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# Define dataset | |
dataset = dict( | |
type="VideoTextDataset", | |
data_path=None, | |
num_frames=64, | |
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="PixArt-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=128, | |
) | |
text_encoder = dict( | |
type="t5", | |
from_pretrained="DeepFloyd/t5-v1_1-xxl", | |
model_max_length=120, | |
shardformer=True, | |
) | |
scheduler = dict( | |
type="iddpm", | |
timestep_respacing="", | |
) | |
# Others | |
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 | |