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# LICENSE file in the root directory of this source tree.
"""
Samples a large number of images from a pre-trained SiT model using DDP.
Subsequently saves a .npz file that can be used to compute FID and other
evaluation metrics via the ADM repo: https://github.com/openai/guided-diffusion/tree/main/evaluations
For a simple single-GPU/CPU sampling script, see sample.py.
"""
import torch
import torch.distributed as dist
from models import SiT_models
from download import find_model
from transport import create_transport, Sampler
from diffusers.models import AutoencoderKL
from train_utils import parse_ode_args, parse_sde_args, parse_transport_args
from tqdm import tqdm
import os
from PIL import Image
import numpy as np
import math
import argparse
import sys
def create_npz_from_sample_folder(sample_dir, num=50_000):
"""
Builds a single .npz file from a folder of .png samples.
"""
samples = []
for i in tqdm(range(num), desc="Building .npz file from samples"):
sample_pil = Image.open(f"{sample_dir}/{i:06d}.png")
sample_np = np.asarray(sample_pil).astype(np.uint8)
samples.append(sample_np)
samples = np.stack(samples)
assert samples.shape == (num, samples.shape[1], samples.shape[2], 3)
npz_path = f"{sample_dir}.npz"
np.savez(npz_path, arr_0=samples)
print(f"Saved .npz file to {npz_path} [shape={samples.shape}].")
return npz_path
def main(mode, args):
"""
Run sampling.
"""
torch.backends.cuda.matmul.allow_tf32 = args.tf32 # True: fast but may lead to some small numerical differences
assert torch.cuda.is_available(), "Sampling with DDP requires at least one GPU. sample.py supports CPU-only usage"
torch.set_grad_enabled(False)
# Setup DDP:
dist.init_process_group("nccl")
rank = dist.get_rank()
device = rank % torch.cuda.device_count()
seed = args.global_seed * dist.get_world_size() + rank
torch.manual_seed(seed)
torch.cuda.set_device(device)
print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
if args.ckpt is None:
assert args.model == "SiT-XL/2", "Only SiT-XL/2 models are available for auto-download."
assert args.image_size in [256, 512]
assert args.num_classes == 1000
assert args.image_size == 256, "512x512 models are not yet available for auto-download." # remove this line when 512x512 models are available
learn_sigma = args.image_size == 256
else:
learn_sigma = False
# Load model:
latent_size = args.image_size // 8
model = SiT_models[args.model](
input_size=latent_size,
num_classes=args.num_classes,
learn_sigma=learn_sigma,
).to(device)
# Auto-download a pre-trained model or load a custom SiT checkpoint from train.py:
ckpt_path = args.ckpt or f"SiT-XL-2-{args.image_size}x{args.image_size}.pt"
state_dict = find_model(ckpt_path)
model.load_state_dict(state_dict)
model.eval() # important!
transport = create_transport(
args.path_type,
args.prediction,
args.loss_weight,
args.train_eps,
args.sample_eps
)
sampler = Sampler(transport)
if mode == "ODE":
if args.likelihood:
assert args.cfg_scale == 1, "Likelihood is incompatible with guidance"
sample_fn = sampler.sample_ode_likelihood(
sampling_method=args.sampling_method,
num_steps=args.num_sampling_steps,
atol=args.atol,
rtol=args.rtol,
)
else:
sample_fn = sampler.sample_ode(
sampling_method=args.sampling_method,
num_steps=args.num_sampling_steps,
atol=args.atol,
rtol=args.rtol,
reverse=args.reverse
)
elif mode == "SDE":
sample_fn = sampler.sample_sde(
sampling_method=args.sampling_method,
diffusion_form=args.diffusion_form,
diffusion_norm=args.diffusion_norm,
last_step=args.last_step,
last_step_size=args.last_step_size,
num_steps=args.num_sampling_steps,
)
vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device)
assert args.cfg_scale >= 1.0, "In almost all cases, cfg_scale be >= 1.0"
using_cfg = args.cfg_scale > 1.0
# Create folder to save samples:
model_string_name = args.model.replace("/", "-")
ckpt_string_name = os.path.basename(args.ckpt).replace(".pt", "") if args.ckpt else "pretrained"
if mode == "ODE":
folder_name = f"{model_string_name}-{ckpt_string_name}-" \
f"cfg-{args.cfg_scale}-{args.per_proc_batch_size}-"\
f"{mode}-{args.num_sampling_steps}-{args.sampling_method}"
elif mode == "SDE":
folder_name = f"{model_string_name}-{ckpt_string_name}-" \
