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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 | |
# Configs | |
resume_path = './models/control_sd21_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_v21.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) | |
trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger]) | |
# Train! | |
trainer.fit(model, dataloader) | |