diffusers/pr_13921/en/_app/immutable/nodes/4.054b88c7.js

import{s as Aa,n as xa,o as ya}from"../chunks/scheduler.53228c21.js";import{S as Ca,i as wa,e as i,s as r,c as l,h as Da,a,d as t,b as n,f as _,g as c,j as h,k as g,l as d,m as o,n as f,t as m,o as p,p as u}from"../chunks/index.cac5d66a.js";import{C as Fa}from"../chunks/CopyLLMTxtMenu.0ef49226.js";import{D as $}from"../chunks/Docstring.9de32ff4.js";import{H as b,E as Ta}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.48d5cb47.js";function Ia(bi){let A,Us,Ws,Qs,he,Bs,$e,Ys,be,vi="An attention processor is a class for applying different types of attention mechanisms.",Zs,ve,eo,x,Pe,Pn,Ot,Pi="Default processor for performing attention-related computations.",to,y,Ae,An,Ut,Ai="Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0).",so,C,xe,xn,Qt,xi=`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.`,ro,D,Ce,Cn,Yt,Ci=`Processor for implementing flash attention using torch_npu. Torch_npu supports only fp16 and bf16 data types. If
fp32 is used, F.scaled_dot_product_attention will be used for computation, but the acceleration effect on NPU is
not significant.`,no,v,we,wn,Zt,wi=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0). It uses
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used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.`,To,q,Ye,Mn,ps,Mi=`Processor for implementing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0) with fused
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For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to
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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_13921/src/diffusers/models/attention_processor.py#L1175"}}),He=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:": int | None = None"},{name:"cross_attention_dim",val:": int | None = 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 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;
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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;
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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;
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The number of steps to compute attention. Uses as many slices as <code>attention_head_dim // slice_size</code>, and
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The base
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase" rel="nofollow">operator</a> to
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The base
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase" rel="nofollow">operator</a> to
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The base
<a href="https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase" rel="nofollow">operator</a> to
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The base
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