mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-06 13:47:06 +00:00
1158 lines
48 KiB
Python
1158 lines
48 KiB
Python
# temporary minimum implementation of LoRA
|
|
# FLUX doesn't have Conv2d, so we ignore it
|
|
# TODO commonize with the original implementation
|
|
|
|
# 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 Dict, List, Optional, Tuple, Type, Union
|
|
from diffusers import AutoencoderKL
|
|
from transformers import CLIPTextModel
|
|
import numpy as np
|
|
import torch
|
|
import re
|
|
from library.utils import setup_logging
|
|
from library.sdxl_original_unet import SdxlUNet2DConditionModel
|
|
|
|
setup_logging()
|
|
import logging
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
NUM_DOUBLE_BLOCKS = 19
|
|
NUM_SINGLE_BLOCKS = 38
|
|
|
|
|
|
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,
|
|
dropout=None,
|
|
rank_dropout=None,
|
|
module_dropout=None,
|
|
split_dims: Optional[List[int]] = None,
|
|
):
|
|
"""
|
|
if alpha == 0 or None, alpha is rank (no scaling).
|
|
|
|
split_dims is used to mimic the split qkv of FLUX as same as Diffusers
|
|
"""
|
|
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
|
|
|
|
self.lora_dim = lora_dim
|
|
self.split_dims = split_dims
|
|
|
|
if split_dims is None:
|
|
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)
|
|
|
|
torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
|
|
torch.nn.init.zeros_(self.lora_up.weight)
|
|
else:
|
|
# conv2d not supported
|
|
assert sum(split_dims) == out_dim, "sum of split_dims must be equal to out_dim"
|
|
assert org_module.__class__.__name__ == "Linear", "split_dims is only supported for Linear"
|
|
# print(f"split_dims: {split_dims}")
|
|
self.lora_down = torch.nn.ModuleList(
|
|
[torch.nn.Linear(in_dim, self.lora_dim, bias=False) for _ in range(len(split_dims))]
|
|
)
|
|
self.lora_up = torch.nn.ModuleList([torch.nn.Linear(self.lora_dim, split_dim, bias=False) for split_dim in split_dims])
|
|
for lora_down in self.lora_down:
|
|
torch.nn.init.kaiming_uniform_(lora_down.weight, a=math.sqrt(5))
|
|
for lora_up in self.lora_up:
|
|
torch.nn.init.zeros_(lora_up.weight)
|
|
|
|
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
|
|
self.multiplier = multiplier
|
|
self.org_module = org_module # remove in applying
|
|
self.dropout = dropout
|
|
self.rank_dropout = rank_dropout
|
|
self.module_dropout = module_dropout
|
|
|
|
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):
|
|
org_forwarded = self.org_forward(x)
|
|
|
|
# module dropout
|
|
if self.module_dropout is not None and self.training:
|
|
if torch.rand(1) < self.module_dropout:
|
|
return org_forwarded
|
|
|
|
if self.split_dims is None:
|
|
lx = self.lora_down(x)
|
|
|
|
# normal dropout
|
|
if self.dropout is not None and self.training:
|
|
lx = torch.nn.functional.dropout(lx, p=self.dropout)
|
|
|
|
# rank dropout
|
|
if self.rank_dropout is not None and self.training:
|
|
mask = torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout
|
|
if len(lx.size()) == 3:
|
|
mask = mask.unsqueeze(1) # for Text Encoder
|
|
elif len(lx.size()) == 4:
|
|
mask = mask.unsqueeze(-1).unsqueeze(-1) # for Conv2d
|
|
lx = lx * mask
|
|
|
|
# scaling for rank dropout: treat as if the rank is changed
|
|
# maskから計算することも考えられるが、augmentation的な効果を期待してrank_dropoutを用いる
|
|
scale = self.scale * (1.0 / (1.0 - self.rank_dropout)) # redundant for readability
|
|
else:
|
|
scale = self.scale
|
|
|
|
lx = self.lora_up(lx)
|
|
|
|
return org_forwarded + lx * self.multiplier * scale
|
|
else:
|
|
lxs = [lora_down(x) for lora_down in self.lora_down]
|
|
|
|
# normal dropout
|
|
if self.dropout is not None and self.training:
|
|
lxs = [torch.nn.functional.dropout(lx, p=self.dropout) for lx in lxs]
|
|
|
|
# rank dropout
|
|
if self.rank_dropout is not None and self.training:
|
|
masks = [torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout for lx in lxs]
|
|
for i in range(len(lxs)):
|
|
if len(lx.size()) == 3:
|
|
masks[i] = masks[i].unsqueeze(1)
|
|
elif len(lx.size()) == 4:
|
|
masks[i] = masks[i].unsqueeze(-1).unsqueeze(-1)
|
|
lxs[i] = lxs[i] * masks[i]
|
|
|
|
# scaling for rank dropout: treat as if the rank is changed
|
|
scale = self.scale * (1.0 / (1.0 - self.rank_dropout)) # redundant for readability
|
|
else:
|
|
scale = self.scale
|
|
|
|
lxs = [lora_up(lx) for lora_up, lx in zip(self.lora_up, lxs)]
|
|
|
|
return org_forwarded + torch.cat(lxs, dim=-1) * self.multiplier * scale
|
|
|
|
|
|
class LoRAInfModule(LoRAModule):
|
|
def __init__(
|
|
self,
|
|
lora_name,
|
|
org_module: torch.nn.Module,
|
|
multiplier=1.0,
|
|
lora_dim=4,
|
|
alpha=1,
|
|
**kwargs,
|
|
):
|
|
# no dropout for inference
|
|
super().__init__(lora_name, org_module, multiplier, lora_dim, alpha)
|
|
|
|
self.org_module_ref = [org_module] # 後から参照できるように
|
|
self.enabled = True
|
|
self.network: LoRANetwork = None
|
|
|
|
def set_network(self, network):
|
|
