Buckets:
hf-doc-build/doc / diffusers /main /ko /_app /pages /training /custom_diffusion.mdx-hf-doc-builder.js
| import{S as fi,i as ci,s as Mi,e as p,k as f,w as m,t as a,M as mi,c as i,d as t,m as c,a as r,x as u,h as o,b as M,G as e,g as n,y as _,L as ui,q as d,o as y,B as w,v as _i}from"../../chunks/vendor-hf-doc-builder.js";import{I as ws}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as J}from"../../chunks/CodeBlock-hf-doc-builder.js";function di(So){let v,Ht,Z,G,rt,q,Us,nt,Js,qt,P,O,hs,bs,Pt,F,Ts,K,Es,js,Ot,N,$,ft,ll,vs,ct,Zs,Kt,I,B,Mt,tl,Ns,mt,Is,le,Ql,Cs,te,Ll,ut,Ws,ee,A,Xs,_t,Rs,Vs,se,el,ae,sl,al,Gs,Fs,oe,ol,pe,xl,$s,ie,pl,re,S,Bs,il,As,Ss,ne,rl,fe,Dl,zs,ce,nl,Me,gl,Ys,me,fl,ue,cl,ks,_e,E,Qs,Ml,Ls,xs,Hl,Ds,gs,de,U,Hs,dt,qs,Ps,yt,Os,Ks,wt,la,ta,Ut,ea,sa,Jt,aa,oa,ht,pa,ia,ye,ml,we,ql,bt,Tt,ul,ra,_l,na,fa,Ue,z,ca,Et,Ma,ma,Je,dl,he,Pl,C,ua,jt,_a,da,yl,ya,wa,be,Y,Ua,vt,Ja,ha,Te,j,k,Zt,ba,Ta,Nt,Ea,ja,va,wl,Za,It,Na,Ia,Ca,b,Wa,Ct,Xa,Ra,Wt,Va,Ga,Xt,Fa,$a,Ul,Rt,Vt,Ba,Aa,Gt,Ft,Sa,Ee,Jl,je,Q,za,hl,Ya,ka,ve,W,$t,Qa,La,bl,xa,Da,Ze,Tl,ga,Ne,X,El,Ha,qa,jl,Pa,Oa,Ie,Ol,Ka,Ce,vl,We,Kl,lo,Xe,Zl,Re,L,to,Nl,eo,so,Ve,Il,ao,Ge,T,oo,Bt,po,io,At,ro,no,St,fo,co,Fe,lt,Mo,$e,Cl,Be,tt,mo,Ae,Wl,Se,Xl,uo,ze,Rl,_o,zt,yo,Ye,Vl,ke,et,wo,Qe,Gl,Le,st,Uo,xe,Fl,De,at,Jo,ge,$l,ho,He,Bl,Yt,bo,To,qe,R,x,kt,Al,Eo,Qt,jo,Pe,D,vo,Lt,Zo,No,Oe,Sl,Io,zl,Co,Ke,Yl,Wo,ls,g,Xo,kl,Ro,Vo,ts;return q=new ws({}),ll=new ws({}),tl=new ws({}),el=new J({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmdpdGh1Yi5jb20lMkZodWdnaW5nZmFjZSUyRmRpZmZ1c2VycyUwQWNkJTIwZGlmZnVzZXJzJTBBcGlwJTIwaW5zdGFsbCUyMC1lJTIwLg==",highlighted:`git <span class="hljs-built_in">clone</span> https://github.com/huggingface/diffusers | |
| <span class="hljs-built_in">cd</span> diffusers | |
| pip install -e .`}}),ol=new J({props:{code:"Y2QlMjBleGFtcGxlcyUyRmN1c3RvbV9kaWZmdXNpb24=",highlighted:'<span class="hljs-built_in">cd</span> examples/custom_diffusion'}}),pl=new J({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1yJTIwcmVxdWlyZW1lbnRzLnR4dCUwQXBpcCUyMGluc3RhbGwlMjBjbGlwLXJldHJpZXZhbCUyMA==",highlighted:`pip install -r requirements.txt | |
| pip install clip-retrieval `}}),rl=new J({props:{code:"YWNjZWxlcmF0ZSUyMGNvbmZpZw==",highlighted:"accelerate config"}}),nl=new J({props:{code:"YWNjZWxlcmF0ZSUyMGNvbmZpZyUyMGRlZmF1bHQ=",highlighted:"accelerate config default"}}),fl=new J({props:{code:"ZnJvbSUyMGFjY2VsZXJhdGUudXRpbHMlMjBpbXBvcnQlMjB3cml0ZV9iYXNpY19jb25maWclMEElMEF3cml0ZV9iYXNpY19jb25maWcoKQ==",highlighted:`<span class="hljs-keyword">from</span> accelerate.utils <span class="hljs-keyword">import</span> write_basic_config | |
