import{S as wn,i as bn,s as vn,e as t,k as c,w as f,t as p,M as Zn,c as r,d as l,m as u,a as i,x as d,h as o,b as h,N as Ql,G as s,g as n,y as m,q as M,o as y,B as w,v as Un}from"../../chunks/vendor-hf-doc-builder.js";import{T as jn}from"../../chunks/Tip-hf-doc-builder.js";import{I as zl}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Z}from"../../chunks/CodeBlock-hf-doc-builder.js";function Jn(ws){let b,ee,U,_,Y,T,g,$,Q,le,S,z,G,Me;return{c(){b=t("p"),ee=p("The following Flax schedulers are "),U=t("em"),_=p("not yet compatible"),Y=p(" with the Flax Stable Diffusion Pipeline:"),T=c(),g=t("ul"),$=t("li"),Q=t("code"),le=p("FlaxLMSDiscreteScheduler"),S=c(),z=t("li"),G=t("code"),Me=p("FlaxDDPMScheduler")},l(j){b=r(j,"P",{});var E=i(b);ee=o(E,"The following Flax schedulers are "),U=r(E,"EM",{});var k=i(U);_=o(k,"not yet compatible"),k.forEach(l),Y=o(E," with the Flax Stable Diffusion Pipeline:"),E.forEach(l),T=u(j),g=r(j,"UL",{});var se=i(g);$=r(se,"LI",{});var te=i($);Q=r(te,"CODE",{});var Qe=i(Q);le=o(Qe,"FlaxLMSDiscreteScheduler"),Qe.forEach(l),te.forEach(l),S=u(se),z=r(se,"LI",{});var ze=i(z);G=r(ze,"CODE",{});var ye=i(G);Me=o(ye,"FlaxDDPMScheduler"),ye.forEach(l),ze.forEach(l),se.forEach(l)},m(j,E){n(j,b,E),s(b,ee),s(b,U),s(U,_),s(b,Y),n(j,T,E),n(j,g,E),s(g,$),s($,Q),s(Q,le),s(g,S),s(g,z),s(z,G),s(G,Me)},d(j){j&&l(b),j&&l(T),j&&l(g)}}}function En(ws){let b,ee,U,_,Y,T,g,$,Q,le,S,z,G,Me,j,E,k,se,te,Qe,ze,ye,C,Hl,Vt,It,Pl,Wt,Bt,ql,Nt,bs,A,Xt,Ll,Ct,At,Kl,Rt,xt,vs,He,Dt,Zs,H,re,Ol,we,Ft,es,Yt,Us,R,Qt,be,ls,zt,Ht,Pe,Pt,qt,js,ve,Js,qe,Lt,Es,Ze,_s,P,ae,ss,Ue,Kt,ts,Ot,Ts,x,er,rs,lr,sr,as,tr,rr,gs,je,Ss,Je,is,ar,ir,Gs,Ee,$s,ie,nr,Le,pr,or,ks,_e,Vs,Ke,cr,Is,Te,Ws,V,ur,hr,Oe,ri,fr,dr,Bs,q,ne,ns,ge,mr,ps,Mr,Ns,pe,yr,os,wr,br,Xs,Se,Cs,Ge,cs,vr,Zr,As,$e,Rs,el,Ur,xs,v,ll,sl,jr,Jr,Er,tl,rl,_r,Tr,gr,al,il,Sr,Gr,$r,nl,pl,kr,Vr,Ir,ol,cl,Wr,Br,Nr,ul,hl,Xr,Cr,Ar,fl,dl,Rr,xr,Ds,D,Dr,us,Fr,Yr,ml,Qr,zr,Fs,ke,Ys,Ml,Hr,Qs,Ve,hs,Pr,qr,zs,Ie,Hs,oe,Lr,yl,Kr,Or,Ps,We,qs,wl,ea,Ls,Be,Ks,I,la,sa,bl,ai,ta,ra,Os,ce,aa,vl,ia,na,et,L,ue,fs,Ne,pa,ds,oa,lt,F,ca,Zl,ua,ha,Ul,fa,da,st,Xe,jl,ma,Ma,tt,Ce,rt,W,ya,wa,Jl,ii,ba,va,at,K,El,Za,Ua,_l,ja,Ja,it,Ae,nt,B,Ea,_a,Tl,ni,Ta,ga,pt,gl,Sa,ot,Re,ct,N,Ga,$a,Sl,pi,ka,Va,ut,he,Ia,Gl,Wa,Ba,ht,xe,ft,X,Na,Xa,$l,oi,Ca,Aa,dt,kl,Ra,mt,O,fe,ms,De,xa,Ms,Da,Mt,de,Fa,Vl,Ya,Qa,yt,Fe,wt,me,bt;return T=new zl({}),we=new zl({}),ve=new Z({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGxvZ2luJTBBZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEFsb2dpbigpJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYp",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

login()

pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16)`}}),Ze=new Z({props:{code:"cGlwZWxpbmUudG8oJTIyY3VkYSUyMik=",highlighted:'pipeline.to(<span class="hljs-string">&quot;cuda&quot;</span>)'}}),Ue=new zl({}),je=new Z({props:{code:"cGlwZWxpbmUuc2NoZWR1bGVy",highlighted:"pipeline.scheduler"}}),Ee=new Z({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>
