Delete train_laion_face.py

train_laion_face.py

@@ -1,46 +0,0 @@
-from share import *
-
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader
-from laion_face_dataset import LaionDataset
-from cldm.logger import ImageLogger
-from cldm.model import create_model, load_state_dict
-
-
-# Configs
-resume_path = './models/controlnet_sd21_laion_face.ckpt'
-batch_size = 4
-logger_freq = 2500
-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
-
-
-# Save every so often:
-ckpt_callback = pl.callbacks.ModelCheckpoint(
-        dirpath="./checkpoints/",
-        filename="ckpt_controlnet_sd21_{epoch}_{step}_{loss}",
-        monitor='train/loss_simple_step',
-        save_top_k=5,
-        every_n_train_steps=5000,
-        save_last=True,
-)
-
-
-# Misc
-dataset = LaionDataset()
-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, ckpt_callback])
-
-
-# Train!
-trainer.fit(model, dataloader)