import{s as Ne,o as Ee,n as je}from"../chunks/scheduler.8c3d61f6.js";import{S as Xe,i as qe,g as c,s as r,r as m,A as Ke,h as f,f as s,c as i,j as v,u as _,x as P,k as $,y as t,a as u,v as p,d as h,t as g,w as A}from"../chunks/index.da70eac4.js";import{T as Ge}from"../chunks/Tip.1d9b8c37.js";import{D as K}from"../chunks/Docstring.6b390b9a.js";import{H as Ve,E as Je}from"../chunks/EditOnGithub.1e64e623.js";function Ue(J){let o,y="This API is 🧪 experimental.";return{c(){o=c("p"),o.textContent=y},l(l){o=f(l,"P",{"data-svelte-h":!0}),P(o)!=="svelte-89q1io"&&(o.textContent=y)},m(l,T){u(l,o,T)},p:je,d(l){l&&s(o)}}}function Re(J){let o,y="This API is 🧪 experimental.";return{c(){o=c("p"),o.textContent=y},l(l){o=f(l,"P",{"data-svelte-h":!0}),P(o)!=="svelte-89q1io"&&(o.textContent=y)},m(l,T){u(l,o,T)},p:je,d(l){l&&s(o)}}}function Oe(J){let o,y,l,T,k,se,z,He='A Diffusion Transformer model for 2D data from <a href="https://github.com/Tencent/HunyuanDiT" rel="nofollow">Hunyuan-DiT</a>.',ne,L,re,n,S,ue,U,we="HunYuanDiT: Diffusion model with a Transformer backbone.",me,R,Ce="Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.",_e,x,I,pe,O,Fe=`Sets the attention processor to use <a href="https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers" rel="nofollow">feed forward
chunking</a>.`,he,M,G,ge,W,ke='The <a href="/docs/diffusers/pr_10037/en/api/models/hunyuan_transformer2d#diffusers.HunyuanDiT2DModel">HunyuanDiT2DModel</a> forward method.',Ae,b,V,Pe,B,ze=`Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.`,ye,H,be,w,j,De,Q,Le="Sets the attention processor to use to compute attention.",ve,C,N,$e,Y,Se="Disables custom attention processors and sets the default attention implementation.",Te,D,E,xe,Z,Ie="Disables the fused QKV projection if enabled.",Me,F,ie,X,ae,oe,de;return k=new Ve({props:{title:"HunyuanDiT2DModel",local:"hunyuandit2dmodel",headingTag:"h1"}}),L=new Ve({props:{title:"HunyuanDiT2DModel",local:"diffusers.HunyuanDiT2DModel",headingTag:"h2"}}),S=new K({props:{name:"class diffusers.HunyuanDiT2DModel",anchor:"diffusers.HunyuanDiT2DModel",parameters:[{name:"num_attention_heads",val:": int = 16"},{name:"attention_head_dim",val:": int = 88"},{name:"in_channels",val:": typing.Optional[int] = None"},{name:"patch_size",val:": typing.Optional[int] = None"},{name:"activation_fn",val:": str = 'gelu-approximate'"},{name:"sample_size",val:" = 32"},{name:"hidden_size",val:" = 1152"},{name:"num_layers",val:": int = 28"},{name:"mlp_ratio",val:": float = 4.0"},{name:"learn_sigma",val:": bool = True"},{name:"cross_attention_dim",val:": int = 1024"},{name:"norm_type",val:": str = 'layer_norm'"},{name:"cross_attention_dim_t5",val:": int = 2048"},{name:"pooled_projection_dim",val:": int = 1024"},{name:"text_len",val:": int = 77"},{name:"text_len_t5",val:": int = 256"},{name:"use_style_cond_and_image_meta_size",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.HunyuanDiT2DModel.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.HunyuanDiT2DModel.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.HunyuanDiT2DModel.in_channels",description:`<strong>in_channels</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of channels in the input and output (specify if the input is <strong>continuous</strong>).`,name:"in_channels"},{anchor:"diffusers.HunyuanDiT2DModel.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The size of the patch to use for the input.`,name:"patch_size"},{anchor:"diffusers.HunyuanDiT2DModel.activation_fn",description:`<strong>activation_fn</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;geglu&quot;</code>) &#x2014;
