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@@ -69,3 +69,5 @@ worldeval/world-r1-code/paper/figures/vis_video.pdf filter=lfs diff=lfs merge=lf
 worldeval/Depth-Anything-3/.venv/bin/python3 filter=lfs diff=lfs merge=lfs -text
 worldeval/Depth-Anything-3/.venv/bin/python3.11 filter=lfs diff=lfs merge=lfs -text
 model_weights/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoints/LongLive/prompts/vidprom_filtered_extended.txt filter=lfs diff=lfs merge=lfs -text
+checkpoints/WoW-1-Wan-1.3B-2M/google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text

batch_test/bench_common.py

@@ -0,0 +1,435 @@
+from __future__ import annotations
+
+import os
+import re
+import shlex
+import shutil
+import subprocess
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable, Sequence
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+VIDEO_EXTENSIONS = (".mp4", ".mov", ".mkv", ".avi", ".webm")
+JSON_SKIP_NAMES = {"prompt.json"}
+DATA_PREFIXES = (
+    "captions_",
+    "caption_",
+    "captions",
+    "caption",
+    "prompts_",
+    "prompt_",
+    "prompts",
+    "prompt",
+    "compressed_",
+    "compressed",
+    "reference_",
+    "ref_",
+    "reference",
+    "ref",
+)
+
+
+@dataclass(frozen=True)
+class PipelineSpec:
+    name: str
+    script: Path
+    batch_script: Path
+    output_prefix: str
+    aliases: tuple[str, ...]
+
+
+PIPELINE_SPECS = (
+    PipelineSpec(
+        name="cosmos-predict",
+        script=Path("examples/run_cosmos_predict2p5_test_stream_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_cosmos_predict2p5_test_stream_from_json.py"),
+        output_prefix="cosmos",
+        aliases=("cosmos", "cosmos-predict", "cosmos_predict", "cosmos-predict2p5", "cosmos_predict2p5"),
+    ),
+    PipelineSpec(
+        name="hunyuan-gamecraft",
+        script=Path("examples/run_hunyuan_gamecraft_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_hunyuan_gamecraft_from_json.py"),
+        output_prefix="hunyuan_gamecraft",
+        aliases=("hunyuan-gamecraft", "hunyuan_gamecraft", "gamecraft"),
+    ),
+    PipelineSpec(
+        name="hunyuan-worldplay",
+        script=Path("examples/run_hunyuan_worldplay_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_hunyuan_worldplay_from_json.py"),
+        output_prefix="hunyuan_worldplay",
+        aliases=("hunyuan-worldplay", "hunyuan_worldplay", "worldplay"),
+    ),
+    PipelineSpec(
+        name="lingbot-world",
+        script=Path("examples/run_lingbot_world_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_lingbot_world_from_json.py"),
+        output_prefix="lingbot_world",
+        aliases=("lingbot-world", "lingbot_world", "lingbot"),
+    ),
+    PipelineSpec(
+        name="longlive",
+        script=Path("examples/run_longlive_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_longlive_from_json.py"),
+        output_prefix="longlive",
+        aliases=("longlive",),
+    ),
+    PipelineSpec(
+        name="matrix-game2",
+        script=Path("examples/run_matrix_game2_kv_cache_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_matrix_game2_kv_cache_from_json.py"),
+        output_prefix="matrix_game2",
+        aliases=("matrix-game2", "matrix_game2", "matrix", "matrix-game-2", "matrix_game_2"),
+    ),
+    PipelineSpec(
+        name="rolling-forcing",
+        script=Path("examples/run_rolling_forcing_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_rolling_forcing_from_json.py"),
+        output_prefix="rolling_forcing",
+        aliases=("rolling-forcing", "rolling_forcing"),
+    ),
+    PipelineSpec(
+        name="wow",
+        script=Path("examples/run_wow_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_wow_from_json.py"),
+        output_prefix="wow",
+        aliases=("wow",),
+    ),
+    PipelineSpec(
+        name="yume1p5",
+        script=Path("examples/run_yume1p5_from_json.py"),
+        batch_script=Path("batch_test/from_json/run_yume1p5_from_json.py"),
+        output_prefix="yume1p5",
+        aliases=("yume1p5", "yume-1p5", "yume_1p5", "yume"),
+    ),
+)
+
+PIPELINE_BY_ALIAS = {
+    alias: spec
+    for spec in PIPELINE_SPECS
+    for alias in (spec.name, *spec.aliases)
+}
+
+
+@dataclass(frozen=True)
+class Sample:
+    sample_id: str
+    json_path: Path
+    video_path: Path
+
+
+def sanitize_filename_part(value: str) -> str:
+    value = value.strip().replace(os.sep, "_")
+    if os.altsep:
+        value = value.replace(os.altsep, "_")
+    value = re.sub(r"[^A-Za-z0-9_.-]+", "_", value)
+    value = value.strip("._-")
+    return value or "sample"
+
+
+def strip_data_prefix(stem: str) -> str:
+    lower_stem = stem.lower()
+    for prefix in DATA_PREFIXES:
+        if lower_stem.startswith(prefix):
+            stripped = stem[len(prefix):].lstrip("_-. ")
+            if stripped:
+                return stripped
+    return stem
+
+
+def pairing_key(stem: str) -> str:
+    stripped = strip_data_prefix(stem)
+    return re.sub(r"[^a-z0-9]+", "", stripped.lower())
+
+
+def iter_json_files(data_dir: Path, json_glob: str, recursive: bool) -> list[Path]:
+    paths = data_dir.rglob(json_glob) if recursive else data_dir.glob(json_glob)
+    json_paths = [
+        path
+        for path in paths
+        if path.is_file()
+        and path.suffix.lower() == ".json"
+        and path.name not in JSON_SKIP_NAMES
+        and "_judge_" not in path.name
+    ]
+    return sorted(json_paths, key=lambda path: str(path))
+
+
+def collect_video_index(data_dir: Path, recursive: bool) -> dict[tuple[Path, str], list[Path]]:
+    video_paths: Iterable[Path]
+    if recursive:
+        video_paths = (
+            path
+            for ext in VIDEO_EXTENSIONS
+            for path in data_dir.rglob(f"*{ext}")
+        )
+    else:
+        video_paths = (
+            path
+            for ext in VIDEO_EXTENSIONS
+            for path in data_dir.glob(f"*{ext}")
+        )
+
+    index: dict[tuple[Path, str], list[Path]] = {}
+    for video_path in video_paths:
+        if not video_path.is_file():
+            continue
+        key = (video_path.parent.resolve(), pairing_key(video_path.stem))
+        index.setdefault(key, []).append(video_path)
+    return index
+
+
+def candidate_video_paths(json_path: Path) -> list[Path]:
+    stem = json_path.stem
+    stripped = strip_data_prefix(stem)
+    stems = [
+        stem,
+        f"compressed_{stem}",
+        f"compressed{stem}",
+        stripped,
+        f"compressed_{stripped}",
+        f"compressed{stripped}",
+        f"ref_{stripped}",
+        f"ref{stripped}",
+        f"reference_{stripped}",
+        f"reference{stripped}",
+    ]
+
+    candidates: list[Path] = []
+    seen: set[Path] = set()
+    for candidate_stem in stems:
+        for ext in VIDEO_EXTENSIONS:
+            path = json_path.with_name(f"{candidate_stem}{ext}")
+            if path not in seen:
+                candidates.append(path)
+                seen.add(path)
+    return candidates
+
+
+def find_video_for_json(
+    json_path: Path,
+    video_index: dict[tuple[Path, str], list[Path]],
+) -> Path:
+    for candidate in candidate_video_paths(json_path):
+        if candidate.exists():
+            return candidate
+
+    key = (json_path.parent.resolve(), pairing_key(json_path.stem))
+    matches = video_index.get(key, [])
+    if len(matches) == 1:
+        return matches[0]
+    if len(matches) > 1:
+        raise ValueError(
+            f"Multiple videos match {json_path}: "
+            + ", ".join(str(path) for path in sorted(matches))
+        )
+    raise FileNotFoundError(f"No matching video found for {json_path}")
+
+
+def discover_samples(args) -> list[Sample]:
+    data_dir = Path(args.data_dir).expanduser().resolve()
+    if not data_dir.is_dir():
+        raise NotADirectoryError(f"--data-dir does not exist or is not a directory: {data_dir}")
+
+    video_index = collect_video_index(data_dir, args.recursive)
+    selected_ids = set(args.sample_ids or [])
+    samples: list[Sample] = []
+    sample_ids_seen: set[str] = set()
+
+    for json_path in iter_json_files(data_dir, args.json_glob, args.recursive):
+        raw_sample_id = strip_data_prefix(json_path.stem)
+        sample_id = sanitize_filename_part(raw_sample_id)
+        if selected_ids and sample_id not in selected_ids:
+            continue
+
+        try:
+            video_path = find_video_for_json(json_path, video_index)
+        except FileNotFoundError:
+            if args.skip_missing:
+                print(f"[SKIP] {json_path}: no matching video")
+                continue
+            raise
+
+        if sample_id in sample_ids_seen:
+            raise ValueError(
+                f"Duplicate sample id after normalization: {sample_id}. "
+                "Use more specific filenames or --sample-ids to disambiguate."
+            )
+        sample_ids_seen.add(sample_id)
+        samples.append(Sample(sample_id=sample_id, json_path=json_path.resolve(), video_path=video_path.resolve()))
+
+    if args.limit is not None:
+        samples = samples[: args.limit]
+    if not samples:
+        raise ValueError(f"No samples found in {data_dir}")
+    return samples
+
+
+def ensure_file_link_or_copy(source: Path, target: Path) -> None:
+    target.parent.mkdir(parents=True, exist_ok=True)
+    if target.exists() or target.is_symlink():
+        target.unlink()
+    try:
+        target.symlink_to(source)
+    except OSError:
+        shutil.copy2(source, target)
+
+
+def stage_sample(sample: Sample, staging_root: Path) -> Path:
+    sample_stage = staging_root / sample.sample_id
+    sample_stage.mkdir(parents=True, exist_ok=True)
+    staged_json = sample_stage / f"{sample.sample_id}.json"
+    staged_video = sample_stage / f"{sample.sample_id}.mp4"
+    ensure_file_link_or_copy(sample.json_path, staged_json)
+    ensure_file_link_or_copy(sample.video_path, staged_video)
+    return staged_json
+
+
+def prepare_sample_output(sample: Sample, output_root: Path, dry_run: bool) -> tuple[Path, Path, Path]:
+    sample_dir = output_root / sample.sample_id
+
+    prompt_path = sample_dir / "prompt.json"
+    ref_path = sample_dir / f"ref_{sample.sample_id}.mp4"
+    if not dry_run:
+        sample_dir.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(sample.json_path, prompt_path)
+        shutil.copy2(sample.video_path, ref_path)
+    return sample_dir, prompt_path, ref_path
+
+
+def output_prefix_for(args, spec: PipelineSpec) -> str:
+    return sanitize_filename_part(args.pipeline_output_prefix or spec.output_prefix)
+
+
+def generated_video_path(output_root: Path, sample: Sample, output_prefix: str) -> Path:
+    return output_root / sample.sample_id / f"{output_prefix}_gen_{sample.sample_id}.mp4"
+
+
+def judge_output_path(output_root: Path, sample: Sample, output_prefix: str) -> Path:
+    return output_root / sample.sample_id / f"{output_prefix}_judge_{sample.sample_id}.json"
+
+
+def build_generation_command(
+    args,
+    spec: PipelineSpec,
+    staged_json: Path,
+    temp_output_root: Path,
+    output_video: Path,
+) -> list[str]:
+    script_path = spec.script if getattr(args, "subprocess_per_sample", False) else spec.batch_script
+    script = REPO_ROOT / script_path
+    if args.launcher == "torchrun":
+        cmd = [
+            "torchrun",
+            "--nproc_per_node",
+            str(args.nproc_per_node),
+            str(script),
+        ]
+    else:
+        cmd = [args.gen_python or args.python, str(script)]
+
+    cmd.extend(
+        [