}`}}),_e=new Z({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>'}}),Te=new Z({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`}}),ge=new zl({}),Se=new Z({props:{code:"cGlwZWxpbmUuc2NoZWR1bGVyLmNvbXBhdGlibGVz",highlighted:"pipeline.scheduler.compatibles"}}),$e=new Z({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>`}}),ke=new Z({props:{code:"cGlwZWxpbmUuc2NoZWR1bGVyLmNvbmZpZw==",highlighted:"pipeline.scheduler.config"}}),Ie=new Z({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)])`}}),We=new Z({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)`}}),Be=new Z({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`}}),Ne=new zl({}),Ce=new Z({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`}}),Ae=new Z({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`}}),Re=new Z({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`}}),xe=new Z({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`}}),De=new zl({}),Fe=new Z({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>:])))`}}),me=new jn({props:{warning:!0,$$slots:{default:[Jn]},$$scope:{ctx:ws}}}),{c(){b=t("meta"),ee=c(),U=t("h1"),_=t("a"),Y=t("span"),f(T.$$.fragment),g=c(),$=t("span"),Q=p("Schedulers"),le=c(),S=t("p"),z=p(`Diffusion pipelines are inherently a collection of diffusion models and schedulers that are partly independent from each other. This means that one is able to switch out parts of the pipeline to better customize
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It is extremely difficult to measure quantitatively which scheduler works best for a given diffusion pipeline, so it is often recommended to simply try out which works best.`),vs=c(),He=t("p"),Dt=p("The following paragraphs show how to do so with the \u{1F9E8} Diffusers library."),Zs=c(),H=t("h2"),re=t("a"),Ol=t("span"),f(we.$$.fragment),Ft=c(),es=t("span"),Yt=p("Load pipeline"),Us=c(),R=t("p"),Qt=p("Let\u2019s start by loading the "),be=t("a"),ls=t("code"),zt=p("runwayml/stable-diffusion-v1-5"),Ht=p(" model in the "),Pe=t("a"),Pt=p("DiffusionPipeline"),qt=p(":"),js=c(),f(ve.$$.fragment),Js=c(),qe=t("p"),Lt=p("Next, we move it to GPU:"),Es=c(),f(Ze.$$.fragment),_s=c(),P=t("h2"),ae=t("a"),ss=t("span"),f(Ue.$$.fragment),Kt=c(),ts=t("span"),Ot=p("Access the scheduler"),Ts=c(),x=t("p"),er=p("The scheduler is always one of the components of the pipeline and is usually called "),rs=t("code"),lr=p('"scheduler"'),sr=p(`.
So it can be accessed via the `),as=t("code"),tr=p('"scheduler"'),rr=p(" property."),gs=c(),f(je.$$.fragment),Ss=c(),Je=t("p"),is=t("strong"),ar=p("Output"),ir=p(":"),Gs=c(),f(Ee.$$.fragment),$s=c(),ie=t("p"),nr=p("We can see that the scheduler is of type "),Le=t("a"),pr=p("PNDMScheduler"),or=p(`.
Cool, now let\u2019s compare the scheduler in its performance to other schedulers.
