_base_ = ['../PixArt_xl2_internal.py']
data_root = 'data'
image_list_json = ['data_info.json',]

data = dict(type='InternalData', root='InternData', image_list_json=image_list_json, transform='default_train', load_vae_feat=True)
image_size = 256

# model setting
window_block_indexes=[]
window_size=0
use_rel_pos=False
model = 'PixArt_XL_2'
fp32_attention = True
load_from = None
vae_pretrained = "output/pretrained_models/sd-vae-ft-ema"
# training setting
eval_sampling_steps = 200

num_workers=10
train_batch_size = 176 # 32  # max 96 for PixArt-L/4 when grad_checkpoint
num_epochs = 200 # 3
gradient_accumulation_steps = 1
grad_checkpointing = True
gradient_clip = 0.01
optimizer = dict(type='AdamW', lr=2e-5, weight_decay=3e-2, eps=1e-10)
lr_schedule_args = dict(num_warmup_steps=1000)

log_interval = 20
save_model_epochs=5
work_dir = 'output/debug'