+            "--json_path",
+            str(staged_json),
+            "--output_root",
+            str(temp_output_root),
+            "--output_dir_name",
+            staged_json.stem,
+            "--output_path",
+            str(output_video),
+        ]
+    )
+    cmd.extend(shlex.split(args.pipeline_extra_args))
+    return cmd
+
+
+def build_judge_command(
+    args,
+    output_video: Path,
+    ref_video: Path,
+    judge_output: Path,
+) -> list[str]:
+    judge_core_cmd = [
+        args.judge_python,
+        str((REPO_ROOT / args.judge_script).resolve()),
+        "--video",
+        str(output_video),
+        "--gt-video",
+        str(ref_video),
+        "--physical-max-frames",
+        str(args.physical_max_frames),
+        "--sam-device",
+        str(args.sam_device),
+        "--three-d-max-frames",
+        str(args.three_d_max_frames),
+        "--three-d-model-name",
+        str(args.three_d_model_name),
+        "--da-device",
+        str(args.da_device),
+        "--vlm-backend",
+        str(args.vlm_backend),
+        "--vlm-model",
+        str(args.vlm_model),
+        "--three-d-vlm-backend",
+        str(args.three_d_vlm_backend),
+        "--three-d-scoring-model",
+        str(args.three_d_scoring_model),
+        "--output",
+        str(judge_output),
+    ]
+    if args.skip_physical:
+        judge_core_cmd.append("--skip-physical")
+    if args.skip_3d:
+        judge_core_cmd.append("--skip-3d")
+    judge_core_cmd.extend(shlex.split(args.judge_extra_args))
+
+    judge_conda_env = str(args.judge_conda_env or "").strip()
+    if judge_conda_env and judge_conda_env.lower() not in {"none", "null", "false", "0"}:
+        return [
+            args.conda_exe,
+            "run",
+            "--no-capture-output",
+            "-n",
+            judge_conda_env,
+            *judge_core_cmd,
+        ]
+    return judge_core_cmd
+
+
+def build_generation_env(args, gpu: str | None = None) -> dict[str, str]:
+    env = os.environ.copy()
+    cuda_visible_devices = gpu or args.gen_cuda_visible_devices or args.cuda_visible_devices
+    if cuda_visible_devices:
+        env["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices
+    return env
+
+
+def build_judge_env(args, gpu: str | None = None) -> dict[str, str]:
+    env = os.environ.copy()
+    cuda_visible_devices = gpu or args.judge_cuda_visible_devices or args.cuda_visible_devices
+    if cuda_visible_devices:
+        env["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices
+    if args.qwenvl_device:
+        env["QWENVL_DEVICE"] = args.qwenvl_device
+    return env
+
+
+def command_to_text(cmd: Sequence[str], env: dict[str, str]) -> str:
+    env_parts = []
+    for key in ("CUDA_VISIBLE_DEVICES", "QWENVL_DEVICE"):
+        if key in env:
+            env_parts.append(f"{key}={shlex.quote(env[key])}")
+    command = shlex.join(str(part) for part in cmd)
+    if env_parts:
+        return " ".join(env_parts + [command])
+    return command
+
+
+def run_command(cmd: Sequence[str], env: dict[str, str], dry_run: bool) -> None:
+    print(command_to_text(cmd, env))
+    if dry_run:
+        return
+    subprocess.run(cmd, cwd=REPO_ROOT, env=env, check=True)

batch_test/eval_bench_single_gpu.py

@@ -0,0 +1,216 @@
+#!/usr/bin/env python3
+"""
+Single-GPU benchmark evaluation for generated videos.
+
+Run after batch_test/test_bench_parallel_4gpu.py has produced videos:
+
+    python batch_test/eval_bench_single_gpu.py \
+        --data-dir test_data \
+        --pipeline longlive \
+        --output-root outputs_batch \
+        --gpu 0
+"""
+
+from __future__ import annotations
+
+import argparse
+import subprocess
+import time
+from pathlib import Path
+from typing import Sequence
+
+from bench_common import (
+    PIPELINE_BY_ALIAS,
+    REPO_ROOT,
+    Sample,
+    build_judge_command,
+    build_judge_env,
+    command_to_text,
+    discover_samples,
+    generated_video_path,
+    judge_output_path,
+    output_prefix_for,
+    prepare_sample_output,
+    sanitize_filename_part,
+)
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Evaluate generated benchmark videos sequentially on one GPU."
+    )
+    parser.add_argument("--data-dir", required=True, help="Directory containing JSON files and reference videos.")
+    parser.add_argument(
+        "--pipeline",
+        required=True,
+        choices=sorted(PIPELINE_BY_ALIAS),
+        help="Pipeline alias, e.g. cosmos-predict, wow, matrix-game2.",
+    )
+    parser.add_argument("--output-root", default="outputs", help="Root output directory produced by generation.")
+    parser.add_argument("--json-glob", default="*.json", help="JSON glob relative to --data-dir.")
+    parser.add_argument("--recursive", action="store_true", help="Search JSON files recursively.")
+    parser.add_argument("--limit", type=int, default=None, help="Optional max number of samples to process.")
+    parser.add_argument("--sample-ids", nargs="+", help="Optional sample ids to process after prefix normalization.")
+    parser.add_argument("--skip-missing", action="store_true", help="Skip JSON files that have no matching reference video.")
+    parser.add_argument("--skip-missing-generated", action="store_true", help="Skip samples without generated videos.")
+    parser.add_argument("--keep-going", action="store_true", help="Continue with later samples after an error.")
+    parser.add_argument("--dry-run", action="store_true", help="Print commands without running judge.")
+
+    parser.add_argument(
+        "--pipeline-output-prefix",
+        default=None,
+        help="Generated/judge filename prefix. Defaults to the selected pipeline prefix.",
+    )
+    parser.add_argument("--gpu", default="0", help="Single GPU id for evaluation.")
