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Running
on
Zero
| 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 | |