f"cfg-{args.cfg_scale}-{args.per_proc_batch_size}-"\
f"{mode}-{args.num_sampling_steps}-{args.sampling_method}-"\
f"{args.diffusion_form}-{args.last_step}-{args.last_step_size}"
sample_folder_dir = f"{args.sample_dir}/{folder_name}"
if rank == 0:
os.makedirs(sample_folder_dir, exist_ok=True)
print(f"Saving .png samples at {sample_folder_dir}")
dist.barrier()
# Figure out how many samples we need to generate on each GPU and how many iterations we need to run:
n = args.per_proc_batch_size
global_batch_size = n * dist.get_world_size()
# To make things evenly-divisible, we'll sample a bit more than we need and then discard the extra samples:
num_samples = len([name for name in os.listdir(sample_folder_dir) if (os.path.isfile(os.path.join(sample_folder_dir, name)) and ".png" in name)])
total_samples = int(math.ceil(args.num_fid_samples / global_batch_size) * global_batch_size)
if rank == 0:
print(f"Total number of images that will be sampled: {total_samples}")
assert total_samples % dist.get_world_size() == 0, "total_samples must be divisible by world_size"
samples_needed_this_gpu = int(total_samples // dist.get_world_size())
assert samples_needed_this_gpu % n == 0, "samples_needed_this_gpu must be divisible by the per-GPU batch size"
iterations = int(samples_needed_this_gpu // n)
done_iterations = int( int(num_samples // dist.get_world_size()) // n)
pbar = range(iterations)
pbar = tqdm(pbar) if rank == 0 else pbar
total = 0
for i in pbar:
# Sample inputs:
z = torch.randn(n, model.in_channels, latent_size, latent_size, device=device)
y = torch.randint(0, args.num_classes, (n,), device=device)
# Setup classifier-free guidance:
if using_cfg:
z = torch.cat([z, z], 0)
y_null = torch.tensor([1000] * n, device=device)
y = torch.cat([y, y_null], 0)
model_kwargs = dict(y=y, cfg_scale=args.cfg_scale)
model_fn = model.forward_with_cfg
else:
model_kwargs = dict(y=y)
model_fn = model.forward
samples = sample_fn(z, model_fn, **model_kwargs)[-1]
if using_cfg:
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
samples = vae.decode(samples / 0.18215).sample
samples = torch.clamp(127.5 * samples + 128.0, 0, 255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()
# Save samples to disk as individual .png files
for i, sample in enumerate(samples):
index = i * dist.get_world_size() + rank + total
Image.fromarray(sample).save(f"{sample_folder_dir}/{index:06d}.png")
total += global_batch_size
dist.barrier()
# Make sure all processes have finished saving their samples before attempting to convert to .npz
dist.barrier()
if rank == 0:
create_npz_from_sample_folder(sample_folder_dir, args.num_fid_samples)
print("Done.")
dist.barrier()
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
if len(sys.argv) < 2:
print("Usage: program.py <mode> [options]")
sys.exit(1)
mode = sys.argv[1]
assert mode[:2] != "--", "Usage: program.py <mode> [options]"
assert mode in ["ODE", "SDE"], "Invalid mode. Please choose 'ODE' or 'SDE'"
parser.add_argument("--model", type=str, choices=list(SiT_models.keys()), default="SiT-XL/2")
parser.add_argument("--vae", type=str, choices=["ema", "mse"], default="ema")
parser.add_argument("--sample-dir", type=str, default="samples")
parser.add_argument("--per-proc-batch-size", type=int, default=4)
parser.add_argument("--num-fid-samples", type=int, default=50_000)
parser.add_argument("--image-size", type=int, choices=[256, 512], default=256)
parser.add_argument("--num-classes", type=int, default=1000)
parser.add_argument("--cfg-scale", type=float, default=1.0)
parser.add_argument("--num-sampling-steps", type=int, default=250)
parser.add_argument("--global-seed", type=int, default=0)
parser.add_argument("--tf32", action=argparse.BooleanOptionalAction, default=True,
help="By default, use TF32 matmuls. This massively accelerates sampling on Ampere GPUs.")
parser.add_argument("--ckpt", type=str, default=None,
help="Optional path to a SiT checkpoint (default: auto-download a pre-trained SiT-XL/2 model).")
parse_transport_args(parser)
if mode == "ODE":
parse_ode_args(parser)
# Further processing for ODE
elif mode == "SDE":
parse_sde_args(parser)
# Further processing for SDE
args = parser.parse_known_args()[0]
main(mode, args)
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