self.network = network
|
|
|
|
# freezeしてマージする
|
|
def merge_to(self, sd, dtype, device):
|
|
# extract weight from org_module
|
|
org_sd = self.org_module.state_dict()
|
|
weight = org_sd["weight"]
|
|
org_dtype = weight.dtype
|
|
org_device = weight.device
|
|
weight = weight.to(torch.float) # calc in float
|
|
|
|
if dtype is None:
|
|
dtype = org_dtype
|
|
if device is None:
|
|
device = org_device
|
|
|
|
if self.split_dims is None:
|
|
# get up/down weight
|
|
down_weight = sd["lora_down.weight"].to(torch.float).to(device)
|
|
up_weight = sd["lora_up.weight"].to(torch.float).to(device)
|
|
|
|
# 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)
|
|
# logger.info(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)
|
|
else:
|
|
# split_dims
|
|
total_dims = sum(self.split_dims)
|
|
for i in range(len(self.split_dims)):
|
|
# get up/down weight
|
|
down_weight = sd[f"lora_down.{i}.weight"].to(torch.float).to(device) # (rank, in_dim)
|
|
up_weight = sd[f"lora_up.{i}.weight"].to(torch.float).to(device) # (split dim, rank)
|
|
|
|
# pad up_weight -> (total_dims, rank)
|
|
padded_up_weight = torch.zeros((total_dims, up_weight.size(0)), device=device, dtype=torch.float)
|
|
padded_up_weight[sum(self.split_dims[:i]) : sum(self.split_dims[: i + 1])] = up_weight
|
|
|
|
# merge weight
|
|
weight = weight + self.multiplier * (up_weight @ down_weight) * self.scale
|
|
|
|
# set weight to org_module
|
|
org_sd["weight"] = weight.to(dtype)
|
|
self.org_module.load_state_dict(org_sd)
|
|
|
|
# 復元できるマージのため、このモジュールのweightを返す
|
|
def get_weight(self, multiplier=None):
|
|
if multiplier is None:
|
|
multiplier = self.multiplier
|
|
|
|
# get up/down weight from module
|
|
up_weight = self.lora_up.weight.to(torch.float)
|
|
down_weight = self.lora_down.weight.to(torch.float)
|
|
|
|
# pre-calculated weight
|
|
if len(down_weight.size()) == 2:
|
|
# linear
|
|
weight = self.multiplier * (up_weight @ down_weight) * self.scale
|
|
elif down_weight.size()[2:4] == (1, 1):
|
|
# conv2d 1x1
|
|
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)
|
|
weight = self.multiplier * conved * self.scale
|
|
|
|
return weight
|
|
|
|
def set_region(self, region):
|
|
self.region = region
|
|
self.region_mask = None
|
|
|
|
def default_forward(self, x):
|
|
# logger.info(f"default_forward {self.lora_name} {x.size()}")
|
|
if self.split_dims is None:
|
|
lx = self.lora_down(x)
|
|
lx = self.lora_up(lx)
|
|
return self.org_forward(x) + lx * self.multiplier * self.scale
|
|
else:
|
|
lxs = [lora_down(x) for lora_down in self.lora_down]
|
|
lxs = [lora_up(lx) for lora_up, lx in zip(self.lora_up, lxs)]
|
|
return self.org_forward(x) + torch.cat(lxs, dim=-1) * self.multiplier * self.scale
|
|
|
|
def forward(self, x):
|
|
if not self.enabled:
|
|
return self.org_forward(x)
|
|
return self.default_forward(x)
|
|
|
|
|
|
def create_network(
|
|
multiplier: float,
|
|
network_dim: Optional[int],
|
|
network_alpha: Optional[float],
|
|
ae: AutoencoderKL,
|
|
text_encoders: List[CLIPTextModel],
|
|
flux,
|
|
neuron_dropout: Optional[float] = None,
|
|
**kwargs,
|
|
):
|
|
if network_dim is None:
|
|
network_dim = 4 # default
|
|
if network_alpha is None:
|
|
network_alpha = 1.0
|
|
|
|
# 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)
|
|
|
|
# attn dim, mlp dim: only for DoubleStreamBlock. SingleStreamBlock is not supported because of combined qkv
|
|
img_attn_dim = kwargs.get("img_attn_dim", None)
|
|
txt_attn_dim = kwargs.get("txt_attn_dim", None)
|
|
img_mlp_dim = kwargs.get("img_mlp_dim", None)
|
|
txt_mlp_dim = kwargs.get("txt_mlp_dim", None)
|
|
img_mod_dim = kwargs.get("img_mod_dim", None)
|
|
txt_mod_dim = kwargs.get("txt_mod_dim", None)
|
|
single_dim = kwargs.get("single_dim", None) # SingleStreamBlock
|
|
single_mod_dim = kwargs.get("single_mod_dim", None) # SingleStreamBlock
|
|
if img_attn_dim is not None:
|
|
img_attn_dim = int(img_attn_dim)
|
|
if txt_attn_dim is not None:
|
|
txt_attn_dim = int(txt_attn_dim)
|
|
if img_mlp_dim is not None:
|
|
img_mlp_dim = int(img_mlp_dim)
|
|
if txt_mlp_dim is not None:
|
|
txt_mlp_dim = int(txt_mlp_dim)
|
|
if img_mod_dim is not None:
|
|
img_mod_dim = int(img_mod_dim)
|
|
if txt_mod_dim is not None:
|
|
txt_mod_dim = int(txt_mod_dim)
|
|
if single_dim is not None:
|
|
single_dim = int(single_dim)
|
|
if single_mod_dim is not None:
|
|
single_mod_dim = int(single_mod_dim)
|
|
type_dims = [img_attn_dim, txt_attn_dim, img_mlp_dim, txt_mlp_dim, img_mod_dim, txt_mod_dim, single_dim, single_mod_dim]
|
|
if all([d is None for d in type_dims]):
|
|
type_dims = None
|
|
|
|
# in_dims [img, time, vector, guidance, txt]
|
|
in_dims = kwargs.get("in_dims", None)
|
|
if in_dims is not None:
|
|
in_dims = in_dims.strip()
|
|
if in_dims.startswith("[") and in_dims.endswith("]"):
|
|
in_dims = in_dims[1:-1]
|
|
in_dims = [int(d) for d in in_dims.split(",")] # is it better to use ast.literal_eval?
