Attention Processor¶
An attention processor is a class for applying different types of attention mechanisms.
mindone.diffusers.models.attention_processor.AttnProcessor
¶
Default processor for performing attention-related computations.
Source code in mindone/diffusers/models/attention_processor.py
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mindone.diffusers.models.attention_processor.AttnAddedKVProcessor
¶
Processor for performing attention-related computations with extra learnable key and value matrices for the text encoder.
Source code in mindone/diffusers/models/attention_processor.py
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mindone.diffusers.models.attention_processor.CustomDiffusionAttnProcessor
¶
Bases: Cell
Processor for implementing attention for the Custom Diffusion method.
PARAMETER | DESCRIPTION |
---|---|
train_kv |
Whether to newly train the key and value matrices corresponding to the text features.
TYPE:
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train_q_out |
Whether to newly train query matrices corresponding to the latent image features.
TYPE:
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hidden_size |
The hidden size of the attention layer.
TYPE:
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cross_attention_dim |
The number of channels in the
TYPE:
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out_bias |
Whether to include the bias parameter in
TYPE:
|
dropout |
The dropout probability to use.
TYPE:
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Source code in mindone/diffusers/models/attention_processor.py
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mindone.diffusers.models.attention_processor.XFormersAttnProcessor
¶
Processor for implementing memory efficient attention using xFormers-like interface.
PARAMETER | DESCRIPTION |
---|---|
attention_op |
The base
operator to
use as the attention operator. It is recommended to set to
TYPE:
|
Source code in mindone/diffusers/models/attention_processor.py
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