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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.`,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).
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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 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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 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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