+    parser.add_argument("--cuda-visible-devices", default=None, help="Fallback CUDA_VISIBLE_DEVICES.")
+    parser.add_argument("--judge-cuda-visible-devices", default=None, help="Fallback judge CUDA_VISIBLE_DEVICES.")
+    parser.add_argument("--qwenvl-device", default="cuda:0", help="QWENVL_DEVICE for local QwenVL judge.")
+
+    parser.add_argument(
+        "--judge-script",
+        default="worldeval/scripts/score_video_physical_3d.py",
+        help="Path to worldeval judge script.",
+    )
+    parser.add_argument(
+        "--judge-python",
+        default="python",
+        help="Python executable used inside the judge environment.",
+    )
+    parser.add_argument(
+        "--judge-conda-env",
+        default="world_eval",
+        help='Conda environment used for judge. Set to "" or "none" to disable conda run.',
+    )
+    parser.add_argument(
+        "--conda-exe",
+        default="conda",
+        help="Conda executable used when --judge-conda-env is enabled.",
+    )
+    parser.add_argument("--physical-max-frames", type=int, default=64)
+    parser.add_argument("--sam-device", default="0")
+    parser.add_argument("--three-d-max-frames", type=int, default=64)
+    parser.add_argument("--three-d-model-name", default="worldeval/weights/da3")
+    parser.add_argument("--da-device", default="0")
+    parser.add_argument("--vlm-backend", default="local")
+    parser.add_argument("--vlm-model", default="worldeval/weights/QwenVL")
+    parser.add_argument("--three-d-vlm-backend", default="local")
+    parser.add_argument("--three-d-scoring-model", default="worldeval/weights/QwenVL")
+    parser.add_argument("--skip-physical", action="store_true", help="Pass --skip-physical to judge.")
+    parser.add_argument("--skip-3d", action="store_true", help="Pass --skip-3d to judge.")
+    parser.add_argument(
+        "--judge-extra-args",
+        default="",
+        help='Extra judge args as one shell-style string, e.g. "--force-sam".',
+    )
+    parser.add_argument("--log-root", default=None, help="Optional directory for per-sample eval logs. Defaults to <output-root>/_eval_logs.")
+    parser.add_argument("--force", action="store_true", help="Re-run judge even if output exists.")
+    parser.add_argument("--force-judge", action="store_true", help="Re-run judge even if output exists.")
+    return parser
+
+
+def parse_args() -> argparse.Namespace:
+    return build_parser().parse_args()
+
+
+def should_eval(args: argparse.Namespace, judge_output: Path) -> bool:
+    return not judge_output.exists() or args.force or args.force_judge
+
+
+def write_header(log_file, sample: Sample, gpu: str, cmd: Sequence[str], env: dict[str, str]) -> None:
+    log_file.write(f"gpu: {gpu}\n")
+    log_file.write(f"sample: {sample.sample_id}\n")
+    log_file.write(f"json: {sample.json_path}\n")
+    log_file.write(f"reference: {sample.video_path}\n")
+    log_file.write(f"command: {command_to_text(cmd, env)}\n")
+    log_file.write("\n")
+    log_file.flush()
+
+
+def eval_sample(
+    args: argparse.Namespace,
+    sample: Sample,
+    output_root: Path,
+    log_root: Path,
+    output_prefix: str,
+) -> None:
+    sample_dir, _prompt_path, ref_video = prepare_sample_output(sample, output_root, args.dry_run)
+    generated_video = generated_video_path(output_root, sample, output_prefix)
+    judge_output = judge_output_path(output_root, sample, output_prefix)
+
+    if not generated_video.exists() and not args.dry_run:
+        message = f"Generated video not found: {generated_video}"
+        if args.skip_missing_generated:
+            print(f"[SKIP][{sample.sample_id}] {message}", flush=True)
+            return
+        raise FileNotFoundError(message)
+
+    if not should_eval(args, judge_output):
+        print(f"[SKIP][{sample.sample_id}] judge exists: {judge_output}", flush=True)
+        return
+
+    cmd = build_judge_command(args, generated_video, ref_video, judge_output)
+    env = build_judge_env(args, gpu=args.gpu)
+
+    print(f"[EVAL][gpu {args.gpu}][{sample.sample_id}] {generated_video}", flush=True)
+    print(command_to_text(cmd, env), flush=True)
+    if args.dry_run:
+        return
+
+    sample_dir.mkdir(parents=True, exist_ok=True)
+    log_root.mkdir(parents=True, exist_ok=True)
+    log_path = log_root / f"{sample.sample_id}_gpu{sanitize_filename_part(args.gpu)}.log"
+    start = time.monotonic()
+    with log_path.open("w", encoding="utf-8") as log_file:
+        write_header(log_file, sample, args.gpu, cmd, env)
+        subprocess.run(
+            cmd,
+            cwd=REPO_ROOT,
+            env=env,
+            check=True,
+            stdout=log_file,
+            stderr=subprocess.STDOUT,
+        )
+    elapsed = time.monotonic() - start
+    print(f"[DONE][gpu {args.gpu}][{sample.sample_id}] {elapsed:.1f}s", flush=True)
+
+
+def main() -> int:
+    args = parse_args()
+    spec = PIPELINE_BY_ALIAS[args.pipeline]
+    output_root = Path(args.output_root).expanduser().resolve()
+    log_root = Path(args.log_root).expanduser().resolve() if args.log_root else output_root / "_eval_logs"
+    output_prefix = output_prefix_for(args, spec)
+
+    judge_script = REPO_ROOT / args.judge_script
+    if not judge_script.exists():
+        raise FileNotFoundError(f"Judge script not found: {judge_script}")
+    if not args.dry_run:
+        output_root.mkdir(parents=True, exist_ok=True)
+
+    samples = discover_samples(args)
+    print(f"Pipeline: {spec.name}")
+    print(f"Samples: {len(samples)}")
+    print(f"Output root: {output_root}")
+    print(f"Log root: {log_root}")
+    print(f"GPU: {args.gpu}")
+
+    errors: list[tuple[str, BaseException]] = []
+    for sample in samples:
+        try:
+            eval_sample(args, sample, output_root, log_root, output_prefix)
+        except Exception as exc:
+            if not args.keep_going:
+                raise
+            print(f"[ERROR][{sample.sample_id}] {exc}", flush=True)
+            errors.append((sample.sample_id, exc))
+
+    if errors:
+        print("\nFailed samples:")
+        for sample_id, exc in errors:
+            print(f"  {sample_id}: {exc}")
+        return 1
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

batch_test/generate_bench_4gpu.py

@@ -0,0 +1,558 @@
+#!/usr/bin/env python3
+"""
+Parallel 4-GPU batch generation for JSON/video benchmark folders.
