| |
| """Generate an image with a local SpaceV1.7 Image / Diffusers checkpoint.""" |
| from __future__ import annotations |
|
|
| import argparse |
| from pathlib import Path |
|
|
|
|
| def main() -> int: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--model", default="SpaceV1.7-Image-model", help="local folder or Hugging Face model ID") |
| parser.add_argument("--prompt", required=True) |
| parser.add_argument("--negative-prompt", default=None) |
| parser.add_argument("--output", default="spacev17-image.png") |
| parser.add_argument("--steps", type=int, default=20) |
| parser.add_argument("--guidance-scale", type=float, default=7.5) |
| parser.add_argument("--seed", type=int, default=42) |
| parser.add_argument("--device", choices=("cuda", "cpu"), default="cuda") |
| args = parser.parse_args() |
|
|
| import torch |
| from diffusers import DiffusionPipeline |
|
|
| if args.steps < 1: |
| parser.error("--steps must be positive") |
| if args.device == "cuda" and not torch.cuda.is_available(): |
| parser.error("CUDA was requested but no CUDA device is available; use --device cpu") |
| dtype = torch.float16 if args.device == "cuda" else torch.float32 |
| pipe = DiffusionPipeline.from_pretrained(args.model, torch_dtype=dtype) |
| pipe = pipe.to(args.device) |
| generator = torch.Generator(device=args.device).manual_seed(args.seed) |
| result = pipe( |
| args.prompt, |
| negative_prompt=args.negative_prompt, |
| num_inference_steps=args.steps, |
| guidance_scale=args.guidance_scale, |
| generator=generator, |
| ) |
| output = Path(args.output) |
| output.parent.mkdir(parents=True, exist_ok=True) |
| result.images[0].save(output) |
| print(f"Saved image: {output}") |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
