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import torch.nn as nn
from refnet.modules.transformer import BasicTransformerBlock, SelfInjectedTransformerBlock
from refnet.util import checkpoint_wrapper
"""
This implementation refers to Multi-ControlNet, thanks for the authors
Paper: Adding Conditional Control to Text-to-Image Diffusion Models
Link: https://github.com/Mikubill/sd-webui-controlnet
"""
def exists(v):
return v is not None
def torch_dfs(model: nn.Module):
result = [model]
for child in model.children():
result += torch_dfs(child)
return result
class AutoMachine():
Read = "read"
Write = "write"
"""
This class controls the attentions of reference unet and denoising unet
"""
class ReferenceAttentionControl:
writer_modules = []
reader_modules = []
def __init__(
self,
reader_module,
writer_module,
time_embed_ch = 0,
only_decoder = True,
*args,
**kwargs
):
self.time_embed_ch = time_embed_ch
self.trainable_layers = []
self.only_decoder = only_decoder
self.hooked = False
self.register("read", reader_module)
self.register("write", writer_module)
if time_embed_ch > 0:
self.insert_time_emb_proj(reader_module)
def insert_time_emb_proj(self, unet):
for module in torch_dfs(unet.output_blocks if self.only_decoder else unet):
if isinstance(module, BasicTransformerBlock):
module.time_proj = nn.Linear(self.time_embed_ch, module.dim)
self.trainable_layers.append(module.time_proj)
def register(self, mode, unet):
@checkpoint_wrapper
def transformer_forward_write(self, x, context=None, mask=None, emb=None, **kwargs):
x_in = self.norm1(x)
x = self.attn1(x_in) + x
if not self.disable_cross_attn:
x = self.attn2(self.norm2(x), context) + x
x = self.ff(self.norm3(x)) + x
self.bank = x_in
return x
@checkpoint_wrapper
def transformer_forward_read(self, x, context=None, mask=None, emb=None, **kwargs):
if exists(self.bank):
bank = self.bank
if bank.shape[0] != x.shape[0]:
bank = bank.repeat(x.shape[0], 1, 1)
if hasattr(self, "time_proj"):
bank = bank + self.time_proj(emb).unsqueeze(1)
x_in = self.norm1(x)
x = self.attn1(
x = x_in,
context = torch.cat([x_in, bank], 1),
mask = mask,
scale_factor = self.scale_factor,
**kwargs
) + x
x = self.attn2(
x = self.norm2(x),
context = context,
mask = mask,
scale = self.reference_scale,
scale_factor = self.scale_factor
) + x
x = self.ff(self.norm3(x)) + x
else:
x = self.original_forward(x, context, mask, emb)
return x
assert mode in ["write", "read"]
if mode == "read":
self.hooked = True
for module in torch_dfs(unet.output_blocks if self.only_decoder else unet):
if isinstance(module, BasicTransformerBlock):
if mode == "write":
module.original_forward = module.forward
module.forward = transformer_forward_write.__get__(module, BasicTransformerBlock)
self.writer_modules.append(module)
else:
if not isinstance(module, SelfInjectedTransformerBlock):
print(f"Hooking transformer block {module.__class__.__name__} for read mode")
module.original_forward = module.forward
module.forward = transformer_forward_read.__get__(module, BasicTransformerBlock)
self.reader_modules.append(module)
def update(self):
for idx in range(len(self.writer_modules)):
self.reader_modules[idx].bank = self.writer_modules[idx].bank
def restore(self):
for idx in range(len(self.writer_modules)):
self.writer_modules[idx].forward = self.writer_modules[idx].original_forward
self.reader_modules[idx].forward = self.reader_modules[idx].original_forward
self.reader_modules[idx].bank = None
self.hooked = False
def clean(self):
for idx in range(len(self.reader_modules)):
self.reader_modules[idx].bank = None
for idx in range(len(self.writer_modules)):