|
|
assert len(in_dims) == 5, f"invalid in_dims: {in_dims}, must be 5 dimensions (img, time, vector, guidance, txt)"
|
|
|
|
# double/single train blocks
|
|
def parse_block_selection(selection: str, total_blocks: int) -> List[bool]:
|
|
"""
|
|
Parse a block selection string and return a list of booleans.
|
|
|
|
Args:
|
|
selection (str): A string specifying which blocks to select.
|
|
total_blocks (int): The total number of blocks available.
|
|
|
|
Returns:
|
|
List[bool]: A list of booleans indicating which blocks are selected.
|
|
"""
|
|
if selection == "all":
|
|
return [True] * total_blocks
|
|
if selection == "none" or selection == "":
|
|
return [False] * total_blocks
|
|
|
|
selected = [False] * total_blocks
|
|
ranges = selection.split(",")
|
|
|
|
for r in ranges:
|
|
if "-" in r:
|
|
start, end = map(str.strip, r.split("-"))
|
|
start = int(start)
|
|
end = int(end)
|
|
assert 0 <= start < total_blocks, f"invalid start index: {start}"
|
|
assert 0 <= end < total_blocks, f"invalid end index: {end}"
|
|
assert start <= end, f"invalid range: {start}-{end}"
|
|
for i in range(start, end + 1):
|
|
selected[i] = True
|
|
else:
|
|
index = int(r)
|
|
assert 0 <= index < total_blocks, f"invalid index: {index}"
|
|
selected[index] = True
|
|
|
|
return selected
|
|
|
|
train_double_block_indices = kwargs.get("train_double_block_indices", None)
|
|
train_single_block_indices = kwargs.get("train_single_block_indices", None)
|
|
if train_double_block_indices is not None:
|
|
train_double_block_indices = parse_block_selection(train_double_block_indices, NUM_DOUBLE_BLOCKS)
|
|
if train_single_block_indices is not None:
|
|
train_single_block_indices = parse_block_selection(train_single_block_indices, NUM_SINGLE_BLOCKS)
|
|
|
|
# rank/module dropout
|
|
rank_dropout = kwargs.get("rank_dropout", None)
|
|
if rank_dropout is not None:
|
|
rank_dropout = float(rank_dropout)
|
|
module_dropout = kwargs.get("module_dropout", None)
|
|
if module_dropout is not None:
|
|
module_dropout = float(module_dropout)
|
|
|
|
# single or double blocks
|
|
train_blocks = kwargs.get("train_blocks", None) # None (default), "all" (same as None), "single", "double"
|
|
if train_blocks is not None:
|
|
assert train_blocks in ["all", "single", "double"], f"invalid train_blocks: {train_blocks}"
|
|
|
|
# split qkv
|
|
split_qkv = kwargs.get("split_qkv", False)
|
|
if split_qkv is not None:
|
|
split_qkv = True if split_qkv == "True" else False
|
|
|
|
# train T5XXL
|
|
train_t5xxl = kwargs.get("train_t5xxl", False)
|
|
if train_t5xxl is not None:
|
|
train_t5xxl = True if train_t5xxl == "True" else False
|
|
|
|
# verbose
|
|
verbose = kwargs.get("verbose", False)
|
|
if verbose is not None:
|
|
verbose = True if verbose == "True" else False
|
|
|
|
# すごく引数が多いな ( ^ω^)･･･
|
|
network = LoRANetwork(
|
|
text_encoders,
|
|
flux,
|
|
multiplier=multiplier,
|
|
lora_dim=network_dim,
|
|
alpha=network_alpha,
|
|
dropout=neuron_dropout,
|
|
rank_dropout=rank_dropout,
|
|
module_dropout=module_dropout,
|
|
conv_lora_dim=conv_dim,
|
|
conv_alpha=conv_alpha,
|
|
train_blocks=train_blocks,
|
|
split_qkv=split_qkv,
|
|
train_t5xxl=train_t5xxl,
|
|
type_dims=type_dims,
|
|
in_dims=in_dims,
|
|
train_double_block_indices=train_double_block_indices,
|
|
train_single_block_indices=train_single_block_indices,
|
|
verbose=verbose,
|
|
)
|
|
|
|
loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
|
|
loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
|
|
loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
|
|
loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
|
|
loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
|
|
loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
|
|
if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
|
|
network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
|
|
|
|
return network
|
|
|
|
|
|
# Create network from weights for inference, weights are not loaded here (because can be merged)
|
|
def create_network_from_weights(multiplier, file, ae, text_encoders, flux, weights_sd=None, for_inference=False, **kwargs):
