import{s as Fe,o as ke,n as we}from"../chunks/scheduler.8c3d61f6.js";import{S as Ce,i as Le,g as a,s as n,r as u,A as je,h as c,f as s,c as i,j as w,u as p,x as $,k as F,y as t,a as m,v as _,d as h,t as g,w as A}from"../chunks/index.da70eac4.js";import{T as Se}from"../chunks/Tip.1d9b8c37.js";import{D as B}from"../chunks/Docstring.6b390b9a.js";import{H as Me,E as Ie}from"../chunks/EditOnGithub.1e64e623.js";function Ge(q){let o,P="This API is 🧪 experimental.";return{c(){o=a("p"),o.textContent=P},l(f){o=c(f,"P",{"data-svelte-h":!0}),$(o)!=="svelte-89q1io"&&(o.textContent=P)},m(f,T){m(f,o,T)},p:we,d(f){f&&s(o)}}}function ze(q){let o,P="This API is 🧪 experimental.";return{c(){o=a("p"),o.textContent=P},l(f){o=c(f,"P",{"data-svelte-h":!0}),$(o)!=="svelte-89q1io"&&(o.textContent=P)},m(f,T){m(f,o,T)},p:we,d(f){f&&s(o)}}}function Ve(q){let o,P,f,T,k,Z,C,be='The Transformer model introduced in <a href="https://hf.co/papers/2403.03206" rel="nofollow">Stable Diffusion 3</a>. Its novelty lies in the MMDiT transformer block.',ee,L,oe,d,j,de,E,De='The Transformer model introduced in <a href="https://huggingface.co/papers/2403.03206" rel="nofollow">Stable Diffusion 3</a>.',ae,v,I,ce,K,$e=`Sets the attention processor to use <a href="https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers" rel="nofollow">feed forward
chunking</a>.`,fe,y,G,le,J,Te='The <a href="/docs/diffusers/main/en/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a> forward method.',me,b,z,ue,U,ve=`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.`,pe,x,_e,S,V,he,R,ye="Sets the attention processor to use to compute attention.",ge,D,H,Ae,O,xe="Disables the fused QKV projection if enabled.",Pe,M,te,N,se,Y,re;return k=new Me({props:{title:"SD3 Transformer Model",local:"sd3-transformer-model",headingTag:"h1"}}),L=new Me({props:{title:"SD3Transformer2DModel",local:"diffusers.SD3Transformer2DModel",headingTag:"h2"}}),j=new B({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>, defaults to <code>128</code>) &#x2014;
The width/height of the latents. 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>, defaults to <code>2</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>, defaults to <code>16</code>) &#x2014;
The number of latent channels in the input.`,name:"in_channels"},{anchor:"diffusers.SD3Transformer2DModel.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, defaults to <code>18</code>) &#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>, defaults to <code>64</code>) &#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>, defaults to <code>18</code>) &#x2014;
The number of heads to use for multi-head attention.`,name:"num_attention_heads"},{anchor:"diffusers.SD3Transformer2DModel.joint_attention_dim",description:`<strong>joint_attention_dim</strong> (<code>int</code>, defaults to <code>4096</code>) &#x2014;
The embedding dimension to use for joint text-image attention.`,name:"joint_attention_dim"},{anchor:"diffusers.SD3Transformer2DModel.caption_projection_dim",description:`<strong>caption_projection_dim</strong> (<code>int</code>, defaults to <code>1152</code>) &#x2014;
The embedding dimension of caption embeddings.`,name:"caption_projection_dim"},{anchor:"diffusers.SD3Transformer2DModel.pooled_projection_dim",description:`<strong>pooled_projection_dim</strong> (<code>int</code>, defaults to <code>2048</code>) &#x2014;
The embedding dimension of pooled text projections.`,name:"pooled_projection_dim"},{anchor:"diffusers.SD3Transformer2DModel.out_channels",description:`<strong>out_channels</strong> (<code>int</code>, defaults to <code>16</code>) &#x2014;
The number of latent channels in the output.`,name:"out_channels"},{anchor:"diffusers.SD3Transformer2DModel.pos_embed_max_size",description:`<strong>pos_embed_max_size</strong> (<code>int</code>, defaults to <code>96</code>) &#x2014;
