| """ | |
| Run this test in Lora adpater checking: | |
| ```shell | |
| python3 test_lora_inference.py --prompt "A girl is ridding a bike." --model_path "THUDM/CogVideoX-5B" --lora_path "path/to/lora" --lora_name "lora_adapter" --output_file "output.mp4" --fps 8 | |
| ``` | |
| """ | |
| import argparse | |
| import torch | |
| from diffusers import CogVideoXPipeline | |
| from diffusers.utils import export_to_video | |
| def generate_video(model_path, prompt, lora_path, lora_name, output_file, fps): | |
| pipe = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=torch.bfloat16).to("cuda") | |
| pipe.load_lora_weights(lora_path, weight_name="pytorch_lora_weights.safetensors", adapter_name=lora_name) | |
| pipe.set_adapters([lora_name], [1.0]) | |
| pipe.enable_model_cpu_offload() | |
| pipe.vae.enable_slicing() | |
| pipe.vae.enable_tiling() | |
| video = pipe(prompt=prompt).frames[0] | |
| export_to_video(video, output_file, fps=fps) | |
| def main(): | |
| parser = argparse.ArgumentParser(description="Generate video using CogVideoX and LoRA weights") | |
| parser.add_argument("--prompt", type=str, required=True, help="Text prompt for the video generation") | |
| parser.add_argument("--model_path", type=str, default="THUDM/CogVideoX-5B", help="Base Model path or HF ID") | |
| parser.add_argument("--lora_path", type=str, required=True, help="Path to the LoRA weights") | |
| parser.add_argument("--lora_name", type=str, default="lora_adapter", help="Name of the LoRA adapter") | |
| parser.add_argument("--output_file", type=str, default="output.mp4", help="Output video file name") | |
| parser.add_argument("--fps", type=int, default=8, help="Frames per second for the output video") | |
| args = parser.parse_args() | |
| generate_video(args.model_path, args.prompt, args.lora_path, args.lora_name, args.output_file, args.fps) | |
| if __name__ == "__main__": | |
| main() | |