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# Copyright 2023 ByteDance and/or its affiliates.
#
# Copyright (2023) MagicAnimate Authors
#
# ByteDance, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from ByteDance or
# its affiliates is strictly prohibited.
import torch
import torch.nn.functional as F
from einops import rearrange
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from diffusers.models.attention import BasicTransformerBlock
from magicanimate.models.attention import BasicTransformerBlock as _BasicTransformerBlock
from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
from .stable_diffusion_controlnet_reference import torch_dfs
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 MutualSelfAttentionControl(AttentionBase):
def __init__(self, total_steps=50, hijack_init_state=True, with_negative_guidance=False, appearance_control_alpha=0.5, mode='enqueue'):
"""
Mutual self-attention control for Stable-Diffusion MODEl
Args:
total_steps: the total number of steps
"""
super().__init__()
self.total_steps = total_steps
self.hijack = hijack_init_state
self.with_negative_guidance = with_negative_guidance
# alpha: mutual self attention intensity
# TODO: make alpha learnable
self.alpha = appearance_control_alpha
self.GLOBAL_ATTN_QUEUE = []
assert mode in ['enqueue', 'dequeue']
MODE = mode
def attn_batch(self, q, k, v, num_heads, **kwargs):
"""
Performing attention for a batch of queries, keys, and values
"""
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 mutual_self_attn(self, q, k, v, num_heads, **kwargs):
q_tgt, q_src = q.chunk(2)
k_tgt, k_src = k.chunk(2)
v_tgt, v_src = v.chunk(2)
# out_tgt = self.attn_batch(q_tgt, k_src, v_src, num_heads, **kwargs) * self.alpha + \
# self.attn_batch(q_tgt, k_tgt, v_tgt, num_heads, **kwargs) * (1 - self.alpha)
out_tgt = self.attn_batch(q_tgt, torch.cat([k_tgt, k_src], dim=1), torch.cat([v_tgt, v_src], dim=1), num_heads, **kwargs)
out_src = self.attn_batch(q_src, k_src, v_src, num_heads, **kwargs)
out = torch.cat([out_tgt, out_src], dim=0)
return out
def mutual_self_attn_wq(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
if self.MODE == 'dequeue' and len(self.kv_queue) > 0:
k_src, v_src = self.kv_queue.pop(0)
out = self.attn_batch(q, torch.cat([k, k_src], dim=1), torch.cat([v, v_src], dim=1), num_heads, **kwargs)
return out
else:
self.kv_queue.append([k.clone(), v.clone()])
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
def get_queue(self):
return self.GLOBAL_ATTN_QUEUE
def set_queue(self, attn_queue):
self.GLOBAL_ATTN_QUEUE = attn_queue
def clear_queue(self):
self.GLOBAL_ATTN_QUEUE = []
def to(self, dtype):
self.GLOBAL_ATTN_QUEUE = [p.to(dtype) for p in self.GLOBAL_ATTN_QUEUE]
def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
"""
Attention forward function
"""
return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
class ReferenceAttentionControl():
def __init__(self,
unet,
mode="write",
do_classifier_free_guidance=False,
attention_auto_machine_weight = float('inf'),
gn_auto_machine_weight = 1.0,
style_fidelity = 1.0,
reference_attn=True,
reference_adain=False,
fusion_blocks="midup",
batch_size=1,
) -> None:
# 10. Modify self attention and group norm
self.unet = unet
assert mode in ["read", "write"]
assert fusion_blocks in ["midup", "full"]
self.reference_attn = reference_attn
self.reference_adain = reference_adain
self.fusion_blocks = fusion_blocks
self.register_reference_hooks(
mode,
do_classifier_free_guidance,
attention_auto_machine_weight,
gn_auto_machine_weight,
style_fidelity,
reference_attn,
reference_adain,
fusion_blocks,
batch_size=batch_size,
)
def register_reference_hooks(
self,
mode,
do_classifier_free_guidance,
attention_auto_machine_weight,
gn_auto_machine_weight,
style_fidelity,
reference_attn,
reference_adain,
dtype=torch.float16,
batch_size=1,
num_images_per_prompt=1,
device=torch.device("cpu"),
fusion_blocks='midup',
):
MODE = mode
do_classifier_free_guidance = do_classifier_free_guidance
attention_auto_machine_weight = attention_auto_machine_weight
gn_auto_machine_weight = gn_auto_machine_weight
style_fidelity = style_fidelity
reference_attn = reference_attn
reference_adain = reference_adain
fusion_blocks = fusion_blocks
num_images_per_prompt = num_images_per_prompt
dtype=dtype
if do_classifier_free_guidance:
uc_mask = (
torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)
.to(device)
.bool()
)
else:
uc_mask = (
torch.Tensor([0] * batch_size * num_images_per_prompt * 2)
.to(device)
.bool()
)
def hacked_basic_transformer_inner_forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
class_labels: Optional[torch.LongTensor] = None,
video_length=None,
):
if self.use_ada_layer_norm:
norm_hidden_states = self.norm1(hidden_states, timestep)
elif self.use_ada_layer_norm_zero:
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
)
else:
norm_hidden_states = self.norm1(hidden_states)
