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+test,watch/image_0032.jpg
+test,watch/image_0221.jpg
+test,watch/image_0171.jpg
+test,watch/image_0036.jpg
+test,watch/image_0060.jpg
+test,watch/image_0134.jpg
+test,watch/image_0118.jpg
+test,watch/image_0070.jpg
+test,watch/image_0111.jpg
+test,watch/image_0071.jpg
+test,watch/image_0204.jpg
+test,watch/image_0006.jpg
+test,watch/image_0203.jpg
+test,watch/image_0015.jpg
+test,watch/image_0235.jpg
+test,watch/image_0144.jpg
+test,watch/image_0211.jpg
+test,watch/image_0220.jpg
+test,watch/image_0003.jpg
+test,watch/image_0115.jpg
+test,watch/image_0050.jpg
+test,watch/image_0040.jpg
+test,watch/image_0218.jpg
+test,watch/image_0210.jpg
+test,watch/image_0150.jpg
+test,water_lilly/image_0002.jpg
+test,water_lilly/image_0017.jpg
+test,water_lilly/image_0003.jpg
+test,water_lilly/image_0026.jpg
+test,water_lilly/image_0018.jpg
+test,water_lilly/image_0027.jpg
+test,water_lilly/image_0033.jpg
+test,water_lilly/image_0034.jpg
+test,water_lilly/image_0016.jpg
+test,water_lilly/image_0004.jpg
+test,water_lilly/image_0008.jpg
+test,water_lilly/image_0031.jpg
+test,wheelchair/image_0041.jpg
+test,wheelchair/image_0048.jpg
+test,wheelchair/image_0017.jpg
+test,wheelchair/image_0040.jpg
+test,wheelchair/image_0034.jpg
+test,wheelchair/image_0056.jpg
+test,wheelchair/image_0021.jpg
+test,wheelchair/image_0007.jpg
+test,wheelchair/image_0003.jpg
+test,wheelchair/image_0049.jpg
+test,wheelchair/image_0010.jpg
+test,wheelchair/image_0029.jpg
+test,wheelchair/image_0033.jpg
+test,wheelchair/image_0031.jpg
+test,wheelchair/image_0030.jpg
+test,wheelchair/image_0039.jpg
+test,wheelchair/image_0047.jpg
+test,wild_cat/image_0008.jpg
+test,wild_cat/image_0002.jpg
+test,wild_cat/image_0026.jpg
+test,wild_cat/image_0013.jpg
+test,wild_cat/image_0014.jpg
+test,wild_cat/image_0019.jpg
+test,wild_cat/image_0012.jpg
+test,wild_cat/image_0016.jpg
+test,wild_cat/image_0034.jpg
+test,wild_cat/image_0021.jpg
+test,windsor_chair/image_0037.jpg
+test,windsor_chair/image_0002.jpg
+test,windsor_chair/image_0050.jpg
+test,windsor_chair/image_0009.jpg
+test,windsor_chair/image_0023.jpg
+test,windsor_chair/image_0053.jpg
+test,windsor_chair/image_0021.jpg
+test,windsor_chair/image_0011.jpg
+test,windsor_chair/image_0036.jpg
+test,windsor_chair/image_0054.jpg
+test,windsor_chair/image_0055.jpg
+test,windsor_chair/image_0026.jpg
+test,windsor_chair/image_0022.jpg
+test,windsor_chair/image_0043.jpg
+test,windsor_chair/image_0044.jpg
+test,windsor_chair/image_0045.jpg
+test,windsor_chair/image_0038.jpg
+test,wrench/image_0009.jpg
+test,wrench/image_0027.jpg
+test,wrench/image_0008.jpg
+test,wrench/image_0005.jpg
+test,wrench/image_0028.jpg
+test,wrench/image_0039.jpg
+test,wrench/image_0023.jpg
+test,wrench/image_0002.jpg
+test,wrench/image_0007.jpg
+test,wrench/image_0003.jpg
+test,wrench/image_0035.jpg
+test,yin_yang/image_0010.jpg
+test,yin_yang/image_0007.jpg
+test,yin_yang/image_0051.jpg
+test,yin_yang/image_0038.jpg
+test,yin_yang/image_0030.jpg
+test,yin_yang/image_0024.jpg
+test,yin_yang/image_0033.jpg
+test,yin_yang/image_0039.jpg
+test,yin_yang/image_0046.jpg
+test,yin_yang/image_0041.jpg
+test,yin_yang/image_0042.jpg
+test,yin_yang/image_0001.jpg
+test,yin_yang/image_0044.jpg
+test,yin_yang/image_0022.jpg
+test,yin_yang/image_0059.jpg
+test,yin_yang/image_0004.jpg
+test,yin_yang/image_0028.jpg
+test,yin_yang/image_0021.jpg
diff --git a/DisEF/caltech101_checkpoint_daemon.py b/DisEF/caltech101_checkpoint_daemon.py
new file mode 100644
index 0000000..71ba094
--- /dev/null
+++ b/DisEF/caltech101_checkpoint_daemon.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python3
+import argparse,json,sys,time,traceback
+from pathlib import Path
+sys.path.insert(0,str(Path(__file__).resolve().parent))
+import caltech101_hf
+
+def backed_count(root):
+ index=root/'metadata/chunk_index.json'
+ if not index.exists(): return 0
+ data=json.loads(index.read_text())
+ return sum(int(c.get('image_count',0)) for c in data.get('chunks',[]))
+
+p=argparse.ArgumentParser()
+p.add_argument('--run-root',default='/marimo/runs/disef_caltech101')
+p.add_argument('--interval',type=int,default=300)
+p.add_argument('--max-chunk-images',type=int,default=500)
+a=p.parse_args(); root=Path(a.run_root)
+while True:
+ sleep_for=a.interval
+ try:
+  s=json.loads((root/'state/caltech101_disef_state.json').read_text())
+  backed=backed_count(root)
+  pending=max(s.get('completed',0)-backed,0)
+  index=root/'metadata/chunk_index.json'
+  age=time.time()-(index.stat().st_mtime if index.exists() else 0)
+  terminal=s.get('status') in {'COMPLETED','PARTIAL','BLOCKED'}
+  if (pending>=50 and age>=300) or (pending>0 and age>=900) or (terminal and pending>0):
+   size=min(max(pending,100),a.max_chunk_images)
+   result=caltech101_hf.checkpoint(root,force=terminal,max_chunk_images=size)
+   print(json.dumps(result),flush=True)
+   pending=max(s.get('completed',0)-backed_count(root),0)
+  if terminal and pending<=0:
+   break
+  if terminal and pending>0:
+   sleep_for=15
+ except Exception:
+  print(traceback.format_exc(),flush=True)
+ time.sleep(sleep_for)
diff --git a/DisEF/caltech101_disef.py b/DisEF/caltech101_disef.py
new file mode 100644
index 0000000..5e8ea00
--- /dev/null
+++ b/DisEF/caltech101_disef.py
@@ -0,0 +1,579 @@
+#!/usr/bin/env python3
+"""Crash-safe DISEF/SPC synthetic generation for Caltech101.
+
+Scientific basis: vturrisi/disef (arXiv:2312.03046). This SPC variant keeps
+Qwen3-VL-Embedding filtering while restoring the paper's cross-sample caption
+conditioning and CALTECH101 diffusion configuration.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import gc
+import hashlib
+import json
+import os
+import random
+import shutil
+import socket
+import subprocess
+import sys
+import time
+import traceback
+from collections import Counter, defaultdict
+from contextlib import contextmanager
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+from PIL import Image, ImageOps
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+SPC_ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_DATA_ROOT = Path("/marimo/data/caltech101/train")
+DEFAULT_SPLIT = SPC_ROOT / "DataSplit/split_coop_Caltech101.csv"
+DEFAULT_QWEN_SOURCE = Path("/marimo/Qwen3-VL-Embedding")
+DEFAULT_CACHE_ROOT = Path("/marimo/runs/disef_caltech101_cache")
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"}
+CLASS_FOLDERS = ['face', 'leopard', 'motorbike', 'accordion', 'airplane', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang']
+FOLDER_TO_LABEL = {'face': 'face', 'leopard': 'leopard', 'motorbike': 'motorbike', 'accordion': 'accordion', 'airplane': 'airplane', 'anchor': 'anchor', 'ant': 'ant', 'barrel': 'barrel', 'bass': 'bass', 'beaver': 'beaver', 'binocular': 'binocular', 'bonsai': 'bonsai', 'brain': 'brain', 'brontosaurus': 'brontosaurus', 'buddha': 'buddha', 'butterfly': 'butterfly', 'camera': 'camera', 'cannon': 'cannon', 'car_side': 'car side', 'ceiling_fan': 'ceiling fan', 'cellphone': 'cellphone', 'chair': 'chair', 'chandelier': 'chandelier', 'cougar_body': 'cougar body', 'cougar_face': 'cougar face', 'crab': 'crab', 'crayfish': 'crayfish', 'crocodile': 'crocodile', 'crocodile_head': 'crocodile head', 'cup': 'cup', 'dalmatian': 'dalmatian', 'dollar_bill': 'dollar bill', 'dolphin': 'dolphin', 'dragonfly': 'dragonfly', 'electric_guitar': 'electric guitar', 'elephant': 'elephant', 'emu': 'emu', 'euphonium': 'euphonium', 'ewer': 'ewer', 'ferry': 'ferry', 'flamingo': 'flamingo', 'flamingo_head': 'flamingo head', 'garfield': 'garfield', 'gerenuk': 'gerenuk', 'gramophone': 'gramophone', 'grand_piano': 'grand piano', 'hawksbill': 'hawksbill', 'headphone': 'headphone', 'hedgehog': 'hedgehog', 'helicopter': 'helicopter', 'ibis': 'ibis', 'inline_skate': 'inline skate', 'joshua_tree': 'joshua tree', 'kangaroo': 'kangaroo', 'ketch': 'ketch', 'lamp': 'lamp', 'laptop': 'laptop', 'llama': 'llama', 'lobster': 'lobster', 'lotus': 'lotus', 'mandolin': 'mandolin', 'mayfly': 'mayfly', 'menorah': 'menorah', 'metronome': 'metronome', 'minaret': 'minaret', 'nautilus': 'nautilus', 'octopus': 'octopus', 'okapi': 'okapi', 'pagoda': 'pagoda', 'panda': 'panda', 'pigeon': 'pigeon', 'pizza': 'pizza', 'platypus': 'platypus', 'pyramid': 'pyramid', 'revolver': 'revolver', 'rhino': 'rhino', 'rooster': 'rooster', 'saxophone': 'saxophone', 'schooner': 'schooner', 'scissors': 'scissors', 'scorpion': 'scorpion', 'sea_horse': 'sea horse', 'snoopy': 'snoopy', 'soccer_ball': 'soccer ball', 'stapler': 'stapler', 'starfish': 'starfish', 'stegosaurus': 'stegosaurus', 'stop_sign': 'stop sign', 'strawberry': 'strawberry', 'sunflower': 'sunflower', 'tick': 'tick', 'trilobite': 'trilobite', 'umbrella': 'umbrella', 'watch': 'watch', 'water_lilly': 'water lilly', 'wheelchair': 'wheelchair', 'wild_cat': 'wild cat', 'windsor_chair': 'windsor chair', 'wrench': 'wrench', 'yin_yang': 'yin yang'}
+FOLDER_TO_SOURCE = {'face': 'Faces', 'leopard': 'Leopards', 'motorbike': 'Motorbikes', 'accordion': 'accordion', 'airplane': 'airplanes', 'anchor': 'anchor', 'ant': 'ant', 'barrel': 'barrel', 'bass': 'bass', 'beaver': 'beaver', 'binocular': 'binocular', 'bonsai': 'bonsai', 'brain': 'brain', 'brontosaurus': 'brontosaurus', 'buddha': 'buddha', 'butterfly': 'butterfly', 'camera': 'camera', 'cannon': 'cannon', 'car_side': 'car_side', 'ceiling_fan': 'ceiling_fan', 'cellphone': 'cellphone', 'chair': 'chair', 'chandelier': 'chandelier', 'cougar_body': 'cougar_body', 'cougar_face': 'cougar_face', 'crab': 'crab', 'crayfish': 'crayfish', 'crocodile': 'crocodile', 'crocodile_head': 'crocodile_head', 'cup': 'cup', 'dalmatian': 'dalmatian', 'dollar_bill': 'dollar_bill', 'dolphin': 'dolphin', 'dragonfly': 'dragonfly', 'electric_guitar': 'electric_guitar', 'elephant': 'elephant', 'emu': 'emu', 'euphonium': 'euphonium', 'ewer': 'ewer', 'ferry': 'ferry', 'flamingo': 'flamingo', 'flamingo_head': 'flamingo_head', 'garfield': 'garfield', 'gerenuk': 'gerenuk', 'gramophone': 'gramophone', 'grand_piano': 'grand_piano', 'hawksbill': 'hawksbill', 'headphone': 'headphone', 'hedgehog': 'hedgehog', 'helicopter': 'helicopter', 'ibis': 'ibis', 'inline_skate': 'inline_skate', 'joshua_tree': 'joshua_tree', 'kangaroo': 'kangaroo', 'ketch': 'ketch', 'lamp': 'lamp', 'laptop': 'laptop', 'llama': 'llama', 'lobster': 'lobster', 'lotus': 'lotus', 'mandolin': 'mandolin', 'mayfly': 'mayfly', 'menorah': 'menorah', 'metronome': 'metronome', 'minaret': 'minaret', 'nautilus': 'nautilus', 'octopus': 'octopus', 'okapi': 'okapi', 'pagoda': 'pagoda', 'panda': 'panda', 'pigeon': 'pigeon', 'pizza': 'pizza', 'platypus': 'platypus', 'pyramid': 'pyramid', 'revolver': 'revolver', 'rhino': 'rhino', 'rooster': 'rooster', 'saxophone': 'saxophone', 'schooner': 'schooner', 'scissors': 'scissors', 'scorpion': 'scorpion', 'sea_horse': 'sea_horse', 'snoopy': 'snoopy', 'soccer_ball': 'soccer_ball', 'stapler': 'stapler', 'starfish': 'starfish', 'stegosaurus': 'stegosaurus', 'stop_sign': 'stop_sign', 'strawberry': 'strawberry', 'sunflower': 'sunflower', 'tick': 'tick', 'trilobite': 'trilobite', 'umbrella': 'umbrella', 'watch': 'watch', 'water_lilly': 'water_lilly', 'wheelchair': 'wheelchair', 'wild_cat': 'wild_cat', 'windsor_chair': 'windsor_chair', 'wrench': 'wrench', 'yin_yang': 'yin_yang'}
+SOURCE_TO_FOLDER = {v:k for k,v in FOLDER_TO_SOURCE.items()}
+TEMPLATES = [
+    "a photo of a {}", "a rendering of a {}", "a cropped photo of the {}",
+    "the photo of a {}", "a clean photo of a {}", "a detailed photo of a {}",
+    "a dark photo of the {}", "a close-up photo of a {}", "a bright photo of the {}",
