import{s as De,o as ye,n as ve}from"../chunks/scheduler.8c3d61f6.js";import{S as Me,i as we,g as f,s as i,r as h,A as Ce,h as l,f as r,c as d,j as N,u as g,x as $,k as H,y as s,a as u,v as A,d as P,t as x,w as b}from"../chunks/index.da70eac4.js";import{T as Fe}from"../chunks/Tip.1d9b8c37.js";import{D as W}from"../chunks/Docstring.932cdff3.js";import{H as Te,E as Le}from"../chunks/index.ef90fe87.js";function je(X){let o,m="This API is 🧪 experimental.";return{c(){o=f("p"),o.textContent=m},l(a){o=l(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-89q1io"&&(o.textContent=m)},m(a,F){u(a,o,F)},p:ve,d(a){a&&r(o)}}}function ke(X){let o,m="This API is 🧪 experimental.";return{c(){o=f("p"),o.textContent=m},l(a){o=l(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-89q1io"&&(o.textContent=m)},m(a,F){u(a,o,F)},p:ve,d(a){a&&r(o)}}}function Se(X){let o,m,a,F,M,B,w,he='A Transformer model for image-like data from <a href="https://blackforestlabs.ai/announcing-black-forest-labs/" rel="nofollow">Flux</a>.',Y,C,Z,n,L,re,E,ge="The Transformer model introduced in Flux.",ne,K,Ae='Reference: <a href="https://blackforestlabs.ai/announcing-black-forest-labs/" rel="nofollow">https://blackforestlabs.ai/announcing-black-forest-labs/</a>',ie,T,j,de,q,Pe='The <a href="/docs/diffusers/main/en/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a> forward method.',ae,_,k,ce,J,xe=`Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.`,fe,v,le,D,S,ue,R,be="Sets the attention processor to use to compute attention.",me,p,G,_e,U,$e="Disables the fused QKV projection if enabled.",pe,y,ee,I,oe,Q,se;return M=new Te({props:{title:"FluxTransformer2DModel",local:"fluxtransformer2dmodel",headingTag:"h1"}}),C=new Te({props:{title:"FluxTransformer2DModel",local:"diffusers.FluxTransformer2DModel",headingTag:"h2"}}),L=new W({props:{name:"class diffusers.FluxTransformer2DModel",anchor:"diffusers.FluxTransformer2DModel",parameters:[{name:"patch_size",val:": int = 1"},{name:"in_channels",val:": int = 64"},{name:"out_channels",val:": typing.Optional[int] = None"},{name:"num_layers",val:": int = 19"},{name:"num_single_layers",val:": int = 38"},{name:"attention_head_dim",val:": int = 128"},{name:"num_attention_heads",val:": int = 24"},{name:"joint_attention_dim",val:": int = 4096"},{name:"pooled_projection_dim",val:": int = 768"},{name:"guidance_embeds",val:": bool = False"},{name:"axes_dims_rope",val:": typing.Tuple[int] = (16, 56, 56)"}],parametersDescription:[{anchor:"diffusers.FluxTransformer2DModel.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, defaults to <code>1</code>) &#x2014;
Patch size to turn the input data into small patches.`,name:"patch_size"},{anchor:"diffusers.FluxTransformer2DModel.in_channels",description:`<strong>in_channels</strong> (<code>int</code>, defaults to <code>64</code>) &#x2014;
The number of channels in the input.`,name:"in_channels"},{anchor:"diffusers.FluxTransformer2DModel.out_channels",description:`<strong>out_channels</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of channels in the output. If not specified, it defaults to <code>in_channels</code>.`,name:"out_channels"},{anchor:"diffusers.FluxTransformer2DModel.num_layers",description:`<strong>num_layers</strong> (<code>int</code>, defaults to <code>19</code>) &#x2014;
The number of layers of dual stream DiT blocks to use.`,name:"num_layers"},{anchor:"diffusers.FluxTransformer2DModel.num_single_layers",description:`<strong>num_single_layers</strong> (<code>int</code>, defaults to <code>38</code>) &#x2014;
