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sample_ddp.py
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sample_ddp.py
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"""
This script is a modification of work originally created by Bill Peebles, Saining Xie, and Ikko Eltociear Ashimine
Original work licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Original source: https://github.com/facebookresearch/DiT
License: https://creativecommons.org/licenses/by-nc/4.0/
Samples a large number of images from a pre-trained 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 os
import argparse
import math
import torch
import torch.distributed as dist
import numpy as np
from PIL import Image
from tqdm import tqdm
from diffusion import create_diffusion
from diffusers.models import AutoencoderKL
from models.edimt import EDiMT_models
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()}.")
# Load model:
latent_size = args.image_size // 8
model = EDiMT_models[args.model](
input_size=latent_size,
num_classes=args.num_classes,
).to(device)
# Load a E-DiMT checkpoint:
state_dict = torch.load(args.ckpt, map_location=lambda storage, loc: storage)
if "ema" in state_dict: # supports checkpoints from train.py
state_dict = state_dict["ema"]
model.load_state_dict(state_dict)
model.eval() # important!
diffusion = create_diffusion(str(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", "")
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}/{model_string_name}/{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)
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)
sample_fn = model.forward_with_cfg
else:
model_kwargs = dict(y=y)
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
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
# 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()
#-- torchrun --nnodes=1 --nproc_per_node=8 --model EDiMT-L/2 --ckpt /path/to/model.pt sample_ddp.py
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, choices=list(EDiMT_models.keys()), default="EDiMT-L/2")
parser.add_argument("--vae", type=str, choices=["ema", "mse"], default="ema")
parser.add_argument("--sample-dir", type=str, default="/evaluator/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=[256, 512], default=256)
parser.add_argument("--num-classes", type=int, default=1000)
parser.add_argument("--cfg-scale", type=float, default=1.5)
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)
args = parser.parse_args()
main(args)