import{s as eo,o as to,n as oo}from"../chunks/scheduler.182ea377.js";import{S as so,i as ro,g as i,s as r,r as d,A as no,h as a,f as o,c as n,j as g,u as c,x as $,k as h,y as u,a as s,v as l,d as m,t as f,w as p}from"../chunks/index.abf12888.js";import{T as io}from"../chunks/Tip.230e2334.js";import{D as b}from"../chunks/Docstring.81ce6c10.js";import{H as A,E as ao}from"../chunks/EditOnGithub.9b8e78e4.js";function co(Ve){let _,q="This API is currently 🧪 experimental in nature and can change in future.";return{c(){_=i("p"),_.textContent=q},l(v){_=a(v,"P",{"data-svelte-h":!0}),$(_)!=="svelte-2dfli5"&&(_.textContent=q)},m(v,ge){s(v,_,ge)},p:oo,d(v){v&&o(_)}}}function lo(Ve){let _,q,v,ge,N,Ke,E,Et="An attention processor is a class for applying different types of attention mechanisms.",Re,I,Xe,x,S,Ct,he,It="Default processor for performing attention-related computations.",qe,O,Ne,C,W,yt,$e,St="Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).",Ee,j,Ie,y,B,wt,Ae,Ot=`Processor for performing attention-related computations with extra learnable key and value matrices for the text
encoder.`,Se,U,Oe,w,H,Dt,be,Wt=`Processor for performing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0), with extra
learnable key and value matrices for the text encoder.`,We,M,je,D,G,Tt,ve,jt="Cross frame attention processor. Each frame attends the first frame.",Be,J,Ue,T,Q,Ft,Pe,Bt="Processor for implementing attention for the Custom Diffusion method.",He,Y,Me,F,Z,kt,xe,Ut=`Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled
dot-product attention.`,Ge,ee,Je,k,te,Lt,Ce,Ht="Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.",Qe,oe,Ye,P,se,zt,ye,Mt=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). It uses
fused projection layers. 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.`,Vt,X,Ze,re,et,L,ne,Kt,we,Gt=`Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text
encoder.`,tt,ie,ot,z,ae,Rt,De,Jt="Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.",st,de,rt,V,ce,Xt,Te,Qt="Processor for implementing sliced attention.",nt,le,it,K,me,qt,Fe,Yt="Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.",at,fe,dt,R,pe,Nt,ke,Zt="Processor for implementing memory efficient attention using xFormers.",ct,ue,lt,ze,mt;return N=new A({props:{title:"Attention Processor",local:"attention-processor",headingTag:"h1"}}),I=new A({props:{title:"AttnProcessor",local:"diffusers.models.attention_processor.AttnProcessor",headingTag:"h2"}}),S=new b({props:{name:"class diffusers.models.attention_processor.AttnProcessor",anchor:"diffusers.models.attention_processor.AttnProcessor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L726"}}),O=new A({props:{title:"AttnProcessor2_0",local:"diffusers.models.attention_processor.AttnProcessor2_0",headingTag:"h2"}}),W=new b({props:{name:"class diffusers.models.attention_processor.AttnProcessor2_0",anchor:"diffusers.models.attention_processor.AttnProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L1210"}}),j=new A({props:{title:"AttnAddedKVProcessor",local:"diffusers.models.attention_processor.AttnAddedKVProcessor",headingTag:"h2"}}),B=new b({props:{name:"class diffusers.models.attention_processor.AttnAddedKVProcessor",anchor:"diffusers.models.attention_processor.AttnAddedKVProcessor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L902"}}),U=new A({props:{title:"AttnAddedKVProcessor2_0",local:"diffusers.models.attention_processor.AttnAddedKVProcessor2_0",headingTag:"h2"}}),H=new b({props:{name:"class diffusers.models.attention_processor.AttnAddedKVProcessor2_0",anchor:"diffusers.models.attention_processor.AttnAddedKVProcessor2_0",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L969"}}),M=new A({props:{title:"CrossFrameAttnProcessor",local:"diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor",headingTag:"h2"}}),G=new b({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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py#L48"}}),J=new A({props:{title:"CustomDiffusionAttnProcessor",local:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor",headingTag:"h2"}}),Q=new b({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:": Optional = None"},{name:"cross_attention_dim",val:": Optional = 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#L798"}}),Y=new A({props:{title:"CustomDiffusionAttnProcessor2_0",local:"diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0",headingTag:"h2"}}),Z=new b({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:": Optional = None"},{name:"cross_attention_dim",val:": Optional = 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#L1516"}}),ee=new A({props:{title:"CustomDiffusionXFormersAttnProcessor",local:"diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor",headingTag:"h2"}}),te=new b({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:": Optional = None"},{name:"cross_attention_dim",val:": Optional = None"},{name:"out_bias",val:": bool = True"},{name:"dropout",val:": float = 0.0"},{name:"attention_op",val:": Optional = 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 number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor.rank",description:`<strong>rank</strong> (<code>int</code>, defaults to 4) &#x2014;
