mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-08 22:35:09 +00:00
591 lines
24 KiB
Python
591 lines
24 KiB
Python
# 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
|
|
from typing import List
|
|
import numpy as np
|
|
import torch
|
|
import re
|
|
|
|
from library import train_util
|
|
|
|
RE_UPDOWN = re.compile(r'(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_')
|
|
|
|
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
|
|
|
|
if org_module.__class__.__name__ == "Conv2d":
|
|
in_dim = org_module.in_channels
|
|
out_dim = org_module.out_channels
|
|
else:
|
|
in_dim = org_module.in_features
|
|
out_dim = org_module.out_features
|
|
|
|
# if limit_rank:
|
|
# self.lora_dim = min(lora_dim, in_dim, out_dim)
|
|
# if self.lora_dim != lora_dim:
|
|
# print(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
|
|
# else:
|
|
self.lora_dim = lora_dim
|
|
|
|
if org_module.__class__.__name__ == "Conv2d":
|
|
kernel_size = org_module.kernel_size
|
|
stride = org_module.stride
|
|
padding = org_module.padding
|
|
self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
|
|
self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
|
|
else:
|
|
self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
|
|
self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
|
|
|
|
if type(alpha) == torch.Tensor:
|
|
alpha = alpha.detach().float().numpy() # without casting, bf16 causes error
|
|
alpha = self.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
|
|
self.region = None
|
|
self.region_mask = None
|
|
|
|
def apply_to(self):
|
|
self.org_forward = self.org_module.forward
|
|
self.org_module.forward = self.forward
|
|
del self.org_module
|
|
|
|
def merge_to(self, sd, dtype, device):
|
|
# get up/down weight
|
|
up_weight = sd["lora_up.weight"].to(torch.float).to(device)
|
|
down_weight = sd["lora_down.weight"].to(torch.float).to(device)
|
|
|
|
# extract weight from org_module
|
|
org_sd = self.org_module.state_dict()
|
|
weight = org_sd["weight"].to(torch.float)
|
|
|
|
# merge weight
|
|
if len(weight.size()) == 2:
|
|
# linear
|
|
weight = weight + self.multiplier * (up_weight @ down_weight) * self.scale
|
|
elif down_weight.size()[2:4] == (1, 1):
|
|
# conv2d 1x1
|
|
weight = (
|
|
weight
|
|
+ self.multiplier
|
|
* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
|
|
* self.scale
|
|
)
|
|
else:
|
|
# conv2d 3x3
|
|
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
|
|
# print(conved.size(), weight.size(), module.stride, module.padding)
|
|
weight = weight + self.multiplier * conved * self.scale
|
|
|
|
# set weight to org_module
|
|
org_sd["weight"] = weight.to(dtype)
|
|
self.org_module.load_state_dict(org_sd)
|
|
|
|
def set_region(self, region):
|
|
self.region = region
|
|
self.region_mask = None
|
|
|
|
def forward(self, x):
|
|
if self.region is None:
|
|
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
|
|
|
|
# regional LoRA FIXME same as additional-network extension
|
|
if x.size()[1] % 77 == 0:
|
|
# print(f"LoRA for context: {self.lora_name}")
|
|
self.region = None
|
|
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
|
|
|
|
# calculate region mask first time
|
|
if self.region_mask is None:
|
|
if len(x.size()) == 4:
|
|
h, w = x.size()[2:4]
|
|
else:
|
|
seq_len = x.size()[1]
|
|
ratio = math.sqrt((self.region.size()[0] * self.region.size()[1]) / seq_len)
|
|
h = int(self.region.size()[0] / ratio + 0.5)
|
|
w = seq_len // h
|
|
|
|
r = self.region.to(x.device)
|
|
if r.dtype == torch.bfloat16:
