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| _base_ = ['../PixArt_xl2_internal.py'] | |
| data_root = 'pixart-sigma-toy-dataset' | |
| image_list_json = ['data_info.json'] | |
| data = dict( | |
| type='InternalDataMSSigma', root='InternData', image_list_json=image_list_json, transform='default_train', | |
| load_vae_feat=False, load_t5_feat=False | |
| ) | |
| image_size = 1024 | |
| # model setting | |
| model = 'PixArtMS_XL_2' | |
| mixed_precision = 'fp16' # ['fp16', 'fp32', 'bf16'] | |
| fp32_attention = True | |
| load_from = None | |
| resume_from = None | |
| vae_pretrained = "output/pretrained_models/pixart_sigma_sdxlvae_T5_diffusers/vae" # sdxl vae | |
| aspect_ratio_type = 'ASPECT_RATIO_1024' | |
| multi_scale = True # if use multiscale dataset model training | |
| pe_interpolation = 2.0 | |
| # training setting | |
| num_workers = 10 | |
| train_batch_size = 2 # 3 for w.o feature extraction; 12 for feature extraction | |
| num_epochs = 2 # 3 | |
| gradient_accumulation_steps = 1 | |
| grad_checkpointing = True | |
| gradient_clip = 0.01 | |
| optimizer = dict(type='CAMEWrapper', lr=2e-5, weight_decay=0.0, betas=(0.9, 0.999, 0.9999), eps=(1e-30, 1e-16)) | |
| lr_schedule_args = dict(num_warmup_steps=1000) | |
| eval_sampling_steps = 500 | |
| visualize = True | |
| log_interval = 20 | |
| save_model_epochs = 1 | |
| save_model_steps = 1000 | |
| work_dir = 'output/debug' | |
| # pixart-sigma | |
| scale_factor = 0.13025 | |
| real_prompt_ratio = 0.5 | |
| model_max_length = 300 | |
| class_dropout_prob = 0.1 | |
| qk_norm = False | |
| skip_step = 0 # skip steps during data loading | |