+    "a good photo of the {}", "a photo of one {}", "a close-up rendering of the {}",
+    "a photo of a nice {}", "a photo of the small {}", "a photo of the large {}",
+    "a photo of a cool {}", "a photo of a small {}",
+]
+
+
+
+def now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def atomic_json(path: Path, obj: Any) -> None:
+    # The generator is the single writer for scientific progress, while the
+    # checkpoint daemon writes immutable remote-chunk metadata. Merge those
+    # markers before every state commit so resume cannot erase durability data.
+    if path.name == "caltech101_disef_state.json" and isinstance(obj, dict) and "records" in obj:
+        chunk_index = path.parent.parent / "metadata" / "chunk_index.json"
+        if chunk_index.exists():
+            try:
+                index = json.loads(chunk_index.read_text())
+                if (index.get("run_id") == obj.get("run_id")
+                        and index.get("config_fingerprint") == obj.get("config_fingerprint")):
+                    backed = 0
+                    for chunk in index.get("chunks", []):
+                        for rec in chunk.get("records", []):
+                            task_id = rec.get("task_id")
+                            if task_id in obj["records"]:
+                                obj["records"][task_id]["remote_chunk"] = chunk["filename"]
+                                backed += 1
+                    obj["remote_backed_up_count"] = backed
+                    if index.get("updated_at"):
+                        obj["latest_remote_checkpoint"] = index["updated_at"]
+            except (OSError, ValueError, KeyError, TypeError):
+                pass
+    path.parent.mkdir(parents=True, exist_ok=True)
+    tmp = path.with_name(path.name + f".tmp.{os.getpid()}")
+    with tmp.open("w", encoding="utf-8") as f:
+        json.dump(obj, f, indent=2, sort_keys=True)
+        f.flush()
+        os.fsync(f.fileno())
+    os.replace(tmp, path)
+
+
+def append_jsonl(path: Path, record: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("a", encoding="utf-8") as f:
+        f.write(json.dumps(record, sort_keys=True) + "\n")
+        f.flush()
+        os.fsync(f.fileno())
+
+
+def sha256_file(path: Path) -> str:
+    h = hashlib.sha256()
+    with path.open("rb") as f:
+        for block in iter(lambda: f.read(8 << 20), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+def validate_image(path: Path, expected_size: tuple[int, int] = (512, 512), class_folder: str | None = None) -> tuple[bool, str]:
+    try:
+        if not path.is_file() or path.stat().st_size <= 0:
+            return False, "missing-or-empty"
+        if class_folder is not None and path.parent.name != class_folder:
+            return False, "wrong-class-directory"
+        with Image.open(path) as im:
+            fmt = im.format
+            size = im.size
+            im.load()
+        if fmt not in {"JPEG", "PNG"}:
+            return False, f"unexpected-format:{fmt}"
+        if size != expected_size:
+            return False, f"unexpected-size:{size}"
+        return True, "ok"
+    except Exception as exc:
+        return False, f"{type(exc).__name__}:{exc}"
+
+
+def padded_image(path: Path, size: int = 512) -> Image.Image:
+    with Image.open(path) as src:
+        image = src.convert("RGB")
+    width, height = image.size
+    side = max(width, height)
+    canvas = Image.new("RGB", (side, side), (0, 0, 0))
+    canvas.paste(image, ((side - width) // 2, (side - height) // 2))
+    return canvas.resize((size, size), Image.Resampling.LANCZOS)
+
+
+def pid_alive(pid: int | None) -> bool:
+    if not pid:
+        return False
+    try:
+        n = int(pid)
+        os.kill(n, 0)
+        stat = Path(f"/proc/{n}/stat")
+        if stat.exists() and stat.read_text().split()[2] == "Z": return False
+        return True
+    except (OSError, ValueError):
+        return False
+
+
+def canonical_scientific_config(args: argparse.Namespace, selected_sha: str) -> dict[str, Any]:
+    return {"dataset":"Caltech101","dataset_version":"torchvision Caltech101 / official DISEF CoOp split",
+        "split_sha256":sha256_file(Path(args.split_csv)),"selected_sources_sha256":selected_sha,"classes":CLASS_FOLDERS,
+        "fewshot":16,"split_seed":args.split_seed,"global_seed":args.global_seed,"images_per_class":args.images_per_class,
+        "diffusion_model":args.diffusion_model,"caption_model":args.caption_model,"filter_model":args.filter_model,
+        "filter_backend":"Qwen3-VL-Embedding-2B (SPC adaptation)","resolution":512,
+        "scheduler":"DPMSolverMultistepScheduler / DPM-Solver++","num_inference_steps":20,"starting_step":15,
+        "strength":0.25,"guidance_scale":8.0,"caption_temperature":0.2,"cross_sample_caption":True,
+        "dtype":args.dtype,"batch_size":args.batch_size,"attention_backend":"diffusers default SDPA",
+        "jpeg_quality":95,"spc_commit":subprocess.check_output(["git","-C",str(SPC_ROOT),"rev-parse","HEAD"],text=True).strip()}
+
+def fingerprint(config: dict[str, Any]) -> str:
+    payload = json.dumps(config, sort_keys=True, separators=(",", ":")).encode()
+    return hashlib.sha256(payload).hexdigest()
+
+
+def load_sources(data_root: Path, split_csv: Path, split_seed: int, limit_classes: int | None = None) -> tuple[dict[str,list[Path]],str]:
+    source_root=data_root/"caltech101"/"101_ObjectCategories"
+    if not source_root.is_dir(): raise FileNotFoundError(source_root)
+    df=pd.read_csv(split_csv)
+    if list(df.columns)!=["split","filename"]: raise ValueError(df.columns.tolist())
+    train=df[df["split"]=="train"]; folders=CLASS_FOLDERS[:limit_classes] if limit_classes else CLASS_FOLDERS
+    rng=random.Random(split_seed); selected={}; selected_rel=[]
+    for folder in folders:
+        sf=FOLDER_TO_SOURCE[folder]; rows=[x for x in train["filename"].tolist() if Path(x).parent.name==sf]
+        if len(rows)<16: raise ValueError(f"{folder} has only {len(rows)} train rows")
+        chosen=rng.sample(rows,16); paths=[source_root/rel for rel in chosen]; missing=[str(p) for p in paths if not p.is_file()]
+        if missing: raise FileNotFoundError(missing[:3])
+        selected[folder]=paths; selected_rel.extend([f"{folder}/{p.name}" for p in paths])
+    return selected,hashlib.sha256(json.dumps(selected_rel,separators=(",",":")).encode()).hexdigest()
+
+def ensure_layout(run_root: Path) -> dict[str, Path]:
+    paths = {
+        "root": run_root,
+        "images": run_root / "images",
+        "metadata": run_root / "metadata",
+        "state": run_root / "state",
+        "logs": run_root / "logs",
+        "artifacts": run_root / "artifacts",
+        "figures": run_root / "artifacts/figures",
+        "checkpoints": run_root / "checkpoints",
+        "quarantine": run_root / "state/quarantine",
+    }
+    for p in paths.values():
+        p.mkdir(parents=True, exist_ok=True)
+    return paths
+
+
+def base_state(run_id: str, fp: str, target: int) -> dict[str, Any]:
+    return {
+        "schema_version": 1, "run_id": run_id, "config_fingerprint": fp,
+        "hostname": socket.gethostname(), "status": "INITIALIZING", "created_at": now_iso(),
+        "updated_at": now_iso(), "generator_pid": None, "supervisor_pid": None,
+        "completed": 0, "target": target, "failed": 0, "retried": 0,
+        "remote_backed_up_count": 0, "latest_remote_checkpoint": None,
+        "current_class": None, "current_source": None, "records": {}, "last_event": "initialized",
+    }
+
+
+def read_state(path: Path) -> dict[str, Any] | None:
+    if not path.exists():
+        return None
+    return json.loads(path.read_text())
+
+
+def save_heartbeat(paths: dict[str, Path], state: dict[str, Any]) -> None:
+    heartbeat = {k: state.get(k) for k in [
+        "run_id", "status", "completed", "target", "current_class", "current_source",
+        "failed", "retried", "latest_remote_checkpoint", "remote_backed_up_count",
+        "generator_pid", "supervisor_pid", "hostname", "last_event"
+    ]}
+    heartbeat["timestamp"] = now_iso()
+    atomic_json(paths["state"] / "heartbeat.json", heartbeat)
+
+
+def append_metric(paths: dict[str, Path], state: dict[str, Any], started: float) -> None:
+    metric_path = paths["metadata"] / "runtime_metrics.csv"
+    elapsed = max(time.time() - started, 0.001)
+    gpu_util = vram_used = vram_total = 0.0
+    try:
+        line = subprocess.check_output([
+            "nvidia-smi", "--query-gpu=utilization.gpu,memory.used,memory.total",
+            "--format=csv,noheader,nounits"
+        ], text=True).splitlines()[0]
+        gpu_util, vram_used, vram_total = [float(x.strip()) for x in line.split(",")]
+    except Exception:
+        pass
+    usage = shutil.disk_usage(paths["root"])
+    row = {
+        "timestamp": now_iso(), "elapsed_seconds": round(elapsed, 3),
+        "completed": state["completed"], "failed": state["failed"], "retried": state["retried"],
+        "images_per_minute": round(state["completed"] / elapsed * 60, 4),
+        "average_images_per_minute": round(state["completed"] / elapsed * 60, 4),
+        "gpu_util_percent": gpu_util, "vram_used_mb": vram_used, "vram_total_mb": vram_total,
+        "disk_used_gb": round((usage.total - usage.free) / 1e9, 3),
+        "disk_free_gb": round(usage.free / 1e9, 3),
+        "remote_backed_up_count": state.get("remote_backed_up_count", 0),
+    }
+    exists = metric_path.exists()
+    with metric_path.open("a", newline="") as f:
+        w = csv.DictWriter(f, fieldnames=list(row))
+        if not exists:
+            w.writeheader()
+        w.writerow(row)
+        f.flush(); os.fsync(f.fileno())
+
+
+def dataset_smoke(args: argparse.Namespace) -> None:
+    selected,digest=load_sources(Path(args.data_root),Path(args.split_csv),args.split_seed,args.limit_classes); df=pd.read_csv(args.split_csv)
+    print(json.dumps({"status":"PASS","dataset":"Caltech101","class_count":len(CLASS_FOLDERS),"selected_class_count":len(selected),
+        "split_counts":dict(Counter(df["split"])),"shots_per_class":{k:len(v) for k,v in selected.items()},
+        "selected_sources_sha256":digest,"source_categories":{k:FOLDER_TO_SOURCE[k] for k in list(selected)[:8]},
+        "sample_paths":[str(v[0]) for v in selected.values()][:5],"prompt_examples":[f"a photo of a {FOLDER_TO_LABEL[k]}." for k in list(selected)[:5]],
+        "expected_total":len(selected)*args.images_per_class},indent=2))
+
+def resolve_model_revisions(args: argparse.Namespace) -> dict[str, str]:
+    from huggingface_hub import HfApi
+    api = HfApi(token=os.environ.get("HF_TOKEN"))
+    revisions = {}
+    for key, model in [("diffusion", args.diffusion_model), ("caption", args.caption_model), ("filter", args.filter_model)]:
+        revisions[key] = api.model_info(model).sha
+    return revisions
+
+
+def load_captioner(args: argparse.Namespace):
+    import torch
+    from transformers import AutoProcessor, LlavaForConditionalGeneration
+    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+    processor = AutoProcessor.from_pretrained(args.caption_model)
+    model = LlavaForConditionalGeneration.from_pretrained(
+        args.caption_model, torch_dtype=dtype, low_cpu_mem_usage=True
+    ).eval().to("cuda")
+    return processor, model
+
+
+def caption_one(processor, model, path: Path, class_label: str, seed: int) -> str:
+    import torch
+    query = f"Provide a detailed caption for the image focusing on the object, knowing it's a {class_label}."
