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"""Samples from k-diffusion models.""" |
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import argparse |
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import math |
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import accelerate |
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import torch |
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from tqdm import trange, tqdm |
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import k_diffusion as K |
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def main(): |
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p = argparse.ArgumentParser(description=__doc__, |
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formatter_class=argparse.ArgumentDefaultsHelpFormatter) |
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p.add_argument('--batch-size', type=int, default=64, |
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help='the batch size') |
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p.add_argument('--checkpoint', type=str, required=True, |
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help='the checkpoint to use') |
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p.add_argument('--config', type=str, required=True, |
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help='the model config') |
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p.add_argument('-n', type=int, default=64, |
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help='the number of images to sample') |
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p.add_argument('--prefix', type=str, default='out', |
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help='the output prefix') |
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p.add_argument('--steps', type=int, default=50, |
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help='the number of denoising steps') |
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args = p.parse_args() |
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config = K.config.load_config(open(args.config)) |
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model_config = config['model'] |
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assert len(model_config['input_size']) == 2 and model_config['input_size'][0] == model_config['input_size'][1] |
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size = model_config['input_size'] |
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accelerator = accelerate.Accelerator() |
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device = accelerator.device |
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print('Using device:', device, flush=True) |
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inner_model = K.config.make_model(config).eval().requires_grad_(False).to(device) |
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inner_model.load_state_dict(torch.load(args.checkpoint, map_location='cpu')['model_ema']) |
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accelerator.print('Parameters:', K.utils.n_params(inner_model)) |
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model = K.Denoiser(inner_model, sigma_data=model_config['sigma_data']) |
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sigma_min = model_config['sigma_min'] |
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sigma_max = model_config['sigma_max'] |
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@torch.no_grad() |
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@K.utils.eval_mode(model) |
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def run(): |
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if accelerator.is_local_main_process: |
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tqdm.write('Sampling...') |
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sigmas = K.sampling.get_sigmas_karras(args.steps, sigma_min, sigma_max, rho=7., device=device) |
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def sample_fn(n): |
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x = torch.randn([n, model_config['input_channels'], size[0], size[1]], device=device) * sigma_max |
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x_0 = K.sampling.sample_lms(model, x, sigmas, disable=not accelerator.is_local_main_process) |
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return x_0 |
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x_0 = K.evaluation.compute_features(accelerator, sample_fn, lambda x: x, args.n, args.batch_size) |
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if accelerator.is_main_process: |
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for i, out in enumerate(x_0): |
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filename = f'{args.prefix}_{i:05}.png' |
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K.utils.to_pil_image(out).save(filename) |
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try: |
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run() |
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except KeyboardInterrupt: |
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pass |
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if __name__ == '__main__': |
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main() |
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