Activation function to use in feed-forward.`,name:"activation_fn"},{anchor:"diffusers.HunyuanDiT2DModel.sample_size",description:`<strong>sample_size</strong> (<code>int</code>, <em>optional</em>) &#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.HunyuanDiT2DModel.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability to use.`,name:"dropout"},{anchor:"diffusers.HunyuanDiT2DModel.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of dimension in the clip text embedding.`,name:"cross_attention_dim"},{anchor:"diffusers.HunyuanDiT2DModel.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The size of hidden layer in the conditioning embedding layers.`,name:"hidden_size"},{anchor:"diffusers.HunyuanDiT2DModel.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of layers of Transformer blocks to use.`,name:"num_layers"},{anchor:"diffusers.HunyuanDiT2DModel.mlp_ratio",description:`<strong>mlp_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
The ratio of the hidden layer size to the input size.`,name:"mlp_ratio"},{anchor:"diffusers.HunyuanDiT2DModel.learn_sigma",description:`<strong>learn_sigma</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to predict variance.`,name:"learn_sigma"},{anchor:"diffusers.HunyuanDiT2DModel.cross_attention_dim_t5",description:`<strong>cross_attention_dim_t5</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number dimensions in t5 text embedding.`,name:"cross_attention_dim_t5"},{anchor:"diffusers.HunyuanDiT2DModel.pooled_projection_dim",description:`<strong>pooled_projection_dim</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The size of the pooled projection.`,name:"pooled_projection_dim"},{anchor:"diffusers.HunyuanDiT2DModel.text_len",description:`<strong>text_len</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The length of the clip text embedding.`,name:"text_len"},{anchor:"diffusers.HunyuanDiT2DModel.text_len_t5",description:`<strong>text_len_t5</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The length of the T5 text embedding.`,name:"text_len_t5"},{anchor:"diffusers.HunyuanDiT2DModel.use_style_cond_and_image_meta_size",description:`<strong>use_style_cond_and_image_meta_size</strong> (<code>bool</code>,  <em>optional</em>) &#x2014;
Whether or not to use style condition and image meta size. True for version &lt;=1.1, False for version &gt;= 1.2`,name:"use_style_cond_and_image_meta_size"}],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/transformers/hunyuan_transformer_2d.py#L203"}}),I=new K({props:{name:"enable_forward_chunking",anchor:"diffusers.HunyuanDiT2DModel.enable_forward_chunking",parameters:[{name:"chunk_size",val:": typing.Optional[int] = None"},{name:"dim",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.HunyuanDiT2DModel.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.HunyuanDiT2DModel.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_10037/src/diffusers/models/transformers/hunyuan_transformer_2d.py#L539"}}),G=new K({props:{name:"forward",anchor:"diffusers.HunyuanDiT2DModel.forward",parameters:[{name:"hidden_states",val:""},{name:"timestep",val:""},{name:"encoder_hidden_states",val:" = None"},{name:"text_embedding_mask",val:" = None"},{name:"encoder_hidden_states_t5",val:" = None"},{name:"text_embedding_mask_t5",val:" = None"},{name:"image_meta_size",val:" = None"},{name:"style",val:" = None"},{name:"image_rotary_emb",val:" = None"},{name:"controlnet_block_samples",val:" = None"},{name:"return_dict",val:" = True"}],parametersDescription:[{anchor:"diffusers.HunyuanDiT2DModel.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.Tensor</code> of shape <code>(batch size, dim, height, width)</code>) &#x2014;
The input tensor.`,name:"hidden_states"},{anchor:"diffusers.HunyuanDiT2DModel.forward.timestep",description:`<strong>timestep</strong> ( <code>torch.LongTensor</code>, <em>optional</em>) &#x2014;