# 1. Self-Attention
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
if self.only_cross_attention:
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
else:
if MODE == "write":
self.bank.append(norm_hidden_states.clone())
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
if MODE == "read":
self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), "b t l c -> (b t) l c")[:hidden_states.shape[0]] for d in self.bank]
hidden_states_uc = self.attn1(norm_hidden_states,
encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
attention_mask=attention_mask) + hidden_states
hidden_states_c = hidden_states_uc.clone()
_uc_mask = uc_mask.clone()
if do_classifier_free_guidance:
if hidden_states.shape[0] != _uc_mask.shape[0]:
_uc_mask = (
torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))
.to(device)
.bool()
)
hidden_states_c[_uc_mask] = self.attn1(
norm_hidden_states[_uc_mask],
encoder_hidden_states=norm_hidden_states[_uc_mask],
attention_mask=attention_mask,
) + hidden_states[_uc_mask]
hidden_states = hidden_states_c.clone()
self.bank.clear()
if self.attn2 is not None:
# Cross-Attention
norm_hidden_states = (
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
)
hidden_states = (
self.attn2(
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
)
+ hidden_states
)
# Feed-forward
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
# Temporal-Attention
if self.unet_use_temporal_attention:
d = hidden_states.shape[1]
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
norm_hidden_states = (
self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
)
hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
return hidden_states
if self.use_ada_layer_norm_zero:
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = attn_output + hidden_states
if self.attn2 is not None:
norm_hidden_states = (
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
)
# 2. Cross-Attention
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
if self.use_ada_layer_norm_zero:
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
ff_output = self.ff(norm_hidden_states)
if self.use_ada_layer_norm_zero:
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = ff_output + hidden_states
return hidden_states
def hacked_mid_forward(self, *args, **kwargs):
eps = 1e-6
x = self.original_forward(*args, **kwargs)
if MODE == "write":
if gn_auto_machine_weight >= self.gn_weight:
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
self.mean_bank.append(mean)
self.var_bank.append(var)
if MODE == "read":
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
var_acc = sum(self.var_bank) / float(len(self.var_bank))
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
x_uc = (((x - mean) / std) * std_acc) + mean_acc
x_c = x_uc.clone()
if do_classifier_free_guidance and style_fidelity > 0:
x_c[uc_mask] = x[uc_mask]
x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
self.mean_bank = []
self.var_bank = []
return x
def hack_CrossAttnDownBlock2D_forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
):
eps = 1e-6
# TODO(Patrick, William) - attention mask is not used
output_states = ()
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
if MODE == "write":
if gn_auto_machine_weight >= self.gn_weight:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
self.mean_bank.append([mean])
self.var_bank.append([var])
if MODE == "read":
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
hidden_states_c = hidden_states_uc.clone()
if do_classifier_free_guidance and style_fidelity > 0:
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
output_states = output_states + (hidden_states,)
if MODE == "read":
self.mean_bank = []
self.var_bank = []
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states = output_states + (hidden_states,)
return hidden_states, output_states
def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
eps = 1e-6
output_states = ()
for i, resnet in enumerate(self.resnets):
hidden_states = resnet(hidden_states, temb)
if MODE == "write":
if gn_auto_machine_weight >= self.gn_weight:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
self.mean_bank.append([mean])
self.var_bank.append([var])
if MODE == "read":
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
hidden_states_c = hidden_states_uc.clone()
if do_classifier_free_guidance and style_fidelity > 0:
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
output_states = output_states + (hidden_states,)
if MODE == "read":
self.mean_bank = []
self.var_bank = []
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states = output_states + (hidden_states,)
return hidden_states, output_states
def hacked_CrossAttnUpBlock2D_forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
upsample_size: Optional[int] = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
):
eps = 1e-6
# TODO(Patrick, William) - attention mask is not used
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