|
|
# if unet is an instance of SdxlUNet2DConditionModel or subclass, set is_sdxl to True
|
|
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, and train t5xxl
|
|
modules_dim = {}
|
|
modules_alpha = {}
|
|
train_t5xxl = None
|
|
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
|
|
# logger.info(lora_name, value.size(), dim)
|
|
|
|
if train_t5xxl is None or train_t5xxl is False:
|
|
train_t5xxl = "lora_te3" in lora_name
|
|
|
|
if train_t5xxl is None:
|
|
train_t5xxl = False
|
|
|
|
# # split qkv
|
|
# double_qkv_rank = None
|
|
# single_qkv_rank = None
|
|
# rank = None
|
|
# for lora_name, dim in modules_dim.items():
|
|
# if "double" in lora_name and "qkv" in lora_name:
|
|
# double_qkv_rank = dim
|
|
# elif "single" in lora_name and "linear1" in lora_name:
|
|
# single_qkv_rank = dim
|
|
# elif rank is None:
|
|
# rank = dim
|
|
# if double_qkv_rank is not None and single_qkv_rank is not None and rank is not None:
|
|
# break
|
|
# split_qkv = (double_qkv_rank is not None and double_qkv_rank != rank) or (
|
|
# single_qkv_rank is not None and single_qkv_rank != rank
|
|
# )
|
|
split_qkv = False # split_qkv is not needed to care, because state_dict is qkv combined
|
|
|
|
module_class = LoRAInfModule if for_inference else LoRAModule
|
|
|
|
network = LoRANetwork(
|
|
text_encoders,
|
|
flux,
|
|
multiplier=multiplier,
|
|
modules_dim=modules_dim,
|
|
modules_alpha=modules_alpha,
|
|
module_class=module_class,
|
|
split_qkv=split_qkv,
|
|
train_t5xxl=train_t5xxl,
|
|
)
|
|
return network, weights_sd
|
|
|
|
|
|
class LoRANetwork(torch.nn.Module):
|
|
# FLUX_TARGET_REPLACE_MODULE = ["DoubleStreamBlock", "SingleStreamBlock"]
|
|
FLUX_TARGET_REPLACE_MODULE_DOUBLE = ["DoubleStreamBlock"]
|
|
FLUX_TARGET_REPLACE_MODULE_SINGLE = ["SingleStreamBlock"]
|
|
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP", "T5Attention", "T5DenseGatedActDense"]
|
|
LORA_PREFIX_FLUX = "lora_unet" # make ComfyUI compatible
|
|
LORA_PREFIX_TEXT_ENCODER_CLIP = "lora_te1"
|
|
LORA_PREFIX_TEXT_ENCODER_T5 = "lora_te3" # make ComfyUI compatible
|
|
|
|
def __init__(
|
|
self,
|
|
text_encoders: Union[List[CLIPTextModel], CLIPTextModel],
|
|
unet,
|
|
multiplier: float = 1.0,
|
|
lora_dim: int = 4,
|
|
alpha: float = 1,
|
|
dropout: Optional[float] = None,
|
|
rank_dropout: Optional[float] = None,
|
|
module_dropout: Optional[float] = None,
|
|
conv_lora_dim: Optional[int] = None,
|
|
conv_alpha: Optional[float] = None,
|
|
module_class: Type[object] = LoRAModule,
|
|
modules_dim: Optional[Dict[str, int]] = None,
|
|
modules_alpha: Optional[Dict[str, int]] = None,
|
|
train_blocks: Optional[str] = None,
|
|
split_qkv: bool = False,
|
|
train_t5xxl: bool = False,
|
|
type_dims: Optional[List[int]] = None,
|
|
in_dims: Optional[List[int]] = None,
|
|
train_double_block_indices: Optional[List[bool]] = None,
|
|
train_single_block_indices: Optional[List[bool]] = None,
|
|
verbose: Optional[bool] = False,
|
|
) -> 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
|
|
self.dropout = dropout
|
|
self.rank_dropout = rank_dropout
|
|
self.module_dropout = module_dropout
|
|
self.train_blocks = train_blocks if train_blocks is not None else "all"
|
|
self.split_qkv = split_qkv
|
|
self.train_t5xxl = train_t5xxl
|
|
|
|
self.type_dims = type_dims
|
|
self.in_dims = in_dims
|
|
self.train_double_block_indices = train_double_block_indices
|
|
self.train_single_block_indices = train_single_block_indices
|
|
|
|
self.loraplus_lr_ratio = None
|
|
self.loraplus_unet_lr_ratio = None
|
|
self.loraplus_text_encoder_lr_ratio = None
|
|
|
|
if modules_dim is not None:
|
|
logger.info(f"create LoRA network from weights")
|
|
self.in_dims = [0] * 5 # create in_dims
|
|
# verbose = True
|
|
else:
|
|
logger.info(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
|
|
logger.info(
|
|
f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}"
|
|
)
|
|
# if self.conv_lora_dim is not None:
|
|
# logger.info(
|
|
# f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}"
|
|
# )
|
|
if self.split_qkv:
