|
|
|
dataset = dict( |
|
type="VideoTextDataset", |
|
data_path=None, |
|
num_frames=64, |
|
frame_interval=3, |
|
image_size=(512, 512), |
|
) |
|
|
|
|
|
num_workers = 4 |
|
dtype = "bf16" |
|
grad_checkpoint = True |
|
plugin = "zero2" |
|
sp_size = 1 |
|
|
|
|
|
model = dict( |
|
type="STDiT-XL/2", |
|
space_scale=1.0, |
|
time_scale=2 / 3, |
|
from_pretrained=None, |
|
enable_flash_attn=True, |
|
enable_layernorm_kernel=True, |
|
) |
|
vae = dict( |
|
type="VideoAutoencoderKL", |
|
from_pretrained="stabilityai/sd-vae-ft-ema", |
|
micro_batch_size=64, |
|
) |
|
text_encoder = dict( |
|
type="t5", |
|
from_pretrained="DeepFloyd/t5-v1_1-xxl", |
|
model_max_length=120, |
|
shardformer=True, |
|
) |
|
scheduler = dict( |
|
type="iddpm", |
|
timestep_respacing="", |
|
) |
|
|
|
|
|
seed = 42 |
|
outputs = "outputs" |
|
wandb = False |
|
|
|
epochs = 1000 |
|
log_every = 10 |
|
ckpt_every = 250 |
|
load = None |
|
|
|
batch_size = 4 |
|
lr = 2e-5 |
|
grad_clip = 1.0 |
|
|
