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db_models.py
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from sqlalchemy.orm import relationship, sessionmaker, declarative_base
from sqlalchemy import Column, Integer, String, Boolean, DateTime, ForeignKey, Integer, LargeBinary, Numeric
Base = declarative_base()
class LoRAModel(Base):
__tablename__ = "lora_models"
id = Column(Integer, primary_key=True)
filepath = Column(String)
filename = Column(String)
preview_image = Column(LargeBinary, nullable=True)
display_name = Column(String, nullable=True)
author = Column(String, nullable=True)
source = Column(String, nullable=True)
keywords = Column(String, nullable=True)
description = Column(String, nullable=True)
rating = Column(Integer, nullable=True)
tags = Column(String, nullable=True)
model_hash = Column(String, nullable=True)
legacy_hash = Column(String, nullable=True)
session_id = Column(Integer, nullable=True)
training_started_at = Column(DateTime, nullable=True)
output_name = Column(String, nullable=True)
learning_rate = Column(Numeric, nullable=True)
text_encoder_lr = Column(Numeric, nullable=True)
unet_lr = Column(Numeric, nullable=True)
num_train_images = Column(Integer, nullable=True)
num_reg_images = Column(Integer, nullable=True)
num_batches_per_epoch = Column(Integer, nullable=True)
num_epochs = Column(Integer, nullable=True)
epoch = Column(Integer, nullable=True)
batch_size_per_device = Column(Integer, nullable=True)
total_batch_size = Column(Integer, nullable=True)
gradient_checkpointing = Column(Boolean, nullable=True)
gradient_accumulation_steps = Column(Integer, nullable=True)
max_train_steps = Column(Integer, nullable=True)
lr_warmup_steps = Column(Integer, nullable=True)
lr_scheduler = Column(String, nullable=True)
network_module = Column(String, nullable=True)
network_dim = Column(Integer, nullable=True)
network_alpha = Column(Numeric, nullable=True)
mixed_precision = Column(Boolean, nullable=True)
full_fp16 = Column(Boolean, nullable=True)
v2 = Column(Boolean, nullable=True)
resolution = Column(String, nullable=True)
clip_skip = Column(Integer, nullable=True)
max_token_length = Column(Integer, nullable=True)
color_aug = Column(Boolean, nullable=True)
flip_aug = Column(Boolean, nullable=True)
random_crop = Column(Boolean, nullable=True)
shuffle_caption = Column(Boolean, nullable=True)
cache_latents = Column(Boolean, nullable=True)
enable_bucket = Column(Boolean, nullable=True)
min_bucket_reso = Column(Integer, nullable=True)
max_bucket_reso = Column(Integer, nullable=True)
seed = Column(Integer, nullable=True)
keep_tokens = Column(Boolean, nullable=True)
noise_offset = Column(Numeric, nullable=True)
dataset_dirs = Column(String, nullable=True)
reg_dataset_dirs = Column(String, nullable=True)
sd_model_name = Column(String, nullable=True)
sd_model_hash = Column(String, nullable=True)
sd_new_model_hash = Column(String, nullable=True)
sd_vae_name = Column(String, nullable=True)
sd_vae_hash = Column(String, nullable=True)
sd_new_vae_hash = Column(String, nullable=True)
vae_name = Column(String, nullable=True)
training_comment = Column(String, nullable=True)
bucket_info = Column(String, nullable=True)
sd_scripts_commit_hash = Column(String, nullable=True)