import{s as je,o as Le,n as ke}from"../chunks/scheduler.8c3d61f6.js";import{S as Ve,i as Ie,g as c,s as i,r as u,A as ze,h as f,f as s,c as d,j as S,u as p,x as D,k as F,y as o,a as m,v as _,d as h,t as g,w as A}from"../chunks/index.da70eac4.js";import{T as Fe}from"../chunks/Tip.1d9b8c37.js";import{D as Y}from"../chunks/Docstring.6b390b9a.js";import{H as Ce,E as Ge}from"../chunks/EditOnGithub.1e64e623.js";function He(X){let t,P="This API is 🧪 experimental.";return{c(){t=c("p"),t.textContent=P},l(l){t=f(l,"P",{"data-svelte-h":!0}),D(t)!=="svelte-89q1io"&&(t.textContent=P)},m(l,v){m(l,t,v)},p:ke,d(l){l&&s(t)}}}function Ne(X){let t,P="This API is 🧪 experimental.";return{c(){t=c("p"),t.textContent=P},l(l){t=f(l,"P",{"data-svelte-h":!0}),D(t)!=="svelte-89q1io"&&(t.textContent=P)},m(l,v){m(l,t,v)},p:ke,d(l){l&&s(t)}}}function Ee(X){let t,P,l,v,C,ee,k,De='The Transformer model introduced in <a href="https://hf.co/papers/2403.03206" rel="nofollow">Stable Diffusion 3</a>. Its novelty lies in the MMDiT transformer block.',oe,j,te,n,L,ae,K,ve="The Transformer model introduced in Stable Diffusion 3.",ce,q,Te='Reference: <a href="https://arxiv.org/abs/2403.03206" rel="nofollow">https://arxiv.org/abs/2403.03206</a>',fe,T,V,le,R,xe=`Sets the attention processor to use <a href="https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers" rel="nofollow">feed forward
chunking</a>.`,me,x,I,ue,U,ye='The <a href="/docs/diffusers/pr_10021/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a> forward method.',pe,$,z,_e,J,we=`Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.`,he,y,ge,w,G,Ae,O,Me="Sets the attention processor to use to compute attention.",Pe,b,H,$e,Q,Se="Disables the fused QKV projection if enabled.",be,M,se,N,re,Z,ne;return C=new Ce({props:{title:"SD3 Transformer Model",local:"sd3-transformer-model",headingTag:"h1"}}),j=new Ce({props:{title:"SD3Transformer2DModel",local:"diffusers.SD3Transformer2DModel",headingTag:"h2"}}),L=new Y({props:{name:"class diffusers.SD3Transformer2DModel",anchor:"diffusers.SD3Transformer2DModel",parameters:[{name:"sample_size",val:": int = 128"},{name:"patch_size",val:": int = 2"},{name:"in_channels",val:": int = 16"},{name:"num_layers",val:": int = 18"},{name:"attention_head_dim",val:": int = 64"},{name:"num_attention_heads",val:": int = 18"},{name:"joint_attention_dim",val:": int = 4096"},{name:"caption_projection_dim",val:": int = 1152"},{name:"pooled_projection_dim",val:": int = 2048"},{name:"out_channels",val:": int = 16"},{name:"pos_embed_max_size",val:": int = 96"},{name:"dual_attention_layers",val:": typing.Tuple[int, ...] = ()"},{name:"qk_norm",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"diffusers.SD3Transformer2DModel.sample_size",description:`<strong>sample_size</strong> (<code>int</code>) &#x2014; The width of the latent images. This is fixed during training since
it is used to learn a number of position embeddings.`,name:"sample_size"},{anchor:"diffusers.SD3Transformer2DModel.patch_size",description:"<strong>patch_size</strong> (<code>int</code>) &#x2014; Patch size to turn the input data into small patches.",name:"patch_size"},{anchor:"diffusers.SD3Transformer2DModel.in_channels",description:"<strong>in_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014; The number of channels in the input.",name:"in_channels"},{anchor:"diffusers.SD3Transformer2DModel.num_layers",description:"<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 18) &#x2014; The number of layers of Transformer blocks to use.",name:"num_layers"},{anchor:"diffusers.SD3Transformer2DModel.attention_head_dim",description:"<strong>attention_head_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014; The number of channels in each head.",name:"attention_head_dim"},{anchor:"diffusers.SD3Transformer2DModel.num_attention_heads",description:"<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 18) &#x2014; The number of heads to use for multi-head attention.",name:"num_attention_heads"},{anchor:"diffusers.SD3Transformer2DModel.cross_attention_dim",description:"<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>) &#x2014; The number of <code>encoder_hidden_states</code> dimensions