Used to indicate denoising step.`,name:"timestep"},{anchor:"diffusers.HunyuanDiT2DModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> ( <code>torch.Tensor</code> of shape <code>(batch size, sequence len, embed dims)</code>, <em>optional</em>) &#x2014;
Conditional embeddings for cross attention layer. This is the output of <code>BertModel</code>.`,name:"encoder_hidden_states"},{anchor:"diffusers.HunyuanDiT2DModel.forward.text_embedding_mask",description:`<strong>text_embedding_mask</strong> &#x2014; torch.Tensor
An attention mask of shape <code>(batch, key_tokens)</code> is applied to <code>encoder_hidden_states</code>. This is the output
of <code>BertModel</code>.`,name:"text_embedding_mask"},{anchor:"diffusers.HunyuanDiT2DModel.forward.encoder_hidden_states_t5",description:`<strong>encoder_hidden_states_t5</strong> ( <code>torch.Tensor</code> of shape <code>(batch size, sequence len, embed dims)</code>, <em>optional</em>) &#x2014;
Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.`,name:"encoder_hidden_states_t5"},{anchor:"diffusers.HunyuanDiT2DModel.forward.text_embedding_mask_t5",description:`<strong>text_embedding_mask_t5</strong> &#x2014; torch.Tensor
An attention mask of shape <code>(batch, key_tokens)</code> is applied to <code>encoder_hidden_states</code>. This is the output
of T5 Text Encoder.`,name:"text_embedding_mask_t5"},{anchor:"diffusers.HunyuanDiT2DModel.forward.image_meta_size",description:`<strong>image_meta_size</strong> (torch.Tensor) &#x2014;
Conditional embedding indicate the image sizes`,name:"image_meta_size"},{anchor:"diffusers.HunyuanDiT2DModel.forward.style",description:`<strong>style</strong> &#x2014; torch.Tensor:
Conditional embedding indicate the style`,name:"style"},{anchor:"diffusers.HunyuanDiT2DModel.forward.image_rotary_emb",description:`<strong>image_rotary_emb</strong> (<code>torch.Tensor</code>) &#x2014;
The image rotary embeddings to apply on query and key tensors during attention calculation.`,name:"image_rotary_emb"},{anchor:"diffusers.HunyuanDiT2DModel.forward.return_dict",description:`<strong>return_dict</strong> &#x2014; bool
Whether to return a dictionary.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/transformers/hunyuan_transformer_2d.py#L426"}}),V=new K({props:{name:"fuse_qkv_projections",anchor:"diffusers.HunyuanDiT2DModel.fuse_qkv_projections",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/transformers/hunyuan_transformer_2d.py#L321"}}),H=new Ge({props:{warning:!0,$$slots:{default:[Ue]},$$scope:{ctx:J}}}),j=new K({props:{name:"set_attn_processor",anchor:"diffusers.HunyuanDiT2DModel.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.HunyuanDiT2DModel.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_10037/src/diffusers/models/transformers/hunyuan_transformer_2d.py#L386"}}),N=new K({props:{name:"set_default_attn_processor",anchor:"diffusers.HunyuanDiT2DModel.set_default_attn_processor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/transformers/hunyuan_transformer_2d.py#L420"}}),E=new K({props:{name:"unfuse_qkv_projections",anchor:"diffusers.HunyuanDiT2DModel.unfuse_qkv_projections",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/transformers/hunyuan_transformer_2d.py#L347"}}),F=new Ge({props:{warning:!0,$$slots:{default:[Re]},$$scope:{ctx:J}}}),X=new Je({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/models/hunyuan_transformer2d.md"}}),{c(){o=c("meta"),y=r(),l=c("p"),T=r(),m(k.$$.fragment),se=r(),z=c("p"),z.innerHTML=He,ne=r(),m(L.$$.fragment),re=r(),n=c("div"),m(S.$$.fragment),ue=r(),U=c("p"),U.textContent=we,me=r(),R=c("p"),R.textContent=Ce,_e=r(),x=c("div"),m(I.$$.fragment),pe=r(),O=c("p"),O.innerHTML=Fe,he=r(),M=c("div"),m(G.$$.fragment),ge=r(),W=c("p"),W.innerHTML=ke,Ae=r(),b=c("div"),m(V.$$.fragment),Pe=r(),B=c("p"),B.textContent=ze,ye=r(),m(H.