| write_basic_config()`}}),ml=new J({props:{code:"cGlwJTIwaW5zdGFsbCUyMGNsaXAtcmV0cmlldmFsJTBBcHl0aG9uJTIwcmV0cmlldmUucHklMjAtLWNsYXNzX3Byb21wdCUyMGNhdCUyMC0tY2xhc3NfZGF0YV9kaXIlMjByZWFsX3JlZyUyRnNhbXBsZXNfY2F0JTIwLS1udW1fY2xhc3NfaW1hZ2VzJTIwMjAw",highlighted:`pip install clip-retrieval | |
| python retrieve.py --class_prompt <span class="hljs-built_in">cat</span> --class_data_dir real_reg/samples_cat --num_class_images 200`}}),dl=new J({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"path-to-save-model"</span> | |
| <span class="hljs-built_in">export</span> INSTANCE_DIR=<span class="hljs-string">"./data/cat"</span> | |
| accelerate launch train_custom_diffusion.py \\ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \\ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \\ | |
| --class_data_dir=./real_reg/samples_cat/ \\ | |
| --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\ | |
| --class_prompt=<span class="hljs-string">"cat"</span> --num_class_images=200 \\ | |
| --instance_prompt=<span class="hljs-string">"photo of a <new1> cat"</span> \\ | |
| --resolution=512 \\ | |
| --train_batch_size=2 \\ | |
| --learning_rate=1e-5 \\ | |
| --lr_warmup_steps=0 \\ | |
| --max_train_steps=250 \\ | |
| --scale_lr --hflip \\ | |
| --modifier_token <span class="hljs-string">"<new1>"</span> \\ | |
| --push_to_hub`}}),Jl=new J({props:{code:"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",highlighted:`accelerate launch train_custom_diffusion.py \\ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \\ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \\ | |
| --class_data_dir=./real_reg/samples_cat/ \\ | |
| --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\ | |
| --class_prompt=<span class="hljs-string">"cat"</span> --num_class_images=200 \\ | |
| --instance_prompt=<span class="hljs-string">"photo of a <new1> cat"</span> \\ | |
| --resolution=512 \\ | |
| --train_batch_size=2 \\ | |
| --learning_rate=1e-5 \\ | |
| --lr_warmup_steps=0 \\ | |
| --max_train_steps=250 \\ | |
| --scale_lr --hflip \\ | |
| --modifier_token <span class="hljs-string">"<new1>"</span> \\ | |
| --validation_prompt=<span class="hljs-string">"<new1> cat sitting in a bucket"</span> \\ | |
| --report_to=<span class="hljs-string">"wandb"</span> \\ | |
| --push_to_hub`}}),vl=new J({props:{code:"cGlwJTIwaW5zdGFsbCUyMGNsaXAtcmV0cmlldmFsJTBBcHl0aG9uJTIwcmV0cmlldmUucHklMjAtLWNsYXNzX3Byb21wdCUyMCU3QiU3RCUyMC0tY2xhc3NfZGF0YV9kaXIlMjAlN0IlN0QlMjAtLW51bV9jbGFzc19pbWFnZXMlMjAyMDA=",highlighted:`pip install clip-retrieval | |
| python retrieve.py --class_prompt {} --class_data_dir {} --num_class_images 200`}}),Zl=new J({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"path-to-save-model"</span> | |
| accelerate launch train_custom_diffusion.py \\ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \\ | |