+    prompt = f"USER: <image>\n{query}\nASSISTANT:"
+    image = padded_image(path)
+    inputs = processor(images=image, text=prompt, return_tensors="pt")
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    torch.manual_seed(seed); torch.cuda.manual_seed_all(seed)
+    with torch.inference_mode():
+        output = model.generate(**inputs, do_sample=True, temperature=0.2, max_new_tokens=160, use_cache=True)
+    new_tokens = output[:, inputs["input_ids"].shape[1]:]
+    text = processor.batch_decode(new_tokens, skip_special_tokens=True)[0].strip()
+    if len(text) < 10:
+        text = f"A detailed photograph of a {class_label}, a type of pet."
+    return text
+
+
+def ensure_captions(args: argparse.Namespace, selected: dict[str, list[Path]], paths: dict[str, Path]) -> dict[str, str]:
+    cache_path = Path(args.caption_cache)
+    cache_path.parent.mkdir(parents=True, exist_ok=True)
+    cache = json.loads(cache_path.read_text()) if cache_path.exists() else {}
+    needed = [(folder, p) for folder, items in selected.items() for p in items if str(p) not in cache]
+    if not needed:
+        return cache
+    state_path = paths["state"] / "caltech101_disef_state.json"
+    state = read_state(state_path) or {}
+    state["last_event"] = f"captioning {len(needed)} uncached support images"
+    state["updated_at"] = now_iso(); atomic_json(state_path, state); save_heartbeat(paths, state)
+    processor, model = load_captioner(args)
+    try:
+        for i, (folder, source) in enumerate(needed):
+            cache[str(source)] = caption_one(processor, model, source, FOLDER_TO_LABEL[folder], args.global_seed + i)
+            atomic_json(cache_path, cache)
+    finally:
+        del model, processor
+        gc.collect()
+        import torch
+        torch.cuda.empty_cache()
+    return cache
+
+
+def load_diffusion(args: argparse.Namespace):
+    import torch
+    from diffusers import DPMSolverMultistepScheduler, StableDiffusionImg2ImgPipeline
+    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(args.diffusion_model, torch_dtype=dtype)
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, algorithm_type="dpmsolver++")
+    pipe.set_progress_bar_config(disable=True)
+    pipe.to("cuda")
+    return pipe
+
+
+def load_qwen(args: argparse.Namespace):
+    import torch
+    source = Path(args.qwen_source)
+    if not source.is_dir():
+        raise FileNotFoundError(f"Qwen source repository missing: {source}")
+    sys.path.insert(0, str(source))
+    from src.models.qwen3_vl_embedding import Qwen3VLEmbedder
+    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+    model = Qwen3VLEmbedder(
+        model_name_or_path=args.filter_model, torch_dtype=dtype,
+        attn_implementation="sdpa", max_length=512, max_pixels=512 * 512,
+    )
+    model.model.eval()
+    return model
+
+
+def build_prototypes(qwen, folders: list[str]):
+    import torch
+    import torch.nn.functional as F
+    vectors = []
+    with torch.inference_mode():
+        for folder in folders:
+            label = FOLDER_TO_LABEL[folder]
+            embeddings = qwen.process([{"text": template.format(label) + "."} for template in TEMPLATES])
+            vectors.append(F.normalize(embeddings.mean(dim=0), dim=-1))
+    return torch.stack(vectors)
+
+
+def qwen_predict(qwen, prototypes, images: list[Image.Image]) -> tuple[list[int], list[float]]:
+    import torch
+    import torch.nn.functional as F
+    with torch.inference_mode():
+        features = qwen.process([{"image": image} for image in images])
+        features = F.normalize(features, dim=-1)
+        sims = features.to(prototypes.dtype) @ prototypes.T
+        pred = sims.argmax(dim=-1)
+    return pred.cpu().tolist(), [float(sims[i, pred[i]].item()) for i in range(len(images))]
+
+
+def model_smoke(args: argparse.Namespace) -> None:
+    revisions = resolve_model_revisions(args)
+    processor, captioner = load_captioner(args)
+    del captioner, processor; gc.collect()
+    import torch
+    torch.cuda.empty_cache()
+    pipe = load_diffusion(args)
+    del pipe; gc.collect(); torch.cuda.empty_cache()
+    qwen = load_qwen(args)
+    prototypes = build_prototypes(qwen, CLASS_FOLDERS[:2])
+    print(json.dumps({"status": "PASS", "revisions": revisions, "qwen_prototype_shape": list(prototypes.shape),
+                      "gpu": torch.cuda.get_device_name(0), "dtype": args.dtype,
+                      "vram_allocated_mb": round(torch.cuda.memory_allocated()/2**20, 1)}, indent=2))
+    del qwen, prototypes; gc.collect(); torch.cuda.empty_cache()
+
+
+def task_seed(global_seed: int, class_idx: int, synth_idx: int, attempt: int) -> int:
+    return global_seed + class_idx * 1_000_000 + synth_idx * 1_000 + attempt
+
+
+def save_candidate(image: Image.Image, final_path: Path) -> None:
+    final_path.parent.mkdir(parents=True, exist_ok=True)
+    tmp = final_path.with_name(final_path.name + f".tmp.{os.getpid()}")
+    image.convert("RGB").save(tmp, format="JPEG", quality=95, optimize=True)
+    with tmp.open("rb") as f:
+        os.fsync(f.fileno())
+    os.replace(tmp, final_path)
+    ok, reason = validate_image(final_path, class_folder=final_path.parent.name)
+    if not ok:
+        raise RuntimeError(f"Saved image failed validation: {reason}")
+
+
+def reconcile_existing(paths: dict[str, Path], state: dict[str, Any]) -> None:
+    records = state.setdefault("records", {})
+    completed = 0
+    for task_id, rec in list(records.items()):
+        if rec.get("completion_status") != "COMPLETE":
+            continue
+        out = paths["root"] / rec["synthetic_filename"]
+        ok, reason = validate_image(out, class_folder=rec["class_folder"])
+        if ok:
+            completed += 1
+        else:
+            if out.exists():
+                quarantine = paths["quarantine"] / f"{out.name}.{int(time.time())}.corrupt"
+                quarantine.parent.mkdir(parents=True, exist_ok=True)
+                os.replace(out, quarantine)
+            rec["completion_status"] = "INVALIDATED"
+            rec["local_validation"] = reason
+    state["completed"] = completed
+
+
+def maybe_checkpoint(run_root: Path, state: dict[str, Any], force: bool = False) -> None:
+    pending = state["completed"] - state.get("remote_backed_up_count", 0)
+    last_ts = state.get("latest_remote_checkpoint_epoch") or time.time()
+    age = time.time() - last_ts
+    should = force or (pending >= 50 and age >= 300) or (pending > 0 and age >= 900)
+    if not should or not os.environ.get("HF_TOKEN"):
+        return
+    try:
+        import caltech101_hf
+        caltech101_hf.checkpoint(run_root, force=force)
+        refreshed = json.loads((run_root / "state" / "caltech101_disef_state.json").read_text())
+        state.clear()
+        state.update(refreshed)
+    except Exception as exc:
+        state["last_event"] = f"HF checkpoint deferred: {type(exc).__name__}: {exc}"
+
+
+def _save_record(paths,state,manifest_path,folder,class_idx,synth_idx,source,caption_source,prompt,seed,pred,score,image,attempt,args):
+    out_dir=paths["images"]/folder; out_dir.mkdir(parents=True,exist_ok=True)
+    out=out_dir/f"{folder}__src-{source.stem}__syn-{synth_idx:03d}__seed-{seed}.jpg"
+    if out.exists():
+        ok,reason=validate_image(out,class_folder=folder)
+        if ok: raise RuntimeError(f"valid output collision: {out}")
+        os.replace(out,paths["quarantine"]/(out.name+".corrupt"))
+    save_candidate(image,out); tid=f"{class_idx:03d}:{synth_idx:03d}"
+    rec={"event":"LOCAL_COMPLETE","timestamp":now_iso(),"task_id":tid,"synthetic_filename":str(out.relative_to(paths["root"])),
+         "class_id":class_idx,"class_folder":folder,"class_name":FOLDER_TO_LABEL[folder],"source_image":str(source),
+         "caption_source_image":str(caption_source),"caption":args._captions[str(caption_source)],"prompt":prompt,"random_seed":seed,
+         "generation_parameters":{"model":args.diffusion_model,"steps":20,"starting_step":15,"strength":0.25,"guidance_scale":8.0,
+             "scheduler":"DPM-Solver++","resolution":[512,512],"batch_size":args.batch_size},
+         "filter_model":args.filter_model,"filter_predicted_class":CLASS_FOLDERS[pred],"filter_similarity":score,
+         "filter_pass":pred==class_idx,"fallback_accept":pred!=class_idx,"completion_status":"COMPLETE","local_validation":"ok",
+         "retry_count":attempt,"remote_chunk":None,"sha256":sha256_file(out),"bytes":out.stat().st_size}
+    append_jsonl(manifest_path,rec); state["records"][tid]=rec; state["completed"]+=1; state.update(updated_at=now_iso(),last_event=f"completed {tid}")
+    atomic_json(paths["state"]/"caltech101_disef_state.json",state); save_heartbeat(paths,state); return rec
+
+def benchmark(args):
+    import torch
+    selected,_=load_sources(Path(args.data_root),Path(args.split_csv),args.split_seed,1); folder=list(selected)[0]
+    pipe=load_diffusion(args); qwen=load_qwen(args); prototypes=build_prototypes(qwen,[folder])
+    base = (selected[folder] * ((max(args.benchmark_batches) + len(selected[folder]) - 1) // len(selected[folder])))[:max(args.benchmark_batches)]
+    rows=[]; 
+    for bs in args.benchmark_batches:
+        try:
+            imgs=[padded_image(p) for p in base[:bs]]; ps=[f"a detailed photo of a {FOLDER_TO_LABEL[folder]}." for _ in imgs]
+            with torch.inference_mode(): pipe(prompt=ps,image=imgs,strength=0.25,guidance_scale=8.0,num_inference_steps=20,
+                generator=[torch.Generator(device="cuda").manual_seed(args.global_seed+i) for i in range(bs)],output_type="pil")
+            torch.cuda.synchronize(); torch.cuda.reset_peak_memory_stats(); start=time.perf_counter()
+            for _ in range(2):
+                with torch.inference_mode(): out=pipe(prompt=ps,image=imgs,strength=0.25,guidance_scale=8.0,num_inference_steps=20,
+                    generator=[torch.Generator(device="cuda").manual_seed(args.global_seed+1000*_+i) for i in range(bs)],output_type="pil").images
+                qwen_predict(qwen,prototypes,out)
+            torch.cuda.synchronize(); elapsed=time.perf_counter()-start
+            util=float(subprocess.check_output(["nvidia-smi","--query-gpu=utilization.gpu","--format=csv,noheader,nounits"],text=True).strip())