+
+This script uses batch_test/bench_common.py for sample discovery, output
+layout, and generation command construction. It dynamically schedules samples
+across up to four single-GPU workers:
+
+    python batch_test/generate_bench_4gpu.py \
+        --data-dir test_data \
+        --pipeline longlive \
+        --output-root outputs_batch_new \
+        --gpus 0,1,2,3
+
+Use batch_test/eval_bench_single_gpu.py after generation if you want to run
+worldeval scoring on one GPU.
+"""
+
+from __future__ import annotations
+
+import argparse
+import contextlib
+import itertools
+import importlib.util
+import multiprocessing as mp
+import os
+import queue
+import subprocess
+import sys
+import tempfile
+import threading
+import time
+from pathlib import Path
+from typing import Sequence
+
+from bench_common import (
+    PIPELINE_BY_ALIAS,
+    REPO_ROOT,
+    Sample,
+    build_generation_env,
+    build_generation_command,
+    command_to_text,
+    discover_samples,
+    generated_video_path,
+    output_prefix_for,
+    prepare_sample_output,
+    sanitize_filename_part,
+    stage_sample,
+)
+
+
+def parse_gpu_list(value: str | None) -> list[str]:
+    if not value:
+        return []
+    return [gpu.strip() for gpu in value.split(",") if gpu.strip()]
+
+
+def auto_detect_gpus(max_gpus: int) -> list[str]:
+    visible = parse_gpu_list(os.environ.get("CUDA_VISIBLE_DEVICES"))
+    if visible:
+        return visible[:max_gpus]
+
+    try:
+        result = subprocess.run(
+            ["nvidia-smi", "--query-gpu=index", "--format=csv,noheader"],
+            cwd=REPO_ROOT,
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.DEVNULL,
+            check=True,
+        )
+    except (OSError, subprocess.CalledProcessError):
+        return ["0"]
+
+    gpus = [line.strip() for line in result.stdout.splitlines() if line.strip()]
+    return gpus[:max_gpus] or ["0"]
+
+
+def build_parallel_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Generate videos for one pipeline over a folder of JSON/ref-video pairs "
+            "with dynamic scheduling across up to four GPUs."
+        )
+    )
+    parser.add_argument("--data-dir", required=True, help="Directory containing JSON files and reference videos.")
+    parser.add_argument(
+        "--pipeline",
+        required=True,
+        choices=sorted(PIPELINE_BY_ALIAS),
+        help="Pipeline alias, e.g. cosmos-predict, wow, matrix-game2.",
+    )
+    parser.add_argument("--output-root", default="outputs", help="Root output directory.")
+    parser.add_argument("--json-glob", default="*.json", help="JSON glob relative to --data-dir.")
+    parser.add_argument("--recursive", action="store_true", help="Search JSON files recursively.")
+    parser.add_argument("--limit", type=int, default=None, help="Optional max number of samples to process.")
+    parser.add_argument("--sample-ids", nargs="+", help="Optional sample ids to process after prefix normalization.")
+    parser.add_argument("--skip-missing", action="store_true", help="Skip JSON files that have no matching video.")
+    parser.add_argument("--keep-going", action="store_true", default=True, help="Continue with later samples after an error.")
+    parser.add_argument("--dry-run", action="store_true", help="Print commands without running generation.")
+
+    parser.add_argument(
+        "--python",
+        default=sys.executable,
+        help="Python executable used for generation unless --gen-python is set.",
+    )
+    parser.add_argument("--gen-python", default=None, help="Python executable used to launch generation scripts.")
+    parser.add_argument(
+        "--launcher",
+        choices=("python", "torchrun"),
+        default="python",
+        help="Launcher used only with --subprocess-per-sample.",
+    )
+    parser.add_argument("--nproc-per-node", type=int, default=1, help="torchrun --nproc_per_node value.")
+    parser.add_argument(
+        "--pipeline-extra-args",
+        default="",
+        help='Extra generation args as one shell-style string, e.g. "--num_frames 16 --seed 1".',
+    )
+    parser.add_argument(
+        "--pipeline-output-prefix",
+        default=None,
+        help="Override generated filename prefix. Defaults to the selected pipeline prefix.",
+    )
+    parser.add_argument(
+        "--cuda-visible-devices",
+        default=None,
+        help="Fallback CUDA_VISIBLE_DEVICES. Parallel workers override it with one GPU each.",
+    )
+    parser.add_argument(
+        "--gen-cuda-visible-devices",
+        default=None,
+        help="Fallback generation CUDA_VISIBLE_DEVICES. Parallel workers override it with one GPU each.",
+    )
+    parser.add_argument(
+        "--gpus",
+        default=None,
+        help=(
+            "Comma-separated physical GPU ids for parallel generation, e.g. 0,1,2,3. "
+            "Defaults to CUDA_VISIBLE_DEVICES or nvidia-smi, capped by --max-workers."
+        ),
+    )
+    parser.add_argument(
+        "--max-workers",
+        type=int,
+        default=4,
+        help="Maximum number of concurrent single-GPU generation workers.",
+    )
+    parser.add_argument(
+        "--fail-fast",
+        action="store_true",
+        help="Stop scheduling new samples after the first generation failure.",
+    )
+    parser.add_argument(
+        "--log-root",
+        default=None,
+        help="Optional directory for per-sample generation logs. Defaults to <output-root>/_logs.",
+    )
+    parser.add_argument("--force", action="store_true", help="Re-run generation even if generated video exists.")
