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6f8c80a ba48d54 e9190ff 6f8c80a e9190ff 6f8c80a e9190ff 6f8c80a e9190ff 6f8c80a e9190ff ba48d54 6f8c80a e9190ff 6f8c80a e9190ff 6f8c80a e9190ff 6f8c80a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | """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()
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