diffusers/v0.27.2/en/_app/immutable/nodes/165.e0979fb2.js

import{s as nl,o as pl,n as il}from"../chunks/scheduler.182ea377.js";import{S as al,i as ol,g as a,s as i,r as c,A as rl,h as o,f as t,c as n,j as ll,u as m,x as r,k as tl,y as dl,a as s,v as u,d as f,t as w,w as y}from"../chunks/index.abf12888.js";import{T as sl}from"../chunks/Tip.230e2334.js";import{C as T}from"../chunks/CodeBlock.57fe6e13.js";import{H as le}from"../chunks/Heading.16916d63.js";function cl(O){let p,M='💡 Take a look at GitHub Issue <a href="https://github.com/huggingface/diffusers/issues/841" rel="nofollow">#841</a> for more context about why we’re adding community pipelines to help everyone easily share their work without being slowed down.';return{c(){p=a("p"),p.innerHTML=M},l(d){p=o(d,"P",{"data-svelte-h":!0}),r(p)!=="svelte-19wo1pm"&&(p.innerHTML=M)},m(d,J){s(d,p,J)},p:il,d(d){d&&t(p)}}}function ml(O){let p,M="💡 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 <code>DiffusionPipeline</code> because this is automatically detected.";return{c(){p=a("p"),p.innerHTML=M},l(d){p=o(d,"P",{"data-svelte-h":!0}),r(p)!=="svelte-143wzg5"&&(p.innerHTML=M)},m(d,J){s(d,p,J)},p:il,d(d){d&&t(p)}}}function ul(O){let p,M,d,J,U,te,h,se,I,ke='Community pipelines allow you to add any additional features you’d like on top of the <a href="/docs/diffusers/v0.27.2/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. The main benefit of building on top of the <code>DiffusionPipeline</code> is anyone can load and use your pipeline by only adding one more argument, making it super easy for the community to access.',ie,Z,He="This guide will show you how to create a community pipeline and explain how they work. To keep things simple, you’ll create a “one-step” pipeline where the <code>UNet</code> does a single forward pass and calls the scheduler once.",ne,_,pe,j,Re='You should start by creating a <code>one_step_unet.py</code> file for your community pipeline. In this file, create a pipeline class that inherits from the <a href="/docs/diffusers/v0.27.2/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a> to be able to load model weights and the scheduler configuration from the Hub. The one-step pipeline needs a <code>UNet</code> and a scheduler, so you’ll need to add these as arguments to the <code>__init__</code> function:',ae,G,oe,V,Ne='To ensure your pipeline and its components (<code>unet</code> and <code>scheduler</code>) can be saved with <a href="/docs/diffusers/v0.27.2/en/api/pipelines/overview#diffusers.DiffusionPipeline.save_pretrained">save_pretrained()</a>, add them to the <code>register_modules</code> function:',re,B,de,$,xe="Cool, the <code>__init__</code> step is done and you can move to the forward pass now! 🔥",ce,g,me,C,Ee="In the forward pass, which we recommend defining as <code>__call__</code>, you have complete creative freedom to add whatever feature you’d like. For our amazing one-step pipeline, create a random image and only call the <code>unet</code> and <code>scheduler</code> once by setting <code>timestep=1</code>:",ue,v,fe,W,Qe="That’s it! 🚀 You can now run this pipeline by passing a <code>unet</code> and <code>scheduler</code> to it:",we,X,ye,k,Se='But what’s even better is you can load pre-existing weights into the pipeline if the pipeline structure is identical. For example, you can load the <a href="https://huggingface.co/google/ddpm-cifar10-32" rel="nofollow"><code>google/ddpm-cifar10-32</code></a> weights into the one-step pipeline:',Me,H,Te,R,Je,N,ze='Open a Pull Request on the 🧨 Diffusers <a href="https://github.com/huggingface/diffusers" rel="nofollow">repository</a> to add your awesome pipeline in <code>one_step_unet.py</code> to the <a href="https://github.com/huggingface/diffusers/tree/main/examples/community" rel="nofollow">examples/community</a> subfolder.',he,x,Pe="Once it is merged, anyone with <code>diffusers &gt;= 0.4.0</code> installed can use this pipeline magically 