The dimension of the LoRA update matrices.`,name:"rank"},{anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor.network_alpha",description:`<strong>network_alpha</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Equivalent to <code>alpha</code> but it&#x2019;s usage is specific to Kohya (A1111) style LoRAs.`,name:"network_alpha"},{anchor:"diffusers.models.attention_processor.LoRAAttnAddedKVProcessor.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>) &#x2014;
Additional keyword arguments to pass to the <code>LoRALinearLayer</code> layers.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L2037"}}),ie=new A({props:{title:"LoRAXFormersAttnProcessor",local:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor",headingTag:"h2"}}),ae=new b({props:{name:"class diffusers.models.attention_processor.LoRAXFormersAttnProcessor",anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor",parameters:[{name:"hidden_size",val:": int"},{name:"cross_attention_dim",val:": int"},{name:"rank",val:": int = 4"},{name:"attention_op",val:": Optional = None"},{name:"network_alpha",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The hidden size of the attention layer.`,name:"hidden_size"},{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of channels in the <code>encoder_hidden_states</code>.`,name:"cross_attention_dim"},{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.rank",description:`<strong>rank</strong> (<code>int</code>, defaults to 4) &#x2014;
The dimension of the LoRA update matrices.`,name:"rank"},{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.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"},{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.network_alpha",description:`<strong>network_alpha</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Equivalent to <code>alpha</code> but it&#x2019;s usage is specific to Kohya (A1111) style LoRAs.`,name:"network_alpha"},{anchor:"diffusers.models.attention_processor.LoRAXFormersAttnProcessor.kwargs",description:`<strong>kwargs</strong> (<code>dict</code>) &#x2014;
Additional keyword arguments to pass to the <code>LoRALinearLayer</code> layers.`,name:"kwargs"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L1958"}}),de=new A({props:{title:"SlicedAttnProcessor",local:"diffusers.models.attention_processor.SlicedAttnProcessor",headingTag:"h2"}}),ce=new b({props:{name:"class diffusers.models.attention_processor.SlicedAttnProcessor",anchor:"diffusers.models.attention_processor.SlicedAttnProcessor",parameters:[{name:"slice_size",val:": int"}],parametersDescription:[{anchor:"diffusers.models.attention_processor.SlicedAttnProcessor.slice_size",description:`<strong>slice_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of steps to compute attention. Uses as many slices as <code>attention_head_dim // slice_size</code>, and
<code>attention_head_dim</code> must be a multiple of the <code>slice_size</code>.`,name:"slice_size"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L1630"}}),le=new A({props:{title:"SlicedAttnAddedKVProcessor",local:"diffusers.models.attention_processor.SlicedAttnAddedKVProcessor",headingTag:"h2"}}),me=new b({props:{name:"class diffusers.models.attention_processor.SlicedAttnAddedKVProcessor",anchor:"diffusers.models.attention_processor.SlicedAttnAddedKVProcessor",parameters:[{name:"slice_size",val:""}],parametersDescription:[{anchor:"diffusers.models.attention_processor.SlicedAttnAddedKVProcessor.slice_size",description:`<strong>slice_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of steps to compute attention. Uses as many slices as <code>attention_head_dim // slice_size</code>, and
<code>attention_head_dim</code> must be a multiple of the <code>slice_size</code>.`,name:"slice_size"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py#L1717"}}),fe=new A({props:{title:"XFormersAttnProcessor",local:"diffusers.models.attention_processor.XFormersAttnProcessor",headingTag:"h2"}}),pe=new b({props:{name:"class diffusers.models.attention_processor.XFormersAttnProcessor",anchor:"diffusers.models.attention_processor.XFormersAttnProcessor",parameters:[{name:"attention_op",val:": Optional = None"}],parametersDescription:[{anchor:"diffusers.models.attention_processor.XFormersAttnProcessor.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
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