import{S as st,i as it,s as tt,e as o,k as u,w as k,t,M as lt,c as n,d as i,m,a,x,h as l,b as f,G as s,g as r,y as C,q as D,o as A,B as H,v as ot}from"../../chunks/vendor-hf-doc-builder.js";import{T as nt}from"../../chunks/Tip-hf-doc-builder.js";import{I as ls}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as os}from"../../chunks/CodeBlock-hf-doc-builder.js";function at(Le){let h,L;return{c(){h=o("p"),L=t(`By loading a custom pipeline from the Hugging Face Hub, you are trusting that the code you are loading
is safe \u{1F512}. Make sure to check out the code online before loading & running it automatically.`)},l(y){h=n(y,"P",{});var v=a(h);L=l(v,`By loading a custom pipeline from the Hugging Face Hub, you are trusting that the code you are loading
is safe \u{1F512}. Make sure to check out the code online before loading & running it automatically.`),v.forEach(i)},m(y,v){r(y,h,v),s(h,L)},d(y){y&&i(h)}}}function pt(Le){let h,L,y,v,pe,N,ns,re,as,qe,b,ps,T,rs,fs,te,us,ms,Se,E,q,fe,V,cs,ue,ds,Me,$,hs,me,_s,ys,K,gs,vs,Fe,S,ws,ce,bs,$s,Oe,U,Ie,M,Es,G,Ps,js,ze,F,Be,P,O,de,J,ks,he,xs,Ne,R,Cs,Z,Ds,Te,d,As,_e,Hs,Ls,ye,qs,Ss,ge,Ms,Fs,ve,Os,Is,we,zs,Bs,be,Ns,Ts,Ve,I,Vs,$e,Ks,Us,Ke,Q,Ue,j,z,Ee,W,Gs,Pe,Js,Ge,w,Rs,le,Zs,Qs,je,Ws,Xs,ke,Ys,ei,Je,oe,si,Re,X,Ze,c,ii,xe,ti,li,Y,oi,ni,ee,ai,pi,Ce,ri,fi,De,ui,mi,Ae,ci,di,He,hi,_i,Qe,se,We;return N=new ls({}),V=new ls({}),U=new os({props:{code:`from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
)`,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;hf-internal-testing/diffusers-dummy-pipeline&quot;</span>
)`}}),F=new nt({props:{warning:!0,$$slots:{default:[at]},$$scope:{ctx:Le}}}),J=new ls({}),Q=new os({props:{code:`from diffusers import DiffusionPipeline
from transformers import CLIPFeatureExtractor, CLIPModel

clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"

feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
clip_model = CLIPModel.from_pretrained(clip_model_id)

pipeline = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    custom_pipeline="clip_guided_stable_diffusion",
    clip_model=clip_model,
    feature_extractor=feature_extractor,
)`,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

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)

pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>,
    custom_pipeline=<span class="hljs-string">&quot;clip_guided_stable_diffusion&quot;</span>,
    clip_model=clip_model,
    feature_extractor=feature_extractor,
)`}}),W=new ls({}),X=new os({props:{code:`import torch
from diffusers import DiffusionPipeline


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

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

    @torch.no_grad()
    def __call__(self, batch_size: int = 1, num_inference_steps: int = 50):
        # Sample gaussian noise to begin loop
        image = torch.randn((batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size))

        image = image.to(self.device)

        # set step values
        self.scheduler.set_timesteps(num_inference_steps)

        for t in self.progress_bar(self.scheduler.timesteps):
            # 1. predict noise model_output
            model_output = self.unet(image, t).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to \u03B7 in paper and should be between [0, 1]
            # do x_t -> x_t-1
            image = self.scheduler.step(model_output, t, image, eta).prev_sample

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()

        return image`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline


<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyPipeline</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__()

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

<span class="hljs-meta">    @torch.no_grad()</span>
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, batch_size: <span class="hljs-built_in">int</span> = <span class="hljs-number">1</span>, num_inference_steps: <span class="hljs-built_in">int</span> = <span class="hljs-number">50</span></span>):
        <span class="hljs-comment"># Sample gaussian noise to begin loop</span>
        image = torch.randn((batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size))

        image = image.to(self.device)

        <span class="hljs-comment"># set step values</span>
        self.scheduler.set_timesteps(num_inference_steps)

        <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> self.progress_bar(self.scheduler.timesteps):
            <span class="hljs-comment"># 1. predict noise model_output</span>
            model_output = self.unet(image, t).sample

            <span class="hljs-comment"># 2. predict previous mean of image x_t-1 and add variance depending on eta</span>
            <span class="hljs-comment"># eta corresponds to \u03B7 in paper and should be between [0, 1]</span>
            <span class="hljs-comment"># do x_t -&gt; x_t-1</span>
            image = self.scheduler.step(model_output, t, image, eta).prev_sample

        image = (image / <span class="hljs-number">2</span> + <span class="hljs-number">0.5</span>).clamp(<span class="hljs-number">0</span>, <span class="hljs-number">1</span>)
        image = image.cpu().permute(<span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>).numpy()

        <span class="hljs-keyword">return</span> image`}}),se=new os({props:{code:`my_pipeline = DiffusionPipeline.from_pretrained(
    "google/ddpm-cifar10-32", custom_pipeline="patrickvonplaten/my_custom_pipeline"
)`,highlighted:`my_pipeline = DiffusionPipeline.from_pretrained(
    <span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>, custom_pipeline=<span class="hljs-string">&quot;patrickvonplaten/my_custom_pipeline&quot;</span>
)`}}),{c(){h=o("meta"),L=u(),y=o("h1"),v=o("a"),pe=o("span"),k(N.$$.fragment),ns=u(),re=o("span"),as=t("Custom Pipelines"),qe=u(),b=o("p"),ps=t("Diffusers allows you to conveniently load any custom pipeline from the Hugging Face Hub as well as any "),T=o("a"),rs=t("official community pipeline"),fs=t(`
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