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Running
on
Zero
Running
on
Zero
| _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=True, load_t5_feat=True, | |
| ) | |
| image_size = 1024 | |
| # model setting | |
| model = 'PixArtMS_XL_2' # model for multi-scale training | |
| fp32_attention = False | |
| 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 = 4 | |
| train_batch_size = 12 # max 12 for PixArt-xL/2 when grad_checkpoint | |
| num_epochs = 10 # 3 | |
| gradient_accumulation_steps = 1 | |
| grad_checkpointing = True | |
| gradient_clip = 0.01 | |
| optimizer = dict(type='CAMEWrapper', lr=1e-5, weight_decay=0.0, betas=(0.9, 0.999, 0.9999), eps=(1e-30, 1e-16)) | |
| lr_schedule_args = dict(num_warmup_steps=100) | |
| save_model_epochs = 10 | |
| save_model_steps = 1000 | |
| valid_num = 0 # take as valid aspect-ratio when sample number >= valid_num | |
| log_interval = 10 | |
| eval_sampling_steps = 5 | |
| visualize = True | |
| work_dir = 'output/debug' | |
| # pixart-sigma | |
| scale_factor = 0.13025 | |
| real_prompt_ratio = 0.5 | |
| model_max_length = 300 | |
| class_dropout_prob = 0.1 | |
| # LCM | |
| loss_type = 'huber' | |
| huber_c = 0.001 | |
| num_ddim_timesteps = 50 | |
| w_max = 15.0 | |
| w_min = 3.0 | |
| ema_decay = 0.95 | |
| cfg_scale = 4.5 | |