|
|
logger.info(f"split qkv for LoRA")
|
|
if self.train_blocks is not None:
|
|
logger.info(f"train {self.train_blocks} blocks only")
|
|
if train_t5xxl:
|
|
logger.info(f"train T5XXL as well")
|
|
|
|
# create module instances
|
|
def create_modules(
|
|
is_flux: bool,
|
|
text_encoder_idx: Optional[int],
|
|
root_module: torch.nn.Module,
|
|
target_replace_modules: List[str],
|
|
filter: Optional[str] = None,
|
|
default_dim: Optional[int] = None,
|
|
) -> List[LoRAModule]:
|
|
prefix = (
|
|
self.LORA_PREFIX_FLUX
|
|
if is_flux
|
|
else (self.LORA_PREFIX_TEXT_ENCODER_CLIP if text_encoder_idx == 0 else self.LORA_PREFIX_TEXT_ENCODER_T5)
|
|
)
|
|
|
|
loras = []
|
|
skipped = []
|
|
for name, module in root_module.named_modules():
|
|
if target_replace_modules is None or module.__class__.__name__ in target_replace_modules:
|
|
if target_replace_modules is None: # dirty hack for all modules
|
|
module = root_module # search all modules
|
|
|
|
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 + "." if name else "") + child_name
|
|
lora_name = lora_name.replace(".", "_")
|
|
|
|
if filter is not None and not filter in lora_name:
|
|
continue
|
|
|
|
dim = None
|
|
alpha = None
|
|
|
|
if modules_dim is not None:
|
|
# モジュール指定あり
|
|
if lora_name in modules_dim:
|
|
dim = modules_dim[lora_name]
|
|
alpha = modules_alpha[lora_name]
|
|
else:
|
|
# 通常、すべて対象とする
|
|
if is_linear or is_conv2d_1x1:
|
|
dim = default_dim if default_dim is not None else self.lora_dim
|
|
alpha = self.alpha
|
|
|
|
if is_flux and type_dims is not None:
|
|
identifier = [
|
|
("img_attn",),
|
|
("txt_attn",),
|
|
("img_mlp",),
|
|
("txt_mlp",),
|
|
("img_mod",),
|
|
("txt_mod",),
|
|
("single_blocks", "linear"),
|
|
("modulation",),
|
|
]
|
|
for i, d in enumerate(type_dims):
|
|
if d is not None and all([id in lora_name for id in identifier[i]]):
|
|
dim = d # may be 0 for skip
|
|
break
|
|
|
|
if (
|
|
is_flux
|
|
and dim
|
|
and (
|
|
self.train_double_block_indices is not None
|
|
or self.train_single_block_indices is not None
|
|
)
|
|
and ("double" in lora_name or "single" in lora_name)
|
|
):
|
|
# "lora_unet_double_blocks_0_..." or "lora_unet_single_blocks_0_..."
|
|
block_index = int(lora_name.split("_")[4]) # bit dirty
|
|
if (
|
|
"double" in lora_name
|
|
and self.train_double_block_indices is not None
|
|
and not self.train_double_block_indices[block_index]
|
|
):
|
|
dim = 0
|
|
elif (
|
|
"single" in lora_name
|
|
and self.train_single_block_indices is not None
|
|
and not self.train_single_block_indices[block_index]
|
|
):
|
|
dim = 0
|
|
|
|
elif self.conv_lora_dim is not None:
|
|
dim = self.conv_lora_dim
|
|
alpha = self.conv_alpha
|
|
|
|
if dim is None or dim == 0:
|
|
# skipした情報を出力
|
|
if is_linear or is_conv2d_1x1 or (self.conv_lora_dim is not None):
|
|
skipped.append(lora_name)
|
|
continue
|
|
|
|
# qkv split
|
|
split_dims = None
|
|
if is_flux and split_qkv:
|
|
if "double" in lora_name and "qkv" in lora_name:
|
|
split_dims = [3072] * 3
|
|
elif "single" in lora_name and "linear1" in lora_name:
|
|
split_dims = [3072] * 3 + [12288]
|
|
|
|
lora = module_class(
|
|
lora_name,
|
|
child_module,
|
|
self.multiplier,
|
|
dim,
|
|
alpha,
|
|
dropout=dropout,
|
|
rank_dropout=rank_dropout,
|
|
module_dropout=module_dropout,
|
|
split_dims=split_dims,
|
|
)
|
|
loras.append(lora)
|
|
|
|
if target_replace_modules is None:
|
|
break # all modules are searched
|
|
return loras, skipped
|
|
|
|
# create LoRA for text encoder
|
|
# 毎回すべてのモジュールを作るのは無駄なので要検討
|
|
self.text_encoder_loras: List[Union[LoRAModule, LoRAInfModule]] = []
|
|
skipped_te = []
|
|
for i, text_encoder in enumerate(text_encoders):
|
|
index = i
|
|
if not train_t5xxl and index > 0: # 0: CLIP, 1: T5XXL, so we skip T5XXL if train_t5xxl is False
|
|
break
|
|
|
|
logger.info(f"create LoRA for Text Encoder {index+1}:")
|
|
|
|
text_encoder_loras, skipped = create_modules(False, index, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
|
|
logger.info(f"create LoRA for Text Encoder {index+1}: {len(text_encoder_loras)} modules.")
