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b910c09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | import os
import sys
import math
import torch
import random
import argparse
import datetime
import numpy as np
from tqdm import tqdm
from PIL import Image
from functools import partial
from omegaconf import OmegaConf
from contextlib import nullcontext
from jutils import instantiate_from_config
from diffusers.models import AutoencoderKL
currentdir = os.path.dirname(__file__)
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
import patch_flow.pt_distributed as dist
NUM_CLASSES = 1000
DATA_SHAPE = (4, 32, 32) # 256x256 images
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}_N{num}.npz"
np.savez(npz_path, arr_0=samples)
print(f"Saved .npz file to {npz_path} [shape={samples.shape}].")
return npz_path
""" Main """
def main(args, sample_fn_overrides=None):
"""Setup distributed"""
dist.init_process_group(backend="nccl", init_method="env://", timeout=datetime.timedelta(seconds=90))
GLOBAL_RANK = dist.get_rank()
LOCAL_RANK = GLOBAL_RANK % torch.cuda.device_count()
DEV = torch.device(f"cuda:{LOCAL_RANK}")
WORLD_SIZE = dist.get_world_size()
is_rank0 = dist.is_primary()
print(f"[RANK {GLOBAL_RANK} | {WORLD_SIZE}] Initializing on device: {DEV}")
seed = args.global_seed * dist.get_world_size() + LOCAL_RANK
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
assert torch.cuda.is_available(), "Sampling with DDP requires at least one GPU."
if args.half_precision:
inference_context = torch.autocast("cuda")
else: # DEFAULT from SiT
torch.backends.cuda.matmul.allow_tf32 = args.tf32 # True: fast but may lead to some small numerical differences
inference_context = nullcontext()
torch.set_grad_enabled(False)
dist.print0(f"Global seed set to {seed}")
dist.print0("=" * 40)
for k, v in vars(args).items():
dist.print0(f"{k:20}: {v}")
dist.print0("=" * 40)
""" sampling function """
timesteps = torch.linspace(0, 1, args.num_sampling_steps + 1)
sample_fn_cfg = OmegaConf.load(args.sample_fn_config)
if sample_fn_overrides is not None: # merge with overrides
sample_fn_cfg = OmegaConf.merge(sample_fn_cfg, sample_fn_overrides)
sampler = instantiate_from_config(sample_fn_cfg)
sample_fn = partial(sampler, timesteps=timesteps)
dist.print0(OmegaConf.to_yaml(sample_fn_cfg))
dist.print0("=" * 40)
""" Load model """
ckpt = torch.load(args.ckpt, map_location="cpu")
config = ckpt["config"]
state_dict = ckpt["state_dict"]
model = instantiate_from_config(config).to(DEV)
model.load_state_dict(state_dict)
model.eval() # important!
vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(DEV)
assert args.cfg_scale >= 1.0, "In almost all cases, cfg_scale be >= 1.0"
""" Saving folder """
sample_dir = os.path.join(os.path.dirname(args.ckpt), "samples")
ckpt_string_name = os.path.basename(args.ckpt).replace(".ckpt", "")
sample_fn_postfix = f"{sampler}" # uses __repr__ method of sampler class
folder_name = (
f"{ckpt_string_name}-"
f"cfg-{args.cfg_scale}-"
f"{args.num_sampling_steps}_seed{args.global_seed}_{sample_fn_postfix}"
)
sample_folder_dir = f"{sample_dir}/{folder_name}"
os.makedirs(sample_folder_dir, exist_ok=True)
dist.print0(f"Saving samples to {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()
total_samples = int(math.ceil(args.num_fid_samples / global_batch_size) * global_batch_size)
dist.print0(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 is_rank0 else pbar
total = 0
all_samples = []
for i in pbar:
# Sample inputs:
z = torch.randn(n, *DATA_SHAPE, device=DEV)
y = torch.randint(0, NUM_CLASSES, (n,), device=DEV)
y_null = torch.tensor([1000] * n, device=DEV) # for cfg
model_kwargs = dict(y=y, uc_cond=y_null, cond_key="y", cfg_scale=args.cfg_scale)
with inference_context:
samples = sample_fn(
model=model,
x=z,
progress=False,
**model_kwargs,
)
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()
all_samples.append(samples)
total += global_batch_size
dist.barrier()
# Make sure all processes have finished saving their samples before attempting to convert to .npz
dist.barrier()
all_samples = np.concatenate(all_samples, axis=0)
# gather all samples over GPUs
all_samples = torch.tensor(all_samples).to(DEV).contiguous()
gathered_samples = dist.gather(all_samples)
gathered_samples = torch.cat(gathered_samples, dim=0).cpu().numpy()
# build the npz file
if is_rank0:
# store the desired number of samples
npz_path = f"{sample_folder_dir}_N{args.num_fid_samples}.npz"
arr_0 = gathered_samples[: args.num_fid_samples]
assert arr_0.shape[0] == args.num_fid_samples, f"Expected {args.num_fid_samples} samples, got {arr_0.shape[0]}"
np.savez(npz_path, arr_0=arr_0)
print(f"Saved .npz file to {npz_path} [shape={arr_0.shape}].")
# store 10k samples
if args.num_fid_samples > 10000 and gathered_samples.shape[0] > 10000:
npz_path = f"{sample_folder_dir}_N10000.npz"
np.savez(npz_path, arr_0=gathered_samples[:10000])
print(f"Saved .npz file to {npz_path} [shape={gathered_samples[:10000].shape}].")
dist.barrier()
dist.destroy_process_group()
""" Parsing utils """
def unknowns_to_dict(unknown):
"""Convert a list of 'key=value' strings (dot-notation) into a nested dict."""
bad = [u for u in unknown if u.startswith("-") or " " in u or u.strip() != u or "=" not in u]
if bad:
raise ValueError(f"Invalid override args (expected key=value without spaces): {bad}")
if not unknown:
return {}
# OmegaConf parses values (int, float, bool, lists, null) automatically
conf = OmegaConf.from_dotlist(unknown)
return OmegaConf.to_container(conf, resolve=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ckpt", type=str, required=True, help="Path to a checkpoint.")
parser.add_argument("--sample-fn-config", type=str, default="configs/sampler/euler-pf.yaml")
parser.add_argument("--per-proc-batch-size", type=int, default=64)
parser.add_argument("--num-fid-samples", type=int, default=10_000)
parser.add_argument("--cfg-scale", type=float, default=1.0)
parser.add_argument("--num-sampling-steps", type=int, default=100)
parser.add_argument("--global-seed", type=int, default=0)
parser.add_argument("--tf32", action=argparse.BooleanOptionalAction, default=True, help="Use TF32 matmuls.")
parser.add_argument("--half_precision", action="store_true", help="Use this flag to enable bf16.")
# Unknown args will be passed as overrides to the sample function config, e.g. following
# dot-notation you can pass, e.g. params.p=0.4
known, unknown = parser.parse_known_args()
unknown = unknowns_to_dict(unknown)
main(known, unknown)
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