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which defines all compatible schedulers. You can take a look at all available, compatible schedulers for the Stable Diffusion pipeline as follows.`),Xs=c(),f(Se.$$.fragment),Cs=c(),Ge=t("p"),cs=t("strong"),vr=p("Output"),Zr=p(":"),As=c(),f($e.$$.fragment),Rs=c(),el=t("p"),Ur=p("Cool, lots of schedulers to look at. Feel free to have a look at their respective class definitions:"),xs=c(),v=t("ul"),ll=t("li"),sl=t("a"),jr=p("LMSDiscreteScheduler"),Jr=p(","),Er=c(),tl=t("li"),rl=t("a"),_r=p("DDIMScheduler"),Tr=p(","),gr=c(),al=t("li"),il=t("a"),Sr=p("DPMSolverMultistepScheduler"),Gr=p(","),$r=c(),nl=t("li"),pl=t("a"),kr=p("EulerDiscreteScheduler"),Vr=p(","),Ir=c(),ol=t("li"),cl=t("a"),Wr=p("PNDMScheduler"),Br=p(","),Nr=c(),ul=t("li"),hl=t("a"),Xr=p("DDPMScheduler"),Cr=p(","),Ar=c(),fl=t("li"),dl=t("a"),Rr=p("EulerAncestralDiscreteScheduler"),xr=p("."),Ds=c(),D=t("p"),Dr=p(`We will now compare the input prompt with all other schedulers. To change the scheduler of the pipeline you can make use of the
convenient `),us=t("code"),Fr=p("ConfigMixin.config"),Yr=p(" property in combination with the "),ml=t("a"),Qr=p("ConfigMixin.from_config()"),zr=p(" function."),Fs=c(),f(ke.$$.fragment),Ys=c(),Ml=t("p"),Hr=p("returns a dictionary of the configuration of the scheduler:"),Qs=c(),Ve=t("p"),hs=t("strong"),Pr=p("Output"),qr=p(":"),zs=c(),f(Ie.$$.fragment),Hs=c(),oe=t("p"),Lr=p(`This configuration can then be used to instantiate a scheduler
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A number of better schedulers have been released that can be run with much fewer steps, let\u2019s compare them here:`),st=c(),Xe=t("p"),jl=t("a"),ma=p("LMSDiscreteScheduler"),Ma=p(" usually leads to better results:"),tt=c(),f(Ce.$$.fragment),rt=c(),W=t("p"),ya=t("br"),wa=c(),Jl=t("img"),ba=c(),va=t("br"),at=c(),K=t("p"),El=t("a"),Za=p("EulerDiscreteScheduler"),Ua=p(" and "),_l=t("a"),ja=p("EulerAncestralDiscreteScheduler"),Ja=p(" can generate high quality results with as little as 30 steps."),it=c(),f(Ae.$$.fragment),nt=c(),B=t("p"),Ea=t("br"),_a=c(),Tl=t("img"),Ta=c(),ga=t("br"),pt=c(),gl=t("p"),Sa=p("and:"),ot=c(),f(Re.$$.fragment),ct=c(),N=t("p"),Ga=t("br"),$a=c(),Sl=t("img"),ka=c(),Va=t("br"),ut=c(),he=t("p"),Ia=p("At the time of writing this doc "),Gl=t("a"),Wa=p("DPMSolverMultistepScheduler"),Ba=p(` gives arguably the best speed/quality trade-off and can be run with as little
as 20 steps.`),ht=c(),f(xe.$$.fragment),ft=c(),X=t("p"),Na=t("br"),Xa=c(),$l=t("img"),Ca=c(),Aa=t("br"),dt=c(),kl=t("p"),Ra=p(`As you can see most images look very similar and are arguably of very similar quality. It often really depends on the specific use case which scheduler to choose. A good approach is always to run multiple different