🪄 by specifying it in the <code>custom_pipeline</code> argument:",be,E,Ue,Q,Le='Another way to share your community pipeline is to upload the <code>one_step_unet.py</code> file directly to your preferred <a href="https://huggingface.co/docs/hub/models-uploading" rel="nofollow">model repository</a> on the Hub. Instead of specifying the <code>one_step_unet.py</code> file, pass the model repository id to the <code>custom_pipeline</code> argument:',Ie,S,Ze,z,Ae="Take a look at the following table to compare the two sharing workflows to help you decide the best option for you:",_e,P,Fe="<thead><tr><th></th> <th>GitHub community pipeline</th> <th>HF Hub community pipeline</th></tr></thead> <tbody><tr><td>usage</td> <td>same</td> <td>same</td></tr> <tr><td>review process</td> <td>open a Pull Request on GitHub and undergo a review process from the Diffusers team before merging; may be slower</td> <td>upload directly to a Hub repository without any review; this is the fastest workflow</td></tr> <tr><td>visibility</td> <td>included in the official Diffusers repository and documentation</td> <td>included on your HF Hub profile and relies on your own usage/promotion to gain visibility</td></tr></tbody>",je,b,Ge,L,Ve,A,Ye='A community pipeline is a class that inherits from <a href="/docs/diffusers/v0.27.2/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a> which means:',Be,F,De="<li>It can be loaded with the <code>custom_pipeline</code> argument.</li> <li>The model weights and scheduler configuration are loaded from <code>pretrained_model_name_or_path</code>.</li> <li>The code that implements a feature in the community pipeline is defined in a <code>pipeline.py</code> file.</li>",$e,Y,qe="Sometimes you can’t load all the pipeline components weights from an official repository. In this case, the other components should be passed directly to the pipeline:",ge,D,Ce,q,Ke="The magic behind community pipelines is contained in the following code. It allows the community pipeline to be loaded from GitHub or the Hub, and it’ll be available to all 🧨 Diffusers packages.",ve,K,We,ee,Xe;return U=new le({props:{title:"Contribute a community pipeline",local:"contribute-a-community-pipeline",headingTag:"h1"}}),h=new sl({props:{$$slots:{default:[cl]},$$scope:{ctx:O}}}),_=new le({props:{title:"Initialize the pipeline",local:"initialize-the-pipeline",headingTag:"h2"}}),G=new T({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEElMEFjbGFzcyUyMFVuZXRTY2hlZHVsZXJPbmVGb3J3YXJkUGlwZWxpbmUoRGlmZnVzaW9uUGlwZWxpbmUpJTNBJTBBJTIwJTIwJTIwJTIwZGVmJTIwX19pbml0X18oc2VsZiUyQyUyMHVuZXQlMkMlMjBzY2hlZHVsZXIpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwc3VwZXIoKS5fX2luaXRfXygp",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__()`,wrap:!1}}),B=new T({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>`,wrap:!1}}),g=new le({props:{title:"Define the forward pass",local:"define-the-forward-pass",headingTag:"h2"}}),v=new T({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>`,wrap:!1}}),X=new T({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()`,wrap:!1}}),H=new T({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()`,wrap:!1}}),R=new le({props:{title:"Share your pipeline",local:"share-your-pipeline",headingTag:"h2"}}),E=new T({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()`,wrap:!1}}),S=new T({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>
)`,wrap:!1}}),b=new sl({props:{$$slots:{default:[ml]},$$scope:{ctx:O}}}),L=new le({props:{title:"How do community pipelines work?",local:"how-do-community-pipelines-work",headingTag:"h2"}}),D=new T({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> CLIPImageProcessor, 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 = CLIPImageProcessor.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>,
)`,wrap:!1}}),K=new T({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 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