to use.",name:"cross_attention_dim"},{anchor:"diffusers.SD3Transformer2DModel.caption_projection_dim",description:"<strong>caption_projection_dim</strong> (<code>int</code>) &#x2014; Number of dimensions to use when projecting the <code>encoder_hidden_states</code>.",name:"caption_projection_dim"},{anchor:"diffusers.SD3Transformer2DModel.pooled_projection_dim",description:"<strong>pooled_projection_dim</strong> (<code>int</code>) &#x2014; Number of dimensions to use when projecting the <code>pooled_projections</code>.",name:"pooled_projection_dim"},{anchor:"diffusers.SD3Transformer2DModel.out_channels",description:"<strong>out_channels</strong> (<code>int</code>, defaults to 16) &#x2014; Number of output channels.",name:"out_channels"}],source:"https://github.com/huggingface/diffusers/blob/vr_10021/src/diffusers/models/transformers/transformer_sd3.py#L109"}}),V=new Y({props:{name:"enable_forward_chunking",anchor:"diffusers.SD3Transformer2DModel.enable_forward_chunking",parameters:[{name:"chunk_size",val:": typing.Optional[int] = None"},{name:"dim",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.SD3Transformer2DModel.enable_forward_chunking.chunk_size",description:`<strong>chunk_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=<code>dim</code>.`,name:"chunk_size"},{anchor:"diffusers.SD3Transformer2DModel.enable_forward_chunking.dim",description:`<strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to <code>0</code>) &#x2014;
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).`,name:"dim"}],source:"https://github.com/huggingface/diffusers/blob/vr_10021/src/diffusers/models/transformers/transformer_sd3.py#L191"}}),I=new Y({props:{name:"forward",anchor:"diffusers.SD3Transformer2DModel.forward",parameters:[{name:"hidden_states",val:": FloatTensor"},{name:"encoder_hidden_states",val:": FloatTensor = None"},{name:"pooled_projections",val:": FloatTensor = None"},{name:"timestep",val:": LongTensor = None"},{name:"block_controlnet_hidden_states",val:": typing.List = None"},{name:"joint_attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"return_dict",val:": bool = True"},{name:"skip_layers",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"diffusers.SD3Transformer2DModel.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch size, channel, height, width)</code>) &#x2014;
Input <code>hidden_states</code>.`,name:"hidden_states"},{anchor:"diffusers.SD3Transformer2DModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch size, sequence_len, embed_dims)</code>) &#x2014;
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.`,name:"encoder_hidden_states"},{anchor:"diffusers.SD3Transformer2DModel.forward.pooled_projections",description:`<strong>pooled_projections</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, projection_dim)</code>) &#x2014; Embeddings projected
from the embeddings of input conditions.`,name:"pooled_projections"},{anchor:"diffusers.SD3Transformer2DModel.forward.timestep",description:`<strong>timestep</strong> (<code>torch.LongTensor</code>) &#x2014;
Used to indicate denoising step.`,name:"timestep"},{anchor:"diffusers.SD3Transformer2DModel.forward.block_controlnet_hidden_states",description:`<strong>block_controlnet_hidden_states</strong> (<code>list</code> of <code>torch.Tensor</code>) &#x2014;
A list of tensors that if specified are added to the residuals of transformer blocks.`,name:"block_controlnet_hidden_states"},{anchor:"diffusers.SD3Transformer2DModel.forward.joint_attention_kwargs",description:`<strong>joint_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow">diffusers.models.attention_processor</a>.`,name:"joint_attention_kwargs"},{anchor:"diffusers.SD3Transformer2DModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <code>~models.transformer_2d.Transformer2DModelOutput</code> instead of a plain
tuple.`,name:"return_dict"},{anchor:"diffusers.SD3Transformer2DModel.forward.skip_layers",description:`<strong>skip_layers</strong> (<code>list</code> of <code>int</code>, <em>optional</em>) &#x2014;
