2022-06-27 19:46:04 +00:00
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import argparse
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import os
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from PIL import Image
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2022-07-01 23:44:24 +00:00
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from min_dalle import MinDalle
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2022-07-14 18:22:06 +00:00
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import torch
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2022-06-27 19:46:04 +00:00
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parser = argparse.ArgumentParser()
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2022-06-27 20:49:42 +00:00
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parser.add_argument('--mega', action='store_true')
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parser.add_argument('--no-mega', dest='mega', action='store_false')
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parser.set_defaults(mega=False)
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2022-07-14 18:22:06 +00:00
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parser.add_argument('--fp16', action='store_true')
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2022-07-04 20:06:49 +00:00
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parser.add_argument('--text', type=str, default='Dali painting of WALL·E')
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2022-07-01 23:44:24 +00:00
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parser.add_argument('--seed', type=int, default=-1)
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2022-07-02 12:45:49 +00:00
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parser.add_argument('--grid-size', type=int, default=1)
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parser.add_argument('--image-path', type=str, default='generated')
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parser.add_argument('--models-root', type=str, default='pretrained')
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2022-07-11 16:28:20 +00:00
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parser.add_argument('--top_k', type=int, default=256)
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2022-06-27 19:46:04 +00:00
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2022-07-04 20:06:49 +00:00
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def ascii_from_image(image: Image.Image, size: int = 128) -> str:
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2022-07-14 15:28:03 +00:00
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gray_pixels = image.resize((size, int(0.55 * size))).convert('L').getdata()
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2022-06-27 19:46:04 +00:00
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chars = list('.,;/IOX')
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2022-07-14 15:28:03 +00:00
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chars = [chars[i * len(chars) // 256] for i in gray_pixels]
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2022-06-27 19:46:04 +00:00
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chars = [chars[i * size: (i + 1) * size] for i in range(size // 2)]
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return '\n'.join(''.join(row) for row in chars)
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2022-06-27 20:49:42 +00:00
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def save_image(image: Image.Image, path: str):
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2022-06-27 19:46:04 +00:00
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if os.path.isdir(path):
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2022-07-14 23:24:04 +00:00
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path = os.path.join(path, 'generated.png')
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elif not path.endswith('.png'):
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path += '.png'
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2022-06-27 19:46:04 +00:00
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print("saving image to", path)
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image.save(path)
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return image
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2022-06-29 13:42:12 +00:00
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def generate_image(
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is_mega: bool,
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text: str,
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seed: int,
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2022-07-02 12:45:49 +00:00
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grid_size: int,
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top_k: int,
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2022-06-29 13:42:12 +00:00
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image_path: str,
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2022-07-14 18:22:06 +00:00
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models_root: str,
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fp16: bool,
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2022-06-29 13:42:12 +00:00
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):
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model = MinDalle(
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2022-07-01 22:16:55 +00:00
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is_mega=is_mega,
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2022-07-02 03:39:48 +00:00
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models_root=models_root,
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is_reusable=False,
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is_verbose=True,
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dtype=torch.float16 if fp16 else torch.float32
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)
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2022-06-29 13:42:12 +00:00
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2022-07-11 16:28:20 +00:00
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image = model.generate_image(
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text,
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seed,
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grid_size,
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top_k=top_k,
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is_verbose=True
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)
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2022-07-05 00:02:33 +00:00
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save_image(image, image_path)
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print(ascii_from_image(image, size=128))
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2022-06-29 13:42:12 +00:00
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2022-06-27 19:46:04 +00:00
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if __name__ == '__main__':
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args = parser.parse_args()
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print(args)
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generate_image(
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is_mega=args.mega,
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text=args.text,
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seed=args.seed,
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grid_size=args.grid_size,
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top_k=args.top_k,
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image_path=args.image_path,
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models_root=args.models_root,
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fp16=args.fp16,
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2022-07-14 23:24:04 +00:00
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)
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