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Constant starting noise for demo() images #28

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Constant starting noise for demo() images #28

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9 changes: 6 additions & 3 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -233,16 +233,19 @@ def main():
metrics_log = K.utils.CSVLogger(f'{args.name}_metrics.csv', ['step', 'fid', 'kid'])
del train_iter

# sample demo noise once per training run
demo_n_per_proc = math.ceil(args.sample_n / accelerator.num_processes)
demo_x = torch.randn([demo_n_per_proc, model_config['input_channels'], size[0], size[1]], device=device) * sigma_max

@torch.no_grad()
@K.utils.eval_mode(model_ema)
def demo():
if accelerator.is_main_process:
tqdm.write('Sampling...')
filename = f'{args.name}_demo_{step:08}.png'
n_per_proc = math.ceil(args.sample_n / accelerator.num_processes)
x = torch.randn([n_per_proc, model_config['input_channels'], size[0], size[1]], device=device) * sigma_max

sigmas = K.sampling.get_sigmas_karras(50, sigma_min, sigma_max, rho=7., device=device)
x_0 = K.sampling.sample_lms(model_ema, x, sigmas, disable=not accelerator.is_main_process)
x_0 = K.sampling.sample_lms(model_ema, demo_x, sigmas, disable=not accelerator.is_main_process)
x_0 = accelerator.gather(x_0)[:args.sample_n]
if accelerator.is_main_process:
grid = utils.make_grid(x_0, nrow=math.ceil(args.sample_n ** 0.5), padding=0)
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