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sample_ddp.py
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sample_ddp.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Samples a large number of images from a pre-trained DiT 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 DiT_models
from models_dig import DiG_models
from download import find_model
from diffusion import create_diffusion
from diffusers.models import AutoencoderKL
from tqdm import tqdm
import os
from PIL import Image
import numpy as np
import math
import argparse
from torchvision.utils import save_image
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(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 == "DiT-XL/2", "Only DiT-XL/2 models are available for auto-download."
assert args.image_size in [256, 512]
assert args.num_classes == 1000
# Load model:
latent_size = args.image_size // 8 if args.num_classes == 1000 else args.image_size
if "DiT" in args.model:
target_model = DiT_models
elif "DiG" in args.model:
target_model = DiG_models
else:
raise ValueError("Please specify a valid model name with --model.")
dtype = torch.float32
model = target_model[args.model](
input_size=latent_size,
num_classes=args.num_classes,
in_channels= 4 if args.num_classes == 1000 else 3,
).to(device).to(dtype)
# Auto-download a pre-trained model or load a custom DiT checkpoint from train.py:
ckpt_path = args.ckpt or f"DiT-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!
diffusion = create_diffusion(str(args.num_sampling_steps))
vae = AutoencoderKL.from_pretrained(f"/mnt/bn/userdata/ckpts/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"
folder_name = f"{model_string_name}-{ckpt_string_name}-size-{args.image_size}-vae-{args.vae}-" \
f"cfg-{args.cfg_scale}-seed-{args.global_seed}"
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:
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)
pbar = range(iterations)
pbar = tqdm(pbar) if rank == 0 else pbar
total = 0
for _ in pbar:
# Sample inputs:
z = torch.randn(n, model.in_channels, latent_size, latent_size, device=device, dtype=dtype)
if args.num_classes > 0:
y = torch.randint(0, args.num_classes, (n,), device=device)
if args.num_classes > 0:
# Setup classifier-free guidance:
if using_cfg:
z = torch.cat([z, z], 0)
y_null = torch.tensor([args.num_classes] * n, device=device)
y = torch.cat([y, y_null], 0)
model_kwargs = dict(y=y, cfg_scale=args.cfg_scale)
sample_fn = model.forward_with_cfg
else:
model_kwargs = dict(y=y)
sample_fn = model.forward
else:
model_kwargs = dict(y=None)
sample_fn = model.forward
# Sample images:
samples = diffusion.p_sample_loop(
sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=False, device=device
)
if using_cfg:
samples, _ = samples.chunk(2, dim=0) # Remove null class samples
if args.num_classes == 1000:
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
else:
# Save samples to disk as individual .png files
for i, sample in enumerate(samples):
index = i * dist.get_world_size() + rank + total
# print i, world_size, rank, total, and index
# print("i: ", i, "world_size: ", dist.get_world_size(), "rank: ", rank, "total: ", total, "index: ", index)
save_image(sample, f"{sample_folder_dir}/{index:06d}.png", normalize=True, value_range=(-1, 1))
total += global_batch_size
# 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()
parser.add_argument("--model", type=str, choices=list(DiT_models.keys()) + list(DiG_models.keys()), default="DiT-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=32)
parser.add_argument("--num-fid-samples", type=int, default=50_000)
parser.add_argument("--image-size", type=int, choices=[32, 64, 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=False,
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 DiT checkpoint (default: auto-download a pre-trained DiT-XL/2 model).")
args = parser.parse_args()
main(args)