self.writer_modules[idx].bank = None
self.hooked = False
def reader_restore(self):
for idx in range(len(self.reader_modules)):
self.reader_modules[idx].forward = self.reader_modules[idx].original_forward
self.reader_modules[idx].bank = None
self.hooked = False
def get_trainable_layers(self):
return self.trainable_layers
"""
This class is for self-injection inside the denoising unet
"""
class UnetHook:
def __init__(self):
super().__init__()
self.attention_auto_machine = AutoMachine.Read
def enhance_reference(
self,
model,
ldm,
bs,
s,
r,
style_cfg=0.5,
control_cfg=0,
gr_indice=None,
injection=False,
start_step=0,
):
def forward(self, x, t, control, context, **kwargs):
if 1 - t[0] / (ldm.num_timesteps - 1) >= outer.start_step:
# Write
outer.attention_auto_machine = AutoMachine.Write
rx = ldm.add_noise(outer.r.cpu(), torch.round(t.float()).long().cpu()).cuda().to(x.dtype)
self.original_forward(rx, t, control=outer.s, context=context, **kwargs)
# Read
outer.attention_auto_machine = AutoMachine.Read
return self.original_forward(x, t, control=control, context=context, **kwargs)
def hacked_basic_transformer_inner_forward(self, x, context=None, mask=None, emb=None, **kwargs):
x_norm1 = self.norm1(x)
self_attn1 = None
if self.disable_self_attn:
# Do not use self-attention
self_attn1 = self.attn1(x_norm1, context=context, **kwargs)
else:
# Use self-attention
self_attention_context = x_norm1
if outer.attention_auto_machine == AutoMachine.Write:
self.bank.append(self_attention_context.detach().clone())
self.style_cfgs.append(outer.current_style_fidelity)
if outer.attention_auto_machine == AutoMachine.Read:
if len(self.bank) > 0:
style_cfg = sum(self.style_cfgs) / float(len(self.style_cfgs))
self_attn1_uc = self.attn1(
x_norm1,
context=torch.cat([self_attention_context] + self.bank, dim=1),
**kwargs
)
self_attn1_c = self_attn1_uc.clone()
if len(outer.current_uc_indices) > 0 and style_cfg > 1e-5:
self_attn1_c[outer.current_uc_indices] = self.attn1(
x_norm1[outer.current_uc_indices],
context=self_attention_context[outer.current_uc_indices],
**kwargs
)
self_attn1 = style_cfg * self_attn1_c + (1.0 - style_cfg) * self_attn1_uc
self.bank = []
self.style_cfgs = []
if self_attn1 is None:
self_attn1 = self.attn1(x_norm1, context=self_attention_context)
x = self_attn1.to(x.dtype) + x
x = self.attn2(self.norm2(x), context, mask, self.reference_scale, self.scale_factor, **kwargs) + x
x = self.ff(self.norm3(x)) + x
return x
self.s = [s.repeat(bs, 1, 1, 1) * control_cfg for s in ldm.control_encoder(s)]
self.r = r
self.injection = injection
self.start_step = start_step
self.current_uc_indices = gr_indice
self.current_style_fidelity = style_cfg
outer = self
model = model.diffusion_model
model.original_forward = model.forward
# TODO: change the class name to target
model.forward = forward.__get__(model, model.__class__)
all_modules = torch_dfs(model)
for module in all_modules:
if isinstance(module, BasicTransformerBlock):
module._unet_hook_original_forward = module.forward
module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
module.bank = []
module.style_cfgs = []
def restore(self, model):
model = model.diffusion_model
if hasattr(model, "original_forward"):
model.forward = model.original_forward
del model.original_forward
all_modules = torch_dfs(model)
for module in all_modules:
if isinstance(module, BasicTransformerBlock):
if hasattr(module, "_unet_hook_original_forward"):
module.forward = module._unet_hook_original_forward
del module._unet_hook_original_forward
if hasattr(module, "bank"):
module.bank = None
if hasattr(module, "style_cfgs"):
del module.style_cfgs |