| """Local or Hub inference for the published Diffusers checkpoint.""" |
| import argparse |
| from pathlib import Path |
|
|
| import torch |
| from scripts.runtime import load_int8_pipeline |
| from scripts.editing_protocol import load_input, validate_seeds, DEFAULT_SEEDS, edit_dimensions |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument('--model', default='models/int8') |
| ap.add_argument('--prompt', required=True) |
| ap.add_argument('--input', nargs='*', default=[]) |
| ap.add_argument('--output', default='output.png') |
| ap.add_argument('--seed', type=int, help='Default: 42 for generation, 1000042 for editing') |
| ap.add_argument('--source-seed', type=int, help='Known source generation seed; reject editing noise replay') |
| ap.add_argument('--width', type=int) |
| ap.add_argument('--height', type=int) |
| ap.add_argument('--resolution', type=int, default=1024, |
| help='Editing reference/target area scale, independent of explicit output width') |
| ap.add_argument('--steps', type=int, default=40) |
| ap.add_argument('--vae-tiling', action='store_true', |
| help='Save VAE memory; may introduce the stripe artifacts documented in EDITING_UPDATE.md') |
| args = ap.parse_args() |
| if ((args.width is None) != (args.height is None)): |
| ap.error('Specify both --width and --height, or neither') |
| if args.steps <= 0 or any(v <= 0 or v % 32 for v in |
| (args.width, args.height, args.resolution) if v is not None): |
| ap.error('Positive steps and dimensions divisible by 32 are required') |
| if Path(args.output).suffix.lower() != '.png': |
| ap.error('Use a .png output to retain the RGBA channel') |
| images = [load_input(path) for path in args.input] |
| seed = args.seed if args.seed is not None else (DEFAULT_SEEDS[0] if images else 42) |
| try: |
| validate_seeds([{'source_seed': args.source_seed}] if images else [], [seed]) |
| except ValueError as exc: |
| ap.error(str(exc)) |
| if args.width is None: |
| args.width, args.height = (edit_dimensions(images[-1].size, args.resolution) |
| if images else (args.resolution, args.resolution)) |
| if Path(args.model).exists(): |
| from scripts.benchmark import load_pipeline |
| pipe = load_pipeline(args.model) |
| else: |
| pipe = load_int8_pipeline(args.model) |
| if args.vae_tiling: |
| pipe.vae.enable_tiling() |
| with torch.inference_mode(): |
| image = pipe(prompt=args.prompt, image=images or None, |
| width=args.width, height=args.height, output_resolution=args.resolution, |
| num_inference_steps=args.steps, true_cfg_scale=1.0, use_kv_cache=True, |
| generator=torch.Generator('cpu').manual_seed(seed)).images[0] |
| Path(args.output).parent.mkdir(parents=True, exist_ok=True) |
| image.save(args.output) |
| print(f'{args.output} (seed={seed}, actual_size={image.size}, mode={image.mode})') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
