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# Modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
# and from https://github.com/MC-E/DragonDiffusion/blob/master/src/unet/attention_processor.py
import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from diffusers.utils.import_utils import is_xformers_available
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
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,
video_length=None,
iter_cur=0,
save_kv=True,
mode="drag",
mask=None,
):
batch_size, sequence_length, _ = hidden_states.shape
encoder_hidden_states = encoder_hidden_states
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)
dim = query.shape[-1]
query = attn.head_to_batch_dim(query)
if attn.added_kv_proj_dim is not None:
raise NotImplementedError
encoder_hidden_states = (
encoder_hidden_states
if encoder_hidden_states is not None
else hidden_states
)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
# Memory bank design
if attn.updown == "up" and not save_kv:
if mode == "move":
if iter_cur >= 5:
key_ref = attn.buffer_key[iter_cur].to("cuda", dtype=query.dtype)
value_ref = attn.buffer_value[iter_cur].to(
"cuda", dtype=query.dtype
)
key = key_ref.repeat(2, 1, 1)
value = value_ref.repeat(2, 1, 1)
elif mode == "drag":
if iter_cur >= 5:
key_ref = (
attn.buffer_key[iter_cur]
.to("cuda", dtype=query.dtype)
.repeat(2, 1, 1)
)
value_ref = (
attn.buffer_value[iter_cur]
.to("cuda", dtype=query.dtype)
.repeat(2, 1, 1)
)
key = torch.cat([key, key_ref], dim=1)
value = torch.cat([value, value_ref], dim=1)
elif mode == "landmark":
if iter_cur >= 5:
key_ref = (
attn.buffer_key[iter_cur]
.to("cuda", dtype=query.dtype)
.repeat(2, 1, 1)
)
value_ref = (
attn.buffer_value[iter_cur]
.to("cuda", dtype=query.dtype)
.repeat(2, 1, 1)
)
key = torch.cat([key, key_ref], dim=1)
value = torch.cat([value, value_ref], dim=1)
elif mode in ["appearance", "paste"]:
if 35 >= iter_cur >= 0:
key_ref = attn.buffer_key[iter_cur].to("cuda", dtype=query.dtype)
value_ref = attn.buffer_value[iter_cur].to(
"cuda", dtype=query.dtype
)
key_fg = key_ref[1:]
value_fg = value_ref[1:]
key_bg = key_ref[:1]
value_bg = value_ref[:1]
mask_fg = mask["replace"]
scale = np.sqrt(
mask_fg.shape[-1] * mask_fg.shape[-2] / value_fg.shape[1]
)
mask_fg = (mask_fg > 0.5).float().to("cuda", dtype=query.dtype)
mask_fg = F.interpolate(
mask_fg[None, None],
(
int(mask_fg.shape[-2] / scale),
int(mask_fg.shape[-1] / scale),
),
)[0].unsqueeze(-1)
mask_fg = mask_fg.reshape(1, -1, 1) > 0.5
mask_bg = mask["base"]
mask_bg = (mask_bg > 0.5).float().to("cuda", dtype=query.dtype)
mask_bg = F.interpolate(
mask_bg[None, None],
(
int(mask_bg.shape[-2] / scale),
int(mask_bg.shape[-1] / scale),
),
)[0].unsqueeze(-1)
mask_bg = mask_bg.reshape(1, -1, 1) < 0.5
key_fg = (
key_fg[mask_fg.repeat(key_fg.shape[0], 1, key_fg.shape[2])]
.reshape(key_fg.shape[0], -1, key_fg.shape[2])
.repeat(2, 1, 1)
)
value_fg = (
value_fg[
mask_fg.repeat(value_fg.shape[0], 1, value_fg.shape[2])
]
.reshape(value_fg.shape[0], -1, value_fg.shape[2])
.repeat(2, 1, 1)
)
key_bg = (
key_bg[mask_bg.repeat(key_bg.shape[0], 1, key_bg.shape[2])]
.reshape(key_bg.shape[0], -1, key_bg.shape[2])
.repeat(2, 1, 1)
)
value_bg = (
value_bg[
mask_bg.repeat(value_bg.shape[0], 1, value_bg.shape[2])
]
.reshape(value_bg.shape[0], -1, value_bg.shape[2])
.repeat(2, 1, 1)
)
key = torch.cat([key_bg, key_fg], dim=1)
value = torch.cat([value_bg, value_fg], dim=1)
elif mode == "mix":
