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generate.py
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generate.py
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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/
"""Generate random images using the techniques described in the paper
"Elucidating the Design Space of Diffusion-Based Generative Models"."""
import os
import re
import click
import tqdm
import pickle
import numpy as np
import torch
import PIL.Image
import dnnlib
from torch_utils import distributed as dist
from torch.autograd.functional import jvp
import torch.autograd.forward_ad as fwAD
#----------------------------------------------------------------------------
# Our new 2nd-order sampler. 1000img FID = 41.5904
def edm_sampler(
net, latents, class_labels=None, randn_like=torch.randn_like,
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, k=0
):
# # 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)
A = torch.tensor(sigma_max**(1/rho), dtype=torch.float64)
B = torch.tensor(sigma_min**(1/rho) - sigma_max**(1/rho), dtype=torch.float64)
# # Time step discretization, turn time-steps into sigma-schedule.
step_indices = torch.arange(num_steps, dtype=torch.float64, device=latents.device) # [0,1,...,num_steps-1]
sigmas = (A + step_indices/(num_steps-1)*B).pow(rho)
sigmas = torch.cat([net.round_sigma(sigmas), torch.zeros_like(sigmas[:1])]) # t_steps[num_steps] = 0
dt_org = torch.tensor(1/(num_steps-1), dtype=torch.float64, device=latents.device)
# # Main sampling loop.
x_next = latents.to(torch.float64) * sigmas[0] # amplify to sigma_max variance
for i, (sigma_cur, sigma_next) in enumerate(zip(sigmas[:-1], sigmas[1:])): # 0, ..., N-1
x_cur = x_next
dt = dt_org
# # increase nosie level except last iteration
if i<num_steps-1:
diffusion_coff = dt**((rho-1)/2)*((-B)**(rho)).sqrt()
if randn_like.__name__ == 'multiGaussian_like':
noise = randn_like(x_cur, dt)
else:
noise = [dt**0.5*randn_like(x_cur)]
x_cur = x_cur + diffusion_coff * noise[0]
sigma_cur = (sigma_cur**2 + dt*diffusion_coff**2).sqrt()
dt = (sigma_next**(1/rho) - sigma_cur**(1/rho))/B
gamma=torch.tensor(0.0, dtype=torch.float64, device=latents.device)
prod=torch.tensor(1.0, dtype=torch.float64, device=latents.device)
for j in range(1, int(rho)):
prod *= (B*(rho-j+1)/j)
gamma += dt**(j-1)*prod*sigma_cur**((rho-j)/rho)
'''
# # Apply 2nd order correction.
if i < num_steps - 2 and sigma_cur > S_max:
# # Euler step.
denoised = net(x_cur, sigma_cur, class_labels).to(torch.float64)
f_cur = (x_cur - denoised) / sigma_cur
x_next = x_cur + gamma*f_cur*dt
# # v1.0 implementation
# dsigma = B*rho*(sigma_cur.pow((rho-1)/rho))
# ddsigma = rho*(rho-1)*B*B*(sigma_cur.pow((rho-2)/rho))
# g_cur = ddsigma*sigma_cur
# gamma = dsigma + 0.5*dt*ddsigma
with fwAD.dual_level():
dual_x = fwAD.make_dual(x_next, 0.5*dt*dt*gamma/sigma_cur*f_cur)
dual_t = fwAD.make_dual(sigma_next, 0.5*dt*dt/sigma_cur)
dual_out = net(dual_x, dual_t, class_labels)
denoised, jfp = fwAD.unpack_dual(dual_out)
jfp = (0.5*dt*dt*gamma/sigma_cur*f_cur + 0.5*dt*dt*gamma/sigma_cur*f_cur -jfp).to(torch.float64) # 0.5*dt^2 * (x_next-x_cur)
# print((f_next-f_cur-JF*2/dt).pow(2).sum().sqrt()) # Jf*2/dt = d(gamma_cur*f(x_next, t_cur) - gamma_cur*f(x_cur, t_cur))
# x_next = x_cur + 0.5*(f_cur*gamma+f_next*gamma)*dt``
x_next = x_cur + (f_cur*dt + jfp)*gamma
'''
