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| _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 = 1024 | |
| # model setting | |
| model = 'PixArt_XL_2' | |
| fp32_attention = True | |
| load_from = None | |
| vae_pretrained = "output/pretrained_models/sd-vae-ft-ema" | |
| pe_interpolation = 2.0 | |
| # training setting | |
| num_workers=10 | |
| train_batch_size = 2 # 32 | |
| 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) | |
| eval_sampling_steps = 200 | |
| log_interval = 20 | |
| save_model_epochs=1 | |
| save_model_steps=2000 | |
| work_dir = 'output/debug' | |