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from demo import GetAnchorVideos
import os
from datetime import datetime
import argparse
import torch
def get_parser():
parser = argparse.ArgumentParser()
## general
parser.add_argument('--video_path', type=str, help='Input path')
parser.add_argument(
'--out_dir', type=str, required=True, help='Output dir'
)
parser.add_argument(
'--device', type=str, default='cuda:0', help='The device to use'
)
parser.add_argument(
'--exp_name',
type=str,
default=None,
help='Experiment name, use video file name by default',
)
parser.add_argument(
'--save_name',
type=str,
default=None,
help='Experiment name, use video file name by default',
)
parser.add_argument(
'--seed', type=int, default=43, help='Random seed for reproducibility'
)
parser.add_argument(
'--video_length', type=int, default=49, help='Length of the video frames'
)
parser.add_argument('--fps', type=int, default=10, help='Fps for saved video')
parser.add_argument(
'--stride', type=int, default=1, help='Sampling stride for input video'
)
parser.add_argument('--server_name', type=str, help='Server IP address')
## render
parser.add_argument(
'--radius_scale',
type=float,
default=1.0,
help='Scale factor for the spherical radius',
)
parser.add_argument('--camera', type=str, default='traj', help='traj or target')
parser.add_argument(
'--mode', type=str, default='gradual', help='gradual, bullet or direct'
)
parser.add_argument(
'--mask', action='store_true', default=False, help='Clean the pcd if true'
)
parser.add_argument(
'--traj_txt',
type=str,
help="Required for 'traj' camera, a txt file that specify camera trajectory",
)
parser.add_argument(
'--target_pose',
nargs=5,
type=float,
help="Required for 'target' mode, specify target camera pose, <theta phi r x y>",
)
parser.add_argument(
'--near', type=float, default=0.0001, help='Near clipping plane distance'
)
parser.add_argument(
'--far', type=float, default=10000.0, help='Far clipping plane distance'
)
parser.add_argument('--anchor_idx', type=int, default=0, help='One GT frame')
## diffusion
parser.add_argument(
'--low_gpu_memory_mode',
type=bool,
default=False,
help='Enable low GPU memory mode',
)
# parser.add_argument('--model_name', type=str, default='checkpoints/CogVideoX-Fun-V1.1-5b-InP', help='Path to the model')
parser.add_argument(
'--model_name',
type=str,
default='/app/pretrained/CogVideoX-Fun-V1.1-5b-InP',
help='Path to the model',
)
parser.add_argument(
'--sampler_name',
type=str,
choices=["Euler", "Euler A", "DPM++", "PNDM", "DDIM_Cog", "DDIM_Origin"],
default='DDIM_Origin',
help='Choose the sampler',
)
# parser.add_argument('--transformer_path', type=str, default='checkpoints/TrajectoryCrafter/crosstransformer', help='Path to the pretrained transformer model')
parser.add_argument(
'--transformer_path',
type=str,
default="/app/pretrained/TrajectoryCrafter",
help='Path to the pretrained transformer model',
)
parser.add_argument(
'--sample_size',
type=int,
nargs=2,
default=[384, 672],
help='Sample size as [height, width]',
)
parser.add_argument(
'--diffusion_guidance_scale',
type=float,
default=6.0,
help='Guidance scale for inference',
)
parser.add_argument(
'--diffusion_inference_steps',
type=int,
default=50,
help='Number of inference steps',
)
parser.add_argument(
'--prompt', type=str, default=None, help='Prompt for video generation'
)
parser.add_argument(
'--negative_prompt',
type=str,
default="The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion.",
help='Negative prompt for video generation',
)
parser.add_argument(
'--refine_prompt',
type=str,
default=". The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic.",
help='Prompt for video generation',
)
parser.add_argument('--qwen_path', type=str, default="/app/pretrained/Qwen2.5-VL-7B-Instruct")
## depth
# parser.add_argument('--unet_path', type=str, default='checkpoints/DepthCrafter', help='Path to the UNet model')
parser.add_argument(
'--unet_path',
type=str,
default="/app/pretrained/DepthCrafter",
help='Path to the UNet model',
)
# parser.add_argument('--pre_train_path', type=str, default='checkpoints/stable-video-diffusion-img2vid-xt', help='Path to the pre-trained model')
parser.add_argument(
'--pre_train_path',
type=str,
default="/app/pretrained/stable-video-diffusion-img2vid",
help='Path to the pre-trained model',
)
parser.add_argument(
'--cpu_offload', type=str, default='model', help='CPU offload strategy'
)
parser.add_argument(
'--depth_inference_steps', type=int, default=5, help='Number of inference steps'
)
parser.add_argument(
'--depth_guidance_scale',
type=float,
default=1.0,
help='Guidance scale for inference',
)
parser.add_argument(
'--window_size', type=int, default=110, help='Window size for processing'
)
parser.add_argument(
'--overlap', type=int, default=25, help='Overlap size for processing'
)
parser.add_argument(
'--max_res', type=int, default=1024, help='Maximum resolution for processing'
)
return parser
if __name__ == "__main__":
parser = get_parser() # infer config.py
opts = parser.parse_args()
opts.weight_dtype = torch.bfloat16
pvd = GetAnchorVideos(opts)
if opts.mode == 'gradual':
pvd.infer_gradual(opts)
elif opts.mode == 'direct':
pvd.infer_direct(opts)
elif opts.mode == 'bullet':
pvd.infer_bullet(opts)
elif opts.mode == 'image':
pvd.infer_image(opts)
elif opts.mode == 'start_end':
pvd.infer_start_end(opts)
elif opts.mode == 'zoom':
pvd.infer_zoom(opts) |