if 35 >= iter_cur >= 0:
key_ref = attn.buffer_key[iter_cur].to("cuda", dtype=query.dtype)
value_ref = attn.buffer_value[iter_cur].to(
"cuda", dtype=query.dtype
)
key_fg = key_ref[1:]
value_fg = value_ref[1:]
key_bg = key_ref[:1]
value_bg = value_ref[:1]
mask_fg = mask["replace"]
scale = np.sqrt(
mask_fg.shape[-1] * mask_fg.shape[-2] / value_fg.shape[1]
)
# Mask the uninterested area of foreground latent
mask_fg = (mask_fg > 0.5).float().to("cuda", dtype=query.dtype)
mask_fg = F.interpolate(
mask_fg[None, None],
(
int(mask_fg.shape[-2] / scale),
int(mask_fg.shape[-1] / scale),
),
)[0].unsqueeze(-1)
mask_fg = mask_fg.reshape(1, -1, 1) > 0.5
key_fg = (
key_fg[mask_fg.repeat(key_fg.shape[0], 1, key_fg.shape[2])]
.reshape(key_fg.shape[0], -1, key_fg.shape[2])
.repeat(2, 1, 1)
)
value_fg = (
value_fg[
mask_fg.repeat(value_fg.shape[0], 1, value_fg.shape[2])
]
.reshape(value_fg.shape[0], -1, value_fg.shape[2])
.repeat(2, 1, 1)
)
key_bg = key_bg.repeat(2, 1, 1)
value_bg = value_bg.repeat(2, 1, 1)
key = torch.cat([key_bg, key_fg], dim=1)
value = torch.cat([value_bg, value_fg], dim=1)
elif mode == "remove":
if iter_cur >= 5:
# # Use kv from base only
# key_ref = attn.buffer_key[iter_cur][:1].to(
# "cuda", dtype=query.dtype
# )
# value_ref = attn.buffer_value[iter_cur][:1].to(
# "cuda", dtype=query.dtype
# )
# key = key_ref.repeat(2, 1, 1)
# value = value_ref.repeat(2, 1, 1)
# if 35 >= iter_cur >= 0:
key_ref = attn.buffer_key[iter_cur].to("cuda", dtype=query.dtype)
value_ref = attn.buffer_value[iter_cur].to(
"cuda", dtype=query.dtype
)
key_bg = key_ref[:1]
value_bg = value_ref[:1]
mask_bg = mask["base"]
scale = np.sqrt(
mask_bg.shape[-1] * mask_bg.shape[-2] / value_bg.shape[1]
)
# mask_bg = (mask_bg > 0.5).float().to("cuda", dtype=query.dtype)
# mask_bg = F.interpolate(
# mask_bg[None, None],
# (
# int(mask_bg.shape[-2] / scale),
# int(mask_bg.shape[-1] / scale),
# ),
# )[0].unsqueeze(-1)
# mask_bg = mask_bg.reshape(1, -1, 1) < 0.5
# key_bg = (
# key_bg[mask_bg.repeat(key_bg.shape[0], 1, key_bg.shape[2])]
# .reshape(key_bg.shape[0], -1, key_bg.shape[2])
# .repeat(2, 1, 1)
# )
# value_bg = (
# value_bg[
# mask_bg.repeat(value_bg.shape[0], 1, value_bg.shape[2])
# ]
# .reshape(value_bg.shape[0], -1, value_bg.shape[2])
# .repeat(2, 1, 1)
# )
# key = key_bg
# value = value_bg
elif mode == "style_transfer":
if iter_cur >= 5:
key_ref = attn.buffer_key[iter_cur].to("cuda", dtype=query.dtype)
value_ref = attn.buffer_value[iter_cur].to(
"cuda", dtype=query.dtype
)
key = key_ref.repeat(2, 1, 1)
value = value_ref.repeat(2, 1, 1)
else:
raise NameError(f"Cannot operate with mode '{mode}'")
if attn.updown == "up" and save_kv:
if not hasattr(attn, "buffer_key"):
attn.buffer_key = {}
attn.buffer_value = {}
attn.buffer_key[iter_cur] = key.cpu()
attn.buffer_value[iter_cur] = value.cpu()
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
if attention_mask is not None:
if attention_mask.shape[-1] != query.shape[1]:
target_length = query.shape[1]
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
attention_mask = attention_mask.repeat_interleave(attn.heads, dim=0)
if is_xformers_available():
hidden_states = xformers.ops.memory_efficient_attention(
query, key, value, attn_bias=attention_mask
)
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
hidden_states = hidden_states.to(query.dtype)
else:
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)
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`.