+            rows.append({"batch_size":bs,"dtype":args.dtype,"attention_backend":"SDPA","peak_vram_mb":round(torch.cuda.max_memory_allocated()/2**20,1),
+                         "gpu_util_percent":util,"images_per_second":2*bs/elapsed,"images_per_minute":120*bs/elapsed,"duration_seconds":elapsed,"status":"PASS"})
+            del out; torch.cuda.empty_cache()
+        except torch.cuda.OutOfMemoryError:
+            rows.append({"batch_size":bs,"dtype":args.dtype,"attention_backend":"SDPA","peak_vram_mb":round(torch.cuda.max_memory_allocated()/2**20,1),
+                         "gpu_util_percent":0,"images_per_second":0,"images_per_minute":0,"duration_seconds":0,"status":"OOM"}); torch.cuda.empty_cache(); break
+        except Exception as exc:
+            rows.append({"batch_size":bs,"dtype":args.dtype,"attention_backend":"SDPA","peak_vram_mb":round(torch.cuda.max_memory_allocated()/2**20,1),
+                         "gpu_util_percent":0,"images_per_second":0,"images_per_minute":0,"duration_seconds":0,"status":type(exc).__name__}); break
+    out_path=Path(args.benchmark_output); out_path.parent.mkdir(parents=True,exist_ok=True); pd.DataFrame(rows).to_csv(out_path,index=False); print(json.dumps({"status":"PASS","rows":rows},indent=2))
+    del pipe,qwen,prototypes; gc.collect(); torch.cuda.empty_cache()
+
+def generate(args):
+    import torch
+    run_root=Path(args.run_root).resolve(); paths=ensure_layout(run_root); selected,selected_sha=load_sources(Path(args.data_root),Path(args.split_csv),args.split_seed,args.limit_classes)
+    folders=list(selected); scientific=canonical_scientific_config(args,selected_sha); scientific["classes"]=folders; fp=fingerprint(scientific); config_path=paths["metadata"]/"generation_config.json"
+    if config_path.exists():
+        prior=json.loads(config_path.read_text())
+        if prior.get("config_fingerprint")!=fp: raise RuntimeError(f"incompatible resume: {prior.get('config_fingerprint')} != {fp}")
+        run_id=prior["run_id"]
+    else:
+        run_id=args.run_id or f"caltech101_disef_{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%SZ')}_{fp[:12]}"
+        atomic_json(config_path,dict(scientific,config_fingerprint=fp,run_id=run_id,expected_total=len(folders)*args.images_per_class,
+            model_revisions=resolve_model_revisions(args),created_at=now_iso()))
+    target=len(folders)*args.images_per_class; state_path=paths["state"]/"caltech101_disef_state.json"; state=read_state(state_path) or base_state(run_id,fp,target)
+    if state["config_fingerprint"]!=fp: raise RuntimeError("state/config fingerprint mismatch")
+    lock_path=paths["state"]/"run.lock"; old_lock=read_state(lock_path)
+    if old_lock and pid_alive(old_lock.get("generator_pid")) and old_lock.get("generator_pid")!=os.getpid(): raise RuntimeError(f"live generator {old_lock.get('generator_pid')}")
+    supervisor_pid=old_lock.get("supervisor_pid") if old_lock else os.environ.get("DISEF_SUPERVISOR_PID")
+    atomic_json(lock_path,{"run_id":run_id,"config_fingerprint":fp,"generator_pid":os.getpid(),"supervisor_pid":int(supervisor_pid) if supervisor_pid else None,"hostname":socket.gethostname(),"updated_at":now_iso()})
+    state.update(status="RUNNING",generator_pid=os.getpid(),supervisor_pid=int(supervisor_pid) if supervisor_pid else None,hostname=socket.gethostname(),updated_at=now_iso(),last_event="reconciling outputs")
+    reconcile_existing(paths,state); atomic_json(state_path,state); save_heartbeat(paths,state)
+    if args.init_only:
+        state.update(status="INITIALIZED",updated_at=now_iso(),last_event="initialized"); atomic_json(state_path,state); save_heartbeat(paths,state); return
+    manifest_path=paths["metadata"]/"caltech101_disef_manifest.jsonl"; started=time.time(); last_metric=0.0; captions=ensure_captions(args,selected,paths); args._captions=captions
+    state["last_event"]="loading diffusion and Qwen models"; atomic_json(state_path,state); save_heartbeat(paths,state); pipe=load_diffusion(args); qwen=load_qwen(args); prototypes=build_prototypes(qwen,folders)
+    state["last_event"]="models loaded; batched generation active"; atomic_json(state_path,state); save_heartbeat(paths,state); attempts={}; new_count=0
+    try:
+        for class_idx,folder in enumerate(folders):
+            sources=selected[folder]; pending=[]
+            for i in range(args.images_per_class):
+                tid=f"{class_idx:03d}:{i:03d}"; rec=state["records"].get(tid)
+                if not (rec and rec.get("completion_status")=="COMPLETE" and validate_image(paths["root"]/rec["synthetic_filename"],class_folder=folder)[0]): pending.append(i)
+            while pending:
+                if args.max_new is not None and new_count>=args.max_new:
+                    state.update(status="STOPPED",updated_at=now_iso(),last_event=f"intentional max-new stop at {new_count}"); atomic_json(state_path,state); save_heartbeat(paths,state); maybe_checkpoint(run_root,state,True); return
+                remaining_cap = (args.max_new - new_count) if args.max_new is not None else args.batch_size
+                batch_ids=pending[:min(args.batch_size, remaining_cap)]; rest=pending[len(batch_ids):]; tasks=[]; images=[]; prompts=[]; gens=[]
+                for synth_idx in batch_ids:
+                    tid=f"{class_idx:03d}:{synth_idx:03d}"; attempt=attempts.get(tid,0); seed=task_seed(args.global_seed,class_idx,synth_idx,attempt); rng=random.Random(seed); ai=rng.randrange(len(sources)); choices=[j for j in range(len(sources)) if j!=ai]; ci=choices[rng.randrange(len(choices))]; source=sources[ai]; caption_source=sources[ci]
+                    tasks.append((synth_idx,tid,attempt,seed,source,caption_source)); images.append(padded_image(source)); prompts.append(captions[str(caption_source)]); gens.append(torch.Generator(device="cuda").manual_seed(seed))
+                state.update(current_class=folder,current_source=str(tasks[0][4]),updated_at=now_iso(),last_event=f"generating batch {len(tasks)}"); save_heartbeat(paths,state)
+                try:
+                    with torch.inference_mode(): result=pipe(prompt=prompts,image=images,strength=0.25,guidance_scale=8.0,num_inference_steps=20,generator=gens,output_type="pil")
+                    pred,scores=qwen_predict(qwen,prototypes,result.images); retry=[]
+                    for j,(synth_idx,tid,attempt,seed,source,caption_source) in enumerate(tasks):
+                        if pred[j]!=class_idx and attempt<args.max_filter_attempts-1:
+                            attempts[tid]=attempt+1; state["retried"]+=1; retry.append(synth_idx); continue
+                        _save_record(paths,state,manifest_path,folder,class_idx,synth_idx,source,caption_source,prompts[j],seed,pred[j],scores[j],result.images[j],attempt,args)
+                        new_count += 1
+                    pending=retry+rest; atomic_json(state_path,state); save_heartbeat(paths,state)
+                    if time.time()-last_metric>=60 or state["completed"]==target: append_metric(paths,state,started); last_metric=time.time()
+                    maybe_checkpoint(run_root,state)
+                except torch.cuda.OutOfMemoryError:
+                    torch.cuda.empty_cache(); gc.collect()
+                    if len(tasks)>1:
+                        args.batch_size=max(1,len(tasks)//2); state["last_event"]=f"OOM; operational batch reduced to {args.batch_size}"; state["retried"]+=len(tasks); attempts.update({t[1]:t[2]+1 for t in tasks}); pending=batch_ids+rest; save_heartbeat(paths,state); continue
+                    raise
+                except Exception as exc:
+                    state["last_event"]=f"batch error {type(exc).__name__}: {exc}"; state["retried"]+=len(tasks); attempts.update({t[1]:t[2]+1 for t in tasks}); pending=batch_ids+rest; (paths["logs"]/"generation_errors.log").open("a").write(f"[{now_iso()}] {state['last_event']}\\n{traceback.format_exc()}\\n")
+                    if any(attempts[t[1]]>=3 for t in tasks): state["failed"]+=len(tasks); pending=rest
+                    save_heartbeat(paths,state)
+        state.update(status="COMPLETED" if state["completed"]==target else "PARTIAL",updated_at=now_iso(),current_class=None,current_source=None,last_event="generation loop finished"); atomic_json(state_path,state); save_heartbeat(paths,state); append_metric(paths,state,started); maybe_checkpoint(run_root,state,True)
+    except BaseException as exc:
+        state.update(status="BLOCKED" if isinstance(exc,RuntimeError) else "STOPPED",updated_at=now_iso(),last_event=f"{type(exc).__name__}: {exc}"); atomic_json(state_path,state); save_heartbeat(paths,state)
+        try: maybe_checkpoint(run_root,state,True)
+        except Exception: pass
+        raise
+    finally:
+        del pipe,qwen,prototypes; gc.collect(); torch.cuda.empty_cache()
+
+def build_parser():
+    p=argparse.ArgumentParser(description=__doc__); p.add_argument("--mode",choices=["dataset-smoke","model-smoke","benchmark","generate"],default="generate")
+    p.add_argument("--data-root",default=str(DEFAULT_DATA_ROOT)); p.add_argument("--split-csv",default=str(DEFAULT_SPLIT)); p.add_argument("--run-root",default="/marimo/runs/disef_caltech101")
+    p.add_argument("--caption-cache",default=str(DEFAULT_CACHE_ROOT/"caltech101_llava_captions_seed1.json")); p.add_argument("--qwen-source",default=str(DEFAULT_QWEN_SOURCE))
+    p.add_argument("--diffusion-model",default="SG161222/Realistic_Vision_V2.0"); p.add_argument("--caption-model",default="llava-hf/llava-1.5-7b-hf"); p.add_argument("--filter-model",default="Qwen/Qwen3-VL-Embedding-2B")
+    p.add_argument("--split-seed",type=int,default=1); p.add_argument("--global-seed",type=int,default=20261005); p.add_argument("--images-per-class",type=int,default=64); p.add_argument("--batch-size",type=int,default=1)
+    p.add_argument("--dtype",choices=["float16","bfloat16"],default="bfloat16"); p.add_argument("--limit-classes",type=int); p.add_argument("--max-new",type=int); p.add_argument("--max-filter-attempts",type=int,default=100); p.add_argument("--run-id"); p.add_argument("--init-only",action="store_true")
+    p.add_argument("--benchmark-batches",type=int,nargs="+",default=[1,2,4,8,16,32]); p.add_argument("--benchmark-output",default="/marimo/runs/disef_caltech101/metadata/optimization_benchmark.csv"); return p
+def torch_cuda_available():
