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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/
"""Minimal standalone example to reproduce the main results from the paper
"Elucidating the Design Space of Diffusion-Based Generative Models"."""
import tqdm
import pickle
import numpy as np
import torch
import PIL.Image
import dnnlib
#----------------------------------------------------------------------------
def generate_image_grid(
network_pkl, dest_path,
seed=0, gridw=8, gridh=8, device=torch.device('cuda'),
num_steps=18, sigma_min=0.002, sigma_max=80, rho=7,
S_churn=0, S_min=0, S_max=float('inf'), S_noise=1,
):
batch_size = gridw * gridh
torch.manual_seed(seed)
# Load network.
print(f'Loading network from "{network_pkl}"...')
with dnnlib.util.open_url(network_pkl) as f:
net = pickle.load(f)['ema'].to(device)
# Pick latents and labels.
print(f'Generating {batch_size} images...')
latents = torch.randn([batch_size, net.img_channels, net.img_resolution, net.img_resolution], device=device)
class_labels = None
if net.label_dim:
class_labels = torch.eye(net.label_dim, device=device)[torch.randint(net.label_dim, size=[batch_size], device=device)]
# Adjust noise levels based on what's supported by the network.
sigma_min = max(sigma_min, net.sigma_min)
sigma_max = min(sigma_max, net.sigma_max)
# Time step discretization.
step_indices = torch.arange(num_steps, dtype=torch.float64, device=device)
t_steps = (sigma_max ** (1 / rho) + step_indices / (num_steps - 1) * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))) ** rho
t_steps = torch.cat([net.round_sigma(t_steps), torch.zeros_like(t_steps[:1])]) # t_N = 0
# Main sampling loop.
x_next = latents.to(torch.float64) * t_steps[0]
for i, (t_cur, t_next) in tqdm.tqdm(list(enumerate(zip(t_steps[:-1], t_steps[1:]))), unit='step'): # 0, ..., N-1
x_cur = x_next
# Increase noise temporarily.
gamma = min(S_churn / num_steps, np.sqrt(2) - 1) if S_min <= t_cur <= S_max else 0
t_hat = net.round_sigma(t_cur + gamma * t_cur)
x_hat = x_cur + (t_hat ** 2 - t_cur ** 2).sqrt() * S_noise * torch.randn_like(x_cur)
# Euler step.
denoised = net(x_hat, t_hat, class_labels).to(torch.float64)
d_cur = (x_hat - denoised) / t_hat
x_next = x_hat + (t_next - t_hat) * d_cur
# Apply 2nd order correction.
if i < num_steps - 1:
denoised = net(x_next, t_next, class_labels).to(torch.float64)
d_prime = (x_next - denoised) / t_next
x_next = x_hat + (t_next - t_hat) * (0.5 * d_cur + 0.5 * d_prime)
# Save image grid.
print(f'Saving image grid to "{dest_path}"...')
image = (x_next * 127.5 + 128).clip(0, 255).to(torch.uint8)
image = image.reshape(gridh, gridw, *image.shape[1:]).permute(0, 3, 1, 4, 2)
image = image.reshape(gridh * net.img_resolution, gridw * net.img_resolution, net.img_channels)
image = image.cpu().numpy()
PIL.Image.fromarray(image, 'RGB').save(dest_path)
print('Done.')
#----------------------------------------------------------------------------
def main():
model_root = 'https://nvlabs-fi-cdn.nvidia.com/edm/pretrained'
generate_image_grid(f'{model_root}/edm-cifar10-32x32-cond-vp.pkl', 'cifar10-32x32.png', num_steps=18) # FID = 1.79, NFE = 35
generate_image_grid(f'{model_root}/edm-ffhq-64x64-uncond-vp.pkl', 'ffhq-64x64.png', num_steps=40) # FID = 2.39, NFE = 79
generate_image_grid(f'{model_root}/edm-afhqv2-64x64-uncond-vp.pkl', 'afhqv2-64x64.png', num_steps=40) # FID = 1.96, NFE = 79
generate_image_grid(f'{model_root}/edm-imagenet-64x64-cond-adm.pkl', 'imagenet-64x64.png', num_steps=256, S_churn=40, S_min=0.05, S_max=50, S_noise=1.003) # FID = 1.36, NFE = 511
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------
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