text stringlengths 1 93.6k |
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drop_last=True,
|
pin_memory=True,
|
num_workers=NUM_WORKERS,
|
persistent_workers=bool(NUM_WORKERS),
|
)
|
# loader warmup
|
next(iter(loader))
|
vae: AutoencoderKL = AutoencoderKL.from_pretrained(BASE_MODEL, subfolder=SUB_FOLDER)
|
if GRAD_CKPT:
|
vae.enable_gradient_checkpointing()
|
vae.encoder = torch.compile(vae.encoder)
|
vae.get_last_layer = lambda: vae.decoder.conv_out.weight
|
trainer_module = LatentTrainer(
|
vae=vae,
|
recon_loss=ReconLoss(
|
loss_type=LOSS_TYPE,
|
lpips_net=LPIPS_NET,
|
convnext_type=ConvNextType.TINY if USE_CONVNEXT else None,
|
convnext_kwargs={
|
"feature_layers": [10, 12, 14],
|
"use_gram": False,
|
"input_range": (-1, 1),
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"device": "cuda",
|
},
|
),
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adv_loss=AdvLoss(start_iter=ADV_START_ITER, n_layers=5),
|
img_deprocess=deprocess,
|
log_interval=100,
|
transform_prob=0.5,
|
latent_transform=RandomAffineTransform(
|
rotate_range=(-180, 180),
|
scale_range=(0.8, 1.2),
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shear_range=((-10, 10), (-5, 5)),
|
translate_range=(0.1, 0.1),
|
method="random",
|
), # Thanks AmericanPresidentJimmyCarter
|
loss_weights={
|
"recon": 1.0,
|
"adv": 0.25,
|
"kl": 5e-8,
|
},
|
name="EQ-SDXL-VAE-random-affine",
|
lr=LR,
|
lr_disc=DLR,
|
optimizer="torch.optim.AdamW",
|
opt_configs={"betas": (0.9, 0.98)},
|
lr_sch_configs={
|
"lr": {
|
"end": len(loader) * EPOCHS // GRAD_ACC,
|
"value": 1.0,
|
"min_value": 0.2,
|
"mode": "cosine",
|
"warmup": 0,
|
}
|
},
|
grad_acc=GRAD_ACC,
|
)
|
logger = WandbLogger(
|
project="HakuLatent",
|
name="EQ-SDXL-VAE-random-affine",
|
offline=True
|
)
|
trainer = pl.Trainer(
|
logger=logger,
|
devices=1,
|
max_epochs=EPOCHS,
|
precision="16-mixed",
|
callbacks=[
|
ModelCheckpoint(every_n_train_steps=1000),
|
LearningRateMonitor(logging_interval="step"),
|
],
|
log_every_n_steps=1,
|
)
|
trainer.fit(trainer_module, loader)
|
# <FILESEP>
|
from django.utils import encoding
|
from pandas import DataFrame
|
import pandas as pd
|
import argparse
|
import os
|
from pandas import ExcelWriter
|
parser = argparse.ArgumentParser(description='convert tweets text to excel format')
|
parser.add_argument('-i', '--infile', type=str,
|
help='input file (csv format).', required=True)
|
parser.add_argument('-o', '--outfile', type=str,
|
help='output file (excel format).', required=True)
|
parser.add_argument('-d', '--delimiter', type=str,
|
help='field delimiter.', required=True)
|
parser.add_argument('-l', '--lines-number', type=int,
|
help='number of lines per excel file', required=True)
|
# input - df: a Dataframe, chunkSize: the chunk size
|
# output - a list of DataFrame
|
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