|
|
r = r.to(torch.float)
|
|
r = r.unsqueeze(0).unsqueeze(1)
|
|
# print(self.lora_name, self.region.size(), x.size(), r.size(), h, w)
|
|
r = torch.nn.functional.interpolate(r, (h, w), mode="bilinear")
|
|
r = r.to(x.dtype)
|
|
|
|
if len(x.size()) == 3:
|
|
r = torch.reshape(r, (1, x.size()[1], -1))
|
|
|
|
self.region_mask = r
|
|
|
|
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale * self.region_mask
|
|
|
|
|
|
def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
|
|
if network_dim is None:
|
|
network_dim = 4 # default
|
|
|
|
# extract dim/alpha for conv2d, and block dim
|
|
conv_dim = kwargs.get("conv_dim", None)
|
|
conv_alpha = kwargs.get("conv_alpha", None)
|
|
if conv_dim is not None:
|
|
conv_dim = int(conv_dim)
|
|
if conv_alpha is None:
|
|
conv_alpha = 1.0
|
|
else:
|
|
conv_alpha = float(conv_alpha)
|
|
|
|
"""
|
|
block_dims = kwargs.get("block_dims")
|
|
block_alphas = None
|
|
|
|
if block_dims is not None:
|
|
block_dims = [int(d) for d in block_dims.split(',')]
|
|
assert len(block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
|
|
block_alphas = kwargs.get("block_alphas")
|
|
if block_alphas is None:
|
|
block_alphas = [1] * len(block_dims)
|
|
else:
|
|
block_alphas = [int(a) for a in block_alphas(',')]
|
|
assert len(block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
|
|
|
|
conv_block_dims = kwargs.get("conv_block_dims")
|
|
conv_block_alphas = None
|
|
|
|
if conv_block_dims is not None:
|
|
conv_block_dims = [int(d) for d in conv_block_dims.split(',')]
|
|
assert len(conv_block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
|
|
conv_block_alphas = kwargs.get("conv_block_alphas")
|
|
if conv_block_alphas is None:
|
|
conv_block_alphas = [1] * len(conv_block_dims)
|
|
else:
|
|
conv_block_alphas = [int(a) for a in conv_block_alphas(',')]
|
|
assert len(conv_block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
|
|
"""
|
|
|
|
network = LoRANetwork(
|
|
text_encoder,
|
|
unet,
|
|
multiplier=multiplier,
|
|
lora_dim=network_dim,
|
|
alpha=network_alpha,
|
|
conv_lora_dim=conv_dim,
|
|
conv_alpha=conv_alpha,
|
|
)
|
|
|
|
up_weight=None
|
|
if 'up_weight' in kwargs:
|
|
up_weight = kwargs.get('up_weight',None)
|
|
if "," in up_weight:
|
|
up_weight = [float(s) for s in up_weight.split(",") if s]
|
|
down_weight=None
|
|
if 'down_weight' in kwargs:
|
|
down_weight = kwargs.get('down_weight',None)
|
|
if "," in down_weight:
|
|
down_weight = [float(s) for s in down_weight.split(",") if s]
|
|
|
|
network.set_stratified_lr_weight(up_weight,float(kwargs.get('mid_weight', 1.0)),down_weight,float(kwargs.get('lr_weight_threshold', 0.0)))
|
|
|
|
return network
|
|
|
|
|
|
def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, **kwargs):
|
|
if weights_sd is None:
|
|
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/alpha mapping
|
|
modules_dim = {}
|
|
modules_alpha = {}
|
|
for key, value in weights_sd.items():
|
|
if "." not in key:
|
|
continue
|
|
|
|
lora_name = key.split(".")[0]
|
|
if "alpha" in key:
|
|
modules_alpha[lora_name] = value
|
|
elif "lora_down" in key:
|
|
dim = value.size()[0]
|
|
modules_dim[lora_name] = dim
|
|
# print(lora_name, value.size(), dim)
|
|
|
|
# support old LoRA without alpha
|
|
for key in modules_dim.keys():
|
|
if key not in modules_alpha:
|
|
modules_alpha = modules_dim[key]
|
|
|
|
network = LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
|
|
network.weights_sd = weights_sd
|
|
return network
|
|
|
|
|
|
class LoRANetwork(torch.nn.Module):
