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num_frames = 16
frame_interval = 3
fps = 24
image_size = (240, 426)
multi_resolution = "STDiT2"
# Condition
prompt_path = None
prompt = [
"A car driving on the ocean.",
'Drone view of waves crashing against the rugged cliffs along Big Sur\'s garay point beach. The crashing blue waters create white-tipped waves, while the golden light of the setting sun illuminates the rocky shore. A small island with a lighthouse sits in the distance, and green shrubbery covers the cliff\'s edge. The steep drop from the road down to the beach is a dramatic feat, with the cliff\'s edges jutting out over the sea. This is a view that captures the raw beauty of the coast and the rugged landscape of the Pacific Coast Highway.{"reference_path": "assets/images/condition/cliff.png", "mask_strategy": "0"}',
"In an ornate, historical hall, a massive tidal wave peaks and begins to crash. Two surfers, seizing the moment, skillfully navigate the face of the wave.",
]
loop = 2
condition_frame_length = 4
reference_path = [
"https://cdn.openai.com/tmp/s/interp/d0.mp4",
None,
"assets/images/condition/wave.png",
]
# valid when reference_path is not None
# (loop id, ref id, ref start, length, target start)
mask_strategy = [
"0,0,0,0,8,0.3",
None,
"0",
]
# Define model
model = dict(
type="STDiT2-XL/2",
from_pretrained=None,
input_sq_size=512,
qk_norm=True,
enable_flashattn=True,
enable_layernorm_kernel=True,
)
vae = dict(
type="VideoAutoencoderKL",
from_pretrained="stabilityai/sd-vae-ft-ema",
cache_dir=None, # "/mnt/hdd/cached_models",
micro_batch_size=4,
)
text_encoder = dict(
type="t5",
from_pretrained="DeepFloyd/t5-v1_1-xxl",
cache_dir=None, # "/mnt/hdd/cached_models",
model_max_length=200,
)
scheduler = dict(
type="iddpm",
num_sampling_steps=100,
cfg_scale=7.0,
cfg_channel=3, # or None
)
dtype = "bf16"
# Others
batch_size = 1
seed = 42
save_dir = "./samples/samples/"
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