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import os
import torch
import torch.nn.functional as F
import numpy as np
from einops import rearrange
from .masactrl_utils import AttentionBase
from torchvision.utils import save_image
class MutualSelfAttentionControl(AttentionBase):
def __init__(self, start_step=4, start_layer=10, layer_idx=None, step_idx=None, total_steps=50):
"""
Mutual self-attention control for Stable-Diffusion model
Args:
start_step: the step to start mutual self-attention control
start_layer: the layer to start mutual self-attention control
layer_idx: list of the layers to apply mutual self-attention control
step_idx: list the steps to apply mutual self-attention control
total_steps: the total number of steps
"""
super().__init__()
self.total_steps = total_steps
self.start_step = start_step
self.start_layer = start_layer
self.layer_idx = layer_idx if layer_idx is not None else list(range(start_layer, 16))
self.step_idx = step_idx if step_idx is not None else list(range(start_step, total_steps))
print("step_idx: ", self.step_idx)
print("layer_idx: ", self.layer_idx)
def attn_batch(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
b = q.shape[0] // num_heads
q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
attn = sim.softmax(-1)
out = torch.einsum("h i j, h j d -> h i d", attn, v)
out = rearrange(out, "h (b n) d -> b n (h d)", b=b)
return out
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
if is_cross or self.cur_step not in self.step_idx or self.cur_att_layer // 2 not in self.layer_idx:
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
qu, qc = q.chunk(2)
ku, kc = k.chunk(2)
vu, vc = v.chunk(2)
attnu, attnc = attn.chunk(2)
out_u = self.attn_batch(qu, ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c = self.attn_batch(qc, kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
out = torch.cat([out_u, out_c], dim=0)
return out
class MutualSelfAttentionControlMask(MutualSelfAttentionControl):
def __init__(self, start_step=4, start_layer=10, layer_idx=None, step_idx=None, total_steps=50, mask_s=None, mask_t=None, mask_save_dir=None):
"""
Maske-guided MasaCtrl to alleviate the problem of fore- and background confusion
Args:
start_step: the step to start mutual self-attention control
start_layer: the layer to start mutual self-attention control
layer_idx: list of the layers to apply mutual self-attention control
step_idx: list the steps to apply mutual self-attention control
total_steps: the total number of steps
mask_s: source mask with shape (h, w)
mask_t: target mask with same shape as source mask
"""
super().__init__(start_step, start_layer, layer_idx, step_idx, total_steps)
self.mask_s = mask_s # source mask with shape (h, w)
self.mask_t = mask_t # target mask with same shape as source mask
print("Using mask-guided MasaCtrl")
if mask_save_dir is not None:
os.makedirs(mask_save_dir, exist_ok=True)
save_image(self.mask_s.unsqueeze(0).unsqueeze(0), os.path.join(mask_save_dir, "mask_s.png"))
save_image(self.mask_t.unsqueeze(0).unsqueeze(0), os.path.join(mask_save_dir, "mask_t.png"))
def attn_batch(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
B = q.shape[0] // num_heads
H = W = int(np.sqrt(q.shape[1]))
q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
if kwargs.get("is_mask_attn") and self.mask_s is not None:
print("masked attention")
mask = self.mask_s.unsqueeze(0).unsqueeze(0)
mask = F.interpolate(mask, (H, W)).flatten(0).unsqueeze(0)
mask = mask.flatten()
# background
sim_bg = sim + mask.masked_fill(mask == 1, torch.finfo(sim.dtype).min)
