import{s as Pt,o as kt,n as rt}from"../chunks/scheduler.8c3d61f6.js";import{S as It,i as Jt,g as r,s as n,r as b,A as Lt,h as i,f as l,c as o,j as J,u as w,x as d,k as L,y as t,a as j,v as $,d as y,t as M,w as x}from"../chunks/index.da70eac4.js";import{T as Tt}from"../chunks/Tip.1d9b8c37.js";import{D as G}from"../chunks/Docstring.91dd4639.js";import{C as Ut}from"../chunks/CodeBlock.a9c4becf.js";import{E as Et}from"../chunks/ExampleCodeBlock.d368b7c4.js";import{H as jt,E as Vt}from"../chunks/getInferenceSnippets.28ca3596.js";function Nt(U){let s,T="This is an experimental feature and is likely to change in the future.";return{c(){s=r("p"),s.textContent=T},l(c){s=i(c,"P",{"data-svelte-h":!0}),d(s)!=="svelte-ozwf5m"&&(s.textContent=T)},m(c,_){j(c,s,_)},p:rt,d(c){c&&l(s)}}}function Bt(U){let s,T=`If the pipeline already has the correct torch.dtype and torch.device, then it is returned as is. Otherwise,
the returned pipeline is a copy of self with the desired torch.dtype and torch.device.`;return{c(){s=r("p"),s.textContent=T},l(c){s=i(c,"P",{"data-svelte-h":!0}),d(s)!=="svelte-18gkv0g"&&(s.textContent=T)},m(c,_){j(c,s,_)},p:rt,d(c){c&&l(s)}}}function Zt(U){let s,T="Examples:",c,_,P;return _=new Ut({props:{code:"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",highlighted:`<span class="hljs-comment"># Update multiple components at once</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>)

<span class="hljs-comment"># Update both components and configs together</span>
pipeline.update_components(unet=new_unet_model, requires_safety_checker=<span class="hljs-literal">False</span>)

<span class="hljs-comment"># Update with ComponentSpec objects (from_config only)</span>
pipeline.update_components(
    guider=ComponentSpec(
        name=<span class="hljs-string">&quot;guider&quot;</span>,
        type_hint=ClassifierFreeGuidance,
        config={<span class="hljs-string">&quot;guidance_scale&quot;</span>: <span class="hljs-number">5.0</span>},
        default_creation_method=<span class="hljs-string">&quot;from_config&quot;</span>,
    )
)`,wrap:!1}}),{c(){s=r("p"),s.textContent=T,c=n(),b(_.$$.fragment)},l(g){s=i(g,"P",{"data-svelte-h":!0}),d(s)!=="svelte-kvfsh7"&&(s.textContent=T),c=o(g),w(_.$$.fragment,g)},m(g,k){j(g,s,k),j(g,c,k),$(_,g,k),P=!0},p:rt,i(g){P||(y(_.$$.fragment,g),P=!0)},o(g){M(_.$$.fragment,g),P=!1},d(g){g&&(l(s),l(c)),x(_,g)}}}function Dt(U){let s,T,c,_,P,g,k,$e,a,X,je,O,it="Base class for all Modular pipelines.",Pe,E,ke,V,R,Ie,Q,st="Load a ModularPipeline from a huggingface hub repo.",Je,K,W,Le,N,H,Ue,ee,at="Load selected components from specs.",Ee,f,A,Ve,te,lt="Register components with their corresponding specifications.",Ne,ne,dt="This method is responsible for:",Be,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>`,Ze,re,ct="This method is called when:",De,ie,mt=`<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>`,Ge,se,ut="Notes:",Xe,ae,ft=`<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>`,Re,B,S,We,le,gt="Save the pipeline to a directory. It does not save components, you need to save them separately.",He,C,F,Ae,de,ht=`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>`,Se,Z,Fe,pe,_t="Here are the ways to call <code>to</code>:",qe,ce,vt=`<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>`,ze,m,q,Ye,me,bt="Update components and configuration values and specs after the pipeline has been instantiated.",Oe,ue,wt="This method allows you to:",Qe,fe,$t="<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>",Ke,ge,yt=`In addition to updating the components and configuration values as pipeline attributes, the method also
updates:`,et,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>",tt,D,nt,_e,xt="Notes:",ot,ve,Ct=`<li>Components with trained weights must be created using ComponentSpec.load(). If the component has not been
shared in huggingface hub and you don’t have loading specs, you can upload it using <code>push_to_hub()</code></li> <li>ConfigMixin objects without weights (e.g., schedulers, guiders) can be passed directly</li> <li>ComponentSpec objects with default_creation_method=“from_pretrained” are not supported in
update_components()</li>`,ye,z,Me,be,xe;return P=new jt({props:{title:"Pipeline",local:"pipeline",headingTag:"h1"}}),k=new jt({props:{title:"ModularPipeline",local:"diffusers.ModularPipeline",headingTag:"h2"}}),X=new G({props:{name:"class diffusers.ModularPipeline",anchor:"diffusers.ModularPipeline",parameters:[{name:"blocks",val:": typing.Optional[diffusers.modular_pipelines.modular_pipeline.ModularPipelineBlocks] = None"},{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike, NoneType] = None"},{name:"components_manager",val:": typing.Optional[diffusers.modular_pipelines.components_manager.ComponentsManager] = None"},{name:"collection",val:": typing.Optional[str] = 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/main/src/diffusers/modular_pipelines/modular_pipeline.py#L1422"}}),E=new Tt({props:{warning:!0,$$slots:{default:[Nt]},$$scope:{ctx:U}}}),R=new G({props:{name:"from_pretrained",anchor:"diffusers.ModularPipeline.