diffusers/pr_12652/en/_app/immutable/nodes/100.73088515.js

import{s as Ht,o as Nt,n as Dt}from"../chunks/scheduler.53228c21.js";import{S as jt,i as zt,e as r,s as o,c as v,h as It,a as i,d as l,b as n,f as N,g as b,j as s,k as P,l as t,m as k,n as x,t as w,o as $,p as y}from"../chunks/index.100fac89.js";import{C as Vt}from"../chunks/CopyLLMTxtMenu.50ab6782.js";import{D as V}from"../chunks/Docstring.d95185c4.js";import{C as Et}from"../chunks/CodeBlock.d30a6509.js";import{E as Ut}from"../chunks/ExampleCodeBlock.fd8b68b3.js";import{H as Tt,E as Rt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.720a8c3c.js";function qt(be){let h,E="Examples:",T,C,M;return C=new Et({props:{code:"JTIzJTIwVXBkYXRlJTIwcHJlLXRyYWluZWQlMjBtb2RlbCUwQXBpcGVsaW5lLnVwZGF0ZV9jb21wb25lbnRzKHVuZXQlM0RuZXdfdW5ldF9tb2RlbCUyQyUyMHRleHRfZW5jb2RlciUzRG5ld190ZXh0X2VuY29kZXIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMHZhbHVlcyUwQXBpcGVsaW5lLnVwZGF0ZV9jb21wb25lbnRzKHJlcXVpcmVzX3NhZmV0eV9jaGVja2VyJTNERmFsc2Up",highlighted:`<span class="hljs-comment"># Update pre-trained model</span>
pipeline.update_components(unet=new_unet_model, text_encoder=new_text_encoder)

<span class="hljs-comment"># Update configuration values</span>
pipeline.update_components(requires_safety_checker=<span class="hljs-literal">False</span>)`,wrap:!1}}),{c(){h=r("p"),h.textContent=E,T=o(),v(C.$$.fragment)},l(u){h=i(u,"P",{"data-svelte-h":!0}),s(h)!=="svelte-kvfsh7"&&(h.textContent=E),T=n(u),b(C.$$.fragment,u)},m(u,L){k(u,h,L),k(u,T,L),x(C,u,L),M=!0},p:Dt,i(u){M||(w(C.$$.fragment,u),M=!0)},o(u){$(C.$$.fragment,u),M=!1},d(u){u&&(l(h),l(T)),y(C,u)}}}function Bt(be){let h,E,T,C,M,u,L,xe,U,we,a,R,Le,O,it="Base class for all Modular pipelines.",Te,q,at="<p>&gt; This is an experimental feature and is likely to change in the future.</p>",He,D,B,Ne,Q,st="Load a ModularPipeline from a huggingface hub repo.",De,K,F,je,j,W,ze,Y,lt="Load selected components from specs.",Ie,p,X,Ve,ee,dt="Register components with their corresponding specifications.",Ee,te,ct="This method is responsible for:",Ue,oe,pt=`<li>Sets component objects as attributes on the loader (e.g., self.unet = unet)</li> <li>Updates the config dict, which will be saved as <code>modular_model_index.json</code> during <code>save_pretrained</code> (only
for from_pretrained components)</li> <li>Adds components to the component manager if one is attached (only for from_pretrained components)</li>`,Re,ne,mt="This method is called when:",qe,re,ut=`<li>Components are first initialized in <strong>init</strong>:
<ul><li>from_pretrained components not loaded during <strong>init</strong> so they are registered as None;</li> <li>non from_pretrained components are created during <strong>init</strong> and registered as the object itself</li></ul></li> <li>Components are updated with the <code>update_components()</code> method: e.g. loader.update_components(unet=unet) or
loader.update_components(guider=guider_spec)</li> <li>(from_pretrained) Components are loaded with the <code>load_components()</code> method: e.g.
loader.load_components(names=[“unet”]) or loader.load_components() to load all default components</li>`,Be,ie,ft="Notes:",Fe,ae,ht=`<li>When registering None for a component, it sets attribute to None but still syncs specs with the config
dict, which will be saved as <code>modular_model_index.json</code> during <code>save_pretrained</code></li> <li>component_specs are updated to match the new component outside of this method, e.g. in
<code>update_components()</code> method</li>`,We,z,S,Xe,se,gt=`Save the pipeline and all its components to a directory, so that it can be re-loaded using the
<a href="/docs/diffusers/pr_12652/en/api/modular_diffusers/pipeline#diffusers.ModularPipeline.from_pretrained">from_pretrained()</a> class method.`,Se,_,Z,Ze,le,_t=`Performs Pipeline dtype and/or device conversion. A torch.dtype and torch.device are inferred from the
arguments of <code>self.to(*args, **kwargs).</code>`,Je,J,vt=`<p>&gt; If the pipeline already has the correct torch.dtype and torch.device, then it is returned as is.
