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import torch
from contextlib import contextmanager
from typing import Union, Tuple
_size_2_t = Union[int, Tuple[int, int]]
class LinearWithLoRA(torch.nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
device=None,
dtype=None) -> None:
super().__init__()
self.weight_module = None
self.up = None
self.down = None
self.bias = None
self.in_features = in_features
self.out_features = out_features
self.device = device
self.dtype = dtype
self.weight = None
def bind_lora(self, weight_module):
self.weight_module = [weight_module]
def unbind_lora(self):
if self.up is not None and self.down is not None: # SAI's model is weird and needs this
self.weight_module = None
def get_original_weight(self):
if self.weight_module is None:
return None
return self.weight_module[0].weight
def forward(self, x):
if self.weight is not None:
return torch.nn.functional.linear(x, self.weight.to(x),
self.bias.to(x) if self.bias is not None else None)
original_weight = self.get_original_weight()
if original_weight is None:
return None # A1111 needs first_time_calculation
if self.up is not None and self.down is not None:
weight = original_weight.to(x) + torch.mm(self.up, self.down).to(x)
else:
weight = original_weight.to(x)
return torch.nn.functional.linear(x, weight, self.bias.to(x) if self.bias is not None else None)
class Conv2dWithLoRA(torch.nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_2_t,
stride: _size_2_t = 1,
padding: Union[str, _size_2_t] = 0,
dilation: _size_2_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
device=None,
dtype=None
) -> None:
super().__init__()
self.stride = stride
self.padding = padding
self.dilation = dilation
self.groups = groups
self.weight_module = None
self.bias = None
self.up = None
self.down = None
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.padding_mode = padding_mode
self.device = device
self.dtype = dtype
self.weight = None
def bind_lora(self, weight_module):
self.weight_module = [weight_module]
def unbind_lora(self):
if self.up is not None and self.down is not None: # SAI's model is weird and needs this
self.weight_module = None
def get_original_weight(self):
if self.weight_module is None:
return None
return self.weight_module[0].weight
def forward(self, x):
if self.weight is not None:
return torch.nn.functional.conv2d(x, self.weight.to(x), self.bias.to(x) if self.bias is not None else None,
self.stride, self.padding, self.dilation, self.groups)
original_weight = self.get_original_weight()
if original_weight is None:
return None # A1111 needs first_time_calculation
if self.up is not None and self.down is not None:
weight = original_weight.to(x) + torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1)).reshape(original_weight.shape).to(x)
else:
weight = original_weight.to(x)
return torch.nn.functional.conv2d(x, weight, self.bias.to(x) if self.bias is not None else None,
self.stride, self.padding, self.dilation, self.groups)
@contextmanager
def controlnet_lora_hijack():
linear, conv2d = torch.nn.Linear, torch.nn.Conv2d
torch.nn.Linear, torch.nn.Conv2d = LinearWithLoRA, Conv2dWithLoRA
try:
yield
finally:
torch.nn.Linear, torch.nn.Conv2d = linear, conv2d
def recursive_set(obj, key, value):
if obj is None:
return
if '.' in key:
k1, k2 = key.split('.', 1)
recursive_set(getattr(obj, k1, None), k2, value)
else:
setattr(obj, key, value)
def force_load_state_dict(model, state_dict):
for k in list(state_dict.keys()):
recursive_set(model, k, torch.nn.Parameter(state_dict[k]))
del state_dict[k]
return
def recursive_bind_lora(obj, key, value):
if obj is None:
return
if '.' in key:
k1, k2 = key.split('.', 1)
recursive_bind_lora(getattr(obj, k1, None), k2, value)
else:
target = getattr(obj, key, None)
if target is not None and hasattr(target, 'bind_lora'):
target.bind_lora(value)
def recursive_get(obj, key):
if obj is None:
return
if '.' in key:
k1, k2 = key.split('.', 1)
return recursive_get(getattr(obj, k1, None), k2)
else:
return getattr(obj, key, None)
def bind_control_lora(base_model, control_lora_model):
sd = base_model.state_dict()
keys = list(sd.keys())
keys = list(set([k.rsplit('.', 1)[0] for k in keys]))
module_dict = {k: recursive_get(base_model, k) for k in keys}
for k, v in module_dict.items():
recursive_bind_lora(control_lora_model, k, v)
def torch_dfs(model: torch.nn.Module):
result = [model]
for child in model.children():
result += torch_dfs(child)
return result
def unbind_control_lora(control_lora_model):
for m in torch_dfs(control_lora_model):
if hasattr(m, 'unbind_lora'):
m.unbind_lora()
return
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