app.py

import gc
import json
import logging
import os
import shutil
import subprocess
import sys
import tempfile
import traceback
import uuid
import zipfile
from dataclasses import dataclass
from pathlib import Path
from types import SimpleNamespace

import gradio as gr
import spaces
import torch
from huggingface_hub import hf_hub_download, snapshot_download


logging.basicConfig(level=logging.INFO, format="[%(asctime)s] %(levelname)s: %(message)s")

ROOT = Path(__file__).resolve().parent


def _default_storage_root() -> Path:
    if os.getenv("SCAIL_STORAGE_ROOT"):
        return Path(os.environ["SCAIL_STORAGE_ROOT"])
    data_mount = Path("/data")
    if data_mount.exists() and os.access(data_mount, os.W_OK):
        return data_mount
    return Path("/tmp")


STORAGE_ROOT = _default_storage_root()
STAGING_ROOT = Path(os.getenv("SCAIL_STAGING_ROOT", "/tmp"))
OUTPUT_DIR = Path(os.getenv("SCAIL_OUTPUT_DIR", str(STORAGE_ROOT / "scail2_outputs")))
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

MODEL_REPO_ID = os.getenv("SCAIL_MODEL_REPO_ID", "zai-org/SCAIL-2")
SAFETENSORS_REPO_ID = os.getenv("SCAIL_SAFETENSORS_REPO_ID")
SAFETENSORS_FILENAME = os.getenv("SCAIL_SAFETENSORS_FILENAME", "SCAIL-2.safetensors")
MODEL_NAME = os.getenv("SCAIL_MODEL_NAME", "SCAIL-14B")
GPU_SIZE = os.getenv("SCAIL_ZEROGPU_SIZE", "xlarge")
GPU_DURATION_COLD = int(os.getenv("SCAIL_GPU_DURATION_COLD", "600"))
GPU_DURATION_WARM = int(os.getenv("SCAIL_GPU_DURATION_WARM", "330"))
DEFAULT_TARGET_H = int(os.getenv("SCAIL_TARGET_H", "512"))
DEFAULT_TARGET_W = int(os.getenv("SCAIL_TARGET_W", "896"))
DEFAULT_SEGMENT_LEN = int(os.getenv("SCAIL_SEGMENT_LEN", "81"))
DEFAULT_SEGMENT_OVERLAP = int(os.getenv("SCAIL_SEGMENT_OVERLAP", "5"))
DEFAULT_SHIFT = float(os.getenv("SCAIL_SAMPLE_SHIFT", "3.0"))
DEFAULT_GUIDE_SCALE = float(os.getenv("SCAIL_GUIDE_SCALE", "5.0"))
DEFAULT_SOLVER = os.getenv("SCAIL_SAMPLE_SOLVER", "unipc")
AUTO_CONVERT = os.getenv("SCAIL_AUTO_CONVERT", "1") == "1"
PRELOAD_PIPELINE = os.getenv("SCAIL_PRELOAD_PIPELINE", "1") == "1"
STAGE_SAFETENSORS_FOR_LOAD = os.getenv("SCAIL_STAGE_SAFETENSORS_FOR_LOAD", "1") == "1"
CONVERT_TO_STAGING_FIRST = os.getenv("SCAIL_CONVERT_TO_STAGING_FIRST", "1") == "1"
MAX_ADDITIONAL_REFS = int(os.getenv("SCAIL_MAX_ADDITIONAL_REFS", "16"))

CLIP_CKPT_NAME = "models_clip_open-clip-xlm-roberta-large-vit-huge-14-onlyvisual.pth"
ORIGINAL_DIT_REL_PATH = "model/1/fsdp2_rank_0000_checkpoint.pt"
BASE_ALLOW_PATTERNS = [
    "Wan2.1_VAE.pth",
    "umt5-xxl/**",
    CLIP_CKPT_NAME,
]

_PIPELINE = None
_PIPELINE_KEY = None
_ASSET_STATUS = "Assets were not prepared yet."
_ASSET_ERROR = None
_RUNTIME_STATUS = "Runtime was not prepared yet."
_RUNTIME_ERROR = None
_PIPELINE_STATUS = "Pipeline was not preloaded."
_PIPELINE_ERROR = None
_LAST_CONVERTED_SAFