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import argparse
import os
import safetensors
import torch
from diffusers.utils import export_to_video, load_image
from cogvideox_interpolation.pipeline import CogVideoXInterpolationPipeline
def parse_args():
parser = argparse.ArgumentParser(description='Video interpolation with different checkpoints')
parser.add_argument('--model_path', type=str, help='Path to the base model')
parser.add_argument('--base_ckpt_path', type=str, default=None, help='Base path for checkpoints')
parser.add_argument('--output_dir', type=str, help='Directory for output videos')
parser.add_argument('--first_image', type=str, help='Path to the first image')
parser.add_argument('--last_image', type=str, help='Path to the last image')
return parser.parse_args()
def main():
args = parse_args()
pipe = CogVideoXInterpolationPipeline.from_pretrained(
args.model_path,
torch_dtype=torch.bfloat16
)
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()
prompt = 'a 3D consistent video scene'
# checkpoints = ['ori', 800]
checkpoints = [800]
os.makedirs(args.output_dir, exist_ok=True)
for ckpt_num in checkpoints:
print(f"Processing checkpoint-{ckpt_num}")
pipe = CogVideoXInterpolationPipeline.from_pretrained(
args.model_path,
torch_dtype=torch.bfloat16
)
if args.base_ckpt_path is not None:
# ckpt_path = os.path.join(args.base_ckpt_path, f"checkpoint-{ckpt_num}")
ckpt_path = args.base_ckpt_path
state_dict = safetensors.torch.load_file(os.path.join(ckpt_path, "model.safetensors"))
pipe.transformer.load_state_dict(state_dict)
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()
first_image = load_image(args.first_image)
last_image = load_image(args.last_image)
videos = pipe(
prompt=prompt,
first_image=first_image,
last_image=last_image,
num_videos_per_prompt=50,
num_inference_steps=50,
num_frames=49,
guidance_scale=6,
generator=torch.Generator(device="cuda").manual_seed(42),
)
video = videos[0]
prefix = "ori" if ckpt_num == 'ori' else "video"
output_path = os.path.join(args.output_dir, f"{prefix}_ckpt_{ckpt_num}.mp4")
export_to_video(video[0], output_path, fps=8)
print(f"{prefix}_ckpt_{ckpt_num}.mp4 saved")
del pipe
torch.cuda.empty_cache()
print("All checkpoints processing completed!")
if __name__ == "__main__":
main() |