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike, NoneType]"},{name:"trust_remote_code",val:": typing.Optional[bool] = None"},{name:"components_manager",val:": typing.Optional[diffusers.modular_pipelines.components_manager.ComponentsManager] = None"},{name:"collection",val:": typing.Optional[str] = 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 repo 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/main/src/diffusers/modular_pipelines/modular_pipeline.py#L1606"}}),W=new G({props:{name:"get_component_spec",anchor:"diffusers.ModularPipeline.get_component_spec",parameters:[{name:"name",val:": str"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/modular_pipelines/modular_pipeline.py#L1948",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li>a copy of the ComponentSpec object for the given component name</li>
</ul>
`}}),H=new G({props:{name:"load_components",anchor:"diffusers.ModularPipeline.load_components",parameters:[{name:"names",val:": typing.Union[str, typing.List[str], NoneType] = 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>repo</code>,
<code>variant</code>, <code>revision</code>, etc.</li>
</ul>`,name:"*kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/modular_pipelines/modular_pipeline.py#L2086"}}),A=new G({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/main/src/diffusers/modular_pipelines/modular_pipeline.py#L1743"}}),S=new G({props:{name:"save_pretrained",anchor:"diffusers.ModularPipeline.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{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;
Path to the directory where the pipeline will be saved.`,name:"save_directory"},{anchor:"diffusers.ModularPipeline.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, optional) &#x2014;
Whether to push the pipeline to the huggingface hub.`,name:"push_to_hub"},{anchor:"diffusers.ModularPipeline.save_pretrained.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional arguments passed to <code>save_config()</code> method",name:"*kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/modular_pipelines/modular_pipeline.py#L1699"}}),F=new G({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/main/src/diffusers/modular_pipelines/modular_pipeline.py#L2160",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/main/en/api/pipelines/overview#diffusers.DiffusionPipeline"
>DiffusionPipeline</a></p>
`}}),Z=new Tt({props:{$$slots:{default:[Bt]},$$scope:{ctx:U}}}),q=new G({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, ComponentSpec objects, or configuration values to update:<ul>
<li>Component objects: Only supports components we can extract specs using
<code>ComponentSpec.from_component()</code> method i.e. components created with ComponentSpec.load() or
ConfigMixin subclasses that aren&#x2019;t nn.Modules (e.g., <code>unet=new_unet, text_encoder=new_encoder</code>)</li>
<li>ComponentSpec objects: Only supports default_creation_method == &#x201C;from_config&#x201D;, will call create()
method to create a new component (e.g., <code>guider=ComponentSpec(name=&quot;guider&quot;, type_hint=ClassifierFreeGuidance, config={...}, default_creation_method=&quot;from_config&quot;)</code>)</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/main/src/diffusers/modular_pipelines/modular_pipeline.py#L1955",raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValueError</code> — If a component object is not supported in ComponentSpec.from_component() method:<ul>
<li>nn.Module components without a valid <code>_diffusers_load_id</code> attribute</li>
<li>Non-ConfigMixin components without a valid <code>_diffusers_load_id</code> attribute</li>
</ul></li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValueError</code></p>
`}}),D=new Et({props:{anchor:"diffusers.ModularPipeline.update_components.example",$$slots:{default:[Zt]},$$scope:{ctx:U}}}),z=new Vt({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/modular_diffusers/pipeline.md"}}),{c(){s=r("meta"),T=n(),c=r("p"),_=n(),b(P.$$.fragment),g=n(),b(k.$$.fragment),$e=n(),a=r("div"),b(X.$$.fragment),je=n(),O=r("p"),O.textContent=it,Pe=n(),b(E.$$.fragment),ke=n(),V=r("div"),b(R.$$.fragment),Ie=n(),Q=r("p"),Q.textContent=st,Je=n(),K=r("div"),b(W.$$.fragment),Le=n(),N=r("div"),b(H.$$.fragment),Ue=n(),ee=r("p"),ee.textContent=at,Ee=n(),f=r("div"),b(A.$$.fragment),Ve=n(),te=r("p"),te.textContent=lt,Ne=n(),ne=r("p"),ne.textContent=dt,Be=n(),oe=r("ol"),oe.innerHTML=pt,Ze=n(),re=r("p"),re.textContent=ct,De=n(),ie=r("ul"),ie.innerHTML=mt,Ge=n(),se=r("p"),se.textContent=ut,Xe=n(),ae=r("ul"),ae.innerHTML=ft,Re=n(),B=r("div"),b(S.$$.fragment),We=n(),le=r("p"),le.textContent=gt,He=n(),C=r("div"),b(F.$$.fragment),Ae=n(),de=r("p"),de.innerHTML=ht,Se=n(),b(Z.$$.fragment),Fe=n(),pe=r("p"),pe.innerHTML=_t,qe=n(),ce=r("ul"),ce.innerHTML=vt,ze=n(),m=r("div"),b(q.$$.fragment),Ye=n(),me=r("p"),me.textContent=bt,Oe=n(),ue=r("p"),ue.textContent=wt,Qe=n(),fe=r("ol"),fe.innerHTML=$t,Ke=n(),ge=r("p"),ge.textContent=yt,et=n(),he=r("ul"),he.innerHTML=Mt,tt=n(),b(D.$$.fragment),nt=n(),_e=r("p"),_e.textContent=xt,ot=n(),ve=r("ul"),ve.innerHTML=Ct,ye=n(),b(z.$$.fragment),Me=n(),be=r("p"),this.h()},l(e){const 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