You can experiment with:
Here are some real-world examples of R63 script in action:
# -------------------------------------------------------------- # CLI entry point # -------------------------------------------------------------- if __name__ == "__main__": parser = argparse.ArgumentParser( description="Quick Rule‑63 image‑to‑image generation using Stable Diffusion." ) parser.add_argument("input", type=Path, help="Path to the source character image.") parser.add_argument( "--prompt", type=str, default="female version, soft lighting, realistic skin", help="Textual prompt describing the gender‑swapped target.", ) parser.add_argument( "--out", type=Path, default=Path("./r63_outputs"), help="Directory where generated images will be stored.", ) parser.add_argument( "--strength", type=float, default=0.75, help="How much the model should transform the input (0‑1).", ) parser.add_argument( "--num", type=int, default=1, help="Number of variations to generate.", ) parser.add_argument( "--seed", type=int, default=42, help="Random seed for reproducibility.", ) args = parser.parse_args() main( input_path=args.input, output_dir=args.out, prompt=args.prompt, strength=args.strength, num_images=args.num, seed=args.seed, )
| Type | Link | Why It’s Useful | |------|------|-----------------| | | https://huggingface.co/stabilityai/stable-diff
R63 Script | __top__
You can experiment with:
Here are some real-world examples of R63 script in action: r63 script
# -------------------------------------------------------------- # CLI entry point # -------------------------------------------------------------- if __name__ == "__main__": parser = argparse.ArgumentParser( description="Quick Rule‑63 image‑to‑image generation using Stable Diffusion." ) parser.add_argument("input", type=Path, help="Path to the source character image.") parser.add_argument( "--prompt", type=str, default="female version, soft lighting, realistic skin", help="Textual prompt describing the gender‑swapped target.", ) parser.add_argument( "--out", type=Path, default=Path("./r63_outputs"), help="Directory where generated images will be stored.", ) parser.add_argument( "--strength", type=float, default=0.75, help="How much the model should transform the input (0‑1).", ) parser.add_argument( "--num", type=int, default=1, help="Number of variations to generate.", ) parser.add_argument( "--seed", type=int, default=42, help="Random seed for reproducibility.", ) args = parser.parse_args() main( input_path=args.input, output_dir=args.out, prompt=args.prompt, strength=args.strength, num_images=args.num, seed=args.seed, ) You can experiment with: Here are some real-world
| Type | Link | Why It’s Useful | |------|------|-----------------| | | https://huggingface.co/stabilityai/stable-diff ) parser.add_argument( "--out"
