mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
395 lines
14 KiB
Python
395 lines
14 KiB
Python
# copied from https://github.com/tencent-ailab/IP-Adapter (Apache License 2.0)
|
|
# and modified as needed
|
|
|
|
# tencent-ailab comment:
|
|
# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class AttnProcessor(nn.Module):
|
|
r"""
|
|
Default processor for performing attention-related computations.
|
|
"""
|
|
def __init__(
|
|
self,
|
|
hidden_size=None,
|
|
cross_attention_dim=None,
|
|
):
|
|
super().__init__()
|
|
|
|
def __call__(
|
|
self,
|
|
attn,
|
|
hidden_states,
|
|
encoder_hidden_states=None,
|
|
attention_mask=None,
|
|
temb=None,
|
|
):
|
|
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
|
|
)
|
|
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
|
|
|
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)
|
|
|
|
query = attn.head_to_batch_dim(query)
|
|
key = attn.head_to_batch_dim(key)
|
|
value = attn.head_to_batch_dim(value)
|
|
|
|
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
|
hidden_states = torch.bmm(attention_probs, value)
|
|
hidden_states = attn.batch_to_head_dim(hidden_states)
|
|
|
|
# linear proj
|
|
hidden_states = attn.to_out[0](hidden_states)
|
|
# dropout
|
|
hidden_states = attn.to_out[1](hidden_states)
|
|
|
|
if input_ndim == 4:
|
|
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
|
|
|
|
return hidden_states
|
|
|
|
|
|
class IPAttnProcessor(nn.Module):
|
|
r"""
|
|
Attention processor for IP-Adapater.
|
|
Args:
|
|
hidden_size (`int`):
|
|
The hidden size of the attention layer.
|
|
cross_attention_dim (`int`):
|
|
The number of channels in the `encoder_hidden_states`.
|
|
text_context_len (`int`, defaults to 77):
|
|
The context length of the text features.
|
|
scale (`float`, defaults to 1.0):
|
|
the weight scale of image prompt.
|
|
"""
|
|
|
|
def __init__(self, hidden_size, cross_attention_dim=None, text_context_len=77, scale=1.0):
|
|
super().__init__()
|
|
|
|
self.hidden_size = hidden_size
|
|
self.cross_attention_dim = cross_attention_dim
|
|
self.text_context_len = text_context_len
|
|
self.scale = scale
|
|
|
|
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
|
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
|
|
|
def __call__(
|
|
self,
|
|
attn,
|
|
hidden_states,
|
|
encoder_hidden_states=None,
|
|
attention_mask=None,
|
|
temb=None,
|
|
):
|
|
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
|
|
)
|
|
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
|
|
|
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)
|
|
|
|
# split hidden states
|
|
encoder_hidden_states, ip_hidden_states = encoder_hidden_states[:, :self.text_context_len, :], encoder_hidden_states[:, self.text_context_len:, :]
|
|
|
|
key = attn.to_k(encoder_hidden_states)
|
|
value = attn.to_v(encoder_hidden_states)
|
|
|
|
query = attn.head_to_batch_dim(query)
|
|
key = attn.head_to_batch_dim(key)
|
|
value = attn.head_to_batch_dim(value)
|
|
|
|
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
|
hidden_states = torch.bmm(attention_probs, value)
|
|
hidden_states = attn.batch_to_head_dim(hidden_states)
|
|
|
|
# for ip-adapter
|
|
ip_key = self.to_k_ip(ip_hidden_states)
|
|
ip_value = self.to_v_ip(ip_hidden_states)
|
|
|
|
ip_key = attn.head_to_batch_dim(ip_key)
|
|
ip_value = attn.head_to_batch_dim(ip_value)
|
|
|
|
ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
|
|
ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
|
|
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
|
|
|
|
hidden_states = hidden_states + self.scale * ip_hidden_states
|
|
|
|
# linear proj
|
|
hidden_states = attn.to_out[0](hidden_states)
|
|
# dropout
|
|
hidden_states = attn.to_out[1](hidden_states)
|
|
|
|
if input_ndim == 4:
|
|
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
|
|
|
|
return hidden_states
|
|
|
|
|
|
class AttnProcessor2_0(torch.nn.Module):
|
|
r"""
|
|
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
|
"""
|
|
def __init__(
|
|
self,
|
|
hidden_size=None,
|
|
cross_attention_dim=None,
|
|
):
|
|
super().__init__()
|
|
if not hasattr(F, "scaled_dot_product_attention"):
|
|
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
|
|
|
def __call__(
|
|
self,
|
|
attn,
|
|
hidden_states,
|
|
encoder_hidden_states=None,
|
|
attention_mask=None,
|
|
temb=None,
|
|
):
|
|
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
|
|
)
|
|
|
|
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)
|
|
|
|
# linear proj
|
|
hidden_states = attn.to_out[0](hidden_states)
|
|
# dropout
|
|
hidden_states = attn.to_out[1](hidden_states)
|
|
|
|
if input_ndim == 4:
|
|
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
|
|
|
|
return hidden_states
|
|
|
|
|
|
class IPAttnProcessor2_0(torch.nn.Module):
|
|
r"""
|
|
Attention processor for IP-Adapater for PyTorch 2.0.
|
|
Args:
|
|
hidden_size (`int`):
|
|
The hidden size of the attention layer.
|
|
cross_attention_dim (`int`):
|
|
The number of channels in the `encoder_hidden_states`.
|
|
text_context_len (`int`, defaults to 77):
|
|
The context length of the text features.
|
|
scale (`float`, defaults to 1.0):
|
|
the weight scale of image prompt.
|
|
"""
|
|
|
|
def __init__(self, hidden_size, cross_attention_dim=None, text_context_len=77, scale=1.0):
|
|
super().__init__()
|
|
|
|
if not hasattr(F, "scaled_dot_product_attention"):
|
|
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
|
|
|
self.hidden_size = hidden_size
|
|
self.cross_attention_dim = cross_attention_dim
|
|
self.text_context_len = text_context_len
|
|
self.scale = scale
|
|
|
|
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
|
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
|
|
|
def __call__(
|
|
self,
|
|
attn,
|
|
hidden_states,
|
|
encoder_hidden_states=None,
|
|
attention_mask=None,
|
|
temb=None,
|
|
):
|
|
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
|
|
)
|
|
|
|
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)
|
|
|
|
# split hidden states
|
|
encoder_hidden_states, ip_hidden_states = encoder_hidden_states[:, :self.text_context_len, :], encoder_hidden_states[:, self.text_context_len:, :]
|
|
|
|
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)
|
|
|
|
# for ip-adapter
|
|
ip_key = self.to_k_ip(ip_hidden_states)
|
|
ip_value = self.to_v_ip(ip_hidden_states)
|
|
|
|
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)
|
|
|
|
# the output of sdp = (batch, num_heads, 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
|
|
)
|
|
|
|
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)
|
|
|
|
hidden_states = hidden_states + self.scale * ip_hidden_states
|
|
|
|
# linear proj
|
|
hidden_states = attn.to_out[0](hidden_states)
|
|
# dropout
|
|
hidden_states = attn.to_out[1](hidden_states)
|
|
|
|
if input_ndim == 4:
|
|
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
|
|
|
|
return hidden_states
|