l(":"),k=d(),b(S.$$.fragment),this.h()},l(u){p=s(u,"P",{});var y=n(p);_=o(y,"With Flax, it\u2019s possible to train a Stable Diffusion model faster on TPUs and GPUs thanks to "),i=s(y,"A",{href:!0,rel:!0});var Ue=n(i);g=o(Ue,"@duongna211"),Ue.forEach(a),W=o(y,". This is very efficient on TPU hardware but works great on GPUs too. The Flax training script doesn\u2019t support features like gradient checkpointing or gradient accumulation yet, so you\u2019ll need a GPU with at least 30GB of memory or a TPU v3."),y.forEach(a),Z=w(u),ue=s(u,"P",{});var Y=n(ue);ge=o(Y,"Before running the script, make sure you have the requirements installed:"),Y.forEach(a),ye=w(u),T(R.$$.fragment,u),D=w(u),N=s(u,"P",{});var oe=n(N);V=o(oe,"Specify the "),Me=s(oe,"CODE",{});var je=n(Me);F=o(je,"MODEL_NAME"),je.forEach(a),re=o(oe," environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the "),se=s(oe,"A",{href:!0,rel:!0});var Ce=n(se);be=s(Ce,"CODE",{});var _e=n(be);O=o(_e,"pretrained_model_name_or_path"),_e.forEach(a),Ce.forEach(a),ne=o(oe," argument."),oe.forEach(a),Te=w(u),j=s(u,"P",{});var fe=n(j);me=o(fe,"Now you can launch the "),C=s(fe,"A",{href:!0,rel:!0});var Ae=n(C);$e=o(Ae,"Flax training script"),Ae.forEach(a),ie=o(fe," like this:"),fe.forEach(a),P=w(u),T(B.$$.fragment,u),Ee=w(u),E=s(u,"P",{});var L=n(E);Re=o(L,"To finetune on your own dataset, prepare the dataset according to the format required by \u{1F917} "),pe=s(L,"A",{href:!0,rel:!0});var Ne=n(pe);Je=o(Ne,"Datasets"),Ne.forEach(a),I=o(L,". You can "),K=s(L,"A",{href:!0,rel:!0});var q=n(K);ee=o(q,"upload your dataset to the Hub"),q.forEach(a),Ie=o(L,", or you can "),te=s(L,"A",{href:!0,rel:!0});var xe=n(te);A=o(xe,"prepare a local folder with your files"),xe.forEach(a),ae=o(L,"."),L.forEach(a),de=w(u),x=s(u,"P",{});var G=n(x);he=o(G,"Modify the script if you want to use custom loading logic. We left pointers in the code in the appropriate places to help you. \u{1F917} The example script below shows how to finetune on a local dataset in "),le=s(G,"CODE",{});var Xe=n(le);H=o(Xe,"TRAIN_DIR"),Xe.forEach(a),z=o(G,":"),G.forEach(a),k=w(u),T(S.$$.fragment,u),this.h()},h(){c(i,"href","https://github.com/duongna21"),c(i,"rel","nofollow"),c(se,"href","https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path"),c(se,"rel","nofollow"),c(C,"href","https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py"),c(C,"rel","nofollow"),c(pe,"href","https://huggingface.co/docs/datasets/index"),c(pe,"rel","nofollow"),c(K,"href","https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub"),c(K,"rel","nofollow"),c(te,"href","https://huggingface.co/docs/datasets/image_dataset#imagefolder"),c(te,"rel","nofollow")},m(u,y){h(u,p,y),t(p,_),t(p,i),t(i,g),t(p,W),h(u,Z,y),h(u,ue,y),t(ue,ge),h(u,ye,y),J(R,u,y),h(u,D,y),h(u,N,y),t(N,V),t(N,Me),t(Me,F),t(N,re),t(N,se),t(se,be),t(be,O),t(N,ne),h(u,Te,y),h(u,j,y),t(j,me),t(j,C),t(C,$e),t(j,ie),h(u,P,y),J(B,u,y),h(u,Ee,y),h(u,E,y),t(E,Re),t(E,pe),t(pe,Je),t(E,I),t(E,K),t(K,ee),t(E,Ie),t(E,te),t(te,A),t(E,ae),h(u,de,y),h(u,x,y),t(x,he),t(x,le),t(le,H),t(x,z),h(u,k,y),J(S,u,y),we=!0},p:Da,i(u){we||(U(R.$$.fragment,u),U(B.$$.fragment,u),U(S.$$.fragment,u),we=!0)},o(u){v(R.$$.fragment,u),v(B.$$.fragment,u),v(S.$$.fragment,u),we=!1},d(u){u&&a(p),u&&a(Z),u&&a(ue),u&&a(ye),$(R,u),u&&a(D),u&&a(N),u&&a(Te),u&&a(j),u&&a(P),$(B,u),u&&a(Ee),u&&a(E),u&&a(de),u&&a(x),u&&a(k),$(S,u)}}}function er(X){let p,_;return p=new Pa({props:{$$slots:{default:[Ko]},$$scope:{ctx:X}}}),{c(){b(p.$$.fragment)},l(i){T(p.$$.fragment,i)},m(i,g){J(p,i,g),_=!0},p(i,g){const W={};g&2&&(W.$$scope={dirty:g,ctx:i}),p.$set(W)},i(i){_||(U(p.$$.fragment,i),_=!0)},o(i){v(p.$$.fragment,i),_=!1},d(i){$(p,i)}}}function tr(X){let p,_;return{c(){p=r("p"),_=l("Training with Min-SNR weighting strategy is only supported in PyTorch.")},l(i){p=s(i,"P",{});var g=n(p);_=o(g,"Training with Min-SNR weighting strategy is only supported in PyTorch."),g.forEach(a)},m(i,g){h(i,p,g),t(p,_)},d(i){i&&a(p)}}}function ar(X){let p,_;return p=new ve({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBJTBBbW9kZWxfcGF0aCUyMCUzRCUyMCUyMnBhdGhfdG9fc2F2ZWRfbW9kZWwlMjIlMEFwaXBlJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKG1vZGVsX3BhdGglMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpJTBBcGlwZS50byglMjJjdWRhJTIyKSUwQSUwQWltYWdlJTIwJTNEJTIwcGlwZShwcm9tcHQlM0QlMjJ5b2RhJTIyKS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2Uuc2F2ZSglMjJ5b2RhLXBva2Vtb24ucG5nJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