# # backward 2nd order sampling.
if i > 0 and sigma_cur >= S_max:
with fwAD.dual_level():
dual_x = fwAD.make_dual(x_cur, 0.5*dt*dt*gamma/sigma_cur*f_cur+diffusion_coff*noise[1]/sigma_cur)
dual_t = fwAD.make_dual(sigma_cur, 0.5*dt*dt/sigma_cur)
dual_out = net(dual_x, dual_t, class_labels)
denoised, jfp = fwAD.unpack_dual(dual_out)
jfp = (dt*dt*gamma/sigma_cur*f_cur + 0.5*dt*dt/sigma_cur*diffusion_coff*noise[0] + noise[1]/sigma_cur -jfp).to(torch.float64) # 0.5*dt^2 * (x_next-x_cur)
f_cur = (x_cur - denoised) / sigma_cur
x_next = x_cur + (f_cur*dt + jfp)*gamma
else:
# # Euler step.
denoised = net(x_cur, sigma_cur, class_labels).to(torch.float64)
f_cur = (x_cur - denoised) / sigma_cur
# # v1.0 implementation
# dsigma = B*rho*(sigma_cur.pow((rho-1)/rho))
# ddsigma = rho*(rho-1)*B*B*(sigma_cur.pow((rho-2)/rho))
# g_cur = ddsigma*sigma_cur
# gamma = dsigma + 0.5*dt*ddsigma
x_next = x_next + f_cur*gamma*dt
# if sigma_cur < S_min and i < num_steps - 1:
# denoised = net(x_next, sigma_next, class_labels).to(torch.float64)
# # gamma_next=torch.tensor(0.0, dtype=torch.float64, device=latents.device)
# # prod=torch.tensor(1.0, dtype=torch.float64, device=latents.device)
# # for j in range(1, int(rho)):
# # prod *= (B*(rho-j+1)/j)
# # gamma_next += dt**(j-1)*prod*sigma_next**((rho-j)/rho)
# f_prime = (x_next - denoised) / sigma_next
# x_next = x_next + 0.5*(f_prime*gamma-f_cur*gamma)*dt
return x_next
#----------------------------------------------------------------------------
# Generalized ablation sampler, representing the superset of all sampling
# methods discussed in the paper.
def ablation_sampler(
net, latents, class_labels=None, randn_like=torch.randn_like,
num_steps=18, sigma_min=None, sigma_max=None, rho=7,
solver='heun', discretization='edm', schedule='linear', scaling='none',
epsilon_s=1e-3, C_1=0.001, C_2=0.008, M=1000, alpha=1,
S_churn=0, S_min=0, S_max=float('inf'), S_noise=1,
):
assert solver in ['euler', 'heun']
assert discretization in ['vp', 've', 'iddpm', 'edm']
assert schedule in ['vp', 've', 'linear']
assert scaling in ['vp', 'none']
# Helper functions for VP & VE noise level schedules.
vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5
vp_sigma_deriv = lambda beta_d, beta_min: lambda t: 0.5 * (beta_min + beta_d * t) * (sigma(t) + 1 / sigma(t))
vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d
ve_sigma = lambda t: t.sqrt()
ve_sigma_deriv = lambda t: 0.5 / t.sqrt()
ve_sigma_inv = lambda sigma: sigma ** 2
# Select default noise level range based on the specified time step discretization.
if sigma_min is None:
vp_def = vp_sigma(beta_d=19.9, beta_min=0.1)(t=epsilon_s)
sigma_min = {'vp': vp_def, 've': 0.02, 'iddpm': 0.002, 'edm': 0.002}[discretization]
if sigma_max is None:
vp_def = vp_sigma(beta_d=19.9, beta_min=0.1)(t=1)
sigma_max = {'vp': vp_def, 've': 100, 'iddpm': 81, 'edm': 80}[discretization]
# 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)
# Compute corresponding betas for VP.
vp_beta_d = 2 * (np.log(sigma_min ** 2 + 1) / epsilon_s - np.log(sigma_max ** 2 + 1)) / (epsilon_s - 1)
