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import math
import numpy as np
import torch
import torch.nn as nn
from diffusers.models.attention_processor import AttnProcessor
from diffusers.utils.import_utils import is_xformers_available
if is_xformers_available():
import xformers
def remove_edlora_unet_attention_forward(unet):
def change_forward(unet): # omit proceesor in new diffusers
for name, layer in unet.named_children():
if layer.__class__.__name__ == 'Attention' and name == 'attn2':
layer.set_processor(AttnProcessor())
else:
change_forward(layer)
change_forward(unet)
class EDLoRA_Control_AttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __init__(self, cross_attention_idx, place_in_unet, controller, attention_op=None):
self.cross_attention_idx = cross_attention_idx
self.place_in_unet = place_in_unet
self.controller = controller
self.attention_op = attention_op
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)
if encoder_hidden_states is None:
is_cross = False
encoder_hidden_states = hidden_states
else:
is_cross = True
if len(encoder_hidden_states.shape) == 4: # multi-layer embedding
encoder_hidden_states = encoder_hidden_states[:, self.cross_attention_idx, ...]
else: # single layer embedding
encoder_hidden_states = encoder_hidden_states
assert not attn.norm_cross
batch_size, sequence_length, _ = 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)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
if is_xformers_available() and not is_cross:
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
hidden_states = hidden_states.to(query.dtype)
else:
attention_probs = attn.get_attention_scores(query, key, attention_mask)
attention_probs = self.controller(attention_probs, is_cross, self.place_in_unet)
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 EDLoRA_AttnProcessor:
def __init__(self, cross_attention_idx, attention_op=None):
self.attention_op = attention_op
self.cross_attention_idx = cross_attention_idx
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)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
else:
if len(encoder_hidden_states.shape) == 4: # multi-layer embedding
encoder_hidden_states = encoder_hidden_states[:, self.cross_attention_idx, ...]
else: # single layer embedding
encoder_hidden_states = encoder_hidden_states
assert not attn.norm_cross
batch_size, sequence_length, _ = 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)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
if is_xformers_available():
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
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)
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 revise_edlora_unet_attention_forward(unet):
def change_forward(unet, count):
for name, layer in unet.named_children():
if layer.__class__.__name__ == 'Attention' and 'attn2' in name:
layer.set_processor(EDLoRA_AttnProcessor(count))
count += 1
else:
count = change_forward(layer, count)
return count
# use this to ensure the order
cross_attention_idx = change_forward(unet.down_blocks, 0)
cross_attention_idx = change_forward(unet.mid_block, cross_attention_idx)
cross_attention_idx = change_forward(unet.up_blocks, cross_attention_idx)
print(f'Number of attention layer registered {cross_attention_idx}')
def revise_edlora_unet_attention_controller_forward(unet, controller):
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.num_att_layers = 0
if controller is None:
controller = DummyController()
def change_forward(unet, count, place_in_unet):
for name, layer in unet.named_children():
if layer.__class__.__name__ == 'Attention' and 'attn2' in name: # only register controller for cross-attention
layer.set_processor(EDLoRA_Control_AttnProcessor(count, place_in_unet, controller))
count += 1
else:
count = change_forward(layer, count, place_in_unet)
return count
# use this to ensure the order
cross_attention_idx = change_forward(unet.down_blocks, 0, 'down')
cross_attention_idx = change_forward(unet.mid_block, cross_attention_idx, 'mid')
cross_attention_idx = change_forward(unet.up_blocks, cross_attention_idx, 'up')
print(f'Number of attention layer registered {cross_attention_idx}')
controller.num_att_layers = cross_attention_idx
class LoRALinearLayer(nn.Module):
def __init__(self, name, original_module, rank=4, alpha=1):
super().__init__()
self.name = name
### Hard coded LoRA rank
rank = 32
if original_module.__class__.__name__ == 'Conv2d':
in_channels, out_channels = original_module.in_channels, original_module.out_channels
self.lora_down = torch.nn.Conv2d(in_channels, rank, (1, 1), bias=False)
self.lora_up = torch.nn.Conv2d(rank, out_channels, (1, 1), bias=False)
else:
in_features, out_features = original_module.in_features, original_module.out_features
self.lora_down = nn.Linear(in_features, rank, bias=False)
self.lora_up = nn.Linear(rank, out_features, bias=False)
self.register_buffer('alpha', torch.tensor(alpha))
### Load and initialize orthogonal B
m = np.load(f"orthogonal_mats/{in_features}.npy")
idxs = np.random.choice(in_features, size = rank, replace = False)
m = m[idxs]/2
with torch.no_grad():
self.lora_down.weight = torch.nn.Parameter(torch.tensor(m, dtype = self.lora_down.weight.dtype))
torch.nn.init.zeros_(self.lora_up.weight)
for param in self.lora_down.parameters():
param.requires_grad = False
self.original_forward = original_module.forward
original_module.forward = self.forward
def forward(self, hidden_states):
hidden_states = self.original_forward(hidden_states) + self.alpha * self.lora_up(self.lora_down(hidden_states))
return hidden_states
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