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40b178e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import logging
from fire import Fire
from depthcrafter.inference import DepthCrafterInference
logging.basicConfig(level=logging.INFO)
def main(
video_path: str,
save_folder: str = "./demo_output",
unet_path: str = "tencent/DepthCrafter",
pre_train_path: str = "stabilityai/stable-video-diffusion-img2vid-xt",
process_length: int = -1,
cpu_offload: str = "model",
target_fps: int = -1,
seed: int = 42,
num_inference_steps: int = 5,
guidance_scale: float = 1.0,
window_size: int = 110,
overlap: int = 25,
max_res: int = 1024,
dataset: str = "open",
save_npz: bool = False,
save_exr: bool = False,
track_time: bool = False,
):
"""
Main function to run DepthCrafter inference.
Args:
video_path (str): Path to the input video(s), separated by comma.
save_folder (str): Folder to save output.
unet_path (str): Path to the UNet model.
pre_train_path (str): Path to the pre-trained model.
process_length (int): Maximum number of frames to process.
cpu_offload (str): CPU offload strategy.
target_fps (int): Target FPS for output video.
seed (int): Random seed.
num_inference_steps (int): Number of denoising steps.
guidance_scale (float): Guidance scale.
window_size (int): Window size for sliding window inference.
overlap (int): Overlap between windows.
max_res (int): Maximum resolution.
dataset (str): Dataset name for resolution settings.
save_npz (bool): Whether to save depth map as .npz.
save_exr (bool): Whether to save depth map as .exr.
track_time (bool): Whether to track execution time.
"""
depthcrafter_inference = DepthCrafterInference(
unet_path=unet_path,
pre_train_path=pre_train_path,
cpu_offload=cpu_offload,
)
# process the videos, the video paths are separated by comma
video_paths = video_path.split(",")
for video in video_paths:
depthcrafter_inference.infer(
video,
num_inference_steps,
guidance_scale,
save_folder=save_folder,
window_size=window_size,
process_length=process_length,
overlap=overlap,
max_res=max_res,
dataset=dataset,
target_fps=target_fps,
seed=seed,
track_time=track_time,
save_npz=save_npz,
save_exr=save_exr,
)
depthcrafter_inference.clear_cache()
if __name__ == "__main__":
Fire(main)
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