+    parser.add_argument("--force-generate", action="store_true", help="Re-run generation even if generated video exists.")
+    parser.add_argument(
+        "--subprocess-per-sample",
+        action="store_true",
+        help="Use the old mode: start a fresh Python subprocess for every sample.",
+    )
+    return parser
+
+
+def parse_args() -> argparse.Namespace:
+    args = build_parallel_parser().parse_args()
+    if args.max_workers < 1:
+        raise ValueError("--max-workers must be >= 1")
+    if not args.subprocess_per_sample and args.launcher != "python":
+        raise ValueError("Persistent batch workers only support --launcher python.")
+    if args.launcher == "torchrun" and args.nproc_per_node != 1:
+        raise ValueError(
+            "Parallel sample scheduling expects one GPU per worker. "
+            "Use --launcher python or keep --nproc-per-node 1."
+        )
+    return args
+
+
+def env_for_gpu(args: argparse.Namespace, gpu: str) -> dict[str, str]:
+    return build_generation_env(args, gpu=gpu)
+
+
+def sample_paths(args: argparse.Namespace, sample: Sample, output_root: Path) -> tuple[Path, Path]:
+    output_prefix = output_prefix_for(args, PIPELINE_BY_ALIAS[args.pipeline])
+    sample_dir = output_root / sample.sample_id
+    return sample_dir, generated_video_path(output_root, sample, output_prefix)
+
+
+def should_generate(args: argparse.Namespace, generated_video: Path) -> bool:
+    return not generated_video.exists() or args.force or args.force_generate
+
+
+def write_header(log_file, worker_id: int, gpu: str, sample: Sample, cmd: Sequence[str], env: dict[str, str]) -> None:
+    log_file.write(f"worker: {worker_id}\n")
+    log_file.write(f"gpu: {gpu}\n")
+    log_file.write(f"sample: {sample.sample_id}\n")
+    log_file.write(f"json: {sample.json_path}\n")
+    log_file.write(f"reference: {sample.video_path}\n")
+    log_file.write(f"command: {command_to_text(cmd, env)}\n")
+    log_file.write("\n")
+    log_file.flush()
+
+
+def run_generation(
+    args: argparse.Namespace,
+    sample: Sample,
+    gpu: str,
+    worker_id: int,
+    staging_root: Path,
+    temp_output_root: Path,
+    output_root: Path,
+    log_root: Path,
+) -> None:
+    spec = PIPELINE_BY_ALIAS[args.pipeline]
+    sample_dir, generated_video = sample_paths(args, sample, output_root)
+    prepare_sample_output(sample, output_root, args.dry_run)
+
+    if not should_generate(args, generated_video):
+        print(f"[SKIP][gpu {gpu}][{sample.sample_id}] generation exists: {generated_video}", flush=True)
+        return
+
+    staged_json = stage_sample(sample, staging_root / f"worker_{worker_id}")
+    worker_temp_output_root = temp_output_root / f"worker_{worker_id}"
+    cmd = build_generation_command(args, spec, staged_json, worker_temp_output_root, generated_video)
+    env = env_for_gpu(args, gpu)
+
+    print(f"[START][gpu {gpu}][{sample.sample_id}] {generated_video}", flush=True)
+    print(command_to_text(cmd, env), flush=True)
+    if args.dry_run:
+        return
+
+    sample_dir.mkdir(parents=True, exist_ok=True)
+    log_root.mkdir(parents=True, exist_ok=True)
+    log_path = log_root / f"{sample.sample_id}_gpu{sanitize_filename_part(gpu)}.log"
+    start = time.monotonic()
+    with log_path.open("w", encoding="utf-8") as log_file:
+        write_header(log_file, worker_id, gpu, sample, cmd, env)
+        subprocess.run(
+            cmd,
+            cwd=REPO_ROOT,
+            env=env,
+            check=True,
+            stdout=log_file,
+            stderr=subprocess.STDOUT,
+        )
+    elapsed = time.monotonic() - start
+    print(f"[DONE][gpu {gpu}][{sample.sample_id}] {elapsed:.1f}s", flush=True)
+
+
+def import_batch_module(script_path: Path):
+    from_json_dir = str(script_path.parent)
+    if from_json_dir not in sys.path:
+        sys.path.insert(0, from_json_dir)
+    module_name = f"batch_from_json_{script_path.stem}_{os.getpid()}"
+    spec = importlib.util.spec_from_file_location(module_name, script_path)
+    if spec is None or spec.loader is None:
+        raise ImportError(f"Cannot import batch script: {script_path}")
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+@contextlib.contextmanager
+def patched_argv(argv: list[str]):
+    previous_argv = sys.argv
+    sys.argv = argv
+    try:
+        yield
+    finally:
+        sys.argv = previous_argv
+
+
+def run_generation_in_process(
+    args: argparse.Namespace,
+    module,
+    sample: Sample,
+    gpu: str,
+    worker_id: int,
+    staging_root: Path,
+    temp_output_root: Path,
+    output_root: Path,
+    log_root: Path,