+    try: import torch; return torch.cuda.is_available()
+    except: return False
+def main():
+    args=build_parser().parse_args()
+    if args.mode in {"model-smoke","benchmark","generate"} and not torch_cuda_available(): raise RuntimeError("CUDA required")
+    if args.mode=="dataset-smoke": dataset_smoke(args)
+    elif args.mode=="model-smoke": model_smoke(args)
+    elif args.mode=="benchmark": benchmark(args)
+    else: generate(args)
+if __name__=="__main__": main()
diff --git a/DisEF/caltech101_finalize.py b/DisEF/caltech101_finalize.py
new file mode 100644
index 0000000..a510010
--- /dev/null
+++ b/DisEF/caltech101_finalize.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+import argparse,csv,hashlib,json,os,shutil,sys,tempfile,zipfile
+from collections import Counter
+from datetime import datetime,timezone
+from pathlib import Path
+sys.path.insert(0,str(Path(__file__).resolve().parent))
+from caltech101_disef import validate_image,atomic_json
+
+def sha(p):
+ h=hashlib.sha256()
+ with p.open('rb') as f:
+  for b in iter(lambda:f.read(8<<20),b''): h.update(b)
+ return h.hexdigest()
+def add(z,src,arc):
+ if src.exists() and src.is_file(): z.write(src,arcname=str(arc))
+def main():
+ ap=argparse.ArgumentParser(); ap.add_argument('--run-root',default='/marimo/runs/disef_caltech101'); ap.add_argument('--output-dir',default='/marimo/final'); ap.add_argument('--report',default='/marimo/DISEF_CALTECH101_RUN_REPORT.md'); ap.add_argument('--patch',default='/marimo/DISEF_CALTECH101_PATCH.diff'); ap.add_argument('--upload',action='store_true'); a=ap.parse_args()
+ root=Path(a.run_root).resolve(); out=Path(a.output_dir).resolve(); out.mkdir(parents=True,exist_ok=True)
+ state=json.loads((root/'state/caltech101_disef_state.json').read_text()); config=json.loads((root/'metadata/generation_config.json').read_text())
+ if state['status']!='COMPLETED' or state['completed']!=state['target']: raise RuntimeError(f"run incomplete: {state['status']} {state['completed']}/{state['target']}")
+ records=[r for _,r in sorted(state['records'].items()) if r.get('completion_status')=='COMPLETE']
+ if len(records)!=state['target']: raise RuntimeError('complete record count mismatch')
+ invalid=[]; names=[]; counts=Counter(); sizes=Counter(); formats=Counter()
+ for r in records:
+  q=root/r['synthetic_filename']; ok,reason=validate_image(q,class_folder=r['class_folder']);
+  if not ok: invalid.append((str(q),reason))
+  names.append(r['synthetic_filename']); counts[r['class_folder']]+=1
+  from PIL import Image
+  with Image.open(q) as im: im.load(); sizes[str(im.size)]+=1; formats[im.format]+=1
+ expected=config['images_per_class']; bad_counts={c:counts.get(c,0) for c in config['classes'] if counts.get(c,0)!=expected}
+ if invalid or bad_counts or len(names)!=len(set(names)): raise RuntimeError(f'integrity failure invalid={invalid[:3]} bad_counts={bad_counts} duplicate={len(names)-len(set(names))}')
+ # Final manifest CSV from authoritative state records.
+ csv_path=root/'metadata/caltech101_disef_manifest.csv'; fields=['synthetic_filename','class_id','class_folder','class_name','source_image','caption_source_image','caption','prompt','random_seed','filter_model','filter_predicted_class','filter_similarity','filter_pass','fallback_accept','completion_status','local_validation','retry_count','remote_chunk','sha256','bytes']
+ with csv_path.open('w',newline='') as f:
+  w=csv.DictWriter(f,fieldnames=fields,extrasaction='ignore'); w.writeheader(); w.writerows(records)
+ # Ensure figures/previews current.
+ import subprocess
+ subprocess.run([sys.executable,str(Path(__file__).with_name('caltech101_monitor.py')),'--run-root',str(root)],check=True)
+ images_zip=out/'caltech101_disef_images.zip'
+ with zipfile.ZipFile(images_zip,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=1) as z:
+  for r in records:
+   q=root/r['synthetic_filename']; z.write(q,arcname=str(Path(r['class_folder'])/q.name))
+ # Strict extraction and ImageFolder validation.
+ with tempfile.TemporaryDirectory(prefix='caltech101_zip_validate_',dir='/marimo') as td:
+  td=Path(td)
+  with zipfile.ZipFile(images_zip) as z:
+   members=z.infolist()
+   if any(Path(m.filename).suffix.lower() not in {'.jpg','.jpeg','.png'} for m in members if not m.is_dir()): raise RuntimeError('non-image member in image zip')
+   if any(len(Path(m.filename).parts)!=2 for m in members if not m.is_dir()): raise RuntimeError('image zip root layout invalid')
+   z.extractall(td)
+  from torchvision.datasets import ImageFolder
+  ds=ImageFolder(td)
+  if len(ds)!=state['target'] or ds.classes!=sorted(config['classes']): raise RuntimeError(f'ImageFolder mismatch {len(ds)} classes={ds.classes}')
+  for q,_ in ds.samples:
+   ok,reason=validate_image(Path(q),class_folder=Path(q).parent.name)
+   if not ok: raise RuntimeError(f'extracted image invalid: {q}: {reason}')
+ checksums=out/'caltech101_disef_checksums.txt'
+ # Artifacts ZIP.
+ artifacts_zip=out/'caltech101_disef_artifacts.zip'
+ amap={
+  root/'metadata/caltech101_disef_manifest.jsonl':'caltech101_disef_artifacts/metadata/caltech101_disef_manifest.jsonl', csv_path:'caltech101_disef_artifacts/metadata/caltech101_disef_manifest.csv',
+  root/'metadata/generation_config.json':'caltech101_disef_artifacts/metadata/generation_config.json', root/'metadata/run_metadata.json':'caltech101_disef_artifacts/metadata/run_metadata.json',
+  root/'metadata/runtime_metrics.csv':'caltech101_disef_artifacts/metadata/runtime_metrics.csv', Path(a.report):'caltech101_disef_artifacts/reports/DISEF_CALTECH101_RUN_REPORT.md',
+  root/'logs/generation.log':'caltech101_disef_artifacts/logs/generation.log', root/'logs/supervisor.log':'caltech101_disef_artifacts/logs/supervisor.log',
+  root/'logs/optimization.log':'caltech101_disef_artifacts/logs/optimization.log',
+ }
+ for fig in (root/'artifacts/figures').glob('*.png'): amap[fig]=f'caltech101_disef_artifacts/figures/{fig.name}'
+ for name in ['requirements_molab.txt','run_caltech101_disef.sh','resume_caltech101_disef.sh','restore_caltech101_disef_from_hf.sh','status_caltech101_disef.sh']:
+  amap[Path(__file__).with_name(name)]=f'caltech101_disef_artifacts/reproducibility/{name}'
+ with zipfile.ZipFile(artifacts_zip,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=6) as z:
+  for src,arc in amap.items(): add(z,src,arc)
+ # Corrected source ZIP.
+ code_zip=out/'caltech101_disef_code.zip'; spc=Path(__file__).resolve().parents[1]
+ code_files=[spc/'README.md',spc/'data_train.py',spc/'util_data.py',spc/'utils.py',spc/'DataSplit/split_coop_Caltech101.csv',spc/'DisEF/main_disef.py']
+ code_files += [q for q in (spc/'DisEF').iterdir() if q.is_file() and q.suffix in {'.py','.sh','.txt','.md'}]
+ with zipfile.ZipFile(code_zip,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=6) as z:
+  for src in sorted(set(code_files)): add(z,src,Path('caltech101_disef_code/SPC')/src.relative_to(spc))
+  add(z,Path(a.patch),'caltech101_disef_code/DISEF_CALTECH101_PATCH.diff')
+ checksums.write_text(f"{sha(images_zip)}  {images_zip.name}\n{sha(code_zip)}  {code_zip.name}\n")
+ # The artifacts archive contains hashes of the two independent deliverables;
+ # its own final hash is recorded externally to avoid a self-referential hash.
+ with zipfile.ZipFile(artifacts_zip,'a',compression=zipfile.ZIP_DEFLATED,compresslevel=6) as z: z.write(checksums,'caltech101_disef_artifacts/checksums/caltech101_disef_checksums.txt')
+ lines=[f"{sha(q)}  {q.name}" for q in [images_zip,artifacts_zip,code_zip]]; checksums.write_text('\n'.join(lines)+'\n')
+ summary={"status":"SUCCESS","run_id":state['run_id'],"config_fingerprint":state['config_fingerprint'],"expected":state['target'],"actual":len(records),"missing":0,"failed":state['failed'],"corrupt":0,"classes":len(counts),"per_class":dict(counts),"sizes":dict(sizes),"formats":dict(formats),"dataset_bytes":sum((root/r['synthetic_filename']).stat().st_size for r in records),"archives":{q.name:{"path":str(q),"bytes":q.stat().st_size,"sha256":sha(q)} for q in [images_zip,artifacts_zip,code_zip]},"validated_at":datetime.now(timezone.utc).isoformat()}
+ atomic_json(root/'metadata/final_validation.json',summary)
+ if a.upload:
+  from huggingface_hub import CommitOperationAdd,HfApi,hf_hub_download
+  api=HfApi(token=os.environ.get('HF_TOKEN'))
+  image_repo='DanielNguyen121/DisEF-datasets'
+  image_commit=api.create_commit(repo_id=image_repo,repo_type='dataset',operations=[CommitOperationAdd(path_in_repo=images_zip.name,path_or_fileobj=str(images_zip))],commit_message=f"DISEF CALTECH101 image dataset: {state['run_id']}")
+  if images_zip.name not in set(api.list_repo_files(image_repo,repo_type='dataset',revision=image_commit.oid)): raise RuntimeError('final image archive missing remotely')
+  downloaded=Path(hf_hub_download(image_repo,images_zip.name,repo_type='dataset',token=os.environ.get('HF_TOKEN'),revision=image_commit.oid,force_download=True))
+  if sha(downloaded)!=sha(images_zip): raise RuntimeError('remote image checksum mismatch')
+  support_repo='DanielNguyen121/DisEF-Caltech101'
+  support=[artifacts_zip,code_zip,Path(a.report),Path(a.patch),csv_path,root/'metadata/generation_config.json',checksums]
+  support_commit=api.create_commit(repo_id=support_repo,repo_type='dataset',operations=[CommitOperationAdd(path_in_repo=q.name,path_or_fileobj=str(q)) for q in support],commit_message=f"DISEF CALTECH101 reproducibility deliverables: {state['run_id']}")
+  remote=set(api.list_repo_files(support_repo,repo_type='dataset',revision=support_commit.oid)); missing=[q.name for q in support if q.name not in remote]
+  if missing: raise RuntimeError(f'support remote files missing: {missing}')
+  summary['hf_image_commit']=image_commit.oid; summary['hf_support_commit']=support_commit.oid
+  summary['hf_image_path']=f'{image_repo}/{images_zip.name}'; summary['hf_support_paths']=[f'{support_repo}/{q.name}' for q in support]
+  atomic_json(root/'metadata/final_validation.json',summary)
+ print(json.dumps(summary,indent=2))
+if __name__=='__main__': main()
diff --git a/DisEF/caltech101_hf.py b/DisEF/caltech101_hf.py
new file mode 100644
index 0000000..f19f7e3
--- /dev/null
+++ b/DisEF/caltech101_hf.py
@@ -0,0 +1,215 @@
+#!/usr/bin/env python3
+"""Immutable Hugging Face checkpoint and restore utilities for DISEF CALTECH101."""