schedulers to compare results.`),mt=c(),O=t("h2"),fe=t("a"),ms=t("span"),f(De.$$.fragment),xa=c(),Ms=t("span"),Da=p("Changing the Scheduler in Flax"),Mt=c(),de=t("p"),Fa=p("If you are a JAX/Flax user, you can also change the default pipeline scheduler. This is a complete example of how to run inference using the Flax Stable Diffusion pipeline and the super-fast "),Vl=t("a"),Ya=p("DDPM-Solver++ scheduler"),Qa=p(":"),yt=c(),f(Fe.$$.fragment),wt=c(),f(me.$$.fragment),this.h()},l(e){const a=Zn('[data-svelte="svelte-1phssyn"]',document.head);b=r(a,"META",{name:!0,content:!0}),a.forEach(l),ee=u(e),U=r(e,"H1",{class:!0});var Ye=i(U);_=r(Ye,"A",{id:!0,class:!0,href:!0});var ci=i(_);Y=r(ci,"SPAN",{});var ui=i(Y);d(T.$$.fragment,ui),ui.forEach(l),ci.forEach(l),g=u(Ye),$=r(Ye,"SPAN",{});var hi=i($);Q=o(hi,"Schedulers"),hi.forEach(l),Ye.forEach(l),le=u(e),S=r(e,"P",{});var vt=i(S);z=o(vt,`Diffusion pipelines are inherently a collection of diffusion models and schedulers that are partly independent from each other. This means that one is able to switch out parts of the pipeline to better customize
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First we define a prompt on which we will test all the different schedulers:`),Jt.forEach(l),ks=u(e),d(_e.$$.fragment,e),Vs=u(e),Ke=r(e,"P",{});var Wi=i(Ke);cr=o(Wi,"Next, we create a generator from a random seed that will ensure that we can generate similar images as well as run the pipeline:"),Wi.forEach(l),Is=u(e),d(Te.$$.fragment,e),Ws=u(e),V=r(e,"P",{align:!0});var Xl=i(V);ur=r(Xl,"BR",{}),hr=u(Xl),Oe=r(Xl,"IMG",{src:!0,width:!0}),fr=u(Xl),dr=r(Xl,"BR",{}),Xl.forEach(l),Bs=u(e),q=r(e,"H2",{class:!0});var Et=i(q);ne=r(Et,"A",{id:!0,class:!0,href:!0});var Bi=i(ne);ns=r(Bi,"SPAN",{});var Ni=i(ns);d(ge.$$.fragment,Ni),Ni.forEach(l),Bi.forEach(l),mr=u(Et),ps=r(Et,"SPAN",{});var Xi=i(ps);Mr=o(Xi,"Changing the scheduler"),Xi.forEach(l),Et.forEach(l),Ns=u(e),pe=r(e,"P",{});var _t=i(pe);yr=o(_t,"Now we show how easy it is to change the scheduler of a pipeline. Every scheduler has a property "),os=r(_t,"CODE",{});var Ci=i(os);wr=o(Ci,"SchedulerMixin.compatibles"),Ci.forEach(l),br=o(_t,`
which defines all compatible schedulers. You can take a look at all available, compatible schedulers for the Stable Diffusion pipeline as follows.`),_t.forEach(l),Xs=u(e),d(Se.$$.fragment,e),Cs=u(e),Ge=r(e,"P",{});var Ha=i(Ge);cs=r(Ha,"STRONG",{});var Ai=i(cs);vr=o(Ai,"Output"),Ai.forEach(l),Zr=o(Ha,":"),Ha.forEach(l),As=u(e),d($e.$$.fragment,e),Rs=u(e),el=r(e,"P",{});var Ri=i(el);Ur=o(Ri,"Cool, lots of schedulers to look at. Feel free to have a look at their respective class definitions:"),Ri.forEach(l),xs=u(e),v=r(e,"UL",{});var J=i(v);ll=r(J,"LI",{});var Pa=i(ll);sl=r(Pa,"A",{href:!0});var xi=i(sl);jr=o(xi,"LMSDiscreteScheduler"),xi.forEach(l),Jr=o(Pa,","),Pa.forEach(l),Er=u(J),tl=r(J,"LI",{});var qa=i(tl);rl=r(qa,"A",{href:!0});var Di=i(rl);_r=o(Di,"DDIMScheduler"),Di.forEach(l),Tr=o(qa,","),qa.forEach(l),gr=u(J),al=r(J,"LI",{});var