diffusers/pr_11234/en/_app/immutable/nodes/3.fb871e48.js

import{s as Pt,n as Dt,o as Ct}from"../chunks/scheduler.8c3d61f6.js";import{S as Ht,i as Mt,g as o,s as n,r as d,A as qt,h as r,f as i,c as a,j as _,u as m,x as R,k as x,y as p,a as s,v as l,d as f,t as c,w as u}from"../chunks/index.da70eac4.js";import{D as B}from"../chunks/Docstring.567bc132.js";import{H as V,E as It}from"../chunks/index.5d4ab994.js";function Ft(Gt){let v,N,J,Q,G,X,U,Ut="Customized activation functions for supporting various models in 🤗 Diffusers.",Y,E,Z,g,w,$t,W,Et="GELU activation function with tanh approximation support with <code>approximate=&quot;tanh&quot;</code>.",tt,T,et,h,y,bt,z,wt='A <a href="https://arxiv.org/abs/2002.05202" rel="nofollow">variant</a> of the gated linear unit activation function.',it,A,st,$,S,Lt,j,Tt=`The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this
<a href="https://arxiv.org/abs/1606.08415" rel="nofollow">paper</a>.`,nt,P,at,b,D,_t,k,yt=`A <a href="https://arxiv.org/abs/2002.05202" rel="nofollow">variant</a> of the gated linear unit activation function. It’s similar to <code>GEGLU</code>
but uses SiLU / Swish instead of GeLU.`,ot,C,rt,L,H,xt,O,At="SiLU activation function with input upcasted to torch.float32.",dt,M,mt,q,I,lt,F,ft,K,ct;return G=new V({props:{title:"Activation functions",local:"activation-functions",headingTag:"h1"}}),E=new V({props:{title:"GELU",local:"diffusers.models.activations.GELU",headingTag:"h2"}}),w=new B({props:{name:"class diffusers.models.activations.GELU",anchor:"diffusers.models.activations.GELU",parameters:[{name:"dim_in",val:": int"},{name:"dim_out",val:": int"},{name:"approximate",val:": str = 'none'"},{name:"bias",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.models.activations.GELU.dim_in",description:"<strong>dim_in</strong> (<code>int</code>) &#x2014; The number of channels in the input.",name:"dim_in"},{anchor:"diffusers.models.activations.GELU.dim_out",description:"<strong>dim_out</strong> (<code>int</code>) &#x2014; The number of channels in the 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True"}],parametersDescription:[{anchor:"diffusers.models.activations.GEGLU.dim_in",description:"<strong>dim_in</strong> (<code>int</code>) &#x2014; The number of channels in the input.",name:"dim_in"},{anchor:"diffusers.models.activations.GEGLU.dim_out",description:"<strong>dim_out</strong> (<code>int</code>) &#x2014; The number of channels in the output.",name:"dim_out"},{anchor:"diffusers.models.activations.GEGLU.bias",description:"<strong>bias</strong> (<code>bool</code>, defaults to True) &#x2014; Whether to use a bias in the linear layer.",name:"bias"}],source:"https://github.com/huggingface/diffusers/blob/vr_11234/src/diffusers/models/activations.py#L93"}}),A=new V({props:{title:"ApproximateGELU",local:"diffusers.models.activations.ApproximateGELU",headingTag:"h2"}}),S=new B({props:{name:"class diffusers.models.activations.ApproximateGELU",anchor:"diffusers.models.activations.ApproximateGELU",parameters:[{name:"dim_in",val:": int"},{name:"dim_out",val:": int"},{name:"bias",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.models.activations.ApproximateGELU.dim_in",description:"<strong>dim_in</strong> (<code>int</code>) &#x2014; The number of channels in the input.",name:"dim_in"},{anchor:"diffusers.models.activations.ApproximateGELU.dim_out",description:"<strong>dim_out</strong> (<code>int</code>) &#x2014; The number of channels in the output.",name:"dim_out"},{anchor:"diffusers.models.activations.ApproximateGELU.bias",description:"<strong>bias</strong> (<code>bool</code>, defaults to True) &#x2014; Whether to use a bias in the linear layer.",name:"bias"}],source:"https://github.com/huggingface/diffusers/blob/vr_11234/src/diffusers/models/activations.py#L149"}}),P=new V({props:{title:"SwiGLU",local:"diffusers.models.activations.SwiGLU",headingTag:"h2"}}),D=new B({props:{name:"class diffusers.models.activations.SwiGLU",anchor:"diffusers.models.activations.SwiGLU",parameters:[{name:"dim_in",val:": int"},{name:"dim_out",val:": int"},{name:"bias",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.models.activations.SwiGLU.dim_in",description:"<strong>dim_in</strong> (<code>int</code>) &#x2014; The number of channels in the input.",name:"dim_in"},{anchor:"diffusers.models.activations.SwiGLU.dim_out",description:"<strong>dim_out</strong> (<code>int</code>) &#x2014; The number of channels in the output.",name:"dim_out"},{anchor:"diffusers.models.activations.SwiGLU.bias",description:"<strong>bias</strong> (<code>bool</code>, defaults to True) &#x2014; Whether to use a bias in the linear layer.",name:"bias"}],source:"https://github.com/huggingface/diffusers/blob/vr_11234/src/diffusers/models/activations.py#L126"}}),C=new V({props:{title:"FP32SiLU",local:"diffusers.models.activations.FP32SiLU",headingTag:"h2"}}),H=new B({props:{name:"class 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