sample_panorama_lcm.py

from pathlib import Path
import argparse
import numpy as np
import torch

from merge_attend_lcm import MergeAttendLCM
from sampling_utils import seed_everything


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--prompt', type=str, default='Boyscout campfire at night')
    parser.add_argument('--model_key', type=str, default='SimianLuo/LCM_Dreamshaper_v7')
    parser.add_argument('--H', type=int, default=512)
    parser.add_argument('--W', type=int, default=3072)
    parser.add_argument('--steps', type=int, default=4)
    parser.add_argument('--num_samples', type=int, default=3)
    parser.add_argument('--save_dir', type=str, default='results')
    parser.add_argument('--out_name', type=str, default='mad_lcd')
    parser.add_argument('--dtype', type=str, default='fp32')
    parser.add_argument('--mad_threshold', type=int, default=2)
    parser.add_argument('--mad_blocks', type=str, default='all')
    parser.add_argument('--stride', type=int, default=16, help='window stride for MultiDiffusion')
    parser.add_argument('--seed', type=int)
    args = parser.parse_args()

    device = torch.device('cuda:0') if torch.cuda.is_available() else 'cpu'

    if args.seed is not None:
        seed_everything(args.seed)

    # Load LCM model
    model = MergeAttendLCM(args.model_key, device, args.dtype)
    save_dir = Path(args.save_dir)
    save_dir.mkdir(exist_ok=True, parents=True)
    out_file = Path(args.out_name).stem

    print(f"{device} - Prompt: {args.prompt}")

    try:
        for sample_idx in range(args.num_samples):
            save_path = Path(save_dir) / args.prompt.replace(' ', '_') / f"{out_file}_{sample_idx:04d}.png"
            save_path.parent.mkdir(exist_ok=True, parents=True)

            # Generate images
            img = model.sample(
                prompts=args.prompt,
                height=args.H,
                width=args.W,
                num_inference_steps=args.steps,
                stride=args.stride,
                mad_blocks=args.mad_blocks,
                mad_threshold=args.mad_threshold
            )
            img.save(save_path)
            torch.cuda.empty_cache()
    except KeyboardInterrupt:
        print(f"Interrupted! Saving results...")
    print(f"Done!")


if __name__ == "__main__":
    main()