import{s as ki,o as Ei,n as Si}from"../chunks/scheduler.8c3d61f6.js";import{S as qi,i as Xi,g as i,s as r,r as l,A as Gi,h as a,f as t,c as n,j as g,u as c,x as $,k as _,y as d,a as o,v as f,d as m,t as p,w as u}from"../chunks/index.da70eac4.js";import{T as Hi}from"../chunks/Tip.1d9b8c37.js";import{D as h}from"../chunks/Docstring.6b390b9a.js";import{H as b,E as Ni}from"../chunks/EditOnGithub.1e64e623.js";function Mi(Vs){let v,$e="This API is currently 🧪 experimental in nature and can change in future.";return{c(){v=i("p"),v.textContent=$e},l(P){v=a(P,"P",{"data-svelte-h":!0}),$(v)!=="svelte-2dfli5"&&(v.textContent=$e)},m(P,Et){o(P,v,Et)},p:Si,d(P){P&&t(v)}}}function ji(Vs){let v,$e,P,Et,he,zs,be,Gn="An attention processor is a class for applying different types of attention mechanisms.",ks,ve,Es,x,Pe,jr,St,Hn="Default processor for performing attention-related computations.",Ss,y,Ae,Jr,qt,Nn="Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).",qs,C,xe,Rr,Xt,Mn=`Processor for performing attention-related computations with extra learnable key and value matrices for the text
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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.`,lo,M,We,fn,ss,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://arxiv.org/abs/2403.17377" rel="nofollow">Pertubed Attention Guidance</a>.`,co,j,Ue,mn,os,ai=`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
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diffusers.models.attention_processor.FusedAuraFlowAttnProcessor2_0",anchor:"diffusers.models.attention_processor.FusedAuraFlowAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L2157"}}),Ve=new b({props:{title:"CogVideoX",local:"diffusers.models.attention_processor.CogVideoXAttnProcessor2_0",headingTag:"h2"}}),ze=new h({props:{name:"class diffusers.models.attention_processor.CogVideoXAttnProcessor2_0",anchor:"diffusers.models.attention_processor.CogVideoXAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L2799"}}),ke=new 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/vr_10037/src/diffusers/models/attention_processor.py#L2870"}}),Ee=new b({props:{title:"CrossFrameAttnProcessor",local:"diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor",headingTag:"h2"}}),Se=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 frames.
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2, due to classifier-free guidance.`,name:"batch_size"}],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L62"}}),qe=new b({props:{title:"Custom Diffusion",local:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor",headingTag:"h2"}}),Xe=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/vr_10037/src/diffusers/models/attention_processor.py#L1152"}}),Ge=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/vr_10037/src/diffusers/models/attention_processor.py#L4407"}}),He=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;
Whether to newly train the key and value matrices corresponding to the text features.`,name:"train_kv"},{anchor:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor.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.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
as the attention operator. It is recommended to set to <code>None</code>, and allow xFormers to choose the best operator.`,name:"attention_op"}],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L4291"}}),Ne=new b({props:{title:"Flux",local:"diffusers.models.attention_processor.FluxAttnProcessor2_0",headingTag:"h2"}}),Me=new h({props:{name:"class diffusers.models.attention_processor.FluxAttnProcessor2_0",anchor:"diffusers.models.attention_processor.FluxAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L2254"}}),je=new h({props:{name:"class diffusers.models.attention_processor.FusedFluxAttnProcessor2_0",anchor:"diffusers.models.attention_processor.FusedFluxAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L2451"}}),Je=new h({props:{name:"class diffusers.models.attention_processor.FluxSingleAttnProcessor2_0",anchor:"diffusers.models.attention_processor.FluxSingleAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L5862"}}),Re=new b({props:{title:"Hunyuan",local:"diffusers.models.attention_processor.HunyuanAttnProcessor2_0",headingTag:"h2"}}),Oe=new h({props:{name:"class diffusers.models.attention_processor.HunyuanAttnProcessor2_0",anchor:"diffusers.models.attention_processor.HunyuanAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L3641"}}),Ke=new h({props:{name:"class diffusers.models.attention_processor.FusedHunyuanAttnProcessor2_0",anchor:"diffusers.models.attention_processor.FusedHunyuanAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L3739"}}),We=new h({props:{name:"class diffusers.models.attention_processor.PAGHunyuanAttnProcessor2_0",anchor:"diffusers.models.attention_processor.PAGHunyuanAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L3842"}}),Ue=new h({props:{name:"class diffusers.models.attention_processor.PAGCFGHunyuanAttnProcessor2_0",anchor:"diffusers.models.attention_processor.PAGCFGHunyuanAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L3965"}}),Be=new b({props:{title:"IdentitySelfAttnProcessor2_0",local:"diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0",headingTag:"h2"}}),Qe=new h({props:{name:"class diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0",anchor:"diffusers.models.attention_processor.PAGIdentitySelfAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L5564"}}),Ye=new h({props:{name:"class diffusers.models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0",anchor:"diffusers.models.attention_processor.PAGCFGIdentitySelfAttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_10037/src/diffusers/models/attention_processor.py#L5663"}}),Ze=new b({props:{title:"IP-Adapter",local:"diffusers.models.attention_processor.IPAdapterAttnProcessor",headingTag:"h2"}}),et=new h({props:{name:"class diffusers.models.attention_processor.IPAdapterAttnProcessor",anchor:"diffusers.models.attention_processor.IPAdapterAttnProcessor",parameters:[{name:"hidden_size",val:""},{name:"cross_attention_dim",val:" = None"},{name:"num_tokens",val:" = (4,)"},{name:"scale",val:" = 1.0"}],parametersDescription:[{anchor:"diffusers.models.attention_processor.IPAdapterAttnProcessor.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>) &#x2014;
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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