import{s as _a,o as $a,n as ha}from"../chunks/scheduler.8c3d61f6.js";import{S as ba,i as va,g as i,s as r,r as l,A as Pa,h as a,f as t,c as n,j as g,u as c,x as $,k as _,y as d,a as o,v as f,d as m,t as p,w as u}from"../chunks/index.da70eac4.js";import{T as Aa}from"../chunks/Tip.1d9b8c37.js";import{D as h}from"../chunks/Docstring.dcbc19b1.js";import{H as b,E as xa}from"../chunks/getInferenceSnippets.86beaec3.js";function ya(Ws){let v,Pe="This API is currently 🧪 experimental in nature and can change in future.";return{c(){v=i("p"),v.textContent=Pe},l(P){v=a(P,"P",{"data-svelte-h":!0}),$(v)!=="svelte-2dfli5"&&(v.textContent=Pe)},m(P,Kt){o(P,v,Kt)},p:ha,d(P){P&&t(v)}}}function Ca(Ws){let v,Pe,P,Kt,Ae,Us,xe,$i="An attention processor is a class for applying different types of attention mechanisms.",Bs,ye,Qs,x,Ce,hn,Wt,hi="Default processor for performing attention-related computations.",Ys,y,we,bn,Ut,bi="Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).",Zs,C,Fe,vn,Bt,vi=`Processor for performing attention-related computations with extra learnable key and value matrices for the text
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learnable key and value matrices for the text encoder.`,to,F,Te,An,Yt,Ai=`Processor for implementing flash attention using torch_npu. Torch_npu supports only fp16 and bf16 data types. If
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not significant.`,so,A,De,xn,Zt,xi=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). It uses
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projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on
query and key vector.`,Co,H,Ye,Nn,us,qi=`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>.`,wo,M,Ze,Gn,gs,Ni=`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>.`,Fo,et,Io,j,tt,Jn,_s,Gi=`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.`,Ro,bt,Ko,re,vt,ti,Ds,ea="Attention processor used in Mochi.",Wo,ne,Pt,si,Ls,ta="Attention processor used in Mochi VAE.",Uo,At,Bo,ie,xt,oi,Vs,sa="Processor for implementing scaled dot-product linear attention.",Qo,ae,yt,ri,zs,oa="Processor for implementing multiscale quadratic attention.",Yo,de,Ct,ni,ks,ra="Processor for implementing scaled dot-product linear attention.",Zo,le,wt,ii,Xs,na="Processor for implementing scaled dot-product linear attention.",er,Ft,tr,ce,It,ai,Es,ia=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). This is
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It applies rotary embedding on query and key vector, and allows MHA, GQA or MQA.`,sr,Tt,or,fe,Dt,di,Ss,aa="Processor for implementing sliced attention.",rr,me,Lt,li,qs,da="Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.",nr,Vt,ir,pe,zt,ci,Ns,la="Processor for implementing memory efficient attention using xFormers.",ar,ue,kt,fi,Gs,ca="Processor for implementing memory efficient attention using xFormers.",dr,Xt,lr,ge,Et,mi,Js,fa="Processor for implementing scaled dot-product attention with pallas flash attention kernel if using <code>torch_xla</code>.",cr,St,fr,_e,qt,pi,Hs,ma="Processor for implementing memory efficient attention using xFormers.",mr,Nt,pr,$e,Gt,ui,Ms,pa="Attention processor for IP-Adapter using xFormers.",ur,Jt,gr,he,Ht,gi,js,ua="Flux Attention processor for IP-Adapter.",_r,Mt,$r,be,jt,_i,Os,ga="Processor for implementing scaled dot-product attention with pallas flash attention kernel if using 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h({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#L2896"}}),Ne=new b({props:{title:"CrossFrameAttnProcessor",local:"diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor",headingTag:"h2"}}),Ge=new h({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 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2, due to classifier-free guidance.`,name:"batch_size"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L62"}}),Je=new b({props:{title:"Custom Diffusion",local:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor",headingTag:"h2"}}),He=new h({props:{name:"class diffusers.models.attention_processor.CustomDiffusionAttnProcessor",anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor",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.CustomDiffusionAttnProcessor.train_kv",description:`<strong>train_kv</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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Whether to newly train query matrices corresponding to the latent image features.`,name:"train_q_out"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
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;
Whether to include the bias parameter in <code>train_q_out</code>.`,name:"out_bias"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor.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#L1173"}}),Me=new h({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;
Whether to newly train the key and value matrices corresponding to the text features.`,name:"train_kv"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.train_q_out",description:`<strong>train_q_out</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to newly train query matrices corresponding to the latent image features.`,name:"train_q_out"},{anchor:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
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#L4540"}}),je=new h({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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Whether to newly train query matrices corresponding to the latent image features.`,name:"train_q_out"},{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
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 hidden size of the attention layer.`,name:"hidden_size"},{anchor:"diffusers.models.attention_processor.IPAdapterAttnProcessor.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>) &#x2014;
The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.IPAdapterAttnProcessor.num_tokens",description:`<strong>num_tokens</strong> (<code>int</code>, <code>Tuple[int]</code> or <code>List[int]</code>, defaults to <code>(4,)</code>) &#x2014;
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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