Datasets:
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8ba5a96 | 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
make_text_table.py —— 把池子里出现过的所有 prompt 串缓存成 UMT5 文本表
训练时不再挂 11 GB 的 UMT5：DiT 直接查这张表。
表必须覆盖池里每一个 prompt 串，缺一个训练就会在中途 KeyError。
串的分组（与旧交付一致）：
bare 9 条 场景 dropout 后的裸串（9 个动作）
scene_action ≤162 条 18 场景 × 9 动作里实际出现过的组合
trigger_* 转场语料的从句（burn / hardcut），由 burn_corpus.py 追加
"""
from __future__ import annotations
import argparse
import glob
import os
import sys
import time
import torch
sys.path.insert(0, "/nfs/zhiyangdeng/Incantation/wan") # 只读：modules.t5
CKPT = "/data/zhiyangdeng/wan_base/Wan2.2-TI2V-5B"
def collect(latent_dir: str) -> list[str]:
need = set()
files = sorted(glob.glob(os.path.join(latent_dir, "clip_*.pt")))
for i, f in enumerate(files):
d = torch.load(f, map_location="cpu", weights_only=False)
need.update(d["prompts"])
need.update(d["prompts_bossdrop"])
if (i + 1) % 2000 == 0:
print(f" 扫描 {i+1:,}/{len(files):,} ... 当前 {len(need):,} 个不同串", flush=True)
return sorted(need)
def main():
ap = argparse.ArgumentParser(description="UMT5 文本表")
ap.add_argument("--latent", default="/data/zhiyangdeng/data_eybx/latent/pool")
ap.add_argument("--out", default=None, help="默认 <latent>/../text_table_eybx.pt")
ap.add_argument("--extra", nargs="*", default=[], help="额外要加入的串（转场语料）")
ap.add_argument("--t5_pth", default=os.path.join(CKPT, "models_t5_umt5-xxl-enc-bf16.pth"))
ap.add_argument("--tok", default=os.path.join(CKPT, "google/umt5-xxl"))
ap.add_argument("--text_len", type=int, default=512)
ap.add_argument("--batch", type=int, default=16)
ap.add_argument("--device", default="cuda")
args = ap.parse_args()
out = args.out or os.path.join(os.path.dirname(args.latent.rstrip("/")),
"text_table_eybx.pt")
print("扫描池里的 prompt 串 ...", flush=True)
keys = collect(args.latent)
for e in args.extra:
if e not in keys:
keys.append(e)
keys = sorted(set(keys))
print(f"{len(keys):,} 个不同串 -> {out}", flush=True)
from modules.t5 import T5EncoderModel
t5 = T5EncoderModel(text_len=args.text_len, dtype=torch.bfloat16, device=args.device,
checkpoint_path=args.t5_pth, tokenizer_path=args.tok)
table = {}
t0 = time.time()
for i in range(0, len(keys), args.batch):
chunk = keys[i:i + args.batch]
with torch.no_grad():
outs = t5(chunk, args.device)
for k, v in zip(chunk, outs):
# 统一 pad 到 text_len，训练侧直接 stack
e = torch.zeros(args.text_len, v.shape[-1], dtype=torch.bfloat16)
e[:v.shape[0]] = v.to(torch.bfloat16).cpu()
table[k] = e
if (i // args.batch) % 20 == 0:
el = time.time() - t0
print(f" {i+len(chunk):,}/{len(keys):,} · {el/60:.1f} min", flush=True)
torch.save(table, out)
sz = os.path.getsize(out) / 1e9
print(f"DONE {len(table):,} 键 · {sz:.2f} GB -> {out}")
if __name__ == "__main__":
main()
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