# object
sim_fg = sim + mask.masked_fill(mask == 0, torch.finfo(sim.dtype).min)
sim = torch.cat([sim_fg, sim_bg], dim=0)
attn = sim.softmax(-1)
if len(attn) == 2 * len(v):
v = torch.cat([v] * 2)
out = torch.einsum("h i j, h j d -> h i d", attn, v)
out = rearrange(out, "(h1 h) (b n) d -> (h1 b) n (h d)", b=B, h=num_heads)
return out
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
if is_cross or self.cur_step not in self.step_idx or self.cur_att_layer // 2 not in self.layer_idx:
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
B = q.shape[0] // num_heads // 2
H = W = int(np.sqrt(q.shape[1]))
qu, qc = q.chunk(2)
ku, kc = k.chunk(2)
vu, vc = v.chunk(2)
attnu, attnc = attn.chunk(2)
out_u_source = self.attn_batch(qu[:num_heads], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_source = self.attn_batch(qc[:num_heads], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
out_u_target = self.attn_batch(qu[-num_heads:], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, is_mask_attn=True, **kwargs)
out_c_target = self.attn_batch(qc[-num_heads:], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, is_mask_attn=True, **kwargs)
if self.mask_s is not None and self.mask_t is not None:
out_u_target_fg, out_u_target_bg = out_u_target.chunk(2, 0)
out_c_target_fg, out_c_target_bg = out_c_target.chunk(2, 0)
mask = F.interpolate(self.mask_t.unsqueeze(0).unsqueeze(0), (H, W))
mask = mask.reshape(-1, 1) # (hw, 1)
out_u_target = out_u_target_fg * mask + out_u_target_bg * (1 - mask)
out_c_target = out_c_target_fg * mask + out_c_target_bg * (1 - mask)
out = torch.cat([out_u_source, out_u_target, out_c_source, out_c_target], dim=0)
return out
class MutualSelfAttentionControlMaskAuto(MutualSelfAttentionControl):
def __init__(self, start_step=4, start_layer=10, layer_idx=None, step_idx=None, total_steps=50, thres=0.1, ref_token_idx=[1], cur_token_idx=[1], mask_save_dir=None):
"""
MasaCtrl with mask auto generation from cross-attention map
Args:
start_step: the step to start mutual self-attention control
start_layer: the layer to start mutual self-attention control
layer_idx: list of the layers to apply mutual self-attention control
step_idx: list the steps to apply mutual self-attention control
total_steps: the total number of steps
thres: the thereshold for mask thresholding
ref_token_idx: the token index list for cross-attention map aggregation
cur_token_idx: the token index list for cross-attention map aggregation
mask_save_dir: the path to save the mask image
"""
super().__init__(start_step, start_layer, layer_idx, step_idx, total_steps)
print("using MutualSelfAttentionControlMaskAuto")
self.thres = thres
self.ref_token_idx = ref_token_idx
self.cur_token_idx = cur_token_idx
self.self_attns = []
self.cross_attns = []
self.cross_attns_mask = None
self.self_attns_mask = None
self.mask_save_dir = mask_save_dir
if self.mask_save_dir is not None:
os.makedirs(self.mask_save_dir, exist_ok=True)
def after_step(self):
self.self_attns = []
self.cross_attns = []
def attn_batch(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
B = q.shape[0] // num_heads
H = W = int(np.sqrt(q.shape[1]))
q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
if self.self_attns_mask is not None:
# binarize the mask
mask = self.self_attns_mask
thres = self.thres
mask[mask >= thres] = 1
mask[mask < thres] = 0
sim_fg = sim + mask.masked_fill(mask == 0, torch.finfo(sim.dtype).min)
sim_bg = sim + mask.masked_fill(mask == 1, torch.finfo(sim.dtype).min)
sim = torch.cat([sim_fg, sim_bg])
attn = sim.softmax(-1)
if len(attn) == 2 * len(v):
v = torch.cat([v] * 2)
