import{S as zs,i as Ys,s as Os,e as s,k as c,w as M,t as o,M as Ls,c as a,d as l,m as d,a as n,x as J,h as i,b as f,G as t,g as p,y as T,q as _,o as b,B as v,v as qs}from"../../chunks/vendor-hf-doc-builder.js";import{T as Fs}from"../../chunks/Tip-hf-doc-builder.js";import{I as Nt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Y}from"../../chunks/CodeBlock-hf-doc-builder.js";function Ks(Be){let u,B,h,w,j;return{c(){u=s("p"),B=o("\u{1F4A1} Take a look at GitHub Issue "),h=s("a"),w=o("#841"),j=o(" for more context about why we\u2019re adding community pipelines to help everyone easily share their work without being slowed down."),this.h()},l(m){u=a(m,"P",{});var y=n(u);B=i(y,"\u{1F4A1} Take a look at GitHub Issue "),h=a(y,"A",{href:!0,rel:!0});var V=n(h);w=i(V,"#841"),V.forEach(l),j=i(y," for more context about why we\u2019re adding community pipelines to help everyone easily share their work without being slowed down."),y.forEach(l),this.h()},h(){f(h,"href","https://github.com/huggingface/diffusers/issues/841"),f(h,"rel","nofollow")},m(m,y){p(m,u,y),t(u,B),t(u,h),t(h,w),t(u,j)},d(m){m&&l(u)}}}function ea(Be){let u,B,h,w,j;return{c(){u=s("p"),B=o("\u{1F4A1} You can use whatever package you want in your community pipeline file - as long as the user has it installed, everything will work fine. Make sure you have one and only one pipeline class that inherits from "),h=s("code"),w=o("DiffusionPipeline"),j=o(" because this is automatically detected.")},l(m){u=a(m,"P",{});var y=n(u);B=i(y,"\u{1F4A1} You can use whatever package you want in your community pipeline file - as long as the user has it installed, everything will work fine. Make sure you have one and only one pipeline class that inherits from "),h=a(y,"CODE",{});var V=n(h);w=i(V,"DiffusionPipeline"),V.forEach(l),j=i(y," because this is automatically detected."),y.forEach(l)},m(m,y){p(m,u,y),t(u,B),t(u,h),t(h,w),t(u,j)},d(m){m&&l(u)}}}function ta(Be){let u,B,h,w,j,m,y,V,Vl,Wt,O,At,$,$l,Ge,gl,kl,Qe,Cl,Rl,Dt,L,Xl,Fe,Nl,Wl,Ht,A,q,ze,re,Al,Ye,Dl,St,Z,Hl,Oe,Sl,Pl,Ve,xl,Ql,Le,Fl,zl,qe,Yl,Ol,Pt,pe,xt,I,Ll,Ke,ql,Kl,et,eo,to,$e,lo,oo,tt,io,so,Qt,ce,Ft,K,ao,lt,no,ro,zt,D,ee,ot,de,po,it,co,Yt,E,fo,st,uo,ho,at,mo,yo,nt,wo,Mo,rt,Jo,To,Ot,fe,Lt,g,_o,pt,bo,vo,ct,Zo,Io,qt,ue,Kt,te,Eo,he,dt,Uo,jo,el,me,tl,H,le,ft,ye,Bo,ut,Go,ll,G,Vo,we,$o,go,ht,ko,Co,Me,Ro,Xo,ol,k,No,mt,Wo,Ao,yt,Do,Ho,il,Je,sl,U,So,wt,Po,xo,Te,Qo,Fo,Mt,zo,Yo,Jt,Oo,Lo,al,_e,nl,ge,qo,rl,oe,Tt,S,pl,Ko,_t,ei,ti,bt,li,oi,P,x,vt,ii,si,Zt,ai,ni,It,ri,pi,Q,Et,ci,di,Ut,fi,ui,jt,hi,mi,F,Bt,yi,wi,Gt,Mi,Ji,Vt,Ti,cl,ie,dl,z,se,$t,be,_i,gt,bi,fl,ae,vi,ke,Zi,Ii,ul,C,ve,Ei,kt,Ui,ji,Bi,Ze,Gi,Ct,Vi,$i,gi,Ie,ki,Rt,Ci,Ri,hl,Ce,Xi,ml,Ee,yl,Re,Ni,wl,Ue,Ml;return m=new Nt({}),O=new Fs({props:{$$slots:{default:[Ks]},$$scope:{ctx:Be}}}),re=new Nt({}),pe=new Y({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEElMEFjbGFzcyUyMFVuZXRTY2hlZHVsZXJPbmVGb3J3YXJkUGlwZWxpbmUoRGlmZnVzaW9uUGlwZWxpbmUpJTNBJTBBJTIwJTIwJTIwJTIwZGVmJTIwX19pbml0X18oc2VsZiUyQyUyMHVuZXQlMkMlMjBzY2hlZHVsZXIpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwc3VwZXIoKS5fX2luaXRfXygp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">import</span> torch


