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