+) -> None:
+    spec = PIPELINE_BY_ALIAS[args.pipeline]
+    sample_dir, generated_video = sample_paths(args, sample, output_root)
+    prepare_sample_output(sample, output_root, args.dry_run)
+
+    if not should_generate(args, generated_video):
+        print(f"[SKIP][gpu {gpu}][{sample.sample_id}] generation exists: {generated_video}", flush=True)
+        return
+
+    staged_json = stage_sample(sample, staging_root / f"worker_{worker_id}")
+    worker_temp_output_root = temp_output_root / f"worker_{worker_id}"
+    cmd = build_generation_command(args, spec, staged_json, worker_temp_output_root, generated_video)
+    env = env_for_gpu(args, gpu)
+
+    print(f"[START][gpu {gpu}][{sample.sample_id}] {generated_video}", flush=True)
+    print(command_to_text(cmd, env), flush=True)
+    if args.dry_run:
+        return
+
+    os.environ.update(env)
+    sample_dir.mkdir(parents=True, exist_ok=True)
+    log_root.mkdir(parents=True, exist_ok=True)
+    log_path = log_root / f"{sample.sample_id}_gpu{sanitize_filename_part(gpu)}.log"
+    start = time.monotonic()
+    argv = [str(REPO_ROOT / spec.batch_script), *cmd[2:]]
+    with log_path.open("w", encoding="utf-8") as log_file:
+        write_header(log_file, worker_id, gpu, sample, cmd, env)
+        with contextlib.redirect_stdout(log_file), contextlib.redirect_stderr(log_file), patched_argv(argv):
+            module.main()
+    elapsed = time.monotonic() - start
+    print(f"[DONE][gpu {gpu}][{sample.sample_id}] {elapsed:.1f}s", flush=True)
+
+
+def worker_loop(
+    args: argparse.Namespace,
+    gpu: str,
+    worker_id: int,
+    work_queue: queue.Queue[Sample],
+    stop_event: threading.Event,
+    errors: list[tuple[str, str, BaseException]],
+    error_lock: threading.Lock,
+    staging_root: Path,
+    temp_output_root: Path,
+    output_root: Path,
+    log_root: Path,
+) -> None:
+    for sample in iter(work_queue.get, None):
+        if stop_event.is_set():
+            return
+
+        try:
+            run_generation(
+                args,
+                sample,
+                gpu,
+                worker_id,
+                staging_root,
+                temp_output_root,
+                output_root,
+                log_root,
+            )
+        except Exception as exc:
+            print(f"[ERROR][gpu {gpu}][{sample.sample_id}] {exc}", flush=True)
+            with error_lock:
+                errors.append((sample.sample_id, gpu, exc))
+            if args.fail_fast or not args.keep_going:
+                stop_event.set()
+        finally:
+            work_queue.task_done()
+
+
+def persistent_worker_loop(
+    args: argparse.Namespace,
+    gpu: str,
+    worker_id: int,
+    work_queue: mp.Queue,
+    error_queue: mp.Queue,
+    stop_event: mp.Event,
+    staging_root: Path,
+    temp_output_root: Path,
+    output_root: Path,
+    log_root: Path,
+) -> None:
+    os.environ["CUDA_VISIBLE_DEVICES"] = gpu
+    os.environ["WORLD_SIZE"] = "1"
+    os.environ["RANK"] = "0"
+    os.environ["LOCAL_RANK"] = "0"
+    spec = PIPELINE_BY_ALIAS[args.pipeline]
+    module = import_batch_module(REPO_ROOT / spec.batch_script)
+
+    for sample in iter(work_queue.get, None):
+        if stop_event.is_set():
+            return
+
+        try:
+            run_generation_in_process(
+                args,
+                module,
+                sample,
+                gpu,
+                worker_id,
+                staging_root,
+                temp_output_root,
+                output_root,
+                log_root,
+            )
+        except Exception as exc:
+            print(f"[ERROR][gpu {gpu}][{sample.sample_id}] {exc}", flush=True)
+            error_queue.put((sample.sample_id, gpu, repr(exc)))
+            if args.fail_fast or not args.keep_going:
+                stop_event.set()
+
+
+def main() -> int:
+    args = parse_args()
+    spec = PIPELINE_BY_ALIAS[args.pipeline]
+    script = REPO_ROOT / (spec.script if args.subprocess_per_sample else spec.batch_script)
+    if not script.exists():
+        raise FileNotFoundError(f"Pipeline script not found: {script}")
+
+    output_root = Path(args.output_root).expanduser().resolve()
+    log_root = Path(args.log_root).expanduser().resolve() if args.log_root else output_root / "_logs"
+    if not args.dry_run:
+        output_root.mkdir(parents=True, exist_ok=True)
+
+    samples = discover_samples(args)
+    gpus = parse_gpu_list(args.gpus) or auto_detect_gpus(args.max_workers)
+    gpus = gpus[: args.max_workers]
+    if not gpus:
+        raise ValueError("No GPUs selected. Pass --gpus 0,1,2,3 explicitly.")
