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# LoRA network module
# reference:
# https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
# https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py
import math
import os
import torch
from trainer_util import *
class LoRAModule(torch.nn.Module):
"""
replaces forward method of the original Linear, instead of replacing the original Linear module.
"""
def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1):
""" if alpha == 0 or None, alpha is rank (no scaling). """
super().__init__()
self.lora_name = lora_name
self.lora_dim = lora_dim
if org_module.__class__.__name__ == 'Conv2d':
in_dim = org_module.in_channels
out_dim = org_module.out_channels
self.lora_down = torch.nn.Conv2d(in_dim, lora_dim, (1, 1), bias=False)
self.lora_up = torch.nn.Conv2d(lora_dim, out_dim, (1, 1), bias=False)
else:
in_dim = org_module.in_features
out_dim = org_module.out_features
self.lora_down = torch.nn.Linear(in_dim, lora_dim, bias=False)
self.lora_up = torch.nn.Linear(lora_dim, out_dim, bias=False)
if type(alpha) == torch.Tensor:
alpha = alpha.detach().float().numpy() # without casting, bf16 causes error
alpha = lora_dim if alpha is None or alpha == 0 else alpha
self.scale = alpha / self.lora_dim
self.register_buffer('alpha', torch.tensor(alpha)) # 定数として扱える
# same as microsoft's
torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
torch.nn.init.zeros_(self.lora_up.weight)
self.multiplier = multiplier
self.org_module = org_module # remove in applying
def apply_to(self):
self.org_forward = self.org_module.forward
self.org_module.forward = self.forward
del self.org_module
def forward(self, x):
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
if network_dim is None:
network_dim = 4 # default
network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha)
return network
def create_network_from_weights(multiplier, file, vae, text_encoder, unet, **kwargs):
if os.path.splitext(file)[1] == '.safetensors':
from safetensors.torch import load_file, safe_open
weights_sd = load_file(file)
else:
weights_sd = torch.load(file, map_location='cpu')
# get dim (rank)
network_alpha = None
network_dim = None
for key, value in weights_sd.items():
if network_alpha is None and 'alpha' in key:
network_alpha = value
if network_dim is None and 'lora_down' in key and len(value.size()) == 2:
network_dim = value.size()[0]
if network_alpha is None:
network_alpha = network_dim
network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha)
network.weights_sd = weights_sd
return network
class LoRANetwork(torch.nn.Module):
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = 'lora_unet'
LORA_PREFIX_TEXT_ENCODER = 'lora_te'
def __init__(self, text_encoder, unet, multiplier=1.0, lora_dim=4, alpha=1) -> None:
super().__init__()
self.multiplier = multiplier
self.lora_dim = lora_dim
self.alpha = alpha
# create module instances
def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> list[LoRAModule]:
loras = []
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
for child_name, child_module in module.named_modules():
if child_module.__class__.__name__ == "Linear" or (child_module.__class__.__name__ == "Conv2d" and child_module.kernel_size == (1, 1)):
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
lora = LoRAModule(lora_name, child_module, self.multiplier, self.lora_dim, self.alpha)
loras.append(lora)
return loras
self.text_encoder_loras = create_modules(LoRANetwork.LORA_PREFIX_TEXT_ENCODER,
text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, LoRANetwork.UNET_TARGET_REPLACE_MODULE)
print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
self.weights_sd = None
# assertion
names = set()
for lora in self.text_encoder_loras + self.unet_loras:
assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
names.add(lora.lora_name)
def load_weights(self, file):
if os.path.splitext(file)[1] == '.safetensors':
from safetensors.torch import load_file, safe_open
self.weights_sd = load_file(file)
else:
self.weights_sd = torch.load(file, map_location='cpu')
def apply_to(self, text_encoder, unet, apply_text_encoder=None, apply_unet=None):
if self.weights_sd:
weights_has_text_encoder = weights_has_unet = False
for key in self.weights_sd.keys():
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
weights_has_text_encoder = True
elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
weights_has_unet = True
if apply_text_encoder is None:
apply_text_encoder = weights_has_text_encoder
else:
assert apply_text_encoder == weights_has_text_encoder, f"text encoder weights: {weights_has_text_encoder} but text encoder flag: {apply_text_encoder} / 重みとText Encoderのフラグが矛盾しています"
if apply_unet is None:
apply_unet = weights_has_unet
else:
assert apply_unet == weights_has_unet, f"u-net weights: {weights_has_unet} but u-net flag: {apply_unet} / 重みとU-Netのフラグが矛盾しています"
else:
assert apply_text_encoder is not None and apply_unet is not None, f"internal error: flag not set"
if apply_text_encoder:
print("enable LoRA for text encoder")
else:
self.text_encoder_loras = []
if apply_unet:
print("enable LoRA for U-Net")
else:
self.unet_loras = []
for lora in self.text_encoder_loras + self.unet_loras:
lora.apply_to()
self.add_module(lora.lora_name, lora)
if self.weights_sd:
# if some weights are not in state dict, it is ok because initial LoRA does nothing (lora_up is initialized by zeros)
info = self.load_state_dict(self.weights_sd, False)
print(f"weights are loaded: {info}")
def enable_gradient_checkpointing(self):
# not supported
pass
def prepare_optimizer_params(self, text_encoder_lr, unet_lr):
def enumerate_params(loras):
params = []
for lora in loras:
params.extend(lora.parameters())
return params
self.requires_grad_(True)
all_params = []
if self.text_encoder_loras:
param_data = {'params': enumerate_params(self.text_encoder_loras)}
if text_encoder_lr is not None:
param_data['lr'] = text_encoder_lr
all_params.append(param_data)
if self.unet_loras:
param_data = {'params': enumerate_params(self.unet_loras)}
if unet_lr is not None:
param_data['lr'] = unet_lr
all_params.append(param_data)
return all_params
def prepare_grad_etc(self, text_encoder, unet):
self.requires_grad_(True)
def on_epoch_start(self, text_encoder, unet):
self.train()
def get_trainable_params(self):
return self.parameters()
def save_weights(self, file, dtype, metadata):
if metadata is not None and len(metadata) == 0:
metadata = None
state_dict = self.state_dict()
if dtype is not None:
for key in list(state_dict.keys()):
v = state_dict[key]
v = v.detach().clone().to("cpu").to(dtype)
state_dict[key] = v
if os.path.splitext(file)[1] == '.safetensors':
from safetensors.torch import save_file
# Precalculate model hashes to save time on indexing
if metadata is None:
metadata = {}
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
metadata["sshs_model_hash"] = model_hash
metadata["sshs_legacy_hash"] = legacy_hash
save_file(state_dict, file, metadata)
else:
torch.save(state_dict, file) |