<span class="hljs-keyword">class</span> <span class="hljs-title class_">UnetSchedulerOneForwardPipeline</span>(<span class="hljs-title class_ inherited__">DiffusionPipeline</span>):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, unet, scheduler</span>):
        <span class="hljs-built_in">super</span>().__init__()`}}),ce=new Y({props:{code:"JTIwJTIwZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTIwJTIwaW1wb3J0JTIwdG9yY2glMEElMEElMjAlMjBjbGFzcyUyMFVuZXRTY2hlZHVsZXJPbmVGb3J3YXJkUGlwZWxpbmUoRGlmZnVzaW9uUGlwZWxpbmUpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwZGVmJTIwX19pbml0X18oc2VsZiUyQyUyMHVuZXQlMkMlMjBzY2hlZHVsZXIpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwc3VwZXIoKS5fX2luaXRfXygpJTBBJTBBJTJCJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwc2VsZi5yZWdpc3Rlcl9tb2R1bGVzKHVuZXQlM0R1bmV0JTJDJTIwc2NoZWR1bGVyJTNEc2NoZWR1bGVyKQ==",highlighted:`  from diffusers import DiffusionPipeline
  import torch

  class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
      def __init__(self, unet, scheduler):
          super().__init__()

<span class="hljs-addition">+         self.register_modules(unet=unet, scheduler=scheduler)</span>`}}),de=new Nt({}),fe=new Y({props:{code:"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",highlighted:`  from diffusers import DiffusionPipeline
  import torch


  class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
      def __init__(self, unet, scheduler):
          super().__init__()

          self.register_modules(unet=unet, scheduler=scheduler)

<span class="hljs-addition">+     def __call__(self):</span>
<span class="hljs-addition">+         image = torch.randn(</span>
<span class="hljs-addition">+             (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),</span>
<span class="hljs-addition">+         )</span>
<span class="hljs-addition">+         timestep = 1</span>

<span class="hljs-addition">+         model_output = self.unet(image, timestep).sample</span>
<span class="hljs-addition">+         scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample</span>

<span class="hljs-addition">+         return scheduler_output</span>`}}),ue=new Y({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEREUE1TY2hlZHVsZXIlMkMlMjBVTmV0MkRNb2RlbCUwQSUwQXNjaGVkdWxlciUyMCUzRCUyMEREUE1TY2hlZHVsZXIoKSUwQXVuZXQlMjAlM0QlMjBVTmV0MkRNb2RlbCgpJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBVbmV0U2NoZWR1bGVyT25lRm9yd2FyZFBpcGVsaW5lKHVuZXQlM0R1bmV0JTJDJTIwc2NoZWR1bGVyJTNEc2NoZWR1bGVyKSUwQSUwQW91dHB1dCUyMCUzRCUyMHBpcGVsaW5lKCk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMScheduler, UNet2DModel

scheduler = DDPMScheduler()
unet = UNet2DModel()

pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)

output = pipeline()`}}),me=new Y({props:{code:"cGlwZWxpbmUlMjAlM0QlMjBVbmV0U2NoZWR1bGVyT25lRm9yd2FyZFBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZkZHBtLWNpZmFyMTAtMzIlMjIlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlKSUwQSUwQW91dHB1dCUyMCUzRCUyMHBpcGVsaW5lKCk=",highlighted:`pipeline = UnetSchedulerOneForwardPipeline.from_pretrained(<span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>, use_safetensors=<span class="hljs-literal">True</span>)

output = pipeline()`}}),ye=new Nt({}),Je=new Y({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJnb29nbGUlMkZkZHBtLWNpZmFyMTAtMzIlMjIlMkMlMjBjdXN0b21fcGlwZWxpbmUlM0QlMjJvbmVfc3RlcF91bmV0JTIyJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSUwQSklMEFwaXBlKCk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>, custom_pipeline=<span class="hljs-string">&quot;one_step_unet&quot;</span>, use_safetensors=<span class="hljs-literal">True</span>
)
pipe()`}}),_e=new Y({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyZ29vZ2xlJTJGZGRwbS1jaWZhcjEwLTMyJTIyJTJDJTIwY3VzdG9tX3BpcGVsaW5lJTNEJTIyc3RldmhsaXUlMkZvbmVfc3RlcF91bmV0JTIyJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSUwQSk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>, custom_pipeline=<span class="hljs-string">&quot;stevhliu/one_step_unet&quot;</span>, use_safetensors=<span class="hljs-literal">True</span>
)`}}),ie=new Fs({props:{$$slots:{default:[ea]},$$scope:{ctx:Be}}}),be=new Nt({}),Ee=new Y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPFeatureExtractor, CLIPModel

model_id = <span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
clip_model_id = <span class="hljs-string">&quot;laion/CLIP-ViT-B-32-laion2B-s34B-b79K&quot;</span>

feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)