The number of layers of single stream DiT blocks to use.`,name:"num_single_layers"},{anchor:"diffusers.FluxTransformer2DModel.attention_head_dim",description:`<strong>attention_head_dim</strong> (<code>int</code>, defaults to <code>128</code>) &#x2014;
The number of dimensions to use for each attention head.`,name:"attention_head_dim"},{anchor:"diffusers.FluxTransformer2DModel.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, defaults to <code>24</code>) &#x2014;
The number of attention heads to use.`,name:"num_attention_heads"},{anchor:"diffusers.FluxTransformer2DModel.joint_attention_dim",description:`<strong>joint_attention_dim</strong> (<code>int</code>, defaults to <code>4096</code>) &#x2014;
The number of dimensions to use for the joint attention (embedding/channel dimension of
<code>encoder_hidden_states</code>).`,name:"joint_attention_dim"},{anchor:"diffusers.FluxTransformer2DModel.pooled_projection_dim",description:`<strong>pooled_projection_dim</strong> (<code>int</code>, defaults to <code>768</code>) &#x2014;
The number of dimensions to use for the pooled projection.`,name:"pooled_projection_dim"},{anchor:"diffusers.FluxTransformer2DModel.guidance_embeds",description:`<strong>guidance_embeds</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to use guidance embeddings for guidance-distilled variant of the model.`,name:"guidance_embeds"},{anchor:"diffusers.FluxTransformer2DModel.axes_dims_rope",description:`<strong>axes_dims_rope</strong> (<code>Tuple[int]</code>, defaults to <code>(16, 56, 56)</code>) &#x2014;
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Input <code>hidden_states</code>.`,name:"hidden_states"},{anchor:"diffusers.FluxTransformer2DModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, text_sequence_length, joint_attention_dim)</code>) &#x2014;
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.`,name:"encoder_hidden_states"},{anchor:"diffusers.FluxTransformer2DModel.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.FluxTransformer2DModel.forward.timestep",description:`<strong>timestep</strong> ( <code>torch.LongTensor</code>) &#x2014;
Used to indicate denoising step.`,name:"timestep"},{anchor:"diffusers.FluxTransformer2DModel.forward.block_controlnet_hidden_states",description:`<strong>block_controlnet_hidden_states</strong> &#x2014; (<code>list</code> of <code>torch.Tensor</code>):
A list of tensors that if specified are added to the residuals of transformer blocks.`,name:"block_controlnet_hidden_states"},{anchor:"diffusers.FluxTransformer2DModel.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.FluxTransformer2DModel.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_flux.py#L389",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>
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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.FluxTransformer2DModel.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_flux.py#L315"}}),G=new W({props:{name:"unfuse_qkv_projections",anchor:"diffusers.FluxTransformer2DModel.unfuse_qkv_projections",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_flux.py#L376"}}),y=new Fe({props:{warning:!0,$$slots:{default:[ke]},$$scope:{ctx:X}}}),I=new Le({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/models/flux_transformer.md"}}),{c(){o=f("meta"),m=i(),a=f("p"),F=i(),h(M.$$.fragment),B=i(),w=f("p"),w.innerHTML=he,Y=i(),h(C.