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num_workers = 8
dtype = "bf16"
seed = 42
num_eval_timesteps = 10
# Dataset settings
dataset = dict(
type="VariableVideoTextDataset",
transform_name="resize_crop",
)
bucket_config = {
"144p": {1: (None, 100), 51: (None, 30), 102: (None, 20), 204: (None, 8), 408: (None, 4)},
# ---
"240p": {1: (None, 100), 51: (None, 24), 102: (None, 12), 204: (None, 4), 408: (None, 2)},
# ---
"360p": {1: (None, 60), 51: (None, 12), 102: (None, 6), 204: (None, 2), 408: (None, 1)},
# ---
"480p": {1: (None, 40), 51: (None, 6), 102: (None, 3), 204: (None, 1)},
# ---
"720p": {1: (None, 20), 51: (None, 2), 102: (None, 1)},
# ---
"1080p": {1: (None, 10)},
# ---
"2048": {1: (None, 5)},
}
# 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,
local_files_only=True,
)
text_encoder = dict(
type="t5",
from_pretrained="DeepFloyd/t5-v1_1-xxl",
model_max_length=300,
local_files_only=True,
)
scheduler = dict(type="rflow")
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