|
|
self.text_encoder_loras.extend(text_encoder_loras)
|
|
skipped_te += skipped
|
|
|
|
# create LoRA for U-Net
|
|
if self.train_blocks == "all":
|
|
target_replace_modules = LoRANetwork.FLUX_TARGET_REPLACE_MODULE_DOUBLE + LoRANetwork.FLUX_TARGET_REPLACE_MODULE_SINGLE
|
|
elif self.train_blocks == "single":
|
|
target_replace_modules = LoRANetwork.FLUX_TARGET_REPLACE_MODULE_SINGLE
|
|
elif self.train_blocks == "double":
|
|
target_replace_modules = LoRANetwork.FLUX_TARGET_REPLACE_MODULE_DOUBLE
|
|
|
|
self.unet_loras: List[Union[LoRAModule, LoRAInfModule]]
|
|
self.unet_loras, skipped_un = create_modules(True, None, unet, target_replace_modules)
|
|
|
|
# img, time, vector, guidance, txt
|
|
if self.in_dims:
|
|
for filter, in_dim in zip(["_img_in", "_time_in", "_vector_in", "_guidance_in", "_txt_in"], self.in_dims):
|
|
loras, _ = create_modules(True, None, unet, None, filter=filter, default_dim=in_dim)
|
|
self.unet_loras.extend(loras)
|
|
|
|
logger.info(f"create LoRA for FLUX {self.train_blocks} blocks: {len(self.unet_loras)} modules.")
|
|
if verbose:
|
|
for lora in self.unet_loras:
|
|
logger.info(f"\t{lora.lora_name:50} {lora.lora_dim}, {lora.alpha}")
|
|
|
|
skipped = skipped_te + skipped_un
|
|
if verbose and len(skipped) > 0:
|
|
logger.warning(
|
|
f"because dim (rank) is 0, {len(skipped)} LoRA modules are skipped / dim (rank)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:
|
|
logger.info(f"\t{name}")
|
|
|
|
# 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 set_multiplier(self, multiplier):
|
|
self.multiplier = multiplier
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.multiplier = self.multiplier
|
|
|
|
def set_enabled(self, is_enabled):
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.enabled = is_enabled
|
|
|
|
def load_weights(self, file):
|
|
if os.path.splitext(file)[1] == ".safetensors":
|
|
from safetensors.torch import load_file
|
|
|
|
weights_sd = load_file(file)
|
|
else:
|
|
weights_sd = torch.load(file, map_location="cpu")
|
|
|
|
info = self.load_state_dict(weights_sd, False)
|
|
return info
|
|
|
|
def load_state_dict(self, state_dict, strict=True):
|
|
# override to convert original weight to split qkv
|
|
if not self.split_qkv:
|
|
return super().load_state_dict(state_dict, strict)
|
|
|
|
# split qkv
|
|
for key in list(state_dict.keys()):
|
|
if "double" in key and "qkv" in key:
|
|
split_dims = [3072] * 3
|
|
elif "single" in key and "linear1" in key:
|
|
split_dims = [3072] * 3 + [12288]
|
|
else:
|
|
continue
|
|
|
|
weight = state_dict[key]
|
|
lora_name = key.split(".")[0]
|
|
if "lora_down" in key and "weight" in key:
|
|
# dense weight (rank*3, in_dim)
|
|
split_weight = torch.chunk(weight, len(split_dims), dim=0)
|
|
for i, split_w in enumerate(split_weight):
|
|
state_dict[f"{lora_name}.lora_down.{i}.weight"] = split_w
|
|
|
|
del state_dict[key]
|
|
# print(f"split {key}: {weight.shape} to {[w.shape for w in split_weight]}")
|
|
elif "lora_up" in key and "weight" in key:
|
|
# sparse weight (out_dim=sum(split_dims), rank*3)
|
|
rank = weight.size(1) // len(split_dims)
|
|
i = 0
|
|
for j in range(len(split_dims)):
|
|
state_dict[f"{lora_name}.lora_up.{j}.weight"] = weight[i : i + split_dims[j], j * rank : (j + 1) * rank]
|
|
i += split_dims[j]
|
|
del state_dict[key]
|
|
|
|
# # check is sparse
|
|
# i = 0
|
|
# is_zero = True
|
|
# for j in range(len(split_dims)):
|
|
# for k in range(len(split_dims)):
|
|
# if j == k:
|
|
# continue
|
|
# is_zero = is_zero and torch.all(weight[i : i + split_dims[j], k * rank : (k + 1) * rank] == 0)
|
|
# i += split_dims[j]
|
|
# if not is_zero:
|
|
# logger.warning(f"weight is not sparse: {key}")
|
|
# else:
|
|
# logger.info(f"weight is sparse: {key}")
|
|
|
|
# print(
|
|
# f"split {key}: {weight.shape} to {[state_dict[k].shape for k in [f'{lora_name}.lora_up.{j}.weight' for j in range(len(split_dims))]]}"
|
|
# )
|
|
|
|
# alpha is unchanged
|
|
|
|
return super().load_state_dict(state_dict, strict)
|
|
|
|
def state_dict(self, destination=None, prefix="", keep_vars=False):
|
|
if not self.split_qkv:
|
|
return super().state_dict(destination, prefix, keep_vars)
|
|
|
|
# merge qkv
|
|
state_dict = super().state_dict(destination, prefix, keep_vars)
|
|
new_state_dict = {}
|
|
for key in list(state_dict.keys()):
|
|
if "double" in key and "qkv" in key:
|
|
split_dims = [3072] * 3
|
|
elif "single" in key and "linear1" in key:
|
|
split_dims = [3072] * 3 + [12288]
|
|
else:
|
|
new_state_dict[key] = state_dict[key]
|
|
continue
|
|
|
|
if key not in state_dict:
|
|
continue # already merged
|
|
|
|
lora_name = key.split(".")[0]
|
|
|
