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# Define dataset
dataset = dict(
type="VideoTextDataset",
data_path=None,
num_frames=16,
frame_interval=3,
image_size=(256, 256),
)
# Define acceleration
num_workers = 4
dtype = "bf16"
grad_checkpoint = True
plugin = "zero2"
sp_size = 1
# Define model
model = dict(
type="STDiT-XL/2",
space_scale=0.5,
time_scale=1.0,
# from_pretrained="PixArt-XL-2-512x512.pth",
# from_pretrained = "/home/zhaowangbo/wangbo/PixArt-alpha/pretrained_models/OpenSora-v1-HQ-16x512x512.pth",
# from_pretrained = "OpenSora-v1-HQ-16x512x512.pth",
from_pretrained="PRETRAINED_MODEL",
enable_flash_attn=True,
enable_layernorm_kernel=True,
)
# mask_ratios = [0.5, 0.29, 0.07, 0.07, 0.07]
# mask_ratios = {
# "identity": 0.9,
# "random": 0.06,
# "mask_head": 0.01,
# "mask_tail": 0.01,
# "mask_head_tail": 0.02,
# }
vae = dict(
type="VideoAutoencoderKL",
from_pretrained="stabilityai/sd-vae-ft-ema",
)
text_encoder = dict(
type="t5",
from_pretrained="DeepFloyd/t5-v1_1-xxl",
model_max_length=120,
shardformer=True,
)
scheduler = dict(
type="rflow",
# timestep_respacing="",
)
# Others
seed = 42
outputs = "outputs"
wandb = True
epochs = 1
log_every = 10
ckpt_every = 1000
load = None
batch_size = 16
lr = 2e-5
grad_clip = 1.0
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