+
+    runnable_samples = []
+    skipped = 0
+    for sample in samples:
+        _sample_dir, generated_video = sample_paths(args, sample, output_root)
+        if should_generate(args, generated_video):
+            runnable_samples.append(sample)
+        else:
+            skipped += 1
+
+    print(f"Pipeline: {spec.name} ({script})")
+    print(f"Samples: {len(samples)} total, {len(runnable_samples)} to generate, {skipped} already done")
+    print(f"Output root: {output_root}")
+    print(f"Log root: {log_root}")
+    print(f"GPUs: {','.join(gpus)}")
+
+    if not runnable_samples:
+        return 0
+
+    if args.dry_run:
+        with tempfile.TemporaryDirectory(prefix="openworldlib_parallel_batch_") as temp_dir:
+            temp_root = Path(temp_dir)
+            staging_root = temp_root / "inputs"
+            temp_output_root = temp_root / "script_outputs"
+            for sample_index, sample in enumerate(runnable_samples):
+                worker_id = sample_index % len(gpus)
+                gpu = gpus[worker_id]
+                run_generation(
+                    args,
+                    sample,
+                    gpu,
+                    worker_id,
+                    staging_root,
+                    temp_output_root,
+                    output_root,
+                    log_root,
+                )
+        return 0
+
+    if args.subprocess_per_sample:
+        work_queue: queue.Queue[Sample] = queue.Queue()
+        for sample in runnable_samples:
+            work_queue.put(sample)
+        errors: list[tuple[str, str, BaseException]] = []
+        error_lock = threading.Lock()
+        stop_event = threading.Event()
+
+        with tempfile.TemporaryDirectory(prefix="openworldlib_parallel_batch_") as temp_dir:
+            temp_root = Path(temp_dir)
+            staging_root = temp_root / "inputs"
+            temp_output_root = temp_root / "script_outputs"
+
+            threads = [
+                threading.Thread(
+                    target=worker_loop,
+                    name=f"gpu-{gpu}-worker",
+                    args=(
+                        args,
+                        gpu,
+                        worker_id,
+                        work_queue,
+                        stop_event,
+                        errors,
+                        error_lock,
+                        staging_root,
+                        temp_output_root,
+                        output_root,
+                        log_root,
+                    ),
+                )
+                for worker_id, gpu in enumerate(gpus)
+            ]
+            for thread in threads:
+                thread.start()
+            for thread in threads:
+                thread.join()
+
+        if errors:
+            print("\nFailed samples:")
+            for sample_id, gpu, exc in errors:
+                print(f"  {sample_id} on gpu {gpu}: {exc}")
+            return 1
+        return 0
+
+    ctx = mp.get_context("spawn")
+    work_queue = ctx.Queue()
+    for sample in runnable_samples:
+        work_queue.put(sample)
+    for _gpu in gpus:
+        work_queue.put(None)
+    error_queue = ctx.Queue()
+    stop_event = ctx.Event()
+
+    with tempfile.TemporaryDirectory(prefix="openworldlib_persistent_batch_") as temp_dir:
+        temp_root = Path(temp_dir)
+        staging_root = temp_root / "inputs"
+        temp_output_root = temp_root / "script_outputs"
+
+        worker_items = zip(gpus, itertools.count())
+        processes = [
+            ctx.Process(
+                target=persistent_worker_loop,
+                name=f"gpu-{gpu}-worker",
+                args=(
+                    args,
+                    gpu,
+                    worker_id,
+                    work_queue,
+                    error_queue,
+                    stop_event,
+                    staging_root,
+                    temp_output_root,
+                    output_root,
+                    log_root,
+                ),
+            )
+            for gpu, worker_id in worker_items
+        ]
+        for process in processes:
+            process.start()
+        for process in processes:
+            process.join()
+
+    errors = []
+    while True:
+        try:
+            errors.append(error_queue.get_nowait())
+        except queue.Empty:
+            break
+    for process in processes:
+        if process.exitcode not in (0, None):
+            errors.append((process.name, "unknown", f"worker exitcode={process.exitcode}"))
+    if errors:
+        print("\nFailed samples:")
+        for sample_id, gpu, exc in errors:
+            print(f"  {sample_id} on gpu {gpu}: {exc}")
+        return 1
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

batch_test/test_bench_parallel_4gpu.py

@@ -0,0 +1,8 @@
+#!/usr/bin/env python3
+"""Compatibility wrapper for batch_test/generate_bench_4gpu.py."""
+
+from generate_bench_4gpu import main
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

checkpoints/LongLive/prompts/vidprom_filtered_extended.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7896742f468bc8aef9e4547424d1ce0a951acdb2a82233790155401a99bf5aa5
+size 145875068

checkpoints/WoW-1-Wan-1.3B-2M/google/umt5-xxl/special_tokens_map.json

@@ -0,0 +1,308 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
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outputs_batch_new/AgiBot-g1_battery_storage_b-observation.images.cam_high_rgb-episode_000000/prompt.json

@@ -0,0 +1,17 @@
+[
+  {
+    "interval": "[00:06, 00:11)",
+    "action": "None",
+    "caption": "A dual-arm robotic setup is positioned over a sectioned cardboard box filled with coiled bundles of colored wires. To the right of the box is a small table with a dark surface. The left robotic arm remains stationary throughout the segment. Meanwhile, the right robotic arm executes a downward trajectory, bringing its open two-finger gripper into the rightmost compartment of the wire box. The right gripper then closes to firmly grasp a grey rectangular component connected to a bundle of wires."
+  },
+  {
+    "interval": "[00:11, 00:14)",
+    "action": "None",
+    "caption": "The left robotic arm remains stationary while the active right robotic arm continues its operation. Holding the grey rectangular component, the right arm smoothly lifts it out of the cardboard box, pulling the attached bundle of red, yellow, and black wires along with it. The arm moves upward and then arcs to the right, carrying the component over to the adjacent black table surface, and begins to lower the object."
+  },
+  {
+    "interval": "[00:14, 00:16)",
+    "action": "None",
+    "caption": "The right robotic arm completes its lowering motion, placing the grey rectangular component onto the black table surface. Once the object rests on the table, the right gripper opens to release its hold. The arm then retracts slightly upwards and away from the component, leaving it stationary on the table. The left robotic arm remains entirely stationary throughout the sequence."
+  }
+]

outputs_batch_new/AgiBot-g1_box_storage_a-observation.images.cam_high_rgb-episode_000000/prompt.json

@@ -0,0 +1,17 @@
+[
+  {
+    "interval": "[00:05, 00:06)",
+    "action": "None",
+    "caption": "A white robotic arm with a black three-finger gripper is positioned over a white surface, facing an identical stationary right gripper. An open cardboard box is located on the left. The left robotic arm rotates its end-effector downward towards the edge of the cardboard box. It makes direct physical contact with a rectangular wooden block, closing its fingers to grasp and secure the object without causing deformation."
+  },
+  {
+    "interval": "[00:06, 00:08)",
+    "action": "None",
+    "caption": "The robotic arm on the left, holding the rectangular wooden block with its three-finger gripper, remains completely stationary. Throughout the segment, there is no kinematic trajectory or velocity change. The arm maintains a firm, static grip on the block over the cardboard box without any physical changes, deformation, or object displacement. The second, inactive robotic gripper remains visible on the right side of the frame."
+  },
+  {
+    "interval": "[00:08, 00:11)",
+    "action": "None",
+    "caption": "The left robotic arm smoothly lifts and translates the rectangular wooden block leftward and downward, positioning it directly over the open cardboard box. The three-finger gripper maintains a secure hold during the initial trajectory before opening its fingers to release the block. The block is displaced as it drops into the box without visible deformation. The right gripper and the camera remain entirely stationary throughout the sequence."
+  }
+]