# Dataset settings dataset = dict( type="VariableVideoTextDataset", transform_name="resize_crop", ) # == Config 1: Webvid == # base: (512, 408), 12s/it grad_checkpoint = True base = ("512", "408") base_step_time = 12 bucket_config = { "144p": { 1: (475, 0), 51: (51, 0), 102: (27, 0), 204: (13, 0), 408: (6, 0), }, # --- "240p": { 1: (297, 200), # 8.25 51: (20, 0), 102: (10, 0), 204: (5, 0), 408: (2, 0), }, # --- "512": { 1: (141, 0), 51: (8, 0), 102: (4, 0), 204: (2, 0), 408: (1, 0), }, # --- "480p": { 1: (89, 0), 51: (5, 0), 102: (2, 0), 204: (1, 0), }, # --- "1024": { 1: (36, 0), 51: (1, 0), }, # --- "1080p": {1: (5, 0)}, # --- "2048": {1: (5, 0)}, } # == Config 1 == # base: (512, 408), 16s/it # Acceleration settings num_workers = 8 num_bucket_build_workers = 16 dtype = "bf16" plugin = "zero2" # Model settings model = dict( type="STDiT3-XL/2", from_pretrained=None, qk_norm=True, enable_flash_attn=True, enable_layernorm_kernel=True, ) vae = dict( type="OpenSoraVAE_V1_2", from_pretrained="pretrained_models/vae-pipeline", micro_frame_size=17, micro_batch_size=4, ) text_encoder = dict( type="t5", from_pretrained="DeepFloyd/t5-v1_1-xxl", model_max_length=300, shardformer=True, local_files_only=True, ) scheduler = dict( type="rflow", use_timestep_transform=True, sample_method="logit-normal", ) # Mask settings mask_ratios = { "random": 0.2, "intepolate": 0.01, "quarter_random": 0.01, "quarter_head": 0.01, "quarter_tail": 0.01, "quarter_head_tail": 0.01, "image_random": 0.05, "image_head": 0.1, "image_tail": 0.05, "image_head_tail": 0.05, } # Log settings seed = 42 outputs = "outputs" wandb = False epochs = 1000 log_every = 10 ckpt_every = 500 # optimization settings load = None grad_clip = 1.0 lr = 2e-4 ema_decay = 0.99 adam_eps = 1e-15