La=i(al);il=r(La,"A",{href:!0});var Fi=i(il);Sr=o(Fi,"DPMSolverMultistepScheduler"),Fi.forEach(l),Gr=o(La,","),La.forEach(l),$r=u(J),nl=r(J,"LI",{});var Ka=i(nl);pl=r(Ka,"A",{href:!0});var Yi=i(pl);kr=o(Yi,"EulerDiscreteScheduler"),Yi.forEach(l),Vr=o(Ka,","),Ka.forEach(l),Ir=u(J),ol=r(J,"LI",{});var Oa=i(ol);cl=r(Oa,"A",{href:!0});var Qi=i(cl);Wr=o(Qi,"PNDMScheduler"),Qi.forEach(l),Br=o(Oa,","),Oa.forEach(l),Nr=u(J),ul=r(J,"LI",{});var ei=i(ul);hl=r(ei,"A",{href:!0});var zi=i(hl);Xr=o(zi,"DDPMScheduler"),zi.forEach(l),Cr=o(ei,","),ei.forEach(l),Ar=u(J),fl=r(J,"LI",{});var li=i(fl);dl=r(li,"A",{href:!0});var Hi=i(dl);Rr=o(Hi,"EulerAncestralDiscreteScheduler"),Hi.forEach(l),xr=o(li,"."),li.forEach(l),J.forEach(l),Ds=u(e),D=r(e,"P",{});var Cl=i(D);Dr=o(Cl,`We will now compare the input prompt with all other schedulers. To change the scheduler of the pipeline you can make use of the
convenient `),us=r(Cl,"CODE",{});var Pi=i(us);Fr=o(Pi,"ConfigMixin.config"),Pi.forEach(l),Yr=o(Cl," property in combination with the "),ml=r(Cl,"A",{href:!0});var qi=i(ml);Qr=o(qi,"ConfigMixin.from_config()"),qi.forEach(l),zr=o(Cl," function."),Cl.forEach(l),Fs=u(e),d(ke.$$.fragment,e),Ys=u(e),Ml=r(e,"P",{});var Li=i(Ml);Hr=o(Li,"returns a dictionary of the configuration of the scheduler:"),Li.forEach(l),Qs=u(e),Ve=r(e,"P",{});var si=i(Ve);hs=r(si,"STRONG",{});var Ki=i(hs);Pr=o(Ki,"Output"),Ki.forEach(l),qr=o(si,":"),si.forEach(l),zs=u(e),d(Ie.$$.fragment,e),Hs=u(e),oe=r(e,"P",{});var Tt=i(oe);Lr=o(Tt,`This configuration can then be used to instantiate a scheduler
of a different class that is compatible with the pipeline. Here,
we change the scheduler to the `),yl=r(Tt,"A",{href:!0});var Oi=i(yl);Kr=o(Oi,"DDIMScheduler"),Oi.forEach(l),Or=o(Tt,"."),Tt.forEach(l),Ps=u(e),d(We.$$.fragment,e),qs=u(e),wl=r(e,"P",{});var en=i(wl);ea=o(en,"Cool, now we can run the pipeline again to compare the generation quality."),en.forEach(l),Ls=u(e),d(Be.$$.fragment,e),Ks=u(e),I=r(e,"P",{align:!0});var Al=i(I);la=r(Al,"BR",{}),sa=u(Al),bl=r(Al,"IMG",{src:!0,width:!0}),ta=u(Al),ra=r(Al,"BR",{}),Al.forEach(l),Os=u(e),ce=r(e,"P",{});var gt=i(ce);aa=o(gt,"If you are a JAX/Flax user, please check "),vl=r(gt,"A",{href:!0});var ln=i(vl);ia=o(ln,"this section"),ln.forEach(l),na=o(gt," instead."),gt.forEach(l),et=u(e),L=r(e,"H2",{class:!0});var St=i(L);ue=r(St,"A",{id:!0,class:!0,href:!0});var sn=i(ue);fs=r(sn,"SPAN",{});var tn=i(fs);d(Ne.$$.fragment,tn),tn.forEach(l),sn.forEach(l),pa=u(St),ds=r(St,"SPAN",{});var rn=i(ds);oa=o(rn,"Compare schedulers"),rn.forEach(l),St.forEach(l),lt=u(e),F=r(e,"P",{});var Rl=i(F);ca=o(Rl,"So far we have tried running the stable diffusion pipeline with two schedulers: "),Zl=r(Rl,"A",{href:!0});var an=i(Zl);ua=o(an,"PNDMScheduler"),an.forEach(l),ha=o(Rl," and "),Ul=r(Rl,"A",{href:!0});var nn=i(Ul);fa=o(nn,"DDIMScheduler"),nn.forEach(l),da=o(Rl,`.
