from dataclasses import dataclass
from typing import List, Optional, cast
import torch
import torch.nn.functional as F
from diffusers.models.attention_processor import Attention, AttnProcessor2_0
from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionProcessorWeights
from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import RegionalIPData
from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
@dataclass
class IPAdapterAttentionWeights:
ip_adapter_weights: IPAttentionProcessorWeights
skip: bool
class CustomAttnProcessor2_0(AttnProcessor2_0):
"""A custom implementation of AttnProcessor2_0 that supports additional Invoke features.
This implementation is based on
https://github.com/huggingface/diffusers/blame/fcfa270fbd1dc294e2f3a505bae6bcb791d721c3/src/diffusers/models/attention_processor.py#L1204
Supported custom features:
- IP-Adapter
- Regional prompt attention
"""
def __init__(
self,
ip_adapter_attention_weights: Optional[List[IPAdapterAttentionWeights]] = None,
):
"""Initialize a CustomAttnProcessor2_0.
Note: Arguments that are the same for all attention layers are passed to __call__(). Arguments that are
layer-specific are passed to __init__().
Args:
ip_adapter_weights: The IP-Adapter attention weights. ip_adapter_weights[i] contains the attention weights
for the i'th IP-Adapter.
"""
super().__init__()
self._ip_adapter_attention_weights = ip_adapter_attention_weights
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
# For Regional Prompting:
regional_prompt_data: Optional[RegionalPromptData] = None,
percent_through: Optional[torch.Tensor] = None,
# For IP-Adapter:
regional_ip_data: Optional[RegionalIPData] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
"""Apply attention.
Args:
regional_prompt_data: The regional prompt data for the current batch. If not None, this will be used to
apply regional prompt masking.
regional_ip_data: The IP-Adapter data for the current batch.
"""
# If true, we are doing cross-attention, if false we are doing self-attention.
is_cross_attention = encoder_hidden_states is not None
# Start unmodified block from AttnProcessor2_0.
# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# End unmodified block from AttnProcessor2_0.
_, query_seq_len, _ = hidden_states.shape
# Handle regional prompt attention masks.
if regional_prompt_data is not None and is_cross_attention:
assert percent_through is not None
prompt_region_attention_mask = regional_prompt_data.get_cross_attn_mask(
query_seq_len=query_seq_len, key_seq_len=sequence_length
)
if attention_mask is None:
attention_mask = prompt_region_attention_mask
else:
attention_mask = prompt_region_attention_mask + attention_mask
# Start unmodified block from AttnProcessor2_0.
# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# End unmodified block from AttnProcessor2_0.
# Apply IP-Adapter conditioning.
if is_cross_attention:
if self._ip_adapter_attention_weights:
assert regional_ip_data is not None
ip_masks = regional_ip_data.get_masks(query_seq_len=query_seq_len)
assert (
len(regional_ip_data.image_prompt_embeds)
== len(self._ip_adapter_attention_weights)
== len(regional_ip_data.scales)
== ip_masks.shape[1]
)
for ipa_index, ipa_embed in enumerate(regional_ip_data.image_prompt_embeds):
ipa_weights = self._ip_adapter_attention_weights[ipa_index].ip_adapter_weights
ipa_scale = regional_ip_data.scales[ipa_index]
ip_mask = ip_masks[0, ipa_index, ...]
# The batch dimensions should match.
assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
# The token_len dimensions should match.
assert ipa_embed.shape[-1] == encoder_hidden_states.shape[-1]
ip_hidden_states = ipa_embed
# Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
if not self._ip_adapter_attention_weights[ipa_index].skip:
ip_key = ipa_weights.to_k_ip(ip_hidden_states)
ip_value = ipa_weights.to_v_ip(ip_hidden_states)
# Expected ip_key and ip_value shape:
# (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# Expected ip_key and ip_value shape:
# (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
ip_hidden_states = F.scaled_dot_product_attention(
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
# Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(
batch_size, -1, attn.heads * head_dim
)
ip_hidden_states = ip_hidden_states.to(query.dtype)
# Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
else:
# If IP-Adapter is not enabled, then regional_ip_data should not be passed in.
assert regional_ip_data is None
# Start unmodified block from AttnProcessor2_0.
# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# End of unmodified block from AttnProcessor2_0
# casting torch.Tensor to torch.FloatTensor to avoid type issues
return cast(torch.FloatTensor, hidden_states)