diffusers/pr_10312/en/_app/immutable/nodes/38.3b9f40b2.js

import{s as De,o as ye,n as Te}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 $,k as H,y as t,a as m,v as A,d as P,t as x,w as b}from"../chunks/index.da70eac4.js";import{T as Fe}from"../chunks/Tip.1d9b8c37.js";import{D as W}from"../chunks/Docstring.6b390b9a.js";import{H as ve,E as Le}from"../chunks/EditOnGithub.1e64e623.js";function je(X){let s,u="This API is 🧪 experimental.";return{c(){s=f("p"),s.textContent=u},l(a){s=l(a,"P",{"data-svelte-h":!0}),$(s)!=="svelte-89q1io"&&(s.textContent=u)},m(a,F){m(a,s,F)},p:Te,d(a){a&&r(s)}}}function ke(X){let s,u="This API is 🧪 experimental.";return{c(){s=f("p"),s.textContent=u},l(a){s=l(a,"P",{"data-svelte-h":!0}),$(s)!=="svelte-89q1io"&&(s.textContent=u)},m(a,F){m(a,s,F)},p:Te,d(a){a&&r(s)}}}function Se(X){let s,u,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,v,j,de,z,Pe='The <a href="/docs/diffusers/pr_10312/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)
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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;
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<p>If <code>return_dict</code> is True, an <code>~models.transformer_2d.Transformer2DModelOutput</code> is returned, otherwise a
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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, 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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
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