pipeline = DiffusionPipeline.from_pretrained(
    model_id,
    custom_pipeline=<span class="hljs-string">&quot;clip_guided_stable_diffusion&quot;</span>,
    clip_model=clip_model,
    feature_extractor=feature_extractor,
    scheduler=scheduler,
    torch_dtype=torch.float16,
    use_safetensors=<span class="hljs-literal">True</span>,
)`}}),Ue=new Y({props:{code:"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",highlighted:`<span class="hljs-comment"># 2. Load the pipeline class, if using custom module then load it from the hub</span>
<span class="hljs-comment"># if we load from explicit class, let&#x27;s use it</span>
<span class="hljs-keyword">if</span> custom_pipeline <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">None</span>:
    pipeline_class = get_class_from_dynamic_module(
        custom_pipeline, module_file=CUSTOM_PIPELINE_FILE_NAME, cache_dir=custom_pipeline
    )
<span class="hljs-keyword">elif</span> cls != DiffusionPipeline:
    pipeline_class = cls
<span class="hljs-keyword">else</span>:
    diffusers_module = importlib.import_module(cls.__module__.split(<span class="hljs-string">&quot;.&quot;</span>)[<span class="hljs-number">0</span>])
    pipeline_class = <span class="hljs-built_in">getattr</span>(diffusers_module, config_dict[<span class="hljs-string">&quot;_class_name&quot;</span>])`}}),{c(){u=s("meta"),B=c(),h=s("h1"),w=s("a"),j=s("span"),M(m.$$.fragment),y=c(),V=s("span"),Vl=o("How to contribute a community pipeline"),Wt=c(),M(O.$$.fragment),At=c(),$=s("p"),$l=o("Community pipelines allow you to add any additional features you\u2019d like on top of the "),Ge=s("a"),gl=o("DiffusionPipeline"),kl=o(". The main benefit of building on top of the "),Qe=s("code"),Cl=o("DiffusionPipeline"),Rl=o(" is anyone can load and use your pipeline by only adding one more argument, making it super easy for the community to access."),Dt=c(),L=s("p"),Xl=o("This guide will show you how to create a community pipeline and explain how they work. To keep things simple, you\u2019ll create a \u201Cone-step\u201D pipeline where the "),Fe=s("code"),Nl=o("UNet"),Wl=o(" does a single forward pass and calls the scheduler once."),Ht=c(),A=s("h2"),q=s("a"),ze=s("span"),M(re.$$.fragment),Al=c(),Ye=s("span"),Dl=o("Initialize the pipeline"),St=c(),Z=s("p"),Hl=o("You should start by creating a "),Oe=s("code"),Sl=o("one_step_unet.py"),Pl=o(" file for your community pipeline. In this file, create a pipeline class that inherits from the "),Ve=s("a"),xl=o("DiffusionPipeline"),Ql=o(" to be able to load model weights and the scheduler configuration from the Hub. The one-step pipeline needs a "),Le=s("code"),Fl=o("UNet"),zl=o(" and a scheduler, so you\u2019ll need to add these as arguments to the "),qe=s("code"),Yl=o("__init__"),Ol=o(" function:"),Pt=c(),M(pe.$$.fragment),xt=c(),I=s("p"),Ll=o("To ensure your pipeline and its components ("),Ke=s("code"),ql=o("unet"),Kl=o(" and "),et=s("code"),eo=o("scheduler"),to=o(") can be saved with "),$e=s("a"),lo=o("save_pretrained()"),oo=o(", add them to the "),tt=s("code"),io=o("register_modules"),so=o(" function:"),Qt=c(),M(ce.$$.fragment),Ft=c(),K=s("p"),ao=o("Cool, the "),lt=s("code"),no=o("__init__"),ro=o(" step is done and you can move to the forward pass now! \u{1F525}"),zt=c(),D=s("h2"),ee=s("a"),ot=s("span"),M(de.$$.fragment),po=c(),it=s("span"),co=o("Define the forward pass"),Yt=c(),E=s("p"),fo=o("In the forward pass, which we recommend defining as "),st=s("code"),uo=o("__call__"),ho=o(", you have complete creative freedom to add whatever feature you\u2019d like. For our amazing one-step pipeline, create a random image and only call the "),at=s("code"),mo=o("unet"),yo=o(" and "),nt=s("code"),wo=o("scheduler"),Mo=o(" once by setting "),rt=s("code"),Jo=o("timestep=1"),To=o(":"),Ot=c(),M(fe.$$.fragment),Lt=c(),g=s("p"),_o=o("That\u2019s it! \u{1F680} You can now run this pipeline by passing a "),pt=s("code"),bo=o("unet"),vo=o(" and "),ct=s("code"),Zo=o("scheduler"),Io=o(" to it:"),qt=c(),M(ue.$$.fragment),Kt=c(),te=s("p"),Eo=o("But what\u2019s even better is you can load pre-existing weights into the pipeline if the pipeline structure is identical. 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