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_base_ = ['../PixArt_xl2_internal.py'] | |
data_root = 'data' | |
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 = 2048 | |
# model setting | |
model = 'PixArtMS_XL_2' | |
mixed_precision = 'fp16' | |
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_2048' # base aspect ratio [ASPECT_RATIO_512 or ASPECT_RATIO_256] | |
multi_scale = True # if use multiscale dataset model training | |
pe_interpolation = 4.0 | |
# training setting | |
num_workers = 10 | |
train_batch_size = 4 # 48 | |
num_epochs = 10 # 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=100) | |
eval_sampling_steps = 100 | |
visualize = True | |
log_interval = 10 | |
save_model_epochs = 10 | |
save_model_steps = 100 | |
work_dir = 'output/debug' | |
# pixart-sigma | |
scale_factor = 0.13025 | |
real_prompt_ratio = 0.5 | |
model_max_length = 300 | |
class_dropout_prob = 0.1 | |
kv_compress = False | |
kv_compress_config = { | |
'sampling': 'conv', | |
'scale_factor': 2, | |
'kv_compress_layer': [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], | |
} | |