The maximum latent height/width of positional embeddings.`,name:"pos_embed_max_size"},{anchor:"diffusers.SD3Transformer2DModel.dual_attention_layers",description:`<strong>dual_attention_layers</strong> (<code>Tuple[int, ...]</code>, defaults to <code>()</code>) &#x2014;
The number of dual-stream transformer blocks to use.`,name:"dual_attention_layers"},{anchor:"diffusers.SD3Transformer2DModel.qk_norm",description:`<strong>qk_norm</strong> (<code>str</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The normalization to use for query and key in the attention layer. If <code>None</code>, no normalization is used.`,name:"qk_norm"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_sd3.py#L80"}}),I=new B({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/main/src/diffusers/models/transformers/transformer_sd3.py#L176"}}),G=new B({props:{name:"forward",anchor:"diffusers.SD3Transformer2DModel.forward",parameters:[{name:"hidden_states",val:": Tensor"},{name:"encoder_hidden_states",val:": Tensor = None"},{name:"pooled_projections",val:": Tensor = 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.Tensor</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.Tensor</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.Tensor</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/main/src/diffusers/models/transformers/transformer_sd3.py#L317",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 B({props:{name:"fuse_qkv_projections",anchor:"diffusers.SD3Transformer2DModel.fuse_qkv_projections",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_sd3.py#L278"}}),x=new Se({props:{warning:!0,$$slots:{default:[Ge]},$$scope:{ctx:q}}}),V=new B({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.XLAFlashAttnProcessor2_0, diffusers.models.attention_processor.AttnProcessorNPU, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.MochiVaeAttnProcessor2_0, diffusers.models.attention_processor.MochiAttnProcessor2_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.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.SanaLinearAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGSanaLinearAttnProcessor2_0, diffusers.models.attention_processor.PAGIdentitySanaLinearAttnProcessor2_0, diffusers.models.attention_processor.SanaMultiscaleLinearAttention, diffusers.models.attention_processor.SanaMultiscaleAttnProcessor2_0, diffusers.models.attention_processor.SanaMultiscaleAttentionProjection, diffusers.models.attention_processor.IPAdapterAttnProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor2_0, diffusers.models.attention_processor.IPAdapterXFormersAttnProcessor, diffusers.models.attention_processor.SD3IPAdapterJointAttnProcessor2_0, 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.XLAFlashAttnProcessor2_0, diffusers.models.attention_processor.AttnProcessorNPU, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.MochiVaeAttnProcessor2_0, diffusers.models.attention_processor.MochiAttnProcessor2_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.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.SanaLinearAttnProcessor2_0, diffusers.models.attention_processor.PAGCFGSanaLinearAttnProcessor2_0, diffusers.models.attention_processor.PAGIdentitySanaLinearAttnProcessor2_0, diffusers.models.attention_processor.SanaMultiscaleLinearAttention, diffusers.models.attention_processor.SanaMultiscaleAttnProcessor2_0, diffusers.models.attention_processor.SanaMultiscaleAttentionProjection, diffusers.models.attention_processor.IPAdapterAttnProcessor, diffusers.models.attention_processor.IPAdapterAttnProcessor2_0, diffusers.models.attention_processor.IPAdapterXFormersAttnProcessor, diffusers.models.attention_processor.SD3IPAdapterJointAttnProcessor2_0, 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/main/src/diffusers/models/transformers/transformer_sd3.py#L243"}}),H=new B({props:{name:"unfuse_qkv_projections",anchor:"diffusers.SD3Transformer2DModel.unfuse_qkv_projections",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_sd3.py#L304"}}),M=new Se({props:{warning:!0,$$slots:{default:[ze]},$$scope:{ctx:q}}}),N=new Ie({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/models/sd3_transformer2d.md"}}),{c(){o=a("meta"),P=n(),f=a("p"),T=n(),u(k.