$$.fragment),Z=i(),n=f("div"),h(L.$$.fragment),re=i(),E=f("p"),E.textContent=ge,ne=i(),K=f("p"),K.innerHTML=Ae,ie=i(),T=f("div"),h(j.$$.fragment),de=i(),q=f("p"),q.innerHTML=Pe,ae=i(),_=f("div"),h(k.$$.fragment),ce=i(),J=f("p"),J.textContent=xe,fe=i(),h(v.$$.fragment),le=i(),D=f("div"),h(S.$$.fragment),ue=i(),R=f("p"),R.textContent=be,me=i(),p=f("div"),h(G.$$.fragment),_e=i(),U=f("p"),U.textContent=$e,pe=i(),h(y.$$.fragment),ee=i(),h(I.$$.fragment),oe=i(),Q=f("p"),this.h()},l(e){const t=Ce("svelte-u9bgzb",document.head);o=l(t,"META",{name:!0,content:!0}),t.forEach(r),m=d(e),a=l(e,"P",{}),N(a).forEach(r),F=d(e),g(M.$$.fragment,e),B=d(e),w=l(e,"P",{"data-svelte-h":!0}),$(w)!=="svelte-e6h9db"&&(w.innerHTML=he),Y=d(e),g(C.$$.fragment,e),Z=d(e),n=l(e,"DIV",{class:!0});var c=N(n);g(L.$$.fragment,c),re=d(c),E=l(c,"P",{"data-svelte-h":!0}),$(E)!=="svelte-19p4ty0"&&(E.textContent=ge),ne=d(c),K=l(c,"P",{"data-svelte-h":!0}),$(K)!=="svelte-mxgguy"&&(K.innerHTML=Ae),ie=d(c),T=l(c,"DIV",{class:!0});var V=N(T);g(j.$$.fragment,V),de=d(V),q=l(V,"P",{"data-svelte-h":!0}),$(q)!=="svelte-4l0nim"&&(q.innerHTML=Pe),V.forEach(r),ae=d(c),_=l(c,"DIV",{class:!0});var z=N(_);g(k.$$.fragment,z),ce=d(z),J=l(z,"P",{"data-svelte-h":!0}),$(J)!=="svelte-1254b9i"&&(J.textContent=xe),fe=d(z),g(v.$$.fragment,z),z.forEach(r),le=d(c),D=l(c,"DIV",{class:!0});var te=N(D);g(S.$$.fragment,te),ue=d(te),R=l(te,"P",{"data-svelte-h":!0}),$(R)!=="svelte-1o77hl2"&&(R.textContent=be),te.forEach(r),me=d(c),p=l(c,"DIV",{class:!0});var O=N(p);g(G.$$.fragment,O),_e=d(O),U=l(O,"P",{"data-svelte-h":!0}),$(U)!=="svelte-1vhtc74"&&(U.textContent=$e),pe=d(O),g(y.$$.fragment,O),O.forEach(r),c.forEach(r),ee=d(e),g(I.$$.fragment,e),oe=d(e),Q=l(e,"P",{}),N(Q).forEach(r),this.h()},h(){H(o,"name","hf:doc:metadata"),H(o,"content",Ge),H(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(_,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(p,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(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,t){s(document.head,o),u(e,m,t),u(e,a,t),u(e,F,t),A(M,e,t),u(e,B,t),u(e,w,t),u(e,Y,t),A(C,e,t),u(e,Z,t),u(e,n,t),A(L,n,null),s(n,re),s(n,E),s(n,ne),s(n,K),s(n,ie),s(n,T),A(j,T,null),s(T,de),s(T,q),s(n,ae),s(n,_),A(k,_,null),s(_,ce),s(_,J),s(_,fe),A(v,_,null),s(n,le),s(n,D),A(S,D,null),s(D,ue),s(D,R),s(n,me),s(n,p),A(G,p,null),s(p,_e),s(p,U),s(p,pe),A(y,p,null),u(e,ee,t),A(I,e,t),u(e,oe,t),u(e,Q,t),se=!0},p(e,[t]){const c={};t&2&&(c.$$scope={dirty:t,ctx:e}),v.$set(c);const V={};t&2&&(V.$$scope={dirty:t,ctx:e}),y.$set(V)},i(e){se||(P(M.$$.fragment,e),P(C.$$.fragment,e),P(L.$$.fragment,e),P(j.$$.fragment,e),P(k.$$.fragment,e),P(v.$$.fragment,e),P(S.$$.fragment,e),P(G.$$.fragment,e),P(y.$$.fragment,e),P(I.$$.fragment,e),se=!0)},o(e){x(M.$$.fragment,e),x(C.$$.fragment,e),x(L.$$.fragment,e),x(j.$$.fragment,e),x(k.$$.fragment,e),x(v.$$.fragment,e),x(S.$$.fragment,e),x(G.$$.fragment,e),x(y.$$.fragment,e),x(I.$$.fragment,e),se=!1},d(e){e&&(r(m),r(a),r(F),r(B),r(w),r(Y),r(Z),r(n),r(ee),r(oe),r(Q)),r(o),b(M,e),b(C,e),b(L),b(j),b(k),b(v),b(S),b(G),b(y),b(I,e)}}}const Ge='{"title":"FluxTransformer2DModel","local":"fluxtransformer2dmodel","sections":[{"title":"FluxTransformer2DModel","local":"diffusers.FluxTransformer2DModel","sections":[],"depth":2}],"depth":1}';function Ie(X){return ye(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ke extends Me{constructor(o){super(),we(this,o,Ie,Se,De,{})}}export{Ke as component};