| --concepts_list=./concept_list.json \\ | |
| --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\ | |
| --resolution=512 \\ | |
| --train_batch_size=2 \\ | |
| --learning_rate=1e-5 \\ | |
| --lr_warmup_steps=0 \\ | |
| --max_train_steps=500 \\ | |
| --num_class_images=200 \\ | |
| --scale_lr --hflip \\ | |
| --modifier_token <span class="hljs-string">"<new1>+<new2>"</span> \\ | |
| --push_to_hub`}}),Cl=new J({props:{code:"cGlwJTIwaW5zdGFsbCUyMGNsaXAtcmV0cmlldmFsJTBBcHl0aG9uJTIwcmV0cmlldmUucHklMjAtLWNsYXNzX3Byb21wdCUyMHBlcnNvbiUyMC0tY2xhc3NfZGF0YV9kaXIlMjByZWFsX3JlZyUyRnNhbXBsZXNfcGVyc29uJTIwLS1udW1fY2xhc3NfaW1hZ2VzJTIwMjAw",highlighted:`pip install clip-retrieval | |
| python retrieve.py --class_prompt person --class_data_dir real_reg/samples_person --num_class_images 200`}}),Wl=new J({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"path-to-save-model"</span> | |
| <span class="hljs-built_in">export</span> INSTANCE_DIR=<span class="hljs-string">"path-to-images"</span> | |
| accelerate launch train_custom_diffusion.py \\ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \\ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \\ | |
| --class_data_dir=./real_reg/samples_person/ \\ | |
| --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\ | |
| --class_prompt=<span class="hljs-string">"person"</span> --num_class_images=200 \\ | |
| --instance_prompt=<span class="hljs-string">"photo of a <new1> person"</span> \\ | |
| --resolution=512 \\ | |
| --train_batch_size=2 \\ | |
| --learning_rate=5e-6 \\ | |
| --lr_warmup_steps=0 \\ | |
| --max_train_steps=1000 \\ | |
| --scale_lr --hflip --noaug \\ | |
| --freeze_model crossattn \\ | |
| --modifier_token <span class="hljs-string">"<new1>"</span> \\ | |
| --enable_xformers_memory_efficient_attention \\ | |
| --push_to_hub`}}),Vl=new J({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch | |
| <span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span>, torch_dtype=torch.float16).to(<span class="hljs-string">"cuda"</span>) | |
| pipe.unet.load_attn_procs(<span class="hljs-string">"path-to-save-model"</span>, weight_name=<span class="hljs-string">"pytorch_custom_diffusion_weights.bin"</span>) | |
| pipe.load_textual_inversion(<span class="hljs-string">"path-to-save-model"</span>, weight_name=<span class="hljs-string">"<new1>.bin"</span>) | |
| image = pipe( | |
| <span class="hljs-string">"<new1> cat sitting in a bucket"</span>, | |
| num_inference_steps=<span class="hljs-number">100</span>, | |
| guidance_scale=<span class="hljs-number">6.0</span>, | |
| eta=<span class="hljs-number">1.0</span>, | |
| ).images[<span class="hljs-number">0</span>] | |
| image.save(<span class="hljs-string">"cat.png"</span>)`}}),Gl=new J({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch | |
| <span class="hljs-keyword">from</span> huggingface_hub.repocard <span class="hljs-keyword">import</span> RepoCard | |
| <span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| model_id = <span class="hljs-string">"sayakpaul/custom-diffusion-cat"</span> | |
| card = RepoCard.load(model_id) | |
| base_model_id = card.data.to_dict()[<span class="hljs-string">"base_model"</span>] | |
| pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to(<span class="hljs-string">"cuda"</span>) | |
| pipe.unet.load_attn_procs(model_id, weight_name=<span class="hljs-string">"pytorch_custom_diffusion_weights.bin"</span>) | |
| pipe.load_textual_inversion(model_id, weight_name=<span class="hljs-string">"<new1>.bin"</span>) | |
| image = pipe( | |
| <span class="hljs-string">"<new1> cat sitting in a bucket"</span>, | |
| num_inference_steps=<span class="hljs-number">100</span>, | |
| guidance_scale=<span class="hljs-number">6.0</span>, | |
| eta=<span class="hljs-number">1.0</span>, | |
| ).images[<span class="hljs-number">0</span>] | |
| image.save(<span class="hljs-string">"cat.png"</span>)`}}),Fl=new J({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwaHVnZ2luZ2ZhY2VfaHViLnJlcG9jYXJkJTIwaW1wb3J0JTIwUmVwb0NhcmQlMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwRGlmZnVzaW9uUGlwZWxpbmUlMEElMEFtb2RlbF9pZCUyMCUzRCUyMCUyMnNheWFrcGF1bCUyRmN1c3RvbS1kaWZmdXNpb24tY2F0LXdvb2Rlbi1wb3QlMjIlMEFjYXJkJTIwJTNEJTIwUmVwb0NhcmQubG9hZChtb2RlbF9pZCklMEFiYXNlX21vZGVsX2lkJTIwJTNEJTIwY2FyZC5kYXRhLnRvX2RpY3QoKSU1QiUyMmJhc2VfbW9kZWwlMjIlNUQlMEElMEFwaXBlJTIwJTNEJTIwRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKGJhc2VfbW9kZWxfaWQlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpLnRvKCUyMmN1ZGElMjIpJTBBcGlwZS51bmV0LmxvYWRfYXR0bl9wcm9jcyhtb2RlbF9pZCUyQyUyMHdlaWdodF9uYW1lJTNEJTIycHl0b3JjaF9jdXN0b21fZGlmZnVzaW9uX3dlaWdodHMuYmluJTIyKSUwQXBpcGUubG9hZF90ZXh0dWFsX2ludmVyc2lvbihtb2RlbF9pZCUyQyUyMHdlaWdodF9uYW1lJTNEJTIyJTNDbmV3MSUzRS5iaW4lMjIpJTBBcGlwZS5sb2FkX3RleHR1YWxfaW52ZXJzaW9uKG1vZGVsX2lkJTJDJTIwd2VpZ2h0X25hbWUlM0QlMjIlM0NuZXcyJTNFLmJpbiUyMiklMEElMEFpbWFnZSUyMCUzRCUyMHBpcGUoJTBBJTIwJTIwJTIwJTIwJTIydGhlJTIwJTNDbmV3MSUzRSUyMGNhdCUyMHNjdWxwdHVyZSUyMGluJTIwdGhlJTIwc3R5bGUlMjBvZiUyMGElMjAlM0NuZXcyJTNFJTIwd29vZGVuJTIwcG90JTIyJTJDJTBBJTIwJTIwJTIwJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDEwMCUyQyUwQSUyMCUyMCUyMCUyMGd1aWRhbmNlX3NjYWxlJTNENi4wJTJDJTBBJTIwJTIwJTIwJTIwZXRhJTNEMS4wJTJDJTBBKS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2Uuc2F2ZSglMjJtdWx0aS1zdWJqZWN0LnBuZyUyMik=",highlighted:`<span class="hljs-keyword">import</span> torch | |
| <span class="hljs-keyword">from</span> huggingface_hub.repocard <span class="hljs-keyword">import</span> RepoCard | |