|
# (rank, in_dim) * 3
|
|
down_weights = [state_dict.pop(f"{lora_name}.lora_down.{i}.weight") for i in range(len(split_dims))]
|
|
# (split dim, rank) * 3
|
|
up_weights = [state_dict.pop(f"{lora_name}.lora_up.{i}.weight") for i in range(len(split_dims))]
|
|
|
|
alpha = state_dict.pop(f"{lora_name}.alpha")
|
|
|
|
# merge down weight
|
|
down_weight = torch.cat(down_weights, dim=0) # (rank, split_dim) * 3 -> (rank*3, sum of split_dim)
|
|
|
|
# merge up weight (sum of split_dim, rank*3)
|
|
rank = up_weights[0].size(1)
|
|
up_weight = torch.zeros((sum(split_dims), down_weight.size(0)), device=down_weight.device, dtype=down_weight.dtype)
|
|
i = 0
|
|
for j in range(len(split_dims)):
|
|
up_weight[i : i + split_dims[j], j * rank : (j + 1) * rank] = up_weights[j]
|
|
i += split_dims[j]
|
|
|
|
new_state_dict[f"{lora_name}.lora_down.weight"] = down_weight
|
|
new_state_dict[f"{lora_name}.lora_up.weight"] = up_weight
|
|
new_state_dict[f"{lora_name}.alpha"] = alpha
|
|
|
|
# print(
|
|
# f"merged {lora_name}: {lora_name}, {[w.shape for w in down_weights]}, {[w.shape for w in up_weights]} to {down_weight.shape}, {up_weight.shape}"
|
|
# )
|
|
print(f"new key: {lora_name}.lora_down.weight, {lora_name}.lora_up.weight, {lora_name}.alpha")
|
|
|
|
return new_state_dict
|
|
|
|
def apply_to(self, text_encoders, flux, apply_text_encoder=True, apply_unet=True):
|
|
if apply_text_encoder:
|
|
logger.info(f"enable LoRA for text encoder: {len(self.text_encoder_loras)} modules")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
logger.info(f"enable LoRA for U-Net: {len(self.unet_loras)} modules")
|
|
else:
|
|
self.unet_loras = []
|
|
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.apply_to()
|
|
self.add_module(lora.lora_name, lora)
|
|
|
|
# マージできるかどうかを返す
|
|
def is_mergeable(self):
|
|
return True
|
|
|
|
# TODO refactor to common function with apply_to
|
|
def merge_to(self, text_encoders, flux, weights_sd, dtype=None, device=None):
|
|
apply_text_encoder = apply_unet = False
|
|
for key in weights_sd.keys():
|
|
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER_CLIP) or key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER_T5):
|
|
apply_text_encoder = True
|
|
elif key.startswith(LoRANetwork.LORA_PREFIX_FLUX):
|
|
apply_unet = True
|
|
|
|
if apply_text_encoder:
|
|
logger.info("enable LoRA for text encoder")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
logger.info("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 weights_sd.keys():
|
|
if key.startswith(lora.lora_name):
|
|
sd_for_lora[key[len(lora.lora_name) + 1 :]] = weights_sd[key]
|
|
lora.merge_to(sd_for_lora, dtype, device)
|
|
|
|
logger.info(f"weights are merged")
|
|
|
|
def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
|
|
self.loraplus_lr_ratio = loraplus_lr_ratio
|
|
self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
|
|
self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
|
|
|
|
logger.info(f"LoRA+ UNet LR Ratio: {self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio}")
|
|
logger.info(f"LoRA+ Text Encoder LR Ratio: {self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio}")
|
|
|
|
def prepare_optimizer_params_with_multiple_te_lrs(self, text_encoder_lr, unet_lr, default_lr):
|
|
# make sure text_encoder_lr as list of two elements
|
|
# if float, use the same value for both text encoders
|
|
if text_encoder_lr is None or (isinstance(text_encoder_lr, list) and len(text_encoder_lr) == 0):
|
|
text_encoder_lr = [default_lr, default_lr]
|
|
elif isinstance(text_encoder_lr, float) or isinstance(text_encoder_lr, int):
|
|
text_encoder_lr = [float(text_encoder_lr), float(text_encoder_lr)]
|
|
elif len(text_encoder_lr) == 1:
|
|
text_encoder_lr = [text_encoder_lr[0], text_encoder_lr[0]]
|
|
|
|
self.requires_grad_(True)
|
|
|
|
all_params = []
|
|
lr_descriptions = []
|
|
|
|
def assemble_params(loras, lr, loraplus_ratio):
|
|
param_groups = {"lora": {}, "plus": {}}
|
|
for lora in loras:
|
|
for name, param in lora.named_parameters():
|
|
if loraplus_ratio is not None and "lora_up" in name:
|
|
param_groups["plus"][f"{lora.lora_name}.{name}"] = param
|
|
else:
|
|
param_groups["lora"][f"{lora.lora_name}.{name}"] = param
|
|
|
|
params = []
|
|
descriptions = []
|
|
for key in param_groups.keys():
|
|
param_data = {"params": param_groups[key].values()}
|
|
|
|
if len(param_data["params"]) == 0:
|
|
continue
|
|
|
|
if lr is not None:
|
|
if key == "plus":
|
|
param_data["lr"] = lr * loraplus_ratio
|
|
else:
|
|
param_data["lr"] = lr
|
|
|
|
if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
|
|
logger.info("NO LR skipping!")
