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63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
num_frames = 16
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frame_interval = 3
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fps = 24
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image_size = (240, 426)
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multi_resolution = "STDiT2"
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# Condition
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prompt_path = None
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prompt = [
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"A car driving on the ocean.",
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'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"}',
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"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.",
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]
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loop = 2
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condition_frame_length = 4
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reference_path = [
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"https://cdn.openai.com/tmp/s/interp/d0.mp4",
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None,
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"assets/images/condition/wave.png",
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]
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# valid when reference_path is not None
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# (loop id, ref id, ref start, length, target start)
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mask_strategy = [
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"0,0,0,0,8,0.3",
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None,
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"0",
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]
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# Define model
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model = dict(
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type="STDiT2-XL/2",
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from_pretrained=None,
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input_sq_size=512,
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qk_norm=True,
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enable_flashattn=True,
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enable_layernorm_kernel=True,
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)
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vae = dict(
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type="VideoAutoencoderKL",
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from_pretrained="stabilityai/sd-vae-ft-ema",
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cache_dir=None, # "/mnt/hdd/cached_models",
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micro_batch_size=4,
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)
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text_encoder = dict(
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type="t5",
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from_pretrained="DeepFloyd/t5-v1_1-xxl",
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cache_dir=None, # "/mnt/hdd/cached_models",
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model_max_length=200,
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)
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scheduler = dict(
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type="iddpm",
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num_sampling_steps=100,
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cfg_scale=7.0,
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cfg_channel=3, # or None
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)
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dtype = "bf16"
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# Others
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batch_size = 1
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seed = 42
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save_dir = "./samples/samples/"
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