File size: 1,300 Bytes
0f8411f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | from share import *
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from tutorial_dataset import MyDataset
from cldm.logger import ImageLogger
from cldm.model import create_model, load_state_dict
from lightning.pytorch.callbacks import ModelCheckpoint
if __name__ == '__main__':
# Configs
resume_path = './models/control_sd15_ini.ckpt'
batch_size = 4
logger_freq = 300
learning_rate = 1e-5
sd_locked = True
only_mid_control = False
# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs.
model = create_model('./models/cldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict(resume_path, location='cpu'))
model.learning_rate = learning_rate
model.sd_locked = sd_locked
model.only_mid_control = only_mid_control
# Misc
dataset = MyDataset()
dataloader = DataLoader(dataset, num_workers=0, batch_size=batch_size, shuffle=True)
logger = ImageLogger(batch_frequency=logger_freq)
checkpoint_callback = ModelCheckpoint(dirpath="./checkpoints", filename = "cn_{epoch:02d}", save_top_k=-1, monitor="val_loss")
trainer = pl.Trainer(gpus=2, precision=32, callbacks=[logger, checkpoint_callback])
# Train!
trainer.fit(model, dataloader)
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