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generate.py
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# Copyright 2023 Garena Online Private Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generate random images using EDM Sampler and DPM-Solver++."""
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 dpm_solver import NoiseScheduleEDM, DPM_Solver
#----------------------------------------------------------------------------
# EDM sampler
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,
):
# 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=latents.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 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 <= 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 * 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)
return x_next
#----------------------------------------------------------------------------
# DPM-Solver++
def ablation_sampler(
net, latents, class_labels=None, randn_like=torch.randn_like,
num_steps=18, sigma_min=0.002, sigma_max=80, rho=7,
denoise=True, **kwargs
):
x_next = latents.to(torch.float64) * sigma_max
ns = NoiseScheduleEDM('linear')
with torch.no_grad():
noise_pred_fn = lambda x, t: (x - net(x, t, class_labels).to(torch.float64)) / t
dpm_solver = DPM_Solver(noise_pred_fn, ns, algorithm_type="dpmsolver++")
# Initial sample
x_next = dpm_solver.sample(
x_next,
steps=num_steps - 1 if denoise else num_steps,
t_start=sigma_max,
t_end=sigma_min,
order=3,
skip_type="logSNR",
method="singlestep",
denoise_to_zero=denoise,
lower_order_final=True,
)
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 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])
#----------------------------------------------------------------------------
# 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)
@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('--solver', help='Ablate ODE solver', metavar='edm|dpm', type=click.Choice(['edm', 'dpm']), default='edm')
def main(network_pkl, outdir, subdirs, seeds, class_idx, max_batch_size, device=torch.device('cuda'), **sampler_kwargs):
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 "{network_pkl}"...')
with dnnlib.util.open_url(network_pkl, verbose=(dist.get_rank() == 0)) as f:
net = pickle.load(f)['ema'].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)]
if class_idx is not None:
class_labels[:, :] = 0
class_labels[:, class_idx] = 1
# Generate images.
sampler_kwargs = {key: value for key, value in sampler_kwargs.items() if value is not None}
sampler_fn = edm_sampler if sampler_kwargs.pop('solver', 'edm') == 'edm' else ablation_sampler
images = sampler_fn(net, latents, class_labels, randn_like=rnd.randn_like, **sampler_kwargs)
# Save images.
images_np = (images * 127.5 + 128).clip(0, 255).to(torch.uint8).permute(0, 2, 3, 1).cpu().numpy()
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()
#----------------------------------------------------------------------------