scale (`float`, defaults to 1.0):
the weight scale of image prompt.
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
The context length of the image features.
"""
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
super().__init__()
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.scale = scale
self.num_tokens = num_tokens
# 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)
self.step = 0
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
iter_cur=-1,
):
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)
ip_hidden_states = None
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)
# for image prompt
if ip_hidden_states is not None:
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)
if iter_cur == -1 or 10 <= iter_cur < 20:
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 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`.
# scale (`float`, defaults to 1.0):
# the weight scale of image prompt.
# num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
# The context length of the image features.
# """
# def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
# super().__init__()
# self.hidden_size = hidden_size
# self.cross_attention_dim = cross_attention_dim
# self.scale = scale
# self.num_tokens = num_tokens
# # 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)
# self.step = 0
# def __call__(
# self,
# attn,
# hidden_states,
# encoder_hidden_states=None,
# attention_mask=None,
# temb=None,
# iter_cur=-1,
# ):
# 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
# ip_hidden_states = None
# else:
# # get encoder_hidden_states, ip_hidden_states
# if encoder_hidden_states.shape[1] <= 77:
# ip_hidden_states = None
# else:
# end_pos = encoder_hidden_states.shape[1] - self.num_tokens
# encoder_hidden_states, ip_hidden_states = (
# encoder_hidden_states[:, :end_pos, :],
# encoder_hidden_states[:, end_pos:, :],
# )
# if 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)
# # for image prompt
# if ip_hidden_states is not None:
# 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)
# if iter_cur == -1 or 10 <= iter_cur < 20:
# 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
def FeedForward(dim, mult=4):
inner_dim = int(dim * mult)
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim, bias=False),
nn.GELU(),
nn.Linear(inner_dim, dim, bias=False),
)
def reshape_tensor(x, heads):
bs, length, width = x.shape
# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
x = x.view(bs, length, heads, -1)
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
x = x.transpose(1, 2)
# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
x = x.reshape(bs, heads, length, -1)
return x
class PerceiverAttention(nn.Module):
def __init__(self, *, dim, dim_head=64, heads=8):
super().__init__()
self.scale = dim_head**-0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
def forward(self, x, latents):
"""
Args:
x (torch.Tensor): image features
shape (b, n1, D)
latent (torch.Tensor): latent features
shape (b, n2, D)
"""
x = self.norm1(x)
latents = self.norm2(latents)
b, l, _ = latents.shape
q = self.to_q(latents)
kv_input = torch.cat((x, latents), dim=-2)
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
q = reshape_tensor(q, self.heads)
k = reshape_tensor(k, self.heads)
v = reshape_tensor(v, self.heads)
# attention
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
weight = (q * scale) @ (k * scale).transpose(
-2, -1
) # More stable with f16 than dividing afterwards
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
out = weight @ v
out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
return self.to_out(out)
class Resampler(nn.Module):
def __init__(
self,
dim=1024,
depth=8,
dim_head=64,
heads=16,
num_queries=8,
embedding_dim=768,
output_dim=1024,
ff_mult=4,
):
super().__init__()
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
self.proj_in = nn.Linear(embedding_dim, dim)
self.proj_out = nn.Linear(dim, output_dim)
self.norm_out = nn.LayerNorm(output_dim)
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
FeedForward(dim=dim, mult=ff_mult),
]
)
)
def forward(self, x):
latents = self.latents.repeat(x.size(0), 1, 1)
x = self.proj_in(x)
for attn, ff in self.layers:
latents = attn(x, latents) + latents
latents = ff(latents) + latents
latents = self.proj_out(latents)
return self.norm_out(latents)
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