File size: 5,027 Bytes
6c311ad | 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 | from __future__ import annotations
import argparse
import io
import os
import tarfile
import time
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import torch
from PIL import Image
from diffusers import AutoencoderKL
from transformers import CLIPTokenizer, T5TokenizerFast
from huggingface_hub import hf_hub_download
REPO = "undefined443/cc12m-wds-coco-recaptioned"
def csr(img, size):
img = img.convert("RGB")
w, h = img.size
s = min(w, h)
l, t = (w - s) // 2, (h - s) // 2
return np.asarray(img.crop((l, t, l + s, t + s)).resize((size, size), Image.BICUBIC), dtype=np.uint8)
def load_shard_items(tar_path, size):
t = tarfile.open(tar_path)
raw = {}
for m in t.getmembers():
if not m.isfile():
continue
key, ext = m.name.rsplit(".", 1)
raw.setdefault(key, {})[ext] = t.extractfile(m).read()
t.close()
def proc(kv):
_, d = kv
if "jpg" not in d or "txt" not in d:
return None
try:
arr = csr(Image.open(io.BytesIO(d["jpg"])), size)
cap = d["txt"].decode("utf-8", "ignore").strip()
if not cap:
return None
return arr, cap
except Exception:
return None
results = []
with ThreadPoolExecutor(max_workers=32) as pool:
for r in pool.map(proc, raw.items()):
if r is not None:
results.append(r)
return results
@torch.no_grad()
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--out", default="/root/v6cache/shards")
ap.add_argument("--tmp", default="/root/v6cache/tars")
ap.add_argument("--size", type=int, default=256)
ap.add_argument("--t5-len", type=int, default=32)
ap.add_argument("--clip-len", type=int, default=40)
ap.add_argument("--batch", type=int, default=128)
ap.add_argument("--vae", default="madebyollin/sdxl-vae-fp16-fix")
ap.add_argument("--clip", default="openai/clip-vit-base-patch32")
ap.add_argument("--t5", default="google/flan-t5-base")
ap.add_argument("--start", type=int, default=0)
ap.add_argument("--end", type=int, default=598)
ap.add_argument("--prefetch", type=int, default=2)
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
os.makedirs(args.tmp, exist_ok=True)
dev = "cuda"
vae = AutoencoderKL.from_pretrained(args.vae).to(dev).half().eval()
scale = vae.config.scaling_factor
print(f"[prep] vae={args.vae} scaling_factor={scale}", flush=True)
clip_tok = CLIPTokenizer.from_pretrained(args.clip)
t5_tok = T5TokenizerFast.from_pretrained(args.t5)
shard_names = [f"cc12m-coco-{i:04d}.tar" for i in range(args.start, args.end)]
def fetch(name):
return hf_hub_download(REPO, name, repo_type="dataset", local_dir=args.tmp)
fpool = ThreadPoolExecutor(max_workers=args.prefetch)
futures = {}
def ensure_fetch(idx):
if idx < len(shard_names) and idx not in futures:
futures[idx] = fpool.submit(fetch, shard_names[idx])
for k in range(args.prefetch):
ensure_fetch(k)
t0 = time.time()
total = 0
for i, name in enumerate(shard_names):
out_path = f"{args.out}/shard_{args.start+i:04d}.npz"
if os.path.exists(out_path):
total += np.load(out_path)["latents"].shape[0]
futures.pop(i, None)
ensure_fetch(i + args.prefetch)
continue
tar_path = futures.pop(i).result()
ensure_fetch(i + args.prefetch)
items = load_shard_items(tar_path, args.size)
os.remove(tar_path)
if not items:
print(f"[prep] shard {args.start+i:04d} EMPTY, skipping", flush=True)
continue
imgs = [a for a, c in items]
caps = [c for a, c in items]
lat_chunks = []
for j in range(0, len(imgs), args.batch):
chunk = np.stack(imgs[j:j + args.batch]).astype(np.float32) / 127.5 - 1.0
x = torch.from_numpy(chunk).permute(0, 3, 1, 2).to(dev).half()
z = vae.encode(x).latent_dist.mean * scale
lat_chunks.append(z.cpu().numpy().astype(np.float16))
latents = np.concatenate(lat_chunks)
t5o = t5_tok(caps, padding="max_length", max_length=args.t5_len, truncation=True, return_tensors="np")
clip_ids = clip_tok(caps, padding="max_length", max_length=args.clip_len, truncation=True,
return_tensors="np")["input_ids"]
np.savez(out_path, latents=latents,
t5_ids=t5o["input_ids"].astype(np.int32),
t5_mask=t5o["attention_mask"].astype(np.int8),
clip_ids=clip_ids.astype(np.int64))
total += len(imgs)
el = time.time() - t0
print(f"[prep] shard {args.start+i:04d} +{len(imgs)} total={total} "
f"({total/el:.1f} img/s, {el/3600:.2f}h elapsed)", flush=True)
print(f"[prep] DONE total={total}", flush=True)
if __name__ == "__main__":
main()
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