import{S as Za,i as Ua,s as ja,e as r,k as M,t as i,w as m,M as Ja,c as p,d as e,m as o,a as n,h as c,x as d,b as u,N as he,G as s,g as a,y,q as h,o as Z,B as U,v as ba}from"../../chunks/vendor-hf-doc-builder.js";import{T as wa}from"../../chunks/Tip-hf-doc-builder.js";import{I as Ea}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as b}from"../../chunks/CodeBlock-hf-doc-builder.js";function _a(Pe){let j,Y,_,pl,Q,T,G,v,x,al,z,V,F,H;return{c(){j=r("p"),Y=i("\uB2E4\uC74C Flax \uC2A4\uCF00\uC904\uB7EC\uB294 "),_=r("em"),pl=i("\uC544\uC9C1"),Q=i(" Flax Stable Diffusion \uD30C\uC774\uD504\uB77C\uC778\uACFC \uD638\uD658\uB418\uC9C0 \uC54A\uC2B5\uB2C8\uB2E4."),T=M(),G=r("ul"),v=r("li"),x=r("code"),al=i("FlaxLMSDiscreteScheduler"),z=M(),V=r("li"),F=r("code"),H=i("FlaxDDPMScheduler")},l(f){j=p(f,"P",{});var w=n(j);Y=c(w,"\uB2E4\uC74C Flax \uC2A4\uCF00\uC904\uB7EC\uB294 "),_=p(w,"EM",{});var Xl=n(_);pl=c(Xl,"\uC544\uC9C1"),Xl.forEach(e),Q=c(w," Flax Stable Diffusion \uD30C\uC774\uD504\uB77C\uC778\uACFC \uD638\uD658\uB418\uC9C0 \uC54A\uC2B5\uB2C8\uB2E4."),w.forEach(e),T=o(f),G=p(f,"UL",{});var P=n(G);v=p(P,"LI",{});var L=n(v);x=p(L,"CODE",{});var Cl=n(x);al=c(Cl,"FlaxLMSDiscreteScheduler"),Cl.forEach(e),L.forEach(e),z=o(P),V=p(P,"LI",{});var kl=n(V);F=p(kl,"CODE",{});var q=n(F);H=c(q,"FlaxDDPMScheduler"),q.forEach(e),kl.forEach(e),P.forEach(e)},m(f,w){a(f,j,w),s(j,Y),s(j,_),s(_,pl),s(j,Q),a(f,T,w),a(f,G,w),s(G,v),s(v,x),s(x,al),s(G,z),s(G,V),s(V,F),s(F,H)},d(f){f&&e(j),f&&e(T),f&&e(G)}}}function Ta(Pe){let j,Y,_,pl,Q,T,G,v,x,al,z,V,F,H,f,w,Xl,P,L,Cl,kl,q,st,Le,Nl,tt,qe,il,rt,Oe,O,pt,cl,at,it,Ke,$l,Ze,ct,ls,nl,es,Rl,nt,ss,Ml,ts,ol,Mt,rs,K,ot,Ue,ut,ft,ps,ul,as,fl,je,mt,dt,is,ml,cs,N,yt,Je,ht,Zt,be,Ut,jt,ns,dl,Ms,Dl,Jt,os,yl,us,I,bt,wt,xl,yp,Et,_t,fs,hl,Tt,ms,$,Gt,we,vt,Vt,Ee,Bt,It,ds,Zl,ys,Ul,_e,Wt,St,hs,jl,Zs,Fl,Xt,Us,J,Al,Te,Ct,kt,Nt,gl,Ge,$t,Rt,Dt,Yl,ve,xt,Ft,At,Ql,Ve,gt,Yt,Qt,zl,Be,zt,Ht,Pt,Hl,Ie,Lt,qt,Ot,Pl,We,Kt,lr,js,Ll,er,Js,R,sr,Se,tr,rr,Xe,pr,ar,bs,Jl,ws,bl,Ce,ir,cr,Es,wl,_s,ql,nr,Ts,B,Mr,ke,or,ur,Ne,fr,mr,$e,dr,yr,Gs,El,vs,Ol,hr,Vs,_l,Bs,W,Zr,Ur,Kl,hp,jr,Jr,Is,Tl,br,Ws,D,wr,Re,Er,_r,De,Tr,Gr,Ss,Gl,xe,vr,Vr,Xs,vl,Cs,S,Br,Ir,le,Zp,Wr,Sr,ks,A,Fe,Xr,Cr,Ae,kr,Nr,Ns,Vl,$s,X,$r,Rr,ee,Up,Dr,xr,Rs,Bl,Ds,C,Fr,Ar,se,jp,gr,Yr,xs,ll,Qr,ge,zr,Hr,Fs,Il,As,k,Pr,Lr,te,Jp,qr,Or,gs,re,Kr,Ys,g,el,Ye,Wl,lp,Qe,ep,Qs,sl,sp,pe,tp,rp,zs,Sl,Hs,tl,Ps;return nl=new b({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGxvZ2luJTBBZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEElMjMlMjBmaXJzdCUyMHdlJTIwbmVlZCUyMHRvJTIwbG9naW4lMjB3aXRoJTIwb3VyJTIwYWNjZXNzJTIwdG9rZW4lMEFsb2dpbigpJTBBJTBBJTIzJTIwTm93JTIwd2UlMjBjYW4lMjBkb3dubG9hZCUyMHRoZSUyMHBpcGVsaW5lJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYp",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> login
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch

<span class="hljs-comment"># first we need to login with our access token</span>
login()

<span class="hljs-comment"># Now we can download the pipeline</span>
pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16)`}}),Ml=new b({props:{code:"cGlwZWxpbmUudG8oJTIyY3VkYSUyMik=",highlighted:'pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)'}}),ul=new b({props:{code:"cGlwZWxpbmUuc2NoZWR1bGVy",highlighted:"pipeline.scheduler"}}),ml=new b({props:{code:"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",highlighted:`PNDMScheduler {
  <span class="hljs-string">&quot;_class_name&quot;</span>: <span class="hljs-string">&quot;PNDMScheduler&quot;</span>,
  <span class="hljs-string">&quot;_diffusers_version&quot;</span>: <span class="hljs-string">&quot;0.8.0.dev0&quot;</span>,
  <span class="hljs-string">&quot;beta_end&quot;</span>: <span class="hljs-number">0.012</span>,
  <span class="hljs-string">&quot;beta_schedule&quot;</span>: <span class="hljs-string">&quot;scaled_linear&quot;</span>,
  <span class="hljs-string">&quot;beta_start&quot;</span>: <span class="hljs-number">0.00085</span>,
  <span class="hljs-string">&quot;clip_sample&quot;</span>: <span class="hljs-literal">false</span>,
  <span class="hljs-string">&quot;num_train_timesteps&quot;</span>: <span class="hljs-number">1000</span>,
  <span class="hljs-string">&quot;set_alpha_to_one&quot;</span>: <span class="hljs-literal">false</span>,
  <span class="hljs-string">&quot;skip_prk_steps&quot;</span>: <span class="hljs-literal">true</span>,
  <span class="hljs-string">&quot;steps_offset&quot;</span>: <span class="hljs-number">1</span>,
  <span class="hljs-string">&quot;trained_betas&quot;</span>: <span class="hljs-literal">null</span>
}`}}),dl=new b({props:{code:"cHJvbXB0JTIwJTNEJTIwJTIyQSUyMHBob3RvZ3JhcGglMjBvZiUyMGFuJTIwYXN0cm9uYXV0JTIwcmlkaW5nJTIwYSUyMGhvcnNlJTIwb24lMjBNYXJzJTJDJTIwaGlnaCUyMHJlc29sdXRpb24lMkMlMjBoaWdoJTIwZGVmaW5pdGlvbi4lMjI=",highlighted:'prompt = <span class="hljs-string">&quot;A photograph of an astronaut riding a horse on Mars, high resolution, high definition.&quot;</span>'}}),yl=new b({props:{code:"Z2VuZXJhdG9yJTIwJTNEJTIwdG9yY2guR2VuZXJhdG9yKGRldmljZSUzRCUyMmN1ZGElMjIpLm1hbnVhbF9zZWVkKDgpJTBBaW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IpLmltYWdlcyU1QjAlNUQlMEFpbWFnZQ==",highlighted:`generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">8</span>)
image = pipeline(prompt, generator=generator).images[<span class="hljs-number">0</span>]
image`}}),Zl=new b({props:{code:"cGlwZWxpbmUuc2NoZWR1bGVyLmNvbXBhdGlibGVz",highlighted:"pipeline.scheduler.compatibles"}}),jl=new b({props:{code:"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",highlighted:`[<span class="hljs-keyword">diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,
</span> <span class="hljs-keyword">diffusers.schedulers.scheduling_ddim.DDIMScheduler,
</span> <span class="hljs-keyword">diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,
</span> <span class="hljs-keyword">diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,
</span> <span class="hljs-keyword">diffusers.schedulers.scheduling_pndm.PNDMScheduler,
</span> <span class="hljs-keyword">diffusers.schedulers.scheduling_ddpm.DDPMScheduler,
</span> <span class="hljs-keyword">diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler]</span>`}}),Jl=new b({props:{code:"cGlwZWxpbmUuc2NoZWR1bGVyLmNvbmZpZw==",highlighted:"pipeline.scheduler.config"}}),wl=new b({props:{code:"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",highlighted:`FrozenDict([(&#x27;num_train_timesteps&#x27;, <span class="hljs-number">1000</span>),
            (&#x27;beta_start&#x27;, <span class="hljs-number">0.00085</span>),
            (&#x27;beta_end&#x27;, <span class="hljs-number">0.012</span>),
            (&#x27;beta_schedule&#x27;, &#x27;scaled_linear&#x27;),
            (&#x27;trained_betas&#x27;, None),
            (&#x27;skip_prk_steps&#x27;, True),
            (&#x27;set_alpha_to_one&#x27;, False),
            (&#x27;steps_offset&#x27;, <span class="hljs-number">1</span>),
            (&#x27;_class_name&#x27;, &#x27;PNDMScheduler&#x27;),
            (&#x27;_diffusers_version&#x27;, &#x27;<span class="hljs-number">0.8</span>.<span class="hljs-number">0</span>.dev0&#x27;),
            (&#x27;clip_sample&#x27;, False)])`}}),El=new b({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERESU1TY2hlZHVsZXIlMEElMEFwaXBlbGluZS5zY2hlZHVsZXIlMjAlM0QlMjBERElNU2NoZWR1bGVyLmZyb21fY29uZmlnKHBpcGVsaW5lLnNjaGVkdWxlci5jb25maWcp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMScheduler

pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)`}}),_l=new b({props:{code:"Z2VuZXJhdG9yJTIwJTNEJTIwdG9yY2guR2VuZXJhdG9yKGRldmljZSUzRCUyMmN1ZGElMjIpLm1hbnVhbF9zZWVkKDgpJTBBaW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IpLmltYWdlcyU1QjAlNUQlMEFpbWFnZQ==",highlighted:`generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">8</span>)
image = pipeline(prompt, generator=generator).images[<span class="hljs-number">0</span>]
image`}}),vl=new b({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMExNU0Rpc2NyZXRlU2NoZWR1bGVyJTBBJTBBcGlwZWxpbmUuc2NoZWR1bGVyJTIwJTNEJTIwTE1TRGlzY3JldGVTY2hlZHVsZXIuZnJvbV9jb25maWcocGlwZWxpbmUuc2NoZWR1bGVyLmNvbmZpZyklMEElMEFnZW5lcmF0b3IlMjAlM0QlMjB0b3JjaC5HZW5lcmF0b3IoZGV2aWNlJTNEJTIyY3VkYSUyMikubWFudWFsX3NlZWQoOCklMEFpbWFnZSUyMCUzRCUyMHBpcGVsaW5lKHByb21wdCUyQyUyMGdlbmVyYXRvciUzRGdlbmVyYXRvcikuaW1hZ2VzJTVCMCU1RCUwQWltYWdl",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LMSDiscreteScheduler

pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">8</span>)
image = pipeline(prompt, generator=generator).images[<span class="hljs-number">0</span>]
image`}}),Vl=new b({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEV1bGVyRGlzY3JldGVTY2hlZHVsZXIlMEElMEFwaXBlbGluZS5zY2hlZHVsZXIlMjAlM0QlMjBFdWxlckRpc2NyZXRlU2NoZWR1bGVyLmZyb21fY29uZmlnKHBpcGVsaW5lLnNjaGVkdWxlci5jb25maWcpJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwdG9yY2guR2VuZXJhdG9yKGRldmljZSUzRCUyMmN1ZGElMjIpLm1hbnVhbF9zZWVkKDgpJTBBaW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IlMkMlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNEMzApLmltYWdlcyU1QjAlNUQlMEFpbWFnZQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> EulerDiscreteScheduler

pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">8</span>)
image = pipeline(prompt, generator=generator, num_inference_steps=<span class="hljs-number">30</span>).images[<span class="hljs-number">0</span>]
image`}}),Bl=new b({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEV1bGVyQW5jZXN0cmFsRGlzY3JldGVTY2hlZHVsZXIlMEElMEFwaXBlbGluZS5zY2hlZHVsZXIlMjAlM0QlMjBFdWxlckFuY2VzdHJhbERpc2NyZXRlU2NoZWR1bGVyLmZyb21fY29uZmlnKHBpcGVsaW5lLnNjaGVkdWxlci5jb25maWcpJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwdG9yY2guR2VuZXJhdG9yKGRldmljZSUzRCUyMmN1ZGElMjIpLm1hbnVhbF9zZWVkKDgpJTBBaW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IlMkMlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNEMzApLmltYWdlcyU1QjAlNUQlMEFpbWFnZQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> EulerAncestralDiscreteScheduler

pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">8</span>)
image = pipeline(prompt, generator=generator, num_inference_steps=<span class="hljs-number">30</span>).images[<span class="hljs-number">0</span>]
image`}}),Il=new b({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERQTVNvbHZlck11bHRpc3RlcFNjaGVkdWxlciUwQSUwQXBpcGVsaW5lLnNjaGVkdWxlciUyMCUzRCUyMERQTVNvbHZlck11bHRpc3RlcFNjaGVkdWxlci5mcm9tX2NvbmZpZyhwaXBlbGluZS5zY2hlZHVsZXIuY29uZmlnKSUwQSUwQWdlbmVyYXRvciUyMCUzRCUyMHRvcmNoLkdlbmVyYXRvcihkZXZpY2UlM0QlMjJjdWRhJTIyKS5tYW51YWxfc2VlZCg4KSUwQWltYWdlJTIwJTNEJTIwcGlwZWxpbmUocHJvbXB0JTJDJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yJTJDJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDIwKS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2U=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DPMSolverMultistepScheduler

pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)

generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">8</span>)
image = pipeline(prompt, generator=generator, num_inference_steps=<span class="hljs-number">20</span>).images[<span class="hljs-number">0</span>]
image`}}),Wl=new Ea({}),Sl=new b({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, FlaxDPMSolverMultistepScheduler

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
    model_id,
    subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>
)
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
    model_id,
    scheduler=scheduler,
    revision=<span class="hljs-string">&quot;bf16&quot;</span>,
    dtype=jax.numpy.bfloat16,
)
params[<span class="hljs-string">&quot;scheduler&quot;</span>] = scheduler_state

<span class="hljs-comment"># Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)</span>
prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
num_samples = jax.device_count()
prompt_ids = pipeline.prepare_inputs([prompt] * num_samples)

prng_seed = jax.random.PRNGKey(<span class="hljs-number">0</span>)
num_inference_steps = <span class="hljs-number">25</span>

<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>:])))`}}),tl=new wa({props:{warning:!0,$$slots:{default:[_a]},$$scope:{ctx:Pe}}}),{c(){j=r("meta"),Y=M(),_=r("h1"),pl=i("\uC2A4\uCF00\uC904\uB7EC"),Q=M(),T=r("p"),G=i("diffusion \uD30C\uC774\uD504\uB77C\uC778\uC740 diffusion \uBAA8\uB378, \uC2A4\uCF00\uC904\uB7EC \uB4F1\uC758 \uCEF4\uD3EC\uB10C\uD2B8\uB4E4\uB85C \uAD6C\uC131\uB429\uB2C8\uB2E4. \uADF8\uB9AC\uACE0 \uD30C\uC774\uD504\uB77C\uC778 \uC548\uC758 \uC77C\uBD80 \uCEF4\uD3EC\uB10C\uD2B8\uB97C \uB2E4\uB978 \uCEF4\uD3EC\uB10C\uD2B8\uB85C \uAD50\uCCB4\uD558\uB294 \uC2DD\uC758 \uCEE4\uC2A4\uD130\uB9C8\uC774\uC9D5 \uC5ED\uC2DC \uAC00\uB2A5\uD569\uB2C8\uB2E4.  \uC774\uC640 \uAC19\uC740 \uCEF4\uD3EC\uB10C\uD2B8 \uCEE4\uC2A4\uD130\uB9C8\uC774\uC9D5\uC758 \uAC00\uC7A5 \uB300\uD45C\uC801\uC778 \uC608\uC2DC\uAC00 \uBC14\uB85C "),v=r("a"),x=i("\uC2A4\uCF00\uC904\uB7EC"),al=i("\uB97C \uAD50\uCCB4\uD558\uB294 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