+from __future__ import annotations
+import argparse
+import fcntl
+import hashlib
+import json
+import os
+import shutil
+import tempfile
+import time
+import sys
+import zipfile
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+REPO_ID = "DanielNguyen121/DisEF-Caltech101"
+REPO_TYPE = "dataset"
+
+
+def now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def atomic_json(path: Path, obj: Any) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    tmp = path.with_name(path.name + f".tmp.{os.getpid()}")
+    with tmp.open("w") as f:
+        json.dump(obj, f, indent=2, sort_keys=True); f.flush(); os.fsync(f.fileno())
+    os.replace(tmp, path)
+
+
+def sha(path: Path) -> str:
+    h=hashlib.sha256()
+    with path.open("rb") as f:
+        for b in iter(lambda:f.read(8<<20),b""): h.update(b)
+    return h.hexdigest()
+
+
+def append_backup_events(manifest: Path, records: list[dict], chunk_name: str) -> bytes:
+    content = manifest.read_bytes() if manifest.exists() else b""
+    lines=[]
+    for rec in records:
+        lines.append(json.dumps({"event":"REMOTE_BACKED_UP","timestamp":now_iso(),"task_id":rec["task_id"],
+                                 "synthetic_filename":rec["synthetic_filename"],"remote_chunk":chunk_name},sort_keys=True))
+    return content + (("\n" if content and not content.endswith(b"\n") else "") + "\n".join(lines) + "\n").encode()
+
+
+def _checkpoint_unlocked(run_root: str | Path, force: bool=False, max_chunk_images: int=100) -> dict[str,Any]:
+    root=Path(run_root).resolve(); state_path=root/'state/caltech101_disef_state.json'
+    config=json.loads((root/'metadata/generation_config.json').read_text())
+    state=json.loads(state_path.read_text()); run_id=state['run_id']; fp=state['config_fingerprint']
+    if config['config_fingerprint']!=fp: raise RuntimeError('config/state fingerprint mismatch')
+    chunk_index_path=root/'metadata/chunk_index.json'
+    index=json.loads(chunk_index_path.read_text()) if chunk_index_path.exists() else {"run_id":run_id,"config_fingerprint":fp,"chunks":[]}
+    backed={r["task_id"] for c in index["chunks"] for r in c.get("records",[])}
+    pending=[r for tid,r in sorted(state.get('records',{}).items()) if r.get('completion_status')=='COMPLETE' and tid not in backed]
+    if not pending:
+        return {"status":"NOOP","remote_backed_up_count":len(backed)}
+    selected=pending[:max_chunk_images]
+    chunk_num=len(index['chunks'])+1; chunk_name=f"caltech101_disef_chunk_{chunk_num:06d}.zip"
+    chunk_path=root/'checkpoints'/chunk_name; chunk_path.parent.mkdir(parents=True,exist_ok=True)
+    if chunk_path.exists(): raise RuntimeError(f'immutable chunk already exists: {chunk_path}')
+    from caltech101_disef import validate_image
+    with zipfile.ZipFile(chunk_path,'w',compression=zipfile.ZIP_DEFLATED,compresslevel=1) as z:
+        for rec in selected:
+            p=root/rec['synthetic_filename']; ok,reason=validate_image(p,class_folder=rec['class_folder'])
+            if not ok: raise RuntimeError(f'cannot checkpoint invalid image {p}: {reason}')
+            z.write(p,arcname=rec['synthetic_filename'].removeprefix('images/'))
+    chunk_sha=sha(chunk_path)
+    chunk_record={"chunk_id":chunk_num,"filename":chunk_name,"image_count":len(selected),"sha256":chunk_sha,
+                  "uploaded_at":now_iso(),"records":[{"task_id":r['task_id'],"synthetic_filename":r['synthetic_filename']} for r in selected]}
+    candidate_index=json.loads(json.dumps(index)); candidate_index['chunks'].append(chunk_record); candidate_index['updated_at']=now_iso()
+    candidate_state=json.loads(json.dumps(state));
+    for rec in selected: candidate_state['records'][rec['task_id']]['remote_chunk']=chunk_name
+    candidate_state['remote_backed_up_count']=len(backed)+len(selected)
+    candidate_state['latest_remote_checkpoint']=now_iso(); candidate_state['latest_remote_checkpoint_epoch']=time.time()
+    summary={"run_id":run_id,"config_fingerprint":fp,"status":candidate_state['status'],"completed":candidate_state['completed'],
+             "target":candidate_state['target'],"remote_backed_up_count":candidate_state['remote_backed_up_count'],
+             "latest_chunk":chunk_name,"updated_at":now_iso()}
+    with tempfile.TemporaryDirectory(prefix='caltech101_hf_') as td:
+        td=Path(td)
+        c_state=td/'caltech101_disef_state.json'; c_index=td/'chunk_index.json'; c_summary=td/'progress_summary.json'; c_manifest=td/'caltech101_disef_manifest.jsonl'
+        atomic_json(c_state,candidate_state); atomic_json(c_index,candidate_index); atomic_json(c_summary,summary)
+        c_manifest.write_bytes(append_backup_events(root/'metadata/caltech101_disef_manifest.jsonl',selected,chunk_name))
+        prefix=f"_progress/{run_id}"
+        operations=[
+            CommitOperationAdd(path_in_repo=f"{prefix}/chunks/{chunk_name}",path_or_fileobj=str(chunk_path)),
+            CommitOperationAdd(path_in_repo=f"{prefix}/current/caltech101_disef_state.json",path_or_fileobj=str(c_state)),
+            CommitOperationAdd(path_in_repo=f"{prefix}/current/caltech101_disef_manifest.jsonl",path_or_fileobj=str(c_manifest)),
+            CommitOperationAdd(path_in_repo=f"{prefix}/current/chunk_index.json",path_or_fileobj=str(c_index)),
+            CommitOperationAdd(path_in_repo=f"{prefix}/current/progress_summary.json",path_or_fileobj=str(c_summary)),
+        ]
+        api=HfApi(token=os.environ.get('HF_TOKEN'))
+        commit=api.create_commit(repo_id=REPO_ID,repo_type=REPO_TYPE,operations=operations,
+                                 commit_message=f"CALTECH101 {run_id}: immutable chunk {chunk_num:06d} ({len(selected)} images)")
+        files=set(api.list_repo_files(REPO_ID,repo_type=REPO_TYPE,revision=commit.oid))
+        remote_chunk=f"{prefix}/chunks/{chunk_name}"
+        if remote_chunk not in files: raise RuntimeError('remote chunk verification failed')
+        # Commit succeeded. Merge backup markers into the newest local state;
+        # generation may have advanced while the network upload was in flight.
+        local_manifest = root/'metadata/caltech101_disef_manifest.jsonl'
+        with local_manifest.open('a',encoding='utf-8') as manifest_out:
+            for rec in selected:
+                manifest_out.write(json.dumps({"event":"REMOTE_BACKED_UP","timestamp":now_iso(),"task_id":rec["task_id"],
+                    "synthetic_filename":rec["synthetic_filename"],"remote_chunk":chunk_name},sort_keys=True)+"\n")
+            manifest_out.flush(); os.fsync(manifest_out.fileno())
+        latest_state=json.loads(state_path.read_text())
+        if latest_state['run_id']!=run_id or latest_state['config_fingerprint']!=fp:
+            raise RuntimeError('local state identity changed during checkpoint')
+        for rec in selected:
+            if rec['task_id'] in latest_state.get('records',{}): latest_state['records'][rec['task_id']]['remote_chunk']=chunk_name
+        latest_state['remote_backed_up_count']=len(backed)+len(selected)
+        latest_state['latest_remote_checkpoint']=candidate_state['latest_remote_checkpoint']
+        latest_state['latest_remote_checkpoint_epoch']=candidate_state['latest_remote_checkpoint_epoch']
+        atomic_json(chunk_index_path,candidate_index); atomic_json(state_path,latest_state)
+    return {"status":"UPLOADED","chunk":chunk_name,"sha256":chunk_sha,"count":len(selected),
+            "remote_backed_up_count":len(backed)+len(selected),"commit":commit.oid}
+
+
+def checkpoint(run_root: str | Path, force: bool=False, max_chunk_images: int=100) -> dict[str,Any]:
+    root=Path(run_root).resolve(); lock_path=root/'state/hf_checkpoint.lock'; lock_path.parent.mkdir(parents=True,exist_ok=True)
+    with lock_path.open('a+') as lock:
+        fcntl.flock(lock.fileno(),fcntl.LOCK_EX)
+        return _checkpoint_unlocked(root,force=force,max_chunk_images=max_chunk_images)
+
+
+def upload_bootstrap(run_root: str|Path, files: list[str|Path]) -> dict[str,Any]:
+    root=Path(run_root).resolve(); state=json.loads((root/'state/caltech101_disef_state.json').read_text()); run_id=state['run_id']
+    prefix=f"_progress/{run_id}/bootstrap"
+    api=HfApi(token=os.environ.get('HF_TOKEN')); operations=[]
+    for file in files:
+        p=Path(file)
+        if not p.is_file(): raise FileNotFoundError(p)
+        operations.append(CommitOperationAdd(path_in_repo=f"{prefix}/{p.name}",path_or_fileobj=str(p)))
+    commit=api.create_commit(repo_id=REPO_ID,repo_type=REPO_TYPE,operations=operations,
+                             commit_message=f"CALTECH101 {run_id}: recovery bootstrap")
+    remote=set(api.list_repo_files(REPO_ID,repo_type=REPO_TYPE,revision=commit.oid))
+    missing=[f"{prefix}/{Path(p).name}" for p in files if f"{prefix}/{Path(p).name}" not in remote]
+    if missing: raise RuntimeError(f'bootstrap verification failed: {missing}')
+    return {"status":"UPLOADED","commit":commit.oid,"prefix":prefix,"file_count":len(files)}
+
+
+def restore(run_id: str, dest: str|Path, validate_only: bool=False) -> dict[str,Any]:
+    dest=Path(dest).resolve(); dest.mkdir(parents=True,exist_ok=True)
+    prefix=f"_progress/{run_id}"
+    token=os.environ.get('HF_TOKEN'); api=HfApi(token=token)
+    files=set(api.list_repo_files(REPO_ID,repo_type=REPO_TYPE))
+    needed=[f"{prefix}/current/caltech101_disef_state.json",f"{prefix}/current/caltech101_disef_manifest.jsonl",f"{prefix}/current/chunk_index.json",f"{prefix}/current/progress_summary.json"]
+    missing=[p for p in needed if p not in files]
+    if missing: raise RuntimeError(f'missing remote current files: {missing}')
+    state_dir=dest/'state'; metadata=dest/'metadata'; images=dest/'images'
+    for directory in (state_dir,metadata,images,dest/'checkpoints',dest/'logs',dest/'artifacts/figures'):
+        directory.mkdir(parents=True,exist_ok=True)
