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num_frames = 17
image_size = (256, 256)
# Define dataset
dataset = dict(
type="VideoTextDataset",
data_path=None,
num_frames=num_frames,
frame_interval=1,
image_size=image_size,
)
# Define acceleration
num_workers = 16
dtype = "bf16"
grad_checkpoint = True
plugin = "zero2"
# Define model
model = dict(
type="VideoAutoencoderPipeline",
freeze_vae_2d=False,
from_pretrained=None,
cal_loss=True,
vae_2d=dict(
type="VideoAutoencoderKL",
from_pretrained="PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
subfolder="vae",
local_files_only=True,
),
vae_temporal=dict(
type="VAE_Temporal_SD",
from_pretrained=None,
),
)
discriminator = dict(
type="NLayerDiscriminator",
from_pretrained="/home/shenchenhui/opensoraplan-v1.0.0-discriminator.pt",
input_nc=3,
n_layers=3,
use_actnorm=False,
)
# discriminator hyper-parames TODO
discriminator_factor = 1
discriminator_start = -1
generator_factor = 0.5
generator_loss_type = "hinge"
discriminator_loss_type = "hinge"
lecam_loss_weight = None
gradient_penalty_loss_weight = None
# loss weights
perceptual_loss_weight = 0.1 # use vgg is not None and more than 0
kl_loss_weight = 1e-6
mixed_image_ratio = 0.2
use_real_rec_loss = True
use_z_rec_loss = False
use_image_identity_loss = False
# Others
seed = 42
outputs = "outputs"
wandb = False
epochs = 100
log_every = 1
ckpt_every = 1000
load = None
batch_size = 1
lr = 1e-5
grad_clip = 1.0
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