$$.fragment),Z=n(),C=a("p"),C.innerHTML=be,ee=n(),u(L.$$.fragment),oe=n(),d=a("div"),u(j.$$.fragment),de=n(),E=a("p"),E.innerHTML=De,ae=n(),v=a("div"),u(I.$$.fragment),ce=n(),K=a("p"),K.innerHTML=$e,fe=n(),y=a("div"),u(G.$$.fragment),le=n(),J=a("p"),J.innerHTML=Te,me=n(),b=a("div"),u(z.$$.fragment),ue=n(),U=a("p"),U.textContent=ve,pe=n(),u(x.$$.fragment),_e=n(),S=a("div"),u(V.$$.fragment),he=n(),R=a("p"),R.textContent=ye,ge=n(),D=a("div"),u(H.$$.fragment),Ae=n(),O=a("p"),O.textContent=xe,Pe=n(),u(M.$$.fragment),te=n(),u(N.$$.fragment),se=n(),Y=a("p"),this.h()},l(e){const r=je("svelte-u9bgzb",document.head);o=c(r,"META",{name:!0,content:!0}),r.forEach(s),P=i(e),f=c(e,"P",{}),w(f).forEach(s),T=i(e),p(k.$$.fragment,e),Z=i(e),C=c(e,"P",{"data-svelte-h":!0}),$(C)!=="svelte-hv1bl6"&&(C.innerHTML=be),ee=i(e),p(L.$$.fragment,e),oe=i(e),d=c(e,"DIV",{class:!0});var l=w(d);p(j.$$.fragment,l),de=i(l),E=c(l,"P",{"data-svelte-h":!0}),$(E)!=="svelte-18dv5mn"&&(E.innerHTML=De),ae=i(l),v=c(l,"DIV",{class:!0});var X=w(v);p(I.$$.fragment,X),ce=i(X),K=c(X,"P",{"data-svelte-h":!0}),$(K)!=="svelte-2m23sy"&&(K.innerHTML=$e),X.forEach(s),fe=i(l),y=c(l,"DIV",{class:!0});var ne=w(y);p(G.$$.fragment,ne),le=i(ne),J=c(ne,"P",{"data-svelte-h":!0}),$(J)!=="svelte-1fdt9lr"&&(J.innerHTML=Te),ne.forEach(s),me=i(l),b=c(l,"DIV",{class:!0});var Q=w(b);p(z.$$.fragment,Q),ue=i(Q),U=c(Q,"P",{"data-svelte-h":!0}),$(U)!=="svelte-1254b9i"&&(U.textContent=ve),pe=i(Q),p(x.$$.fragment,Q),Q.forEach(s),_e=i(l),S=c(l,"DIV",{class:!0});var ie=w(S);p(V.$$.fragment,ie),he=i(ie),R=c(ie,"P",{"data-svelte-h":!0}),$(R)!=="svelte-1o77hl2"&&(R.textContent=ye),ie.forEach(s),ge=i(l),D=c(l,"DIV",{class:!0});var W=w(D);p(H.$$.fragment,W),Ae=i(W),O=c(W,"P",{"data-svelte-h":!0}),$(O)!=="svelte-1vhtc74"&&(O.textContent=xe),Pe=i(W),p(M.$$.fragment,W),W.forEach(s),l.forEach(s),te=i(e),p(N.$$.fragment,e),se=i(e),Y=c(e,"P",{}),w(Y).forEach(s),this.h()},h(){F(o,"name","hf:doc:metadata"),F(o,"content",He),F(v,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(y,"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(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(d,"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){t(document.head,o),m(e,P,r),m(e,f,r),m(e,T,r),_(k,e,r),m(e,Z,r),m(e,C,r),m(e,ee,r),_(L,e,r),m(e,oe,r),m(e,d,r),_(j,d,null),t(d,de),t(d,E),t(d,ae),t(d,v),_(I,v,null),t(v,ce),t(v,K),t(d,fe),t(d,y),_(G,y,null),t(y,le),t(y,J),t(d,me),t(d,b),_(z,b,null),t(b,ue),t(b,U),t(b,pe),_(x,b,null),t(d,_e),t(d,S),_(V,S,null),t(S,he),t(S,R),t(d,ge),t(d,D),_(H,D,null),t(D,Ae),t(D,O),t(D,Pe),_(M,D,null),m(e,te,r),_(N,e,r),m(e,se,r),m(e,Y,r),re=!0},p(e,[r]){const l={};r&2&&(l.$$scope={dirty:r,ctx:e}),x.$set(l);const X={};r&2&&(X.$$scope={dirty:r,ctx:e}),M.$set(X)},i(e){re||(h(k.$$.fragment,e),h(L.$$.fragment,e),h(j.$$.fragment,e),h(I.$$.fragment,e),h(G.$$.fragment,e),h(z.$$.fragment,e),h(x.$$.fragment,e),h(V.$$.fragment,e),h(H.$$.fragment,e),h(M.$$.fragment,e),h(N.$$.fragment,e),re=!0)},o(e){g(k.$$.fragment,e),g(L.$$.fragment,e),g(j.$$.fragment,e),g(I.$$.fragment,e),g(G.$$.fragment,e),g(z.$$.fragment,e),g(x.$$.fragment,e),g(V.$$.fragment,e),g(H.$$.fragment,e),g(M.$$.fragment,e),g(N.$$.fragment,e),re=!1},d(e){e&&(s(P),s(f),s(T),s(Z),s(C),s(ee),s(oe),s(d),s(te),s(se),s(Y)),s(o),A(k,e),A(L,e),A(j),A(I),A(G),A(z),A(x),A(V),A(H),A(M),A(N,e)}}}const He='{"title":"SD3 Transformer Model","local":"sd3-transformer-model","sections":[{"title":"SD3Transformer2DModel","local":"diffusers.SD3Transformer2DModel","sections":[],"depth":2}],"depth":1}';function Ne(q){return ke(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ue extends Ce{constructor(o){super(),Le(this,o,Ne,Ve,Fe,{})}}export{Ue as component};