+    mapping={
+        'caltech101_disef_state.json':state_dir/'caltech101_disef_state.json',
+        'caltech101_disef_manifest.jsonl':metadata/'caltech101_disef_manifest.jsonl',
+        'chunk_index.json':metadata/'chunk_index.json',
+        'progress_summary.json':metadata/'progress_summary.json',
+    }
+    for remote in needed:
+        src=Path(hf_hub_download(REPO_ID,remote,repo_type=REPO_TYPE,token=token))
+        shutil.copy2(src,mapping[Path(remote).name])
+    bootstrap_config=f"{prefix}/bootstrap/generation_config.json"
+    if bootstrap_config in files:
+        src=Path(hf_hub_download(REPO_ID,bootstrap_config,repo_type=REPO_TYPE,token=token))
+        shutil.copy2(src,metadata/'generation_config.json')
+    index=json.loads((metadata/'chunk_index.json').read_text())
+    state=json.loads((state_dir/'caltech101_disef_state.json').read_text())
+    if state['run_id']!=run_id or state['config_fingerprint']!=index['config_fingerprint']:
+        raise RuntimeError('remote state/index identity mismatch')
+    restored=0
+    for chunk in index['chunks']:
+        remote=f"{prefix}/chunks/{chunk['filename']}"
+        src=Path(hf_hub_download(REPO_ID,remote,repo_type=REPO_TYPE,token=token))
+        if sha(src)!=chunk['sha256']: raise RuntimeError(f"chunk checksum mismatch: {chunk['filename']}")
+        shutil.copy2(src,dest/'checkpoints'/chunk['filename'])
+        with zipfile.ZipFile(src) as z:
+            for member in z.infolist():
+                target=(images/member.filename).resolve()
+                if images.resolve() not in target.parents: raise RuntimeError('unsafe zip member')
+            z.extractall(images)
+        restored += chunk['image_count']
+    from caltech101_disef import validate_image
+    invalid=[]
+    for chunk in index['chunks']:
+        for rec in chunk['records']:
+            rel=rec['synthetic_filename'].removeprefix('images/')
+            p=images/rel; ok,reason=validate_image(p,class_folder=Path(rel).parent.name)
+            if not ok: invalid.append((rel,reason))
+    if invalid: raise RuntimeError(f'invalid restored images: {invalid[:3]}')
+    missing_count=max(0,int(state['target'])-restored)
+    atomic_json(state_dir/'restore_validation.json',{"run_id":run_id,"config_fingerprint":state['config_fingerprint'],
+                "restored":restored,"target":state['target'],"remaining":missing_count,"invalid":invalid,"validated_at":now_iso()})
+    return {"status":"PASS","run_id":run_id,"restored":restored,"remaining":missing_count,
+            "chunks":len(index['chunks']),"dest":str(dest),"config_fingerprint":state['config_fingerprint']}
+
+
+def main():
+    p=argparse.ArgumentParser(); sub=p.add_subparsers(dest='cmd',required=True)
+    c=sub.add_parser('checkpoint'); c.add_argument('--run-root',required=True); c.add_argument('--force',action='store_true')
+    r=sub.add_parser('restore'); r.add_argument('--run-id',required=True); r.add_argument('--dest',required=True)
+    b=sub.add_parser('bootstrap'); b.add_argument('--run-root',required=True); b.add_argument('files',nargs='+')
+    a=p.parse_args()
+    if a.cmd=='checkpoint': result=checkpoint(a.run_root,a.force)
+    elif a.cmd=='restore': result=restore(a.run_id,a.dest)
+    else: result=upload_bootstrap(a.run_root,a.files)
+    print(json.dumps(result,indent=2))
+
+if __name__=='__main__': main()
diff --git a/DisEF/caltech101_monitor.py b/DisEF/caltech101_monitor.py
new file mode 100644
index 0000000..e4e722e
--- /dev/null
+++ b/DisEF/caltech101_monitor.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""CPU-only metrics figures and qualitative previews; never loads generation models."""
+import argparse,json,time
+from pathlib import Path
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import pandas as pd
+from PIL import Image,ImageOps,ImageDraw
+
+def savefig(fig,path):
+ path.parent.mkdir(parents=True,exist_ok=True); tmp=path.with_suffix('.tmp.png'); fig.tight_layout(); fig.savefig(tmp,dpi=140); plt.close(fig); tmp.replace(path)
+def refresh(root):
+ state_path=root/'state/caltech101_disef_state.json'
+ if not state_path.exists(): return 'NOT INITIALIZED'
+ s=json.loads(state_path.read_text()); figs=root/'artifacts/figures'; figs.mkdir(parents=True,exist_ok=True)
+ metrics=root/'metadata/runtime_metrics.csv'
+ if metrics.exists() and metrics.stat().st_size:
+  df=pd.read_csv(metrics)
+  x=df.elapsed_seconds/60
+  fig,ax=plt.subplots(figsize=(7,4)); ax.plot(x,df.completed,label='completed'); ax.axhline(s['target'],ls='--',color='tab:red',label='target'); ax.set(xlabel='Elapsed (min)',ylabel='Completed images',title='DISEF CALTECH101 generation progress'); ax.legend(); ax.grid(alpha=.25); savefig(fig,figs/'generation_progress.png')
+  fig,ax=plt.subplots(figsize=(7,4)); ax.plot(x,df.images_per_minute); ax.set(xlabel='Elapsed (min)',ylabel='Images/min',title='Generation throughput'); ax.grid(alpha=.25); savefig(fig,figs/'generation_throughput.png')
+  fig,ax=plt.subplots(figsize=(7,4)); ax.plot(x,df.vram_used_mb/1024,label='VRAM GiB'); ax2=ax.twinx(); ax2.plot(x,df.gpu_util_percent,color='tab:orange',alpha=.7,label='GPU %'); ax.set(xlabel='Elapsed (min)',ylabel='VRAM (GiB)',title='GPU and VRAM usage'); ax2.set_ylabel('GPU utilization (%)'); ax.grid(alpha=.25); savefig(fig,figs/'gpu_vram_usage.png')
+ counts={}
+ for rec in s.get('records',{}).values():
+  if rec.get('completion_status')=='COMPLETE': counts[rec['class_folder']]=counts.get(rec['class_folder'],0)+1
+ config=json.loads((root/'metadata/generation_config.json').read_text()); classes=config['classes']; expected=config['images_per_class']
+ fig,ax=plt.subplots(figsize=(14,5)); vals=[counts.get(c,0) for c in classes]; ax.bar(range(len(classes)),[expected]*len(classes),color='lightgray',label='expected'); ax.bar(range(len(classes)),vals,color='tab:blue',label='actual'); ax.set_xticks(range(len(classes)),classes,rotation=75,ha='right',fontsize=7); ax.set(ylabel='Images',title='Expected vs actual images per class'); ax.legend(); savefig(fig,figs/'images_per_class.png')
+ records=[r for r in s.get('records',{}).values() if r.get('completion_status')=='COMPLETE' and (root/r['synthetic_filename']).exists()]
+ if records:
+  chosen=[]; seen=set()
+  for rec in records:
+   if rec['class_folder'] not in seen: chosen.append(rec); seen.add(rec['class_folder'])
+   if len(chosen)>=16: break
+  if len(chosen)<16: chosen=(chosen+records)[:16]
+  canvas=Image.new('RGB',(4*260,4*290),'white'); draw=ImageDraw.Draw(canvas)
+  for i,rec in enumerate(chosen):
+   im=Image.open(root/rec['synthetic_filename']).convert('RGB'); im=ImageOps.fit(im,(250,250)); x=(i%4)*260+5; y=(i//4)*290+5; canvas.paste(im,(x,y)); draw.text((x,y+252),rec['class_folder'][:34],fill='black')
+  canvas.save(figs/'caltech101_disef_preview.png')
+  pairs=chosen[:8]; canvas=Image.new('RGB',(2*260,8*285),'white'); draw=ImageDraw.Draw(canvas)
+  for i,rec in enumerate(pairs):
+   for j,key in enumerate(['source_image','synthetic_filename']):
+    q=Path(rec[key]) if key=='source_image' else root/rec[key]; im=Image.open(q).convert('RGB'); im=ImageOps.fit(im,(250,250)); x=j*260+5; y=i*285+5; canvas.paste(im,(x,y)); draw.text((x,y+252),('REAL | ' if j==0 else 'DISEF | ')+rec['class_folder'][:25],fill='black')
+  canvas.save(figs/'caltech101_real_vs_disef_preview.png')
+ return s.get('status')
+def main():
+ p=argparse.ArgumentParser(); p.add_argument('--run-root',default='/marimo/runs/disef_caltech101'); p.add_argument('--watch',action='store_true'); p.add_argument('--interval',type=int,default=300); a=p.parse_args(); root=Path(a.run_root)
+ while True:
+  status=refresh(root); print(f'{time.strftime("%FT%T%z")} monitor status={status}',flush=True)
+  if not a.watch or status in {'COMPLETED','BLOCKED','PARTIAL'}: return
+  time.sleep(a.interval)
+if __name__=='__main__': main()
diff --git a/DisEF/caltech101_status.py b/DisEF/caltech101_status.py
new file mode 100644
index 0000000..4015b2a
--- /dev/null
+++ b/DisEF/caltech101_status.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+import argparse,json,os,shutil,subprocess,time
+from pathlib import Path
+from datetime import datetime
+from PIL import Image
+
+def alive(pid):
+    try:
+        n=int(pid); os.kill(n,0)
+        stat=Path(f'/proc/{n}/stat')
+        return not stat.exists() or stat.read_text().split()[2] != 'Z'
+    except: return False
+p=argparse.ArgumentParser(); p.add_argument('--run-root',default='/marimo/runs/disef_caltech101'); a=p.parse_args(); root=Path(a.run_root)
+sp=root/'state/caltech101_disef_state.json'; lockp=root/'state/run.lock'
+if not sp.exists(): raise SystemExit('Status: NOT INITIALIZED')
+s=json.loads(sp.read_text()); lock=json.loads(lockp.read_text()) if lockp.exists() else {}
+remote_count=s.get('remote_backed_up_count',0); remote_time=s.get('latest_remote_checkpoint')
+chunk_index=root/'metadata/chunk_index.json'
+if chunk_index.exists():
+    try:
+        ci=json.loads(chunk_index.read_text())
+        if ci.get('run_id')==s.get('run_id') and ci.get('config_fingerprint')==s.get('config_fingerprint'):
+            remote_count=sum(int(c.get('image_count',0)) for c in ci.get('chunks',[]))
+            remote_time=ci.get('updated_at',remote_time)
+    except (OSError,ValueError,TypeError):
+        pass
+status=s.get('status','UNKNOWN')
+if status=='RUNNING' and not alive(lock.get('generator_pid')): status='STOPPED'
+valid=[]
+for q in (root/'images').rglob('*.jpg'):
+    try:
+        with Image.open(q) as im:
+            im.load()
+            if im.format=='JPEG' and im.size==(512,512): valid.append(q)
+    except Exception:
+        pass
+completed=len(valid)
+latest_image=max(valid,key=lambda q:q.stat().st_mtime) if valid else None
+current_class=latest_image.parent.name if latest_image else s.get('current_class')