if MODE == "write":
if gn_auto_machine_weight >= self.gn_weight:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
self.mean_bank.append([mean])
self.var_bank.append([var])
if MODE == "read":
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
hidden_states_c = hidden_states_uc.clone()
if do_classifier_free_guidance and style_fidelity > 0:
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
if MODE == "read":
self.mean_bank = []
self.var_bank = []
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states
def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
eps = 1e-6
for i, resnet in enumerate(self.resnets):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
hidden_states = resnet(hidden_states, temb)
if MODE == "write":
if gn_auto_machine_weight >= self.gn_weight:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
self.mean_bank.append([mean])
self.var_bank.append([var])
if MODE == "read":
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
hidden_states_c = hidden_states_uc.clone()
if do_classifier_free_guidance and style_fidelity > 0:
hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
if MODE == "read":
self.mean_bank = []
self.var_bank = []
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states
if self.reference_attn:
if self.fusion_blocks == "midup":
attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
elif self.fusion_blocks == "full":
attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
for i, module in enumerate(attn_modules):
module._original_inner_forward = module.forward
module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
module.bank = []
module.attn_weight = float(i) / float(len(attn_modules))
if self.reference_adain:
gn_modules = [self.unet.mid_block]
self.unet.mid_block.gn_weight = 0
down_blocks = self.unet.down_blocks
for w, module in enumerate(down_blocks):
module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
gn_modules.append(module)
up_blocks = self.unet.up_blocks
for w, module in enumerate(up_blocks):
module.gn_weight = float(w) / float(len(up_blocks))
gn_modules.append(module)
for i, module in enumerate(gn_modules):
if getattr(module, "original_forward", None) is None:
module.original_forward = module.forward
if i == 0:
# mid_block
module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
elif isinstance(module, CrossAttnDownBlock2D):
module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
elif isinstance(module, DownBlock2D):
module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
elif isinstance(module, CrossAttnUpBlock2D):
module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
elif isinstance(module, UpBlock2D):
module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
module.mean_bank = []
module.var_bank = []
module.gn_weight *= 2
def update(self, writer, dtype=torch.float16):
if self.reference_attn:
if self.fusion_blocks == "midup":
reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]
writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]
elif self.fusion_blocks == "full":
reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]
writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]
reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
for r, w in zip(reader_attn_modules, writer_attn_modules):
r.bank = [v.clone().to(dtype) for v in w.bank]
# w.bank.clear()
if self.reference_adain:
reader_gn_modules = [self.unet.mid_block]
down_blocks = self.unet.down_blocks
for w, module in enumerate(down_blocks):
reader_gn_modules.append(module)
up_blocks = self.unet.up_blocks
for w, module in enumerate(up_blocks):
reader_gn_modules.append(module)
writer_gn_modules = [writer.unet.mid_block]
down_blocks = writer.unet.down_blocks
for w, module in enumerate(down_blocks):
writer_gn_modules.append(module)
up_blocks = writer.unet.up_blocks
for w, module in enumerate(up_blocks):
writer_gn_modules.append(module)
for r, w in zip(reader_gn_modules, writer_gn_modules):
if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):
r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]
r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]
else:
r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]
r.var_bank = [v.clone().to(dtype) for v in w.var_bank]
def clear(self):
if self.reference_attn:
if self.fusion_blocks == "midup":
reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
elif self.fusion_blocks == "full":
reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
for r in reader_attn_modules:
r.bank.clear()
if self.reference_adain:
reader_gn_modules = [self.unet.mid_block]
down_blocks = self.unet.down_blocks
for w, module in enumerate(down_blocks):
reader_gn_modules.append(module)
up_blocks = self.unet.up_blocks
for w, module in enumerate(up_blocks):
reader_gn_modules.append(module)
for r in reader_gn_modules:
r.mean_bank.clear()
r.var_bank.clear()
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