|
|
# is it possible to apply conv_in and conv_out?
|
|
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
|
|
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
|
|
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,
|
|
conv_lora_dim=None,
|
|
conv_alpha=None,
|
|
modules_dim=None,
|
|
modules_alpha=None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.multiplier = multiplier
|
|
|
|
self.lora_dim = lora_dim
|
|
self.alpha = alpha
|
|
self.conv_lora_dim = conv_lora_dim
|
|
self.conv_alpha = conv_alpha
|
|
|
|
if modules_dim is not None:
|
|
print(f"create LoRA network from weights")
|
|
else:
|
|
print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
|
|
|
|
self.apply_to_conv2d_3x3 = self.conv_lora_dim is not None
|
|
if self.apply_to_conv2d_3x3:
|
|
if self.conv_alpha is None:
|
|
self.conv_alpha = self.alpha
|
|
print(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_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:
|
|
# TODO get block index here
|
|
for child_name, child_module in module.named_modules():
|
|
is_linear = child_module.__class__.__name__ == "Linear"
|
|
is_conv2d = child_module.__class__.__name__ == "Conv2d"
|
|
is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
|
|
if is_linear or is_conv2d:
|
|
lora_name = prefix + "." + name + "." + child_name
|
|
lora_name = lora_name.replace(".", "_")
|
|
|
|
if modules_dim is not None:
|
|
if lora_name not in modules_dim:
|
|
continue # no LoRA module in this weights file
|
|
dim = modules_dim[lora_name]
|
|
alpha = modules_alpha[lora_name]
|
|
else:
|
|
if is_linear or is_conv2d_1x1:
|
|
dim = self.lora_dim
|
|
alpha = self.alpha
|
|
elif self.apply_to_conv2d_3x3:
|
|
dim = self.conv_lora_dim
|
|
alpha = self.conv_alpha
|
|
else:
|
|
continue
|
|
|
|
lora = LoRAModule(lora_name, child_module, self.multiplier, dim, 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.")
|
|
|
|
# extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
|
|
target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE
|
|
if modules_dim is not None or self.conv_lora_dim is not None:
|
|
target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
|
|
|
|
self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, target_modules)
|
|
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)
|
|
|
|
self.up_weight:list[float] = None
|
|
self.down_weight:list[float] = None
|
|
self.mid_weight:float = None
|
|
|
|
def set_multiplier(self, multiplier):
|
|
self.multiplier = multiplier
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.multiplier = self.multiplier
|
|
|
|
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 = []
|
|
|
|
skipped = []
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
if self.get_stratified_lr_weight(lora) == 0:
|
|
skipped.append(lora.lora_name)
|
|
continue
|
|
lora.apply_to()
|
|
self.add_module(lora.lora_name, lora)
|
|
if len(skipped)>0:
|
|
print(f"stratified_lr_weightが0の為、次の{len(skipped)}個のLoRAモジュールはスキップされます:")
|
|
for name in skipped:
|
|
print(f"\t{name}")
|
|
|
|
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}")
|
|
|
|
# TODO refactor to common function with apply_to
|
|
def merge_to(self, text_encoder, unet, dtype, device):
|
|
assert self.weights_sd is not None, "weights are not loaded"
|
|
|
|
apply_text_encoder = apply_unet = False
|
|
for key in self.weights_sd.keys():
|
|
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
|
|
apply_text_encoder = True
|
|
elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
|
|
apply_unet = True
|
|
|
|
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:
|
|
sd_for_lora = {}
|
|
for key in self.weights_sd.keys():
|
|
if key.startswith(lora.lora_name):
|
|
sd_for_lora[key[len(lora.lora_name) + 1 :]] = self.weights_sd[key]
|
|
lora.merge_to(sd_for_lora, dtype, device)
|
|
print(f"weights are merged")
|
|
|
|
# 層別学習率用に層ごとの学習率に対する倍率を定義する
|
|
def set_stratified_lr_weight(self, up_weight:list[float]|str=None, mid_weight:float=None, down_weight:list[float]|str=None, zero_threshold:float=0.0):
|
|
max_len = 3 # attentions -> attentions -> attentions で3個のModuleに対して定義
|
|
if self.apply_to_conv2d_3x3:
|
|
max_len = 10 # (resnets -> {up,down}sampler -> attentions) x3 -> resnets で10個のModuleに対して定義