out = torch.einsum("h i j, h j d -> h i d", attn, v)
out = rearrange(out, "(h1 h) (b n) d -> (h1 b) n (h d)", b=B, h=num_heads)
return out
def aggregate_cross_attn_map(self, idx):
attn_map = torch.stack(self.cross_attns, dim=1).mean(1) # (B, N, dim)
B = attn_map.shape[0]
res = int(np.sqrt(attn_map.shape[-2]))
attn_map = attn_map.reshape(-1, res, res, attn_map.shape[-1])
image = attn_map[..., idx]
if isinstance(idx, list):
image = image.sum(-1)
image_min = image.min(dim=1, keepdim=True)[0].min(dim=2, keepdim=True)[0]
image_max = image.max(dim=1, keepdim=True)[0].max(dim=2, keepdim=True)[0]
image = (image - image_min) / (image_max - image_min)
return image
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
if is_cross:
# save cross attention map with res 16 * 16
if attn.shape[1] == 16 * 16:
self.cross_attns.append(attn.reshape(-1, num_heads, *attn.shape[-2:]).mean(1))
if is_cross or self.cur_step not in self.step_idx or self.cur_att_layer // 2 not in self.layer_idx:
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
B = q.shape[0] // num_heads // 2
H = W = int(np.sqrt(q.shape[1]))
qu, qc = q.chunk(2)
ku, kc = k.chunk(2)
vu, vc = v.chunk(2)
attnu, attnc = attn.chunk(2)
out_u_source = self.attn_batch(qu[:num_heads], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_source = self.attn_batch(qc[:num_heads], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
if len(self.cross_attns) == 0:
self.self_attns_mask = None
out_u_target = self.attn_batch(qu[-num_heads:], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_target = self.attn_batch(qc[-num_heads:], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
else:
mask = self.aggregate_cross_attn_map(idx=self.ref_token_idx) # (2, H, W)
mask_source = mask[-2] # (H, W)
res = int(np.sqrt(q.shape[1]))
self.self_attns_mask = F.interpolate(mask_source.unsqueeze(0).unsqueeze(0), (res, res)).flatten()
if self.mask_save_dir is not None:
H = W = int(np.sqrt(self.self_attns_mask.shape[0]))
mask_image = self.self_attns_mask.reshape(H, W).unsqueeze(0)
save_image(mask_image, os.path.join(self.mask_save_dir, f"mask_s_{self.cur_step}_{self.cur_att_layer}.png"))
out_u_target = self.attn_batch(qu[-num_heads:], ku[:num_heads], vu[:num_heads], sim[:num_heads], attnu, is_cross, place_in_unet, num_heads, **kwargs)
out_c_target = self.attn_batch(qc[-num_heads:], kc[:num_heads], vc[:num_heads], sim[:num_heads], attnc, is_cross, place_in_unet, num_heads, **kwargs)
if self.self_attns_mask is not None:
mask = self.aggregate_cross_attn_map(idx=self.cur_token_idx) # (2, H, W)
mask_target = mask[-1] # (H, W)
res = int(np.sqrt(q.shape[1]))
spatial_mask = F.interpolate(mask_target.unsqueeze(0).unsqueeze(0), (res, res)).reshape(-1, 1)
if self.mask_save_dir is not None:
H = W = int(np.sqrt(spatial_mask.shape[0]))
mask_image = spatial_mask.reshape(H, W).unsqueeze(0)
save_image(mask_image, os.path.join(self.mask_save_dir, f"mask_t_{self.cur_step}_{self.cur_att_layer}.png"))
# binarize the mask
thres = self.thres
spatial_mask[spatial_mask >= thres] = 1
spatial_mask[spatial_mask < thres] = 0
out_u_target_fg, out_u_target_bg = out_u_target.chunk(2)
out_c_target_fg, out_c_target_bg = out_c_target.chunk(2)
out_u_target = out_u_target_fg * spatial_mask + out_u_target_bg * (1 - spatial_mask)
out_c_target = out_c_target_fg * spatial_mask + out_c_target_bg * (1 - spatial_mask)
# set self self-attention mask to None
self.self_attns_mask = None
out = torch.cat([out_u_source, out_u_target, out_c_source, out_c_target], dim=0)
return out
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