A list of layer indices to skip during the forward pass.`,name:"skip_layers"}],source:"https://github.com/huggingface/diffusers/blob/vr_10021/src/diffusers/models/transformers/transformer_sd3.py#L336",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is True, an <code>~models.transformer_2d.Transformer2DModelOutput</code> is returned, otherwise a
<code>tuple</code> where the first element is the sample tensor.</p>
`}}),z=new Y({props:{name:"fuse_qkv_projections",anchor:"diffusers.SD3Transformer2DModel.fuse_qkv_projections",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10021/src/diffusers/models/transformers/transformer_sd3.py#L293"}}),y=new Fe({props:{warning:!0,$$slots:{default:[He]},$$scope:{ctx:X}}}),G=new Y({props:{name:"set_attn_processor",anchor:"diffusers.SD3Transformer2DModel.set_attn_processor",parameters:[{name:"processor",val:": typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.JointAttnProcessor2_0, diffusers.models.attention_processor.PAGJointAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGJointAttnProcessor2_0, diffusers.models.attention_processor.FusedJointAttnProcessor2_0, diffusers.models.attention_processor.AllegroAttnProcessor2_0, diffusers.models.attention_processor.AuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FusedAuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.CogVideoXAttnProcessor2_0, diffusers.models.attention_processor.FusedCogVideoXAttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.AttnProcessorNPU, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.MochiVaeAttnProcessor2_0, diffusers.models.attention_processor.StableAudioAttnProcessor2_0, diffusers.models.attention_processor.HunyuanAttnProcessor2_0, diffusers.models.attention_processor.FusedHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.LuminaAttnProcessor2_0, diffusers.models.attention_processor.MochiAttnProcessor2_0, diffusers.models.attention_processor.FusedAttnProcessor2_0, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor2_0, diffusers.models.attention_processor.IPAdapterXFormersAttnProcessor, diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor, typing.Dict[str, typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.JointAttnProcessor2_0, diffusers.models.attention_processor.PAGJointAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGJointAttnProcessor2_0, diffusers.models.attention_processor.FusedJointAttnProcessor2_0, diffusers.models.attention_processor.AllegroAttnProcessor2_0, diffusers.models.attention_processor.AuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FusedAuraFlowAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0, diffusers.models.attention_processor.FluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0, diffusers.models.attention_processor.FusedFluxAttnProcessor2_0_NPU, diffusers.models.attention_processor.CogVideoXAttnProcessor2_0, diffusers.models.attention_processor.FusedCogVideoXAttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.AttnProcessorNPU, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.MochiVaeAttnProcessor2_0, diffusers.models.attention_processor.StableAudioAttnProcessor2_0, diffusers.models.attention_processor.HunyuanAttnProcessor2_0, diffusers.models.attention_processor.FusedHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGHunyuanAttnProcessor2_0, diffusers.models.attention_processor.LuminaAttnProcessor2_0, diffusers.models.attention_processor.MochiAttnProcessor2_0, diffusers.models.attention_processor.FusedAttnProcessor2_0, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor2_0, diffusers.models.attention_processor.IPAdapterXFormersAttnProcessor, diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor]]]"}],parametersDescription:[{anchor:"diffusers.SD3Transformer2DModel.set_attn_processor.processor",description:`<strong>processor</strong> (<code>dict</code> of <code>AttentionProcessor</code> or only <code>AttentionProcessor</code>) &#x2014;
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for <strong>all</strong> <code>Attention</code> layers.</p>
