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import os |
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import sys |
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import argparse |
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import clip |
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import numpy as np |
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from PIL import Image |
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) |
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from dalle.models import Dalle |
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from dalle.utils.utils import set_seed, clip_score |
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parser = argparse.ArgumentParser() |
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parser.add_argument('-n', '--num_candidates', type=int, default=96) |
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parser.add_argument('--prompt', type=str, default='A painting of a tree on the ocean') |
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parser.add_argument('--softmax-temperature', type=float, default=1.0) |
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parser.add_argument('--top-k', type=int, default=256) |
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parser.add_argument('--top-p', type=float, default=None, help='0.0 <= top-p <= 1.0') |
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parser.add_argument('--seed', type=int, default=0) |
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args = parser.parse_args() |
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assert args.top_k <= 256, "It is recommended that top_k is set lower than 256." |
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set_seed(args.seed) |
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device = 'cuda:0' |
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model = Dalle.from_pretrained('minDALL-E/1.3B') |
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model.to(device=device) |
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images = model.sampling(prompt=args.prompt, |
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top_k=args.top_k, |
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top_p=args.top_p, |
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softmax_temperature=args.softmax_temperature, |
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num_candidates=args.num_candidates, |
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device=device).cpu().numpy() |
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images = np.transpose(images, (0, 2, 3, 1)) |
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model_clip, preprocess_clip = clip.load("ViT-B/32", device=device) |
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model_clip.to(device=device) |
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rank = clip_score(prompt=args.prompt, |
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images=images, |
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model_clip=model_clip, |
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preprocess_clip=preprocess_clip, |
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device=device) |
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images = images[rank] |
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print(rank, images.shape) |
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if not os.path.exists('./figures'): |
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os.makedirs('./figures') |
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for i in range(min(16, args.num_candidates)): |
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im = Image.fromarray((images[i]*255).astype(np.uint8)) |
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im.save(f'./figures/{args.prompt}_{i}.png') |
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