A number of better schedulers have been released that can be run with much fewer steps, let\u2019s compare them here:`),Rl.forEach(l),st=u(e),Xe=r(e,"P",{});var ti=i(Xe);jl=r(ti,"A",{href:!0});var pn=i(jl);ma=o(pn,"LMSDiscreteScheduler"),pn.forEach(l),Ma=o(ti," usually leads to better results:"),ti.forEach(l),tt=u(e),d(Ce.$$.fragment,e),rt=u(e),W=r(e,"P",{align:!0});var xl=i(W);ya=r(xl,"BR",{}),wa=u(xl),Jl=r(xl,"IMG",{src:!0,width:!0}),ba=u(xl),va=r(xl,"BR",{}),xl.forEach(l),at=u(e),K=r(e,"P",{});var ys=i(K);El=r(ys,"A",{href:!0});var on=i(El);Za=o(on,"EulerDiscreteScheduler"),on.forEach(l),Ua=o(ys," and "),_l=r(ys,"A",{href:!0});var cn=i(_l);ja=o(cn,"EulerAncestralDiscreteScheduler"),cn.forEach(l),Ja=o(ys," can generate high quality results with as little as 30 steps."),ys.forEach(l),it=u(e),d(Ae.$$.fragment,e),nt=u(e),B=r(e,"P",{align:!0});var Dl=i(B);Ea=r(Dl,"BR",{}),_a=u(Dl),Tl=r(Dl,"IMG",{src:!0,width:!0}),Ta=u(Dl),ga=r(Dl,"BR",{}),Dl.forEach(l),pt=u(e),gl=r(e,"P",{});var un=i(gl);Sa=o(un,"and:"),un.forEach(l),ot=u(e),d(Re.$$.fragment,e),ct=u(e),N=r(e,"P",{align:!0});var Fl=i(N);Ga=r(Fl,"BR",{}),$a=u(Fl),Sl=r(Fl,"IMG",{src:!0,width:!0}),ka=u(Fl),Va=r(Fl,"BR",{}),Fl.forEach(l),ut=u(e),he=r(e,"P",{});var Gt=i(he);Ia=o(Gt,"At the time of writing this doc "),Gl=r(Gt,"A",{href:!0});var hn=i(Gl);Wa=o(hn,"DPMSolverMultistepScheduler"),hn.forEach(l),Ba=o(Gt,` gives arguably the best speed/quality trade-off and can be run with as little
as 20 steps.`),Gt.forEach(l),ht=u(e),d(xe.$$.fragment,e),ft=u(e),X=r(e,"P",{align:!0});var Yl=i(X);Na=r(Yl,"BR",{}),Xa=u(Yl),$l=r(Yl,"IMG",{src:!0,width:!0}),Ca=u(Yl),Aa=r(Yl,"BR",{}),Yl.forEach(l),dt=u(e),kl=r(e,"P",{});var fn=i(kl);Ra=o(fn,`As you can see most images look very similar and are arguably of very similar quality. It often really depends on the specific use case which scheduler to choose. A good approach is always to run multiple different
schedulers to compare results.`),fn.forEach(l),mt=u(e),O=r(e,"H2",{class:!0});var $t=i(O);fe=r($t,"A",{id:!0,class:!0,href:!0});var dn=i(fe);ms=r(dn,"SPAN",{});var mn=i(ms);d(De.$$.fragment,mn),mn.forEach(l),dn.forEach(l),xa=u($t),Ms=r($t,"SPAN",{});var Mn=i(Ms);Da=o(Mn,"Changing the Scheduler in Flax"),Mn.forEach(l),$t.forEach(l),Mt=u(e),de=r(e,"P",{});var kt=i(de);Fa=o(kt,"If you are a JAX/Flax user, you can also change the default pipeline scheduler. This is a complete example of how to run inference using the Flax Stable Diffusion pipeline and the super-fast "),Vl=r(kt,"A",{href:!0});var yn=i(Vl);Ya=o(yn,"DDPM-Solver++ scheduler"),yn.forEach(l),Qa=o(kt,":"),kt.forEach(l),yt=u(e),d(Fe.$$.fragment,e),wt=u(e),d(me.$$.fragment,e),this.h()},h(){h(b,"name","hf:doc:metadata"),h(b,"content",JSON.stringify(_n)),h(_,"id","schedulers"),h(_,"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"),h(_,"href","#schedulers"),h(U,"class","relative group"),h(G,"href","../api/schedulers/overview.mdx"),h(re,"id","load-pipeline"),h(re,"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"),h(re,"href","#load-pipeline"),h(H,"class","relative 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