| <span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| model_id = <span class="hljs-string">"sayakpaul/custom-diffusion-cat-wooden-pot"</span> | |
| card = RepoCard.load(model_id) | |
| base_model_id = card.data.to_dict()[<span class="hljs-string">"base_model"</span>] | |
| pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to(<span class="hljs-string">"cuda"</span>) | |
| pipe.unet.load_attn_procs(model_id, weight_name=<span class="hljs-string">"pytorch_custom_diffusion_weights.bin"</span>) | |
| pipe.load_textual_inversion(model_id, weight_name=<span class="hljs-string">"<new1>.bin"</span>) | |
| pipe.load_textual_inversion(model_id, weight_name=<span class="hljs-string">"<new2>.bin"</span>) | |
| image = pipe( | |
| <span class="hljs-string">"the <new1> cat sculpture in the style of a <new2> wooden pot"</span>, | |
| num_inference_steps=<span class="hljs-number">100</span>, | |
| guidance_scale=<span class="hljs-number">6.0</span>, | |
| eta=<span class="hljs-number">1.0</span>, | |
| ).images[<span class="hljs-number">0</span>] | |
| image.save(<span class="hljs-string">"multi-subject.png"</span>)`}}),Al=new ws({}),{c(){v=p("meta"),Ht=f(),Z=p("h1"),G=p("a"),rt=p("span"),m(q.$$.fragment),Us=f(),nt=p("span"),Js=a("\uCEE4\uC2A4\uD140 Diffusion \uD559\uC2B5 \uC608\uC81C"),qt=f(),P=p("p"),O=p("a"),hs=a("\uCEE4\uC2A4\uD140 Diffusion"),bs=a(`\uC740 \uD53C\uC0AC\uCCB4\uC758 \uC774\uBBF8\uC9C0 \uBA87 \uC7A5(4~5\uC7A5)\uB9CC \uC8FC\uC5B4\uC9C0\uBA74 Stable Diffusion\uCC98\uB7FC text-to-image \uBAA8\uB378\uC744 \uCEE4\uC2A4\uD130\uB9C8\uC774\uC9D5\uD558\uB294 \uBC29\uBC95\uC785\uB2C8\uB2E4. | |
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\uAD00\uB828 \uC778\uC218\uB97C \uAD6C\uC131\uD560 \uC218\uB3C4 \uC788\uC2B5\uB2C8\uB2E4:"),Ul=p("ul"),Rt=p("li"),Vt=p("code"),Ba=a("num_validation_images"),Aa=f(),Gt=p("li"),Ft=p("code"),Sa=a("validation_steps"),Ee=f(),m(Jl.$$.fragment),je=f(),Q=p("p"),za=a("\uB2E4\uC74C\uC740 "),hl=p("a"),Ya=a("Weights and Biases page"),ka=a("\uC758 \uC608\uC2DC\uC774\uBA70, \uC5EC\uB7EC \uD559\uC2B5 \uC138\uBD80 \uC815\uBCF4\uC640 \uD568\uAED8 \uC911\uAC04 \uACB0\uACFC\uB4E4\uC744 \uD655\uC778\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),ve=f(),W=p("p"),$t=p("code"),Qa=a("--push_to_hub"),La=a("\uB97C \uC9C0\uC815\uD558\uBA74 \uD559\uC2B5\uB41C \uD30C\uB77C\uBBF8\uD130\uAC00 \uD5C8\uAE45 \uD398\uC774\uC2A4 \uD5C8\uBE0C\uC758 \uB9AC\uD3EC\uC9C0\uD1A0\uB9AC\uC5D0 \uD478\uC2DC\uB429\uB2C8\uB2E4. \uB2E4\uC74C\uC740 "),bl=p("a"),xa=a("\uC608\uC81C \uB9AC\uD3EC\uC9C0\uD1A0\uB9AC"),Da=a("\uC785\uB2C8\uB2E4."),Ze=f(),Tl=p("h3"),ga=a("\uBA40\uD2F0 \uCEE8\uC149\uC5D0 \uB300\uD55C \uD559\uC2B5 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