vp_beta_min = np.log(sigma_max ** 2 + 1) - 0.5 * vp_beta_d
# Define time steps in terms of noise level.
step_indices = torch.arange(num_steps, dtype=torch.float64, device=latents.device)
if discretization == 'vp':
orig_t_steps = 1 + step_indices / (num_steps - 1) * (epsilon_s - 1)
sigma_steps = vp_sigma(vp_beta_d, vp_beta_min)(orig_t_steps)
elif discretization == 've':
orig_t_steps = (sigma_max ** 2) * ((sigma_min ** 2 / sigma_max ** 2) ** (step_indices / (num_steps - 1)))
sigma_steps = ve_sigma(orig_t_steps)
elif discretization == 'iddpm':
u = torch.zeros(M + 1, dtype=torch.float64, device=latents.device)
alpha_bar = lambda j: (0.5 * np.pi * j / M / (C_2 + 1)).sin() ** 2
for j in torch.arange(M, 0, -1, device=latents.device): # M, ..., 1
u[j - 1] = ((u[j] ** 2 + 1) / (alpha_bar(j - 1) / alpha_bar(j)).clip(min=C_1) - 1).sqrt()
u_filtered = u[torch.logical_and(u >= sigma_min, u <= sigma_max)]
sigma_steps = u_filtered[((len(u_filtered) - 1) / (num_steps - 1) * step_indices).round().to(torch.int64)]
else:
assert discretization == 'edm'
sigma_steps = (sigma_max ** (1 / rho) + step_indices / (num_steps - 1) * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho))) ** rho
# Define noise level schedule.
if schedule == 'vp':
sigma = vp_sigma(vp_beta_d, vp_beta_min)
sigma_deriv = vp_sigma_deriv(vp_beta_d, vp_beta_min)
sigma_inv = vp_sigma_inv(vp_beta_d, vp_beta_min)
elif schedule == 've':
sigma = ve_sigma
sigma_deriv = ve_sigma_deriv
sigma_inv = ve_sigma_inv
else:
assert schedule == 'linear'
sigma = lambda t: t
sigma_deriv = lambda t: 1
sigma_inv = lambda sigma: sigma
# Define scaling schedule.
if scaling == 'vp':
s = lambda t: 1 / (1 + sigma(t) ** 2).sqrt()
s_deriv = lambda t: -sigma(t) * sigma_deriv(t) * (s(t) ** 3)
else:
assert scaling == 'none'
s = lambda t: 1
s_deriv = lambda t: 0
# Compute final time steps based on the corresponding noise levels.
t_steps = sigma_inv(net.round_sigma(sigma_steps))
t_steps = torch.cat([t_steps, torch.zeros_like(t_steps[:1])]) # t_N = 0
# Main sampling loop.
t_next = t_steps[0]
x_next = latents.to(torch.float64) * (sigma(t_next) * s(t_next))
for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): # 0, ..., N-1
x_cur = x_next
# Increase noise temporarily.
gamma = min(S_churn / num_steps, np.sqrt(2) - 1) if S_min <= sigma(t_cur) <= S_max else 0
t_hat = sigma_inv(net.round_sigma(sigma(t_cur) + gamma * sigma(t_cur)))
x_hat = s(t_hat) / s(t_cur) * x_cur + (sigma(t_hat) ** 2 - sigma(t_cur) ** 2).clip(min=0).sqrt() * s(t_hat) * S_noise * randn_like(x_cur)
# Euler step.
h = t_next - t_hat
denoised = net(x_hat / s(t_hat), sigma(t_hat), class_labels).to(torch.float64)
d_cur = (sigma_deriv(t_hat) / sigma(t_hat) + s_deriv(t_hat) / s(t_hat)) * x_hat - sigma_deriv(t_hat) * s(t_hat) / sigma(t_hat) * denoised
x_prime = x_hat + alpha * h * d_cur
t_prime = t_hat + alpha * h
# Apply 2nd order correction.
if solver == 'euler' or i == num_steps - 1:
x_next = x_hat + h * d_cur
else:
assert solver == 'heun'
denoised = net(x_prime / s(t_prime), sigma(t_prime), class_labels).to(torch.float64)
d_prime = (sigma_deriv(t_prime) / sigma(t_prime) + s_deriv(t_prime) / s(t_prime)) * x_prime - sigma_deriv(t_prime) * s(t_prime) / sigma(t_prime) * denoised
x_next = x_hat + h * ((1 - 1 / (2 * alpha)) * d_cur + 1 / (2 * alpha) * d_prime)