model_path = <span class="hljs-string">&quot;path_to_saved_model&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

image = pipe(prompt=<span class="hljs-string">&quot;yoda&quot;</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;yoda-pokemon.png&quot;</span>)`}}),{c(){b(p.$$.fragment)},l(i){T(p.$$.fragment,i)},m(i,g){J(p,i,g),_=!0},p:Da,i(i){_||(U(p.$$.fragment,i),_=!0)},o(i){v(p.$$.fragment,i),_=!1},d(i){$(p,i)}}}function lr(X){let p,_;return p=new Pa({props:{$$slots:{default:[ar]},$$scope:{ctx:X}}}),{c(){b(p.$$.fragment)},l(i){T(p.$$.fragment,i)},m(i,g){J(p,i,g),_=!0},p(i,g){const W={};g&2&&(W.$$scope={dirty:g,ctx:i}),p.$set(W)},i(i){_||(U(p.$$.fragment,i),_=!0)},o(i){v(p.$$.fragment,i),_=!1},d(i){$(p,i)}}}function or(X){let p,_;return p=new ve({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> jax
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> flax.jax_utils <span class="hljs-keyword">import</span> replicate
<span class="hljs-keyword">from</span> flax.training.common_utils <span class="hljs-keyword">import</span> shard
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FlaxStableDiffusionPipeline

model_path = <span class="hljs-string">&quot;path_to_saved_model&quot;</span>
pipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)

prompt = <span class="hljs-string">&quot;yoda pokemon&quot;</span>
prng_seed = jax.random.PRNGKey(<span class="hljs-number">0</span>)
num_inference_steps = <span class="hljs-number">50</span>

num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)

<span class="hljs-comment"># shard inputs and rng</span>
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)

images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=<span class="hljs-literal">True</span>).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-<span class="hljs-number">3</span>:])))
image.save(<span class="hljs-string">&quot;yoda-pokemon.png&quot;</span>)`}}),{c(){b(p.$$.fragment)},l(i){T(p.$$.fragment,i)},m(i,g){J(p,i,g),_=!0},p:Da,i(i){_||(U(p.$$.fragment,i),_=!0)},o(i){v(p.$$.fragment,i),_=!1},d(i){$(p,i)}}}function rr(X){let p,_;return p=new Pa({props:{$$slots:{default:[or]},$$scope:{ctx:X}}}),{c(){b(p.$$.fragment)},l(i){T(p.$$.fragment,i)},m(i,g){J(p,i,g),_=!0},p(i,g){const W={};g&2&&(W.$$scope={dirty:g,ctx:i}),p.$set(W)},i(i){_||(U(p.$$.fragment,i),_=!0)},o(i){v(p.$$.fragment,i),_=!1},d(i){$(p,i)}}}function sr(X){let p,_,i,g,W,Z,ue,ge,ye,R,D,N,V,Me,F,re,se,be,O,ne,Te,j,me,C,$e,ie,P,B,Ee,E,Re,pe,Je,I,K,ee,Ie,te,A,ae,de,x,he,le,H,z,k,S,we,u,y,Ue,Y,oe,je,Ce,_e,fe,Ae,L,Ne,q,xe,G,Xe,m,M,kt,Rt,Ze,De,It,dt,Q,Be,tt,Se,Ge,at,Nt,Pe,He,xt,wt,ce,_t,We,Fe,Yt,gt,Ha,Lt,za,ua,Xt,qa,ma,yt,da,ke,Oa,Qt,Ka,el,Dt,tl,al,Pt,ll,ol,wa,lt,rl,Ht,sl,nl,_a,Mt,ga,ze,ot,zt,bt,il,qt,pl,ya,rt,Ma,qe,st,Ot,Tt,hl,Kt,fl,ba,Ve,cl,Jt,ul,ml,ea,dl,wl,Ta,nt,_l,Ut,gl,yl,Ja,Ye,ta,Ml,bl,vt,Tl,aa,Jl,Ul,vl,$t,$l,la,El,jl,Ua,Zt,Wl,va,Le,kl,oa,Rl,Il,ra,Nl,xl,$a,it,Ea,Oe,pt,sa,Et,Xl,na,Zl,ja,ht,Bl,Bt,Fl,Cl,Wa,Ke,ft,ia,jt,Al,pa,Sl,ka,ct,Gl,Ft,Vl,Yl,Ra,ut,Ia;return Z=new et({}),D=new Vo({props:{warning:!0,$$slots:{default:[zo]},$$scope:{ctx:X}}}),ie=new ve({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGZGlmZnVzZXJzLmdpdCUwQXBpcCUyMGluc3RhbGwlMjAtVSUyMC1yJTIwcmVxdWlyZW1lbnRzLnR4dA==",highlighted:`pip install git+https://github.com/huggingface/diffusers.git
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