Otherwise, &gt; the returned pipeline is a copy of self with the desired torch.dtype and torch.device.</p>`,Ge,de,bt="Here are the ways to call <code>to</code>:",Ae,ce,xt=`<li><code>to(dtype, silence_dtype_warnings=False) → DiffusionPipeline</code> to return a pipeline with the specified
<a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" rel="nofollow"><code>dtype</code></a></li> <li><code>to(device, silence_dtype_warnings=False) → DiffusionPipeline</code> to return a pipeline with the specified
<a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" rel="nofollow"><code>device</code></a></li> <li><code>to(device=None, dtype=None, silence_dtype_warnings=False) → DiffusionPipeline</code> to return a pipeline with the
specified <a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" rel="nofollow"><code>device</code></a> and
<a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" rel="nofollow"><code>dtype</code></a></li>`,Oe,c,G,Qe,pe,wt="Update components and configuration values and specs after the pipeline has been instantiated.",Ke,me,$t="This method allows you to:",Ye,ue,yt="<li>Replace existing components with new ones (e.g., updating <code>self.unet</code> or <code>self.text_encoder</code>)</li> <li>Update configuration values (e.g., changing <code>self.requires_safety_checker</code> flag)</li>",et,fe,Ct=`In addition to updating the components and configuration values as pipeline attributes, the method also
updates:`,tt,he,Mt="<li>the corresponding specs in <code>_component_specs</code> and <code>_config_specs</code></li> <li>the <code>config</code> dict, which will be saved as <code>modular_model_index.json</code> during <code>save_pretrained</code></li>",ot,I,nt,ge,Pt="Notes:",rt,_e,kt=`<li>Components loaded with <code>AutoModel.from_pretrained()</code> or <code>ComponentSpec.load()</code> will have
loading specs preserved for serialization. Custom or locally loaded components without Hub references will
have their <code>modular_model_index.json</code> entries updated automatically during <code>save_pretrained()</code>.</li> <li>ConfigMixin objects without weights (e.g., schedulers, guiders) can be passed directly.</li>`,$e,A,ye,ve,Ce;return M=new Vt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),L=new Tt({props:{title:"Pipeline",local:"pipeline",headingTag:"h1"}}),U=new Tt({props:{title:"ModularPipeline",local:"diffusers.ModularPipeline",headingTag:"h2"}}),R=new V({props:{name:"class diffusers.ModularPipeline",anchor:"diffusers.ModularPipeline",parameters:[{name:"blocks",val:": diffusers.modular_pipelines.modular_pipeline.ModularPipelineBlocks | None = None"},{name:"pretrained_model_name_or_path",val:": str | os.PathLike | None = None"},{name:"components_manager",val:": diffusers.modular_pipelines.components_manager.ComponentsManager | None = None"},{name:"collection",val:": str | None = None"},{name:"modular_config_dict",val:": dict[str, typing.Any] | None = None"},{name:"config_dict",val:": dict[str, typing.Any] | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.ModularPipeline.blocks",description:"<strong>blocks</strong> &#x2014; ModularPipelineBlocks, the blocks to be used in the pipeline",name:"blocks"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L1576"}}),B=new V({props:{name:"from_pretrained",anchor:"diffusers.ModularPipeline.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": str | os.PathLike | None"},{name:"trust_remote_code",val:": bool | None = None"},{name:"components_manager",val:": diffusers.modular_pipelines.components_manager.ComponentsManager | None = None"},{name:"collection",val:": str | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.ModularPipeline.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, optional) &#x2014;
Path to a pretrained pipeline configuration. It will first try to load config from
<code>modular_model_index.json</code>, then fallback to <code>model_index.json</code> for compatibility with standard
non-modular repositories. If the pretrained_model_name_or_path does not contain any pipeline config, it
will be set to None during initialization.`,name:"pretrained_model_name_or_path"},{anchor:"diffusers.ModularPipeline.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, optional) &#x2014;
Whether to trust remote code when loading the pipeline, need to be set to True if you want to create
