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# Dataset settings
dataset = dict(
type="VariableVideoTextDataset",
transform_name="resize_crop",
dummy_text_feature=True,
)
# webvid
bucket_config = { # 12s/it
"144p": {1: (1.0, 475), 51: (1.0, 51), 102: ((1.0, 0.33), 27), 204: ((1.0, 0.1), 13), 408: ((1.0, 0.1), 6)},
# ---
"256": {1: (0.4, 297), 51: (0.5, 20), 102: ((0.5, 0.33), 10), 204: ((0.5, 0.1), 5), 408: ((0.5, 0.1), 2)},
"240p": {1: (0.3, 297), 51: (0.4, 20), 102: ((0.4, 0.33), 10), 204: ((0.4, 0.1), 5), 408: ((0.4, 0.1), 2)},
# ---
"360p": {1: (0.2, 141), 51: (0.15, 8), 102: ((0.15, 0.33), 4), 204: ((0.15, 0.1), 2), 408: ((0.15, 0.1), 1)},
"512": {1: (0.1, 141)},
# ---
"480p": {1: (0.1, 89)},
# ---
"720p": {1: (0.05, 36)},
"1024": {1: (0.05, 36)},
# ---
"1080p": {1: (0.1, 5)},
# ---
"2048": {1: (0.1, 5)},
}
grad_checkpoint = True
load_text_features = True
# Acceleration settings
num_workers = 0
num_bucket_build_workers = 16
dtype = "bf16"
plugin = "zero2"
# Model settings
model = dict(
type="STDiT3-XL/2",
from_pretrained=None,
qk_norm=True,
enable_flash_attn=True,
enable_layernorm_kernel=True,
freeze_y_embedder=True,
skip_y_embedder=True,
)
vae = dict(
type="OpenSoraVAE_V1_2",
from_pretrained="pretrained_models/vae-pipeline",
micro_frame_size=17,
micro_batch_size=4,
)
text_encoder = dict(
type="t5",
from_pretrained="DeepFloyd/t5-v1_1-xxl",
model_max_length=300,
shardformer=True,
local_files_only=True,
)
scheduler = dict(
type="rflow",
use_timestep_transform=True,
sample_method="logit-normal",
)
# Mask settings
mask_ratios = {
"random": 0.2,
"intepolate": 0.01,
"quarter_random": 0.01,
"quarter_head": 0.01,
"quarter_tail": 0.01,
"quarter_head_tail": 0.01,
"image_random": 0.05,
"image_head": 0.1,
"image_tail": 0.05,
"image_head_tail": 0.05,
}
# Log settings
seed = 42
outputs = "outputs"
wandb = False
epochs = 1000
log_every = 10
ckpt_every = 1
# optimization settings
load = None
grad_clip = 1.0
lr = 2e-4
ema_decay = 0.99
adam_eps = 1e-15
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