return x_next
#----------------------------------------------------------------------------
# Wrapper for torch.Generator that allows specifying a different random seed
# for each sample in a minibatch.
class StackedRandomGenerator:
def __init__(self, device, seeds):
super().__init__()
self.generators = [torch.Generator(device).manual_seed(int(seed) % (1 << 32)) for seed in seeds]
def randn(self, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack([torch.randn(size[1:], generator=gen, **kwargs) for gen in self.generators])
def multiGaussian_like(self, input_tensor, d_t, **kwargs):
'''
return d_beta and 2nd-order d_beta
'''
size = input_tensor.shape
device = input_tensor.device
assert size[0] == len(self.generators)
# Cholesky decomposition of covariance matrix
up_left = ((1/3)*d_t**3)**0.5
bottom_left = 0.5*(3*d_t)**0.5
bottom_right = 0.5*d_t**0.5
eps = [torch.randn([2, size[1], size[2], size[3]], generator=gen, **kwargs, device=device) for gen in self.generators]
dd_beta = torch.stack([noise[0]*up_left for noise in eps])
d_beta = torch.stack([noise[0]*bottom_left + noise[1]*bottom_right for noise in eps])
return (d_beta, dd_beta)
def randn_like(self, input):
return self.randn(input.shape, dtype=input.dtype, layout=input.layout, device=input.device)
def randint(self, *args, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack([torch.randint(*args, size=size[1:], generator=gen, **kwargs) for gen in self.generators])
def multiGaussian_like(input_tensor, d_t, **kwargs):
'''
return d_beta and 2nd-order d_beta
'''
size = input_tensor.shape
device = input_tensor.device
# Cholesky decomposition of covariance matrix
up_left = ((1/3)*d_t**3)**0.5
bottom_left = 0.5*(3*d_t)**0.5
bottom_right = 0.5*d_t**0.5
# up_right = torch.zeros_like(up_left)
# left = torch.concat((up_left, bottom_left) , dim=0)
# right = torch.concat((up_right, bottom_right) , dim=0)
# mean = torch.concat((left, right), dim=1)
eps = torch.randn([2, size[1], size[2], size[3]], **kwargs, device=device)
dd_beta = eps[0]*up_left
d_beta = eps[0]*bottom_left + eps[1]*bottom_right
return (d_beta, dd_beta)
#----------------------------------------------------------------------------
# Parse a comma separated list of numbers or ranges and return a list of ints.
# Example: '1,2,5-10' returns [1, 2, 5, 6, 7, 8, 9, 10]
def parse_int_list(s):
if isinstance(s, list): return s
ranges = []
range_re = re.compile(r'^(\d+)-(\d+)$')
for p in s.split(','):
m = range_re.match(p)
if m:
ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
else:
ranges.append(int(p))
return ranges
#----------------------------------------------------------------------------
@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', metavar='PATH|URL', type=str, required=True)
@click.option('--outdir', help='Where to save the output images', metavar='DIR', type=str, required=True)
@click.option('--seeds', help='Random seeds (e.g. 1,2,5-10)', metavar='LIST', type=parse_int_list, default='0-63', show_default=True)
@click.option('--subdirs', help='Create subdirectory for every 1000 seeds', is_flag=True)
@click.option('--class', 'class_idx', help='Class label [default: random]', metavar='INT', type=click.IntRange(min=0), default=None)
@click.option('--batch', 'max_batch_size', help='Maximum batch size', metavar='INT', type=click.IntRange(min=1), default=64, show_default=True)
# sampler
@click.option('--steps', 'num_steps', help='Number of sampling steps', metavar='INT', type=click.IntRange(min=1), default=18, show_default=True)
@click.option('--sigma_min', help='Lowest noise level [default: varies]', metavar='FLOAT', type=click.FloatRange(min=0, min_open=True))
@click.option('--sigma_max', help='Highest noise level [default: varies]', metavar='FLOAT', type=click.FloatRange(min=0, min_open=True))
@click.option('--rho', help='Time step exponent', metavar='FLOAT', type=click.FloatRange(min=0, min_open=True), default=7, show_default=True)
@click.option('--S_churn', 'S_churn', help='Stochasticity strength', metavar='FLOAT', type=click.FloatRange(min=0), default=0, show_default=True)
@click.option('--S_min', 'S_min', help='Stoch. min noise level', metavar='FLOAT', type=click.FloatRange(min=0), default=0, show_default=True)
@click.option('--S_max', 'S_max', help='Stoch. max noise level', metavar='FLOAT', type=click.FloatRange(min=0), default='inf', show_default=True)
@click.option('--S_noise', 'S_noise', help='Stoch. noise inflation', metavar='FLOAT', type=float, default=1, show_default=True)
# @click.option('--k', help='residual order of diffusion-coefficient', metavar='FLOAT', type=click.FloatRange(min=0, min_open=False), default=0, show_default=True)
@click.option('--randn_like', help='Stoch. Brownian motions generator', metavar='db|ddb', type=click.Choice(['db', 'ddb']), default='db')
# ablation
@click.option('--solver', help='Ablate ODE solver', metavar='euler|heun', type=click.Choice(['euler', 'heun']))
@click.option('--disc', 'discretization', help='Ablate time step discretization {t_i}', metavar='vp|ve|iddpm|edm', type=click.Choice(['vp', 've', 'iddpm', 'edm']))
@click.option('--schedule', help='Ablate noise schedule sigma(t)', metavar='vp|ve|linear', type=click.Choice(['vp', 've', 'linear']))
@click.option('--scaling', help='Ablate signal scaling s(t)', metavar='vp|none', type=click.Choice(['vp', 'none']))
def main(network_pkl, outdir, subdirs, seeds, class_idx, max_batch_size, device=torch.device('cuda'), **sampler_kwargs):
"""Generate random images using the techniques described in the paper
"Elucidating the Design Space of Diffusion-Based Generative Models".
