import{S as Kt,i as Qt,s as Xt,e as n,k as f,w as W,t as a,M as Yt,c as r,d as o,m,a as s,x as j,h as i,b as l,G as t,g as c,y as B,q as G,o as H,B as J,v as eo}from"../../../chunks/vendor-hf-doc-builder.js";import{T as to}from"../../../chunks/Tip-hf-doc-builder.js";import{D as Mt}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as xt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function oo(_e){let p,$;return{c(){p=n("p"),$=a("It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don\u2019t contain a prediction for the masked pixel because the unnoised image cannot be masked.")},l(u){p=r(u,"P",{});var h=s(p);$=i(h,"It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don\u2019t contain a prediction for the masked pixel because the unnoised image cannot be masked."),h.forEach(o)},m(u,h){c(u,p,h),t(p,$)},d(u){u&&o(p)}}}function no(_e){let p,$,u,h,te,N,Se,oe,qe,ge,_,Fe,L,Ve,Re,P,Ue,We,Z,je,Be,ve,w,Ge,ne,He,Je,be,v,I,Ze,re,Ke,Qe,Xe,se,Ye,et,ae,tt,Te,M,ot,ie,nt,rt,ye,x,De,b,de,st,at,le,it,dt,fe,lt,$e,T,k,me,z,ft,ce,mt,we,g,C,ct,pe,pt,ut,E,S,ht,q,_t,K,gt,vt,Me,y,O,ue,F,bt,he,Tt,xe,D,V,yt,R,Dt,Q,$t,wt,ke;return N=new xt({}),x=new to({props:{$$slots:{default:[oo]},$$scope:{ctx:_e}}}),z=new xt({}),C=new Mt({props:{name:"class diffusers.Transformer2DModel",anchor:"diffusers.Transformer2DModel",parameters:[{name:"num_attention_heads",val:": int = 16"},{name:"attention_head_dim",val:": int = 88"},{name:"in_channels",val:": typing.Optional[int] = None"},{name:"out_channels",val:": typing.Optional[int] = None"},{name:"num_layers",val:": int = 1"},{name:"dropout",val:": float = 0.0"},{name:"norm_num_groups",val:": int = 32"},{name:"cross_attention_dim",val:": typing.Optional[int] = None"},{name:"attention_bias",val:": bool = False"},{name:"sample_size",val:": typing.Optional[int] = None"},{name:"num_vector_embeds",val:": typing.Optional[int] = None"},{name:"patch_size",val:": typing.Optional[int] = None"},{name:"activation_fn",val:": str = 'geglu'"},{name:"num_embeds_ada_norm",val:": typing.Optional[int] = None"},{name:"use_linear_projection",val:": bool = False"},{name:"only_cross_attention",val:": bool = False"},{name:"upcast_attention",val:": bool = False"},{name:"norm_type",val:": str = 'layer_norm'"},{name:"norm_elementwise_affine",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.Transformer2DModel.num_attention_heads",description:"<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014; The number of heads to use for multi-head attention.",name:"num_attention_heads"},{anchor:"diffusers.Transformer2DModel.attention_head_dim",description:"<strong>attention_head_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 88) &#x2014; The number of channels in each head.",name:"attention_head_dim"},{anchor:"diffusers.Transformer2DModel.in_channels",description:`<strong>in_channels</strong> (<code>int</code>, <em>optional</em>) &#x2014;
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The number of diffusion steps used during training. Pass if at least one of the norm_layers is
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<p>During inference, you can denoise for up to but not more steps than <code>num_embeds_ada_norm</code>.`,name:"num_embeds_ada_norm"},{anchor:"diffusers.Transformer2DModel.attention_bias",description:`<strong>attention_bias</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<li>Mask <code>(batch, sequence_length)</code> True = keep, False = discard.</li>
<li>Bias <code>(batch, 1, sequence_length)</code> 0 = keep, -10000 = discard.</li>
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<p>If <code>ndim == 2</code>: will be interpreted as a mask, then converted into a bias consistent with the format
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