pipeline blocks based on the custom code in <code>pretrained_model_name_or_path</code>`,name:"trust_remote_code"},{anchor:"diffusers.ModularPipeline.from_pretrained.components_manager",description:`<strong>components_manager</strong> (<code>ComponentsManager</code>, optional) &#x2014;
ComponentsManager instance for managing multiple component cross different pipelines and apply
offloading strategies.`,name:"components_manager"},{anchor:"diffusers.ModularPipeline.from_pretrained.collection",description:"<strong>collection</strong> (<code>str</code>, optional) &#x2014;`\nCollection name for organizing components in the ComponentsManager.",name:"collection"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L1788"}}),F=new V({props:{name:"get_component_spec",anchor:"diffusers.ModularPipeline.get_component_spec",parameters:[{name:"name",val:": str"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L2233",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li>a copy of the ComponentSpec object for the given component name</li>
</ul>
`}}),W=new V({props:{name:"load_components",anchor:"diffusers.ModularPipeline.load_components",parameters:[{name:"names",val:": list[str] | str | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.ModularPipeline.load_components.names",description:`<strong>names</strong> &#x2014; list of component names to load. If None, will load all components with
default_creation_method == &#x201C;from_pretrained&#x201D;. If provided as a list or string, will load only the
specified components.`,name:"names"},{anchor:"diffusers.ModularPipeline.load_components.*kwargs",description:`*<strong>*kwargs</strong> &#x2014; additional kwargs to be passed to <code>from_pretrained()</code>.Can be:
<ul>
<li>a single value to be applied to all components to be loaded, e.g. torch_dtype=torch.bfloat16</li>
<li>a dict, e.g. torch_dtype={&#x201C;unet&#x201D;: torch.bfloat16, &#x201C;default&#x201D;: torch.float32}</li>
<li>if potentially override ComponentSpec if passed a different loading field in kwargs, e.g.
<code>pretrained_model_name_or_path</code>, <code>variant</code>, <code>revision</code>, etc.</li>
<li>if potentially override ComponentSpec if passed a different loading field in kwargs, e.g.
<code>pretrained_model_name_or_path</code>, <code>variant</code>, <code>revision</code>, etc.</li>
</ul>`,name:"*kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L2321"}}),X=new V({props:{name:"register_components",anchor:"diffusers.ModularPipeline.register_components",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.ModularPipeline.register_components.*kwargs",description:`*<strong>*kwargs</strong> &#x2014; Keyword arguments where keys are component names and values are component objects.
E.g., register_components(unet=unet_model, text_encoder=encoder_model)`,name:"*kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L2028"}}),S=new V({props:{name:"save_pretrained",anchor:"diffusers.ModularPipeline.save_pretrained",parameters:[{name:"save_directory",val:": str | os.PathLike"},{name:"safe_serialization",val:": bool = True"},{name:"variant",val:": str | None = None"},{name:"max_shard_size",val:": int | str | None = None"},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.ModularPipeline.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory to save the pipeline to. Will be created if it doesn&#x2019;t exist.`,name:"save_directory"},{anchor:"diffusers.ModularPipeline.save_pretrained.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to save the model using <code>safetensors</code> or the traditional PyTorch way with <code>pickle</code>.`,name:"safe_serialization"},{anchor:"diffusers.ModularPipeline.save_pretrained.variant",description:`<strong>variant</strong> (<code>str</code>, <em>optional</em>) &#x2014;
If specified, weights are saved in the format <code>pytorch_model.&lt;variant&gt;.bin</code>.`,name:"variant"},{anchor:"diffusers.ModularPipeline.save_pretrained.max_shard_size",description:`<strong>max_shard_size</strong> (<code>int</code> or <code>str</code>, defaults to <code>None</code>) &#x2014;
The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
lower than this size. If expressed as a string, needs to be digits followed by a unit (like <code>&quot;5GB&quot;</code>).
