import{s as aa,o as da,n as la}from"../chunks/scheduler.8c3d61f6.js";import{S as ca,i as fa,g as i,s as r,r as l,A as ma,h as a,f as t,c as n,j as g,u as c,x as h,k as _,y as d,a as o,v as f,d as m,t as p,w as u}from"../chunks/index.da70eac4.js";import{T as pa}from"../chunks/Tip.1d9b8c37.js";import{D as $}from"../chunks/Docstring.91dd4639.js";import{H as b,E as ua}from"../chunks/getInferenceSnippets.28ca3596.js";function ga(Os){let v,he="This API is currently 🧪 experimental in nature and can change in future.";return{c(){v=i("p"),v.textContent=he},l(P){v=a(P,"P",{"data-svelte-h":!0}),h(v)!=="svelte-2dfli5"&&(v.textContent=he)},m(P,Kt){o(P,v,Kt)},p:la,d(P){P&&t(v)}}}function _a(Os){let v,he,P,Kt,be,Rs,ve,di="An attention processor is a class for applying different types of attention mechanisms.",Ks,Pe,Ws,x,Ae,mn,Wt,li="Default processor for performing attention-related computations.",Us,y,xe,pn,Ut,ci="Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).",Bs,C,ye,un,Bt,fi=`Processor for performing attention-related computations with extra learnable key and value matrices for the text
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used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.`,Po,N,Be,Dn,fs,wi=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0) with fused
projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on
query and key vector.`,Ao,G,Qe,Ln,ms,Fi=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a normalization layer and rotary embedding on query and key vector. This
variant of the processor employs <a href="https://huggingface.co/papers/2403.17377" rel="nofollow">Pertubed Attention Guidance</a>.`,xo,J,Ye,Vn,ps,Ii=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is
used in the HunyuanDiT model. It applies a normalization layer and rotary embedding on query and key vector. This
variant of the processor employs <a href="https://huggingface.co/papers/2403.17377" rel="nofollow">Pertubed Attention Guidance</a>.`,yo,Ze,Co,H,et,zn,us,Ti=`Processor for implementing PAG using scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).
PAG reference: <a href="https://huggingface.co/papers/2403.17377" rel="nofollow">https://huggingface.co/papers/2403.17377</a>`,wo,M,tt,kn,gs,Di=`Processor for implementing PAG using scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).
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used in the LuminaNextDiT model. It applies a s normalization layer and rotary embedding on query and key vector.`,Mo,ht,jo,se,bt,Kn,Is,Mi="Attention processor used in Mochi.",Oo,oe,vt,Wn,Ts,ji="Attention processor used in Mochi VAE.",Ro,Pt,Ko,re,At,Un,Ds,Oi="Processor for implementing scaled dot-product linear attention.",Wo,ne,xt,Bn,Ls,Ri="Processor for implementing multiscale quadratic attention.",Uo,ie,yt,Qn,Vs,Ki="Processor for implementing scaled dot-product linear attention.",Bo,ae,Ct,Yn,zs,Wi="Processor for implementing scaled dot-product linear attention.",Qo,wt,Yo,de,Ft,Zn,ks,Ui=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is
used in the Stable Audio model. It applies rotary embedding on query and key vector, and allows MHA, GQA or MQA.`,Zo,It,er,le,Tt,ei,Xs,Bi="Processor for implementing sliced attention.",tr,ce,Dt,ti,Es,Qi="Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.",sr,Lt,or,fe,Vt,si,Ss,Yi="Processor for implementing memory efficient attention using xFormers.",rr,me,zt,oi,qs,Zi="Processor for implementing memory efficient attention using xFormers.",nr,kt,ir,pe,Xt,ri,Ns,ea="Processor for implementing scaled dot-product attention with pallas flash attention kernel if using <code>torch_xla</code>.",ar,Et,dr,ue,St,ni,Gs,ta="Processor for implementing memory efficient attention using xFormers.",lr,qt,cr,ge,Nt,ii,Js,sa="Attention processor for IP-Adapter using xFormers.",fr,Gt,mr,Jt,Ht,pr,Mt,ur,_e,jt,ai,Hs,oa="Processor for implementing scaled dot-product attention with pallas flash attention kernel if using <code>torch_xla</code>.",gr,Ot,_r,js,$r;return be=new 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$({props:{name:"class diffusers.models.attention_processor.FusedCogVideoXAttnProcessor2_0",anchor:"diffusers.models.attention_processor.FusedCogVideoXAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L2344"}}),Ee=new b({props:{title:"CrossFrameAttnProcessor",local:"diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor",headingTag:"h2"}}),Se=new $({props:{name:"class diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor",anchor:"diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor",parameters:[{name:"batch_size",val:" = 2"}],parametersDescription:[{anchor:"diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor.batch_size",description:`<strong>batch_size</strong> &#x2014; The number that represents actual batch size, other than the frames.
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The hidden size of the attention layer.`,name:"hidden_size"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor.out_bias",description:`<strong>out_bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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The dropout probability to use.`,name:"dropout"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L1173"}}),Ge=new $({props:{name:"class diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0",anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0",parameters:[{name:"train_kv",val:": bool = True"},{name:"train_q_out",val:": bool = True"},{name:"hidden_size",val:": typing.Optional[int] = None"},{name:"cross_attention_dim",val:": typing.Optional[int] = None"},{name:"out_bias",val:": bool = True"},{name:"dropout",val:": float = 0.0"}],parametersDescription:[{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.train_kv",description:`<strong>train_kv</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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The hidden size of the attention layer.`,name:"hidden_size"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.out_bias",description:`<strong>out_bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to include the bias parameter in <code>train_q_out</code>.`,name:"out_bias"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability to use.`,name:"dropout"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L3888"}}),Je=new $({props:{name:"class diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor",anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor",parameters:[{name:"train_kv",val:": bool = True"},{name:"train_q_out",val:": bool = False"},{name:"hidden_size",val:": typing.Optional[int] = None"},{name:"cross_attention_dim",val:": typing.Optional[int] = None"},{name:"out_bias",val:": bool = True"},{name:"dropout",val:": float = 0.0"},{name:"attention_op",val:": typing.Optional[typing.Callable] = None"}],parametersDescription:[{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.train_kv",description:`<strong>train_kv</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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The hidden size of the attention layer.`,name:"hidden_size"},{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.out_bias",description:`<strong>out_bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to include the bias parameter in <code>train_q_out</code>.`,name:"out_bias"},{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.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.models.attention_processor.CustomDiffusionXFormersAttnProcessor.attention_op",description:`<strong>attention_op</strong> (<code>Callable</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The base
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase" rel="nofollow">operator</a> to use
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The context length of the image features.`,name:"num_tokens"},{anchor:"diffusers.models.attention_processor.IPAdapterAttnProcessor.scale",description:`<strong>scale</strong> (<code>float</code> or List<code>float</code>, defaults to 1.0) &#x2014;
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