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# Dataset settings
dataset = dict(
type="VariableVideoTextDataset",
transform_name="resize_crop",
)
# == Config 1: Webvid ==
# base: (512, 408), 12s/it
grad_checkpoint = True
base = ("512", "408")
base_step_time = 12
bucket_config = {
"144p": {
1: (475, 0),
51: (51, 0),
102: (27, 0),
204: (13, 0),
408: (6, 0),
},
# ---
"240p": {
1: (297, 200), # 8.25
51: (20, 0),
102: (10, 0),
204: (5, 0),
408: (2, 0),
},
# ---
"512": {
1: (141, 0),
51: (8, 0),
102: (4, 0),
204: (2, 0),
408: (1, 0),
},
# ---
"480p": {
1: (89, 0),
51: (5, 0),
102: (2, 0),
204: (1, 0),
},
# ---
"1024": {
1: (36, 0),
51: (1, 0),
},
# ---
"1080p": {1: (5, 0)},
# ---
"2048": {1: (5, 0)},
}
# == Config 1 ==
# base: (512, 408), 16s/it
# Acceleration settings
num_workers = 8
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,
)
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 = 500
# optimization settings
load = None
grad_clip = 1.0
lr = 2e-4
ema_decay = 0.99
adam_eps = 1e-15
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