If expressed as an integer, the unit is bytes.`,name:"max_shard_size"},{anchor:"diffusers.ModularPipeline.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to push the pipeline to the Hugging Face model hub after saving it.`,name:"push_to_hub"},{anchor:"diffusers.ModularPipeline.save_pretrained.*kwargs",description:`*<strong>*kwargs</strong> &#x2014; Additional keyword arguments:
<ul>
<li><code>overwrite_modular_index</code> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
When saving a Modular Pipeline, its components in <code>modular_model_index.json</code> may reference repos
different from the destination repo. Setting this to <code>True</code> updates all component references in
<code>modular_model_index.json</code> so they point to the repo specified by <code>repo_id</code>.</li>
<li><code>repo_id</code> (<code>str</code>, <em>optional</em>):
The repository ID to push the pipeline to. Defaults to the last component of <code>save_directory</code>.</li>
<li><code>commit_message</code> (<code>str</code>, <em>optional</em>):
Commit message for the push to hub operation.</li>
<li><code>private</code> (<code>bool</code>, <em>optional</em>):
Whether the repository should be private.</li>
<li><code>create_pr</code> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to create a pull request instead of pushing directly.</li>
<li><code>token</code> (<code>str</code>, <em>optional</em>):
The Hugging Face token to use for authentication.</li>
</ul>`,name:"*kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L1874"}}),Z=new V({props:{name:"to",anchor:"diffusers.ModularPipeline.to",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.ModularPipeline.to.dtype",description:`<strong>dtype</strong> (<code>torch.dtype</code>, <em>optional</em>) &#x2014;
Returns a pipeline with the specified
<a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" rel="nofollow"><code>dtype</code></a>`,name:"dtype"},{anchor:"diffusers.ModularPipeline.to.device",description:`<strong>device</strong> (<code>torch.Device</code>, <em>optional</em>) &#x2014;
Returns a pipeline with the specified
<a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" rel="nofollow"><code>device</code></a>`,name:"device"},{anchor:"diffusers.ModularPipeline.to.silence_dtype_warnings",description:`<strong>silence_dtype_warnings</strong> (<code>str</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to omit warnings if the target <code>dtype</code> is not compatible with the target <code>device</code>.`,name:"silence_dtype_warnings"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L2438",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The pipeline converted to specified <code>dtype</code> and/or <code>dtype</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_12652/en/api/pipelines/overview#diffusers.DiffusionPipeline"
>DiffusionPipeline</a></p>
`}}),G=new V({props:{name:"update_components",anchor:"diffusers.ModularPipeline.update_components",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.ModularPipeline.update_components.*kwargs",description:`*<strong>*kwargs</strong> &#x2014; Component objects or configuration values to update:
<ul>
<li>Component objects: Models loaded with <code>AutoModel.from_pretrained()</code> or <code>ComponentSpec.load()</code>
are automatically tagged with loading information. ConfigMixin objects without weights (e.g.,
schedulers, guiders) can be passed directly.</li>
<li>Configuration values: Simple values to update configuration settings
(e.g., <code>requires_safety_checker=False</code>)</li>
</ul>`,name:"*kwargs"}],source:"https://github.com/huggingface/diffusers/blob/vr_12652/src/diffusers/modular_pipelines/modular_pipeline.py#L2240"}}),I=new Ut({props:{anchor:"diffusers.ModularPipeline.update_components.example",$$slots:{default:[qt]},$$scope:{ctx:be}}}),A=new Rt({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/modular_diffusers/pipeline.md"}}),{c(){h=r("meta"),E=o(),T=r("p"),C=o(),v(M.$$.fragment),u=o(),v(L.$$.fragment),xe=o(),v(U.$$.fragment),we=o(),a=r("div"),v(R.$$.fragment),Le=o(),O=r("p"),O.textContent=it,Te=o(),q=r("blockquote"),q.innerHTML=at,He=o(),D=r("div"),v(B.$$.fragment),Ne=o(),Q=r("p"),Q.textContent=st,De=o(),K=r("div"),v(F.$$.fragment),je=o(),j=r("div"),v(W.$$.fragment),ze=o(),Y=r("p"),Y.textContent=lt,Ie=o(),p=r("div"),v(X.$$.fragment),Ve=o(),ee=r("p"),ee.textContent=dt,Ee=o(),te=r("p"),te.textContent=ct,Ue=o(),oe=r("ol"),oe.innerHTML=pt,Re=o(),ne=r("p"),ne.textContent=mt,qe=o(),re=r("ul"),re.innerHTML=ut,Be=o(),ie=r("p"),ie.textContent=ft,Fe=o(),ae=r("ul"),ae.innerHTML=ht,We=o(),z=r("div"),v(S.$$.fragment),Xe=o(),se=r("p"),se.innerHTML=gt,Se=o(),_=r("div"),v(Z.$$.fragment),Ze=o(),le=r("p"),le.innerHTML=_t,Je=o(),J=r("blockquote"),J.innerHTML=vt,Ge=o(),de=r("p"),de.innerHTML=bt,Ae=o(),ce=r("ul"),ce.innerHTML=xt,Oe=o(),c=r("div"),v(G.$$.fragment),Qe=o(),pe=r("p"),pe.textContent=wt,Ke=o(),me=r("p"),me.textContent=$t,Ye=o(),ue=r("ol"),ue.innerHTML=yt,et=o(),fe=r("p"),fe.textContent=Ct,tt=o(),he=r("ul"),he.innerHTML=Mt,ot=o(),v(I.$$.fragment),nt=o(),ge=r("p"),ge.textContent=Pt,rt=o(),_e=r("ul"),_e.innerHTML=kt,$e=o(),v(A.$$.fragment),ye=o(),ve=r("p"),this.h()},l(e){const 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