$$.fragment),be=r(),w=c("div"),m(j.$$.fragment),De=r(),Q=c("p"),Q.textContent=Le,ve=r(),C=c("div"),m(N.$$.fragment),$e=r(),Y=c("p"),Y.textContent=Se,Te=r(),D=c("div"),m(E.$$.fragment),xe=r(),Z=c("p"),Z.textContent=Ie,Me=r(),m(F.$$.fragment),ie=r(),m(X.$$.fragment),ae=r(),oe=c("p"),this.h()},l(e){const a=Ke("svelte-u9bgzb",document.head);o=f(a,"META",{name:!0,content:!0}),a.forEach(s),y=i(e),l=f(e,"P",{}),v(l).forEach(s),T=i(e),_(k.$$.fragment,e),se=i(e),z=f(e,"P",{"data-svelte-h":!0}),P(z)!=="svelte-nkajjv"&&(z.innerHTML=He),ne=i(e),_(L.$$.fragment,e),re=i(e),n=f(e,"DIV",{class:!0});var d=v(n);_(S.$$.fragment,d),ue=i(d),U=f(d,"P",{"data-svelte-h":!0}),P(U)!=="svelte-rv6x8u"&&(U.textContent=we),me=i(d),R=f(d,"P",{"data-svelte-h":!0}),P(R)!=="svelte-wuyqug"&&(R.textContent=Ce),_e=i(d),x=f(d,"DIV",{class:!0});var q=v(x);_(I.$$.fragment,q),pe=i(q),O=f(q,"P",{"data-svelte-h":!0}),P(O)!=="svelte-2m23sy"&&(O.innerHTML=Fe),q.forEach(s),he=i(d),M=f(d,"DIV",{class:!0});var ce=v(M);_(G.$$.fragment,ce),ge=i(ce),W=f(ce,"P",{"data-svelte-h":!0}),P(W)!=="svelte-16tb3j0"&&(W.innerHTML=ke),ce.forEach(s),Ae=i(d),b=f(d,"DIV",{class:!0});var ee=v(b);_(V.$$.fragment,ee),Pe=i(ee),B=f(ee,"P",{"data-svelte-h":!0}),P(B)!=="svelte-1254b9i"&&(B.textContent=ze),ye=i(ee),_(H.$$.fragment,ee),ee.forEach(s),be=i(d),w=f(d,"DIV",{class:!0});var fe=v(w);_(j.$$.fragment,fe),De=i(fe),Q=f(fe,"P",{"data-svelte-h":!0}),P(Q)!=="svelte-1o77hl2"&&(Q.textContent=Le),fe.forEach(s),ve=i(d),C=f(d,"DIV",{class:!0});var le=v(C);_(N.$$.fragment,le),$e=i(le),Y=f(le,"P",{"data-svelte-h":!0}),P(Y)!=="svelte-1lxcwhv"&&(Y.textContent=Se),le.forEach(s),Te=i(d),D=f(d,"DIV",{class:!0});var te=v(D);_(E.$$.fragment,te),xe=i(te),Z=f(te,"P",{"data-svelte-h":!0}),P(Z)!=="svelte-1vhtc74"&&(Z.textContent=Ie),Me=i(te),_(F.$$.fragment,te),te.forEach(s),d.forEach(s),ie=i(e),_(X.$$.fragment,e),ae=i(e),oe=f(e,"P",{}),v(oe).forEach(s),this.h()},h(){$(o,"name","hf:doc:metadata"),$(o,"content",We),$(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(M,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(b,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(C,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,a){t(document.head,o),u(e,y,a),u(e,l,a),u(e,T,a),p(k,e,a),u(e,se,a),u(e,z,a),u(e,ne,a),p(L,e,a),u(e,re,a),u(e,n,a),p(S,n,null),t(n,ue),t(n,U),t(n,me),t(n,R),t(n,_e),t(n,x),p(I,x,null),t(x,pe),t(x,O),t(n,he),t(n,M),p(G,M,null),t(M,ge),t(M,W),t(n,Ae),t(n,b),p(V,b,null),t(b,Pe),t(b,B),t(b,ye),p(H,b,null),t(n,be),t(n,w),p(j,w,null),t(w,De),t(w,Q),t(n,ve),t(n,C),p(N,C,null),t(C,$e),t(C,Y),t(n,Te),t(n,D),p(E,D,null),t(D,xe),t(D,Z),t(D,Me),p(F,D,null),u(e,ie,a),p(X,e,a),u(e,ae,a),u(e,oe,a),de=!0},p(e,[a]){const d={};a&2&&(d.$$scope={dirty:a,ctx:e}),H.$set(d);const q={};a&2&&(q.$$scope={dirty:a,ctx:e}),F.$set(q)},i(e){de||(h(k.$$.fragment,e),h(L.$$.fragment,e),h(S.$$.fragment,e),h(I.$$.fragment,e),h(G.$$.fragment,e),h(V.$$.fragment,e),h(H.$$.fragment,e),h(j.$$.fragment,e),h(N.$$.fragment,e),h(E.$$.fragment,e),h(F.$$.fragment,e),h(X.$$.fragment,e),de=!0)},o(e){g(k.$$.fragment,e),g(L.$$.fragment,e),g(S.$$.fragment,e),g(I.$$.fragment,e),g(G.$$.fragment,e),g(V.$$.fragment,e),g(H.$$.fragment,e),g(j.$$.fragment,e),g(N.$$.fragment,e),g(E.$$.fragment,e),g(F.$$.fragment,e),g(X.$$.fragment,e),de=!1},d(e){e&&(s(y),s(l),s(T),s(se),s(z),s(ne),s(re),s(n),s(ie),s(ae),s(oe)),s(o),A(k,e),A(L,e),A(S),A(I),A(G),A(V),A(H),A(j),A(N),A(E),A(F),A(X,e)}}}const We='{"title":"HunyuanDiT2DModel","local":"hunyuandit2dmodel","sections":[{"title":"HunyuanDiT2DModel","local":"diffusers.HunyuanDiT2DModel","sections":[],"depth":2}],"depth":1}';function Be(J){return Ee(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ot extends Xe{constructor(o){super(),qe(this,o,Be,Oe,Ne,{})}}export{ot as component};