+current_source=latest_image.name if latest_image else s.get('current_source')
+now=time.time()
+current_rate=sum(q.stat().st_mtime>=now-60 for q in valid)
+elapsed=max(time.time()-datetime.fromisoformat(s['created_at']).timestamp(),1)
+average_rate=completed/elapsed*60; remaining=max(s.get('target',0)-completed,0); eta=remaining/current_rate*60 if current_rate else 0
+try: gpu=subprocess.check_output(['nvidia-smi','--query-gpu=utilization.gpu,memory.used,memory.total','--format=csv,noheader,nounits'],text=True).splitlines()[0]
+except: gpu='unavailable'
+du=shutil.disk_usage(root); log=root/'logs/generation.log'; latest=''
+if log.exists():
+    lines=log.read_text(errors='replace').splitlines(); latest=lines[-1] if lines else ''
+lines=[
+ f"Status: {status}", f"Run ID: {s.get('run_id')}",
+ f"PID: {lock.get('generator_pid')} ({'alive' if alive(lock.get('generator_pid')) else 'dead'})",
+ f"Supervisor: {lock.get('supervisor_pid')} ({'alive' if alive(lock.get('supervisor_pid')) else 'dead'})",
+ f"Progress: {completed}/{s.get('target',0)} ({100*completed/max(s.get('target',1),1):.2f}%)",
+ f"Current class: {current_class}", f"Current source: {current_source}",
+ f"Failed: {s.get('failed',0)}", f"Retried: {s.get('retried',0)}", f"Rate: {current_rate:.2f} img/min; average {average_rate:.2f}",
+ f"Elapsed: {elapsed/3600:.2f} h", f"Estimated remaining: {eta/3600:.2f} h", f"Disk free: {du.free/1e9:.1f} GB",
+ f"GPU util, VRAM used/total MiB: {gpu}", f"Remote backed up: {remote_count}",
+ f"Last HF checkpoint: {remote_time}", f"Latest event: {latest_image.name if latest_image else (s.get('last_event') or latest)}"]
+print('\n'.join(lines))
diff --git a/DisEF/caltech101_supervisor.py b/DisEF/caltech101_supervisor.py
new file mode 100644
index 0000000..31338fe
--- /dev/null
+++ b/DisEF/caltech101_supervisor.py
@@ -0,0 +1,36 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+import argparse, json, os, subprocess, sys, time, traceback
+from pathlib import Path
+sys.path.insert(0,str(Path(__file__).resolve().parent))
+from caltech101_disef import atomic_json, now_iso, pid_alive
+
+def main():
+    p=argparse.ArgumentParser(); p.add_argument('--run-root',required=True); p.add_argument('--max-restarts',type=int,default=3); p.add_argument('--batch-size',type=int,default=16); p.add_argument('generator_args',nargs=argparse.REMAINDER); a=p.parse_args()
+    root=Path(a.run_root).resolve(); (root/'logs').mkdir(parents=True,exist_ok=True); (root/'state').mkdir(parents=True,exist_ok=True)
+    lock_path=root/'state/run.lock'; state_path=root/'state/caltech101_disef_state.json'; log_path=root/'logs/generation.log'
+    if lock_path.exists():
+        lock=json.loads(lock_path.read_text())
+        if pid_alive(lock.get('supervisor_pid')) and lock.get('supervisor_pid')!=os.getpid(): raise SystemExit(f"supervisor already alive: {lock['supervisor_pid']}")
+        if pid_alive(lock.get('generator_pid')): raise SystemExit(f"generator already alive: {lock['generator_pid']}")
+    cmd=[sys.executable,str(Path(__file__).with_name('caltech101_disef.py')),'--mode','generate','--run-root',str(root),'--batch-size',str(a.batch_size),*a.generator_args]
+    for attempt in range(a.max_restarts+1):
+        env=os.environ.copy(); env['DISEF_SUPERVISOR_PID']=str(os.getpid())
+        with log_path.open('a',buffering=1) as log:
+            log.write(f"[{now_iso()}] supervisor start attempt={attempt} cmd={cmd!r}\n")
+            child=subprocess.Popen(cmd,stdout=log,stderr=subprocess.STDOUT,text=True,env=env)
+            state=json.loads(state_path.read_text()) if state_path.exists() else {}
+            atomic_json(lock_path,{"run_id":state.get('run_id'),"config_fingerprint":state.get('config_fingerprint'),"supervisor_pid":os.getpid(),"generator_pid":child.pid,"hostname":os.uname().nodename,"updated_at":now_iso()})
+            rc=child.wait(); log.write(f"[{now_iso()}] generator exit rc={rc}\n")
+        state=json.loads(state_path.read_text()) if state_path.exists() else {}
+        if rc==0 and state.get('status') in {'COMPLETED','PARTIAL','STOPPED'}: return
+        if state.get('status')=='COMPLETED': return
+        try:
+            import caltech101_hf; caltech101_hf.checkpoint(root,force=True)
+        except Exception:
+            with (root/'logs/supervisor.log').open('a') as f: f.write(traceback.format_exc()+"\n")
+        if attempt>=a.max_restarts:
+            state.update(status='BLOCKED',updated_at=now_iso(),last_event=f'generator failed after {a.max_restarts+1} attempts; last rc={rc}')
+            atomic_json(state_path,state); return
+        time.sleep(20)
+if __name__=='__main__': main()
diff --git a/DisEF/requirements_molab.txt b/DisEF/requirements_molab.txt
new file mode 100644
index 0000000..e9cbc04
--- /dev/null
+++ b/DisEF/requirements_molab.txt
@@ -0,0 +1,15 @@
+# Validated Molab environment (Python 3.13, CUDA 13 / Blackwell)
+torch==2.14.1
+torchvision==0.29.1
+diffusers==0.40.0
+transformers==5.18.0
+accelerate==1.15.0
+huggingface-hub==1.33.0
+qwen-vl-utils==0.0.14
+sentencepiece==0.2.2
+open-clip-torch==3.3.0
+peft==0.21.2
+Pillow==12.3.0
+numpy==2.5.3
+pandas==3.0.3
+matplotlib==3.11.1
diff --git a/DisEF/restore_caltech101_disef_from_hf.sh b/DisEF/restore_caltech101_disef_from_hf.sh
new file mode 100644
index 0000000..b284aac
--- /dev/null
+++ b/DisEF/restore_caltech101_disef_from_hf.sh
@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+set -euo pipefail
+RUN_ID="${1:?Usage: $0 RUN_ID [RUN_ROOT]}"
+RUN_ROOT="${2:-/marimo/runs/disef_caltech101}"
+SPC_DIR="${SPC_DIR:-/marimo/SPC}"
+BOOT_DIR="/marimo/caltech101_recovery_bootstrap/$RUN_ID"
+PYTHON_BIN="${PYTHON_BIN:-/tmp/uv-venv/bin/python}"
+if [[ ! -d "$SPC_DIR/.git" ]]; then git clone https://github.com/thanhhai1988/SPC.git "$SPC_DIR"; fi
+mkdir -p "$BOOT_DIR"
+"$PYTHON_BIN" - "$RUN_ID" "$BOOT_DIR" <<'PY'
+import os,sys,shutil
+from pathlib import Path
+from huggingface_hub import snapshot_download
+run_id,dest=sys.argv[1],Path(sys.argv[2])
+snap=Path(snapshot_download('DanielNguyen121/DisEF-Caltech101',repo_type='dataset',token=os.environ.get('HF_TOKEN'),allow_patterns=[f'_progress/{run_id}/bootstrap/*']))
+src=snap/f'_progress/{run_id}/bootstrap'
+for p in src.iterdir(): shutil.copy2(p,dest/p.name)
+PY
+if [[ -f "$BOOT_DIR/DISEF_CALTECH101_PATCH.diff" ]]; then
+  git -C "$SPC_DIR" apply --check "$BOOT_DIR/DISEF_CALTECH101_PATCH.diff" 2>/dev/null && git -C "$SPC_DIR" apply "$BOOT_DIR/DISEF_CALTECH101_PATCH.diff" || true
+fi
+uv pip install -r "$BOOT_DIR/requirements_molab.txt" -p "$PYTHON_BIN"
+if [[ ! -d /marimo/Qwen3-VL-Embedding ]]; then git clone https://github.com/QwenLM/Qwen3-VL-Embedding.git /marimo/Qwen3-VL-Embedding; fi
+"$PYTHON_BIN" - "$SPC_DIR" <<'PY'
+import hashlib, shutil, subprocess, sys, zipfile
+from pathlib import Path
+spc=Path(sys.argv[1])
+root=Path('/marimo/data/caltech101/train')
+archive=root/'caltech-101.zip'
+url='https://data.caltech.edu/records/mzrjq-6wc02/files/caltech-101.zip'
+if not archive.exists():
+    root.mkdir(parents=True,exist_ok=True)
+    subprocess.run(['curl','-k','-fL',url,'-o',str(archive)],check=True)
+if hashlib.md5(archive.read_bytes()).hexdigest() != '3138e1922a9193bfa496528edbbc45d0':
+    raise RuntimeError('Caltech101 archive MD5 mismatch')
+categories=root/'caltech101'/'101_ObjectCategories'
+if not categories.exists():
+    with zipfile.ZipFile(archive) as zf: zf.extractall(root)
+split_source=spc/'DataSplit'/'split_coop_Caltech101.csv'
+if not split_source.exists():
+    raise RuntimeError(f'missing patched DISEF split: {split_source}')
+shutil.copy2(split_source,root/'split_coop.csv')
+PY
+"$PYTHON_BIN" "$SPC_DIR/DisEF/caltech101_hf.py" restore --run-id "$RUN_ID" --dest "$RUN_ROOT"
+exec bash "$SPC_DIR/DisEF/resume_caltech101_disef.sh" "$RUN_ROOT"
diff --git a/DisEF/resume_caltech101_disef.sh b/DisEF/resume_caltech101_disef.sh
new file mode 100644
index 0000000..2570ced
--- /dev/null
+++ b/DisEF/resume_caltech101_disef.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+set -euo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+exec bash "$SCRIPT_DIR/run_caltech101_disef.sh" "${1:-/marimo/runs/disef_caltech101}"
diff --git a/DisEF/run_caltech101_disef.sh b/DisEF/run_caltech101_disef.sh
new file mode 100644
index 0000000..e98f638
--- /dev/null
+++ b/DisEF/run_caltech101_disef.sh
@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+set -euo pipefail
+RUN_ROOT="${1:-/marimo/runs/disef_caltech101}"
+BATCH_SIZE="${DISEF_BATCH_SIZE:-${2:-16}}"
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PYTHON_BIN="${PYTHON_BIN:-/tmp/uv-venv/bin/python}"
+mkdir -p "$RUN_ROOT/logs" "$RUN_ROOT/state"
+"$PYTHON_BIN" "$SCRIPT_DIR/caltech101_status.py" --run-root "$RUN_ROOT" 2>/dev/null | head -4 || true
+nohup setsid "$PYTHON_BIN" "$SCRIPT_DIR/caltech101_supervisor.py" --run-root "$RUN_ROOT" --max-restarts 3 --batch-size "$BATCH_SIZE" \
+  >>"$RUN_ROOT/logs/supervisor.log" 2>&1 </dev/null &
+SUPERVISOR_PID=$!
+nohup setsid "$PYTHON_BIN" "$SCRIPT_DIR/caltech101_monitor.py" --run-root "$RUN_ROOT" --watch --interval 300 \
+  >>"$RUN_ROOT/logs/monitor.log" 2>&1 </dev/null &
+MONITOR_PID=$!
+nohup setsid "$PYTHON_BIN" "$SCRIPT_DIR/caltech101_checkpoint_daemon.py" --run-root "$RUN_ROOT" --interval 300 \
+  >>"$RUN_ROOT/logs/checkpoint_daemon.log" 2>&1 </dev/null &
+CHECKPOINT_PID=$!
+echo "supervisor_pid=$SUPERVISOR_PID monitor_pid=$MONITOR_PID checkpoint_pid=$CHECKPOINT_PID run_root=$RUN_ROOT"
diff --git a/DisEF/status_caltech101_disef.sh b/DisEF/status_caltech101_disef.sh
new file mode 100644
index 0000000..90479b9
--- /dev/null
+++ b/DisEF/status_caltech101_disef.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+set -euo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+exec "${PYTHON_BIN:-/tmp/uv-venv/bin/python}" "$SCRIPT_DIR/caltech101_status.py" --run-root "${1:-/marimo/runs/disef_caltech101}"