<p>If <code>processor</code> is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.`,name:"processor"}],source:"https://github.com/huggingface/diffusers/blob/vr_10021/src/diffusers/models/transformers/transformer_sd3.py#L258"}}),H=new Y({props:{name:"unfuse_qkv_projections",anchor:"diffusers.SD3Transformer2DModel.unfuse_qkv_projections",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10021/src/diffusers/models/transformers/transformer_sd3.py#L319"}}),M=new Fe({props:{warning:!0,$$slots:{default:[Ne]},$$scope:{ctx:X}}}),N=new Ge({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/models/sd3_transformer2d.md"}}),{c(){t=c("meta"),P=i(),l=c("p"),v=i(),u(C.$$.fragment),ee=i(),k=c("p"),k.innerHTML=De,oe=i(),u(j.$$.fragment),te=i(),n=c("div"),u(L.$$.fragment),ae=i(),K=c("p"),K.textContent=ve,ce=i(),q=c("p"),q.innerHTML=Te,fe=i(),T=c("div"),u(V.$$.fragment),le=i(),R=c("p"),R.innerHTML=xe,me=i(),x=c("div"),u(I.$$.fragment),ue=i(),U=c("p"),U.innerHTML=ye,pe=i(),$=c("div"),u(z.$$.fragment),_e=i(),J=c("p"),J.textContent=we,he=i(),u(y.$$.fragment),ge=i(),w=c("div"),u(G.$$.fragment),Ae=i(),O=c("p"),O.textContent=Me,Pe=i(),b=c("div"),u(H.$$.fragment),$e=i(),Q=c("p"),Q.textContent=Se,be=i(),u(M.$$.fragment),se=i(),u(N.$$.fragment),re=i(),Z=c("p"),this.h()},l(e){const r=ze("svelte-u9bgzb",document.head);t=f(r,"META",{name:!0,content:!0}),r.forEach(s),P=d(e),l=f(e,"P",{}),S(l).forEach(s),v=d(e),p(C.$$.fragment,e),ee=d(e),k=f(e,"P",{"data-svelte-h":!0}),D(k)!=="svelte-hv1bl6"&&(k.innerHTML=De),oe=d(e),p(j.$$.fragment,e),te=d(e),n=f(e,"DIV",{class:!0});var a=S(n);p(L.$$.fragment,a),ae=d(a),K=f(a,"P",{"data-svelte-h":!0}),D(K)!=="svelte-1f9jxt2"&&(K.textContent=ve),ce=d(a),q=f(a,"P",{"data-svelte-h":!0}),D(q)!=="svelte-5lf8o6"&&(q.innerHTML=Te),fe=d(a),T=f(a,"DIV",{class:!0});var E=S(T);p(V.$$.fragment,E),le=d(E),R=f(E,"P",{"data-svelte-h":!0}),D(R)!=="svelte-2m23sy"&&(R.innerHTML=xe),E.forEach(s),me=d(a),x=f(a,"DIV",{class:!0});var ie=S(x);p(I.$$.fragment,ie),ue=d(ie),U=f(ie,"P",{"data-svelte-h":!0}),D(U)!=="svelte-12wo6od"&&(U.innerHTML=ye),ie.forEach(s),pe=d(a),$=f(a,"DIV",{class:!0});var W=S($);p(z.$$.fragment,W),_e=d(W),J=f(W,"P",{"data-svelte-h":!0}),D(J)!=="svelte-1254b9i"&&(J.textContent=we),he=d(W),p(y.$$.fragment,W),W.forEach(s),ge=d(a),w=f(a,"DIV",{class:!0});var de=S(w);p(G.$$.fragment,de),Ae=d(de),O=f(de,"P",{"data-svelte-h":!0}),D(O)!=="svelte-1o77hl2"&&(O.textContent=Me),de.forEach(s),Pe=d(a),b=f(a,"DIV",{class:!0});var B=S(b);p(H.$$.fragment,B),$e=d(B),Q=f(B,"P",{"data-svelte-h":!0}),D(Q)!=="svelte-1vhtc74"&&(Q.textContent=Se),be=d(B),p(M.$$.fragment,B),B.forEach(s),a.forEach(s),se=d(e),p(N.$$.fragment,e),re=d(e),Z=f(e,"P",{}),S(Z).forEach(s),this.h()},h(){F(t,"name","hf:doc:metadata"),F(t,"content",Xe),F(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(b,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,r){o(document.head,t),m(e,P,r),m(e,l,r),m(e,v,r),_(C,e,r),m(e,ee,r),m(e,k,r),m(e,oe,r),_(j,e,r),m(e,te,r),m(e,n,r),_(L,n,null),o(n,ae),o(n,K),o(n,ce),o(n,q),o(n,fe),o(n,T),_(V,T,null),o(T,le),o(T,R),o(n,me),o(n,x),_(I,x,null),o(x,ue),o(x,U),o(n,pe),o(n,$),_(z,$,null),o($,_e),o($,J),o($,he),_(y,$,null),o(n,ge),o(n,w),_(G,w,null),o(w,Ae),o(w,O),o(n,Pe),o(n,b),_(H,b,null),o(b,$e),o(b,Q),o(b,be),_(M,b,null),m(e,se,r),_(N,e,r),m(e,re,r),m(e,Z,r),ne=!0},p(e,[r]){const a={};r&2&&(a.$$scope={dirty:r,ctx:e}),y.$set(a);const E={};r&2&&(E.$$scope={dirty:r,ctx:e}),M.$set(E)},i(e){ne||(h(C.$$.fragment,e),h(j.$$.fragment,e),h(L.$$.fragment,e),h(V.$$.fragment,e),h(I.$$.fragment,e),h(z.$$.fragment,e),h(y.$$.fragment,e),h(G.$$.fragment,e),h(H.$$.fragment,e),h(M.$$.fragment,e),h(N.$$.fragment,e),ne=!0)},o(e){g(C.$$.fragment,e),g(j.$$.fragment,e),g(L.$$.fragment,e),g(V.$$.fragment,e),g(I.$$.fragment,e),g(z.$$.fragment,e),g(y.$$.fragment,e),g(G.$$.fragment,e),g(H.$$.fragment,e),g(M.$$.fragment,e),g(N.$$.fragment,e),ne=!1},d(e){e&&(s(P),s(l),s(v),s(ee),s(k),s(oe),s(te),s(n),s(se),s(re),s(Z)),s(t),A(C,e),A(j,e),A(L),A(V),A(I),A(z),A(y),A(G),A(H),A(M),A(N,e)}}}const Xe='{"title":"SD3 Transformer Model","local":"sd3-transformer-model","sections":[{"title":"SD3Transformer2DModel","local":"diffusers.SD3Transformer2DModel","sections":[],"depth":2}],"depth":1}';function Ke(X){return Le(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Qe extends Ve{constructor(t){super(),Ie(this,t,Ke,Ee,je,{})}}export{Qe as component};
