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# Define dataset
dataset = dict(
type="VariableVideoTextDataset",
data_path=None,
num_frames=None,
frame_interval=3,
image_size=(None, None),
transform_name="resize_crop",
)
bucket_config = { # 6s/it
"240p": {16: (1.0, 16), 32: (1.0, 8), 64: (1.0, 4), 128: (1.0, 2)},
"256": {1: (1.0, 256)},
"512": {1: (0.5, 80)},
"480p": {1: (0.4, 52), 16: (0.4, 4), 32: (0.0, None)},
"720p": {16: (0.1, 2), 32: (0.0, None)}, # No examples now
"1024": {1: (0.3, 20)},
"1080p": {1: (0.3, 8)},
}
# Define acceleration
num_workers = 4
num_bucket_build_workers = 16
dtype = "bf16"
grad_checkpoint = True
plugin = "zero2"
sp_size = 1
# Define model
model = dict(
type="STDiT2-XL/2",
from_pretrained=None,
input_sq_size=512, # pretrained model is trained on 512x512
qk_norm=True,
enable_flashattn=True,
enable_layernorm_kernel=True,
)
vae = dict(
type="VideoAutoencoderKL",
from_pretrained="stabilityai/sd-vae-ft-ema",
micro_batch_size=4,
local_files_only=True,
)
text_encoder = dict(
type="t5",
from_pretrained="DeepFloyd/t5-v1_1-xxl",
model_max_length=200,
shardformer=True,
local_files_only=True,
)
scheduler = dict(
type="iddpm",
timestep_respacing="",
)
# Others
seed = 42
outputs = "outputs"
wandb = False
epochs = 1000
log_every = 10
ckpt_every = 500
load = None
batch_size = 10 # only for logging
lr = 2e-5
grad_clip = 1.0
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