|
|
continue
|
|
|
|
params.append(param_data)
|
|
descriptions.append("plus" if key == "plus" else "")
|
|
|
|
return params, descriptions
|
|
|
|
if self.text_encoder_loras:
|
|
loraplus_lr_ratio = self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio
|
|
|
|
# split text encoder loras for te1 and te3
|
|
te1_loras = [lora for lora in self.text_encoder_loras if lora.lora_name.startswith(self.LORA_PREFIX_TEXT_ENCODER_CLIP)]
|
|
te3_loras = [lora for lora in self.text_encoder_loras if lora.lora_name.startswith(self.LORA_PREFIX_TEXT_ENCODER_T5)]
|
|
if len(te1_loras) > 0:
|
|
logger.info(f"Text Encoder 1 (CLIP-L): {len(te1_loras)} modules, LR {text_encoder_lr[0]}")
|
|
params, descriptions = assemble_params(te1_loras, text_encoder_lr[0], loraplus_lr_ratio)
|
|
all_params.extend(params)
|
|
lr_descriptions.extend(["textencoder 1 " + (" " + d if d else "") for d in descriptions])
|
|
if len(te3_loras) > 0:
|
|
logger.info(f"Text Encoder 2 (T5XXL): {len(te3_loras)} modules, LR {text_encoder_lr[1]}")
|
|
params, descriptions = assemble_params(te3_loras, text_encoder_lr[1], loraplus_lr_ratio)
|
|
all_params.extend(params)
|
|
lr_descriptions.extend(["textencoder 2 " + (" " + d if d else "") for d in descriptions])
|
|
|
|
if self.unet_loras:
|
|
params, descriptions = assemble_params(
|
|
self.unet_loras,
|
|
unet_lr if unet_lr is not None else default_lr,
|
|
self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
|
|
)
|
|
all_params.extend(params)
|
|
lr_descriptions.extend(["unet" + (" " + d if d else "") for d in descriptions])
|
|
|
|
return all_params, lr_descriptions
|
|
|
|
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
|
|
from library import train_util
|
|
|
|
# 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)
|
|
|
|
def backup_weights(self):
|
|
# 重みのバックアップを行う
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
if not hasattr(org_module, "_lora_org_weight"):
|
|
sd = org_module.state_dict()
|
|
org_module._lora_org_weight = sd["weight"].detach().clone()
|
|
org_module._lora_restored = True
|
|
|
|
def restore_weights(self):
|
|
# 重みのリストアを行う
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
if not org_module._lora_restored:
|
|
sd = org_module.state_dict()
|
|
sd["weight"] = org_module._lora_org_weight
|
|
org_module.load_state_dict(sd)
|
|
org_module._lora_restored = True
|
|
|
|
def pre_calculation(self):
|
|
# 事前計算を行う
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
sd = org_module.state_dict()
|
|
|
|
org_weight = sd["weight"]
|
|
lora_weight = lora.get_weight().to(org_weight.device, dtype=org_weight.dtype)
|
|
sd["weight"] = org_weight + lora_weight
|
|
assert sd["weight"].shape == org_weight.shape
|
|
org_module.load_state_dict(sd)
|
|
|
|
org_module._lora_restored = False
|
|
lora.enabled = False
|
|
|
|
def apply_max_norm_regularization(self, max_norm_value, device):
|
|
downkeys = []
|
|
upkeys = []
|
|
alphakeys = []
|
|
norms = []
|
|
keys_scaled = 0
|
|
|
|
state_dict = self.state_dict()
|
|
for key in state_dict.keys():
|
|
if "lora_down" in key and "weight" in key:
|
|
downkeys.append(key)
|
|
upkeys.append(key.replace("lora_down", "lora_up"))
|
|
alphakeys.append(key.replace("lora_down.weight", "alpha"))
|
|
|
|
for i in range(len(downkeys)):
|
|
down = state_dict[downkeys[i]].to(device)
|
|
up = state_dict[upkeys[i]].to(device)
|
|
alpha = state_dict[alphakeys[i]].to(device)
|
|
dim = down.shape[0]
|
|
scale = alpha / dim
|
|
|
|
if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
|
|
updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
|
|
elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
|
|
updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
|
|
else:
|
|
updown = up @ down
|
|
|
|
updown *= scale
|
|
|
|
norm = updown.norm().clamp(min=max_norm_value / 2)
|
|
desired = torch.clamp(norm, max=max_norm_value)
|
|
ratio = desired.cpu() / norm.cpu()
|
|
sqrt_ratio = ratio**0.5
|
|
if ratio != 1:
|
|
keys_scaled += 1
|
|
state_dict[upkeys[i]] *= sqrt_ratio
|
|
state_dict[downkeys[i]] *= sqrt_ratio
|
|
scalednorm = updown.norm() * ratio
|
|
norms.append(scalednorm.item())
|
|
|
|
return keys_scaled, sum(norms) / len(norms), max(norms)
|