Examples:
\b
# Generate 64 images and save them as out/*.png
python generate.py --outdir=out --seeds=0-63 --batch=64 \\
--network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-cond-vp.pkl
\b
# Generate 1024 images using 2 GPUs
torchrun --standalone --nproc_per_node=2 generate.py --outdir=out --seeds=0-999 --batch=64 \\
--network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-cond-vp.pkl
"""
dist.init()
num_batches = ((len(seeds) - 1) // (max_batch_size * dist.get_world_size()) + 1) * dist.get_world_size()
all_batches = torch.as_tensor(seeds).tensor_split(num_batches)
rank_batches = all_batches[dist.get_rank() :: dist.get_world_size()]
# Rank 0 goes first.
if dist.get_rank() != 0:
torch.distributed.barrier()
# Load network.
dist.print0(f'Loading network from local directorty "{network_pkl}"...')
with open(network_pkl, 'rb') as f:
net = pickle.load(f).to(device)
# Other ranks follow.
if dist.get_rank() == 0:
torch.distributed.barrier()
# Loop over batches.
dist.print0(f'Generating {len(seeds)} images to "{outdir}"...')
for batch_seeds in tqdm.tqdm(rank_batches, unit='batch', disable=(dist.get_rank() != 0)):
torch.distributed.barrier()
batch_size = len(batch_seeds)
if batch_size == 0:
continue
# Pick latents and labels.
rnd = StackedRandomGenerator(device, batch_seeds)
latents = rnd.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)[rnd.randint(net.label_dim, size=[batch_size], device=device)] # random initialize class vector
if class_idx is not None:
class_labels[:, :] = 0
class_labels[:, class_idx] = 1 # one-hot
# Generate images.
sampler_kwargs = {key: value for key, value in sampler_kwargs.items() if value is not None} # unwrap kwargs, withdraw non-stated params
have_ablation_kwargs = any(x in sampler_kwargs for x in ['solver', 'discretization', 'schedule', 'scaling'])
if 'randn_like' in sampler_kwargs and type(sampler_kwargs['randn_like']) is str:
sampler_kwargs['randn_like'] = rnd.randn_like if sampler_kwargs['randn_like'] == 'db' else rnd.multiGaussian_like
sampler_fn = ablation_sampler if have_ablation_kwargs else edm_sampler
images = sampler_fn(net, latents, class_labels, **sampler_kwargs)
# Save images.
images_np = (images * 127.5 + 128).clip(0, 255).to(torch.uint8).permute(0, 2, 3, 1).cpu().numpy()
# # Save batch images
# images_dir = os.path.join(outdir, f'{class_idx:02d}') if subdirs else outdir
# os.makedirs(images_dir, exist_ok=True)
# images_path = os.path.join(images_dir, f'{class_idx:02d}.png')
# PIL.Image.fromarray(images_np, 'RGB').save(images_path)
for seed, image_np in zip(batch_seeds, images_np):
image_dir = os.path.join(outdir, f'{seed-seed%1000:06d}') if subdirs else outdir
os.makedirs(image_dir, exist_ok=True)
image_path = os.path.join(image_dir, f'{seed:06d}.png')
if image_np.shape[2] == 1:
PIL.Image.fromarray(image_np[:, :, 0], 'L').save(image_path)
else:
PIL.Image.fromarray(image_np, 'RGB').save(image_path)
# Done.
torch.distributed.barrier()
dist.print0('Done.')
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------