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| data_root = '/data/data' | |
| data = dict(type='InternalData', root='images', image_list_json=['data_info.json'], transform='default_train', load_vae_feat=True, load_t5_feat=True) | |
| 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 | |
| fp32_attention = True | |
| # model setting | |
| 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 | |
| pe_interpolation = 1.0 # positional embedding interpolation | |
| # qk norm | |
| qk_norm = False | |
| # kv token compression | |
| kv_compress = False | |
| kv_compress_config = { | |
| 'sampling': None, | |
| 'scale_factor': 1, | |
| 'kv_compress_layer': [], | |
| } | |
| # training setting | |
| num_workers=4 | |
| train_sampling_steps = 1000 | |
| visualize=False | |
| eval_sampling_steps = 250 | |
| model_max_length = 120 | |
| 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 = 0.18215 # ldm vae: 0.18215; sdxl vae: 0.13025 | |
| 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 | |
| real_prompt_ratio = 1.0 | |
| # classifier free guidance | |
| class_dropout_prob = 0.1 | |
| # work dir settings | |
| work_dir = '/cache/exps/' | |
| s3_work_dir = None | |
| micro_condition = False | |
| seed = 43 | |
| skip_step=0 | |
| # LCM | |
| loss_type = 'huber' | |
| huber_c = 0.001 | |
| num_ddim_timesteps=50 | |
| w_max = 15.0 | |
| w_min = 3.0 | |
| ema_decay = 0.95 | |