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https://github.com/ml-explore/mlx-examples.git
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56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
# Copyright © 2023 Apple Inc.
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import argparse
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import mlx.core as mx
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import numpy as np
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from PIL import Image
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from tqdm import tqdm
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from stable_diffusion import StableDiffusion
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Generate images from a textual prompt using stable diffusion"
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)
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parser.add_argument("prompt")
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parser.add_argument("--n_images", type=int, default=4)
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parser.add_argument("--steps", type=int, default=50)
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parser.add_argument("--cfg", type=float, default=7.5)
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parser.add_argument("--negative_prompt", default="")
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parser.add_argument("--n_rows", type=int, default=1)
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parser.add_argument("--decoding_batch_size", type=int, default=1)
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parser.add_argument("--output", default="out.png")
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args = parser.parse_args()
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sd = StableDiffusion()
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# Generate the latent vectors using diffusion
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latents = sd.generate_latents(
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args.prompt,
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n_images=args.n_images,
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cfg_weight=args.cfg,
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num_steps=args.steps,
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negative_text=args.negative_prompt,
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)
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for x_t in tqdm(latents, total=args.steps):
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mx.eval(x_t)
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# Decode them into images
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decoded = []
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for i in tqdm(range(0, args.n_images, args.decoding_batch_size)):
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decoded.append(sd.decode(x_t[i : i + args.decoding_batch_size]))
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mx.eval(decoded[-1])
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# Arrange them on a grid
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x = mx.concatenate(decoded, axis=0)
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x = mx.pad(x, [(0, 0), (8, 8), (8, 8), (0, 0)])
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B, H, W, C = x.shape
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x = x.reshape(args.n_rows, B // args.n_rows, H, W, C).transpose(0, 2, 1, 3, 4)
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x = x.reshape(args.n_rows * H, B // args.n_rows * W, C)
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x = (x * 255).astype(mx.uint8)
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# Save them to disc
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im = Image.fromarray(np.array(x))
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im.save(args.output)
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