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on
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Running
on
Zero
File size: 2,204 Bytes
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data_root = '/data/data'
data = dict(type='TriplaneData', data_base_dir='triplane', data_json_file='/nas8/liuhongyu/HeadGallery_Data/data.json', model_names='configs/gan_model.yaml' )
image_size = 256 # the generated image resolution
train_batch_size = 32
eval_batch_size = 16
use_fsdp=False # if use FSDP mode
valid_num=0 # take as valid aspect-ratio when sample number >= valid_num
triplane_size = (256*4, 256)
# model setting
image_encoder_path = "/home/liuhongyu/code/IP-Adapter/models/image_encoder"
vae_triplane_config_path = "vae_model.yaml"
std_dir = '/nas8/liuhongyu/HeadGallery_Data/final_std.pt'
mean_dir = '/nas8/liuhongyu/HeadGallery_Data/final_mean.pt'
conditioning_params_dir = '/nas8/liuhongyu/HeadGallery_Data/conditioning_params.pkl'
gan_model_base_dir = '/nas8/liuhongyu/HeadGallery_Data/gan_models'
model = 'PixArt_XL_2'
aspect_ratio_type = None # base aspect ratio [ASPECT_RATIO_512 or ASPECT_RATIO_256]
multi_scale = False # if use multiscale dataset model training
lewei_scale = 1.0 # lewei_scale for positional embedding interpolation
# training setting
num_workers=4
train_sampling_steps = 1000
eval_sampling_steps = 250
model_max_length = 8
lora_rank = 4
num_epochs = 80
gradient_accumulation_steps = 1
grad_checkpointing = False
gradient_clip = 1.0
gc_step = 1
auto_lr = dict(rule='sqrt')
# we use different weight decay with the official implementation since it results better result
optimizer = dict(type='AdamW', lr=1e-4, weight_decay=3e-2, eps=1e-10)
lr_schedule = 'constant'
lr_schedule_args = dict(num_warmup_steps=500)
save_image_epochs = 1
save_model_epochs = 1
save_model_steps=1000000
sample_posterior = True
mixed_precision = 'fp16'
scale_factor = 0.3994218
ema_rate = 0.9999
tensorboard_mox_interval = 50
log_interval = 50
cfg_scale = 4
mask_type='null'
num_group_tokens=0
mask_loss_coef=0.
load_mask_index=False # load prepared mask_type index
# load model settings
vae_pretrained = "/cache/pretrained_models/sd-vae-ft-ema"
load_from = None
resume_from = dict(checkpoint=None, load_ema=False, resume_optimizer=True, resume_lr_scheduler=True)
snr_loss=False
# work dir settings
work_dir = '/cache/exps/'
s3_work_dir = None
seed = 43
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