|
|
|
|
def get_list(name) -> list[float]:
|
|
import math
|
|
if name=="cosine":
|
|
return [math.cos(math.pi*(i/(max_len-1))/2) for i in range(max_len)]
|
|
elif name=="sine":
|
|
return [math.sin(math.pi*(i/(max_len-1))/2) for i in range(max_len)]
|
|
elif name=="linear":
|
|
return [i/(max_len-1) for i in range(max_len)]
|
|
elif name=="reverse_linear":
|
|
return [i/(max_len-1) for i in reversed(range(max_len))]
|
|
elif name=="zeros":
|
|
return [0.0] * max_len
|
|
else:
|
|
print("不明なweightの引数 %s が使われました。\n\t有効な引数: cosine, sine, linear, reverse_linear, zeros"%(name))
|
|
return None
|
|
|
|
if type(down_weight)==str:
|
|
down_weight=get_list(down_weight)
|
|
if type(up_weight)==str:
|
|
up_weight=get_list(up_weight)
|
|
|
|
if (up_weight != None and len(up_weight)>max_len) or (down_weight != None and len(down_weight)>max_len):
|
|
print("down_weightもしくはup_weightが長すぎます。%d個目以降のパラメータは無視されます。"%max_len)
|
|
if (up_weight != None and len(up_weight)<max_len) or (down_weight != None and len(down_weight)<max_len):
|
|
print("down_weightもしくはup_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。"%max_len)
|
|
if down_weight != None and len(down_weight)<max_len:
|
|
down_weight = down_weight + [1.0] * (max_len - len(down_weight))
|
|
if up_weight != None and len(up_weight)<max_len:
|
|
up_weight = up_weight + [1.0] * (max_len - len(up_weight))
|
|
if (up_weight != None) or (mid_weight != None) or (down_weight != None):
|
|
print("層別学習率を適用します。")
|
|
if (down_weight != None):
|
|
self.down_weight = [w if w > zero_threshold else 0 for w in down_weight[:max_len]]
|
|
print("down_weight(浅い層->深い層):",self.down_weight)
|
|
if (mid_weight != None):
|
|
self.mid_weight = mid_weight if mid_weight > zero_threshold else 0
|
|
print("mid_weight:",self.mid_weight)
|
|
if (up_weight != None):
|
|
self.up_weight = [w if w > zero_threshold else 0 for w in up_weight[:max_len]]
|
|
print("up_weight(深い層->浅い層):",self.up_weight)
|
|
return
|
|
|
|
def get_stratified_lr_weight(self, lora:LoRAModule) -> float:
|
|
m = RE_UPDOWN.search(lora.lora_name)
|
|
if m:
|
|
idx = 0
|
|
g = m.groups()
|
|
i = int(g[1])
|
|
if self.apply_to_conv2d_3x3:
|
|
if g[2]=="resnets":
|
|
idx=3*i
|
|
elif g[2]=="attentions":
|
|
if g[0]=="down":
|
|
idx=3*i + 2
|
|
else:
|
|
idx=3*i - 1
|
|
elif g[2]=="upsamplers" or g[2]=="downsamplers":
|
|
idx=3*i + 1
|
|
else:
|
|
idx=i
|
|
if g[0]=="up":
|
|
idx=i-1
|
|
|
|
if (g[0]=="up") and (self.up_weight != None):
|
|
return self.up_weight[idx]
|
|
elif (g[0]=="down") and (self.down_weight != None):
|
|
return self.down_weight[idx]
|
|
elif ("mid_block_" in lora.lora_name) and (self.mid_weight != None):
|
|
return self.mid_weight
|
|
# print({'params': lora.parameters(), 'lr':alpha*lr})
|
|
return 1
|
|
|
|
def prepare_optimizer_params(self, text_encoder_lr, unet_lr , default_lr):
|
|
self.requires_grad_(True)
|
|
all_params = []
|
|
|
|
if self.text_encoder_loras:
|
|
params = []
|
|
for lora in self.text_encoder_loras:
|
|
params.extend(lora.parameters())
|
|
param_data = {'params': params}
|
|
if text_encoder_lr is not None:
|
|
param_data['lr'] = text_encoder_lr
|
|
all_params.append(param_data)
|
|
|
|
if self.unet_loras:
|
|
for lora in self.unet_loras:
|
|
param_data={}
|
|
if unet_lr is not None:
|
|
param_data = {'params': lora.parameters(), 'lr':self.get_stratified_lr_weight(lora)*unet_lr}
|
|
elif default_lr is not None:
|
|
param_data = {'params': lora.parameters(), 'lr':self.get_stratified_lr_weight(lora)*default_lr}
|
|
if param_data["lr"]==0:
|
|
continue
|
|
all_params.append(param_data)
|
|
return all_params
|
|
|
|
def enable_gradient_checkpointing(self):
|
|
# not supported
|
|
pass
|
|
|
|
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)
|
|
|
|
@staticmethod
|
|
def set_regions(networks, image):
|
|
image = image.astype(np.float32) / 255.0
|
|
for i, network in enumerate(networks[:3]):
|
|
# NOTE: consider averaging overwrapping area
|
|
region = image[:, :, i]
|
|
if region.max() == 0:
|
|
continue
|
|
region = torch.tensor(region)
|
|
network.set_region(region)
|
|
|
|
def set_region(self, region):
|
|
for lora in self.unet_loras:
|
|
lora.set_region(region)
|