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import os
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Union, Tuple, List, Callable, Dict
from torchvision.utils import save_image
from einops import rearrange, repeat
class AttentionBase:
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
def after_step(self):
pass
def __call__(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
out = self.forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
# after step
self.after_step()
return out
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
out = torch.einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=num_heads)
return out
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
class AttentionStore(AttentionBase):
def __init__(self, res=[32], min_step=0, max_step=1000):
super().__init__()
self.res = res
self.min_step = min_step
self.max_step = max_step
self.valid_steps = 0
self.self_attns = [] # store the all attns
self.cross_attns = []
self.self_attns_step = [] # store the attns in each step
self.cross_attns_step = []
def after_step(self):
if self.cur_step > self.min_step and self.cur_step < self.max_step:
self.valid_steps += 1
if len(self.self_attns) == 0:
self.self_attns = self.self_attns_step
self.cross_attns = self.cross_attns_step
else:
for i in range(len(self.self_attns)):
self.self_attns[i] += self.self_attns_step[i]
self.cross_attns[i] += self.cross_attns_step[i]
self.self_attns_step.clear()
self.cross_attns_step.clear()
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
if attn.shape[1] <= 64 ** 2: # avoid OOM
if is_cross:
self.cross_attns_step.append(attn)
else:
self.self_attns_step.append(attn)
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
def regiter_attention_editor_diffusers(model, editor: AttentionBase):
"""
Register a attention editor to Diffuser Pipeline, refer from [Prompt-to-Prompt]
"""
def ca_forward(self, place_in_unet):
def forward(x, encoder_hidden_states=None, attention_mask=None, context=None, mask=None):
"""
The attention is similar to the original implementation of LDM CrossAttention class
except adding some modifications on the attention
"""
if encoder_hidden_states is not None:
context = encoder_hidden_states
if attention_mask is not None:
mask = attention_mask
to_out = self.to_out
if isinstance(to_out, nn.modules.container.ModuleList):
to_out = self.to_out[0]
else:
to_out = self.to_out
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
if mask is not None:
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
attn = sim.softmax(dim=-1)
# the only difference
out = editor(
q, k, v, sim, attn, is_cross, place_in_unet,
self.heads, scale=self.scale)
return to_out(out)
return forward
def register_editor(net, count, place_in_unet):
for name, subnet in net.named_children():
if net.__class__.__name__ == 'Attention': # spatial Transformer layer
net.forward = ca_forward(net, place_in_unet)
return count + 1
elif hasattr(net, 'children'):
count = register_editor(subnet, count, place_in_unet)
return count
cross_att_count = 0
for net_name, net in model.unet.named_children():
if "down" in net_name:
cross_att_count += register_editor(net, 0, "down")
elif "mid" in net_name:
cross_att_count += register_editor(net, 0, "mid")
elif "up" in net_name:
cross_att_count += register_editor(net, 0, "up")
editor.num_att_layers = cross_att_count
def regiter_attention_editor_ldm(model, editor: AttentionBase):
"""
Register a attention editor to Stable Diffusion model, refer from [Prompt-to-Prompt]
"""
def ca_forward(self, place_in_unet):
def forward(x, encoder_hidden_states=None, attention_mask=None, context=None, mask=None):
"""
The attention is similar to the original implementation of LDM CrossAttention class
except adding some modifications on the attention
"""
if encoder_hidden_states is not None:
context = encoder_hidden_states
if attention_mask is not None:
mask = attention_mask
to_out = self.to_out
if isinstance(to_out, nn.modules.container.ModuleList):
to_out = self.to_out[0]
else:
to_out = self.to_out
h = self.heads
q = self.to_q(x)
is_cross = context is not None
context = context if is_cross else x
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
if mask is not None:
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
mask = mask[:, None, :].repeat(h, 1, 1)
sim.masked_fill_(~mask, max_neg_value)
attn = sim.softmax(dim=-1)
# the only difference
out = editor(
q, k, v, sim, attn, is_cross, place_in_unet,
self.heads, scale=self.scale)
return to_out(out)
return forward
def register_editor(net, count, place_in_unet):
for name, subnet in net.named_children():
if net.__class__.__name__ == 'CrossAttention': # spatial Transformer layer
net.forward = ca_forward(net, place_in_unet)
return count + 1
elif hasattr(net, 'children'):
count = register_editor(subnet, count, place_in_unet)
return count
cross_att_count = 0
for net_name, net in model.model.diffusion_model.named_children():
if "input" in net_name:
cross_att_count += register_editor(net, 0, "input")
elif "middle" in net_name:
cross_att_count += register_editor(net, 0, "middle")
elif "output" in net_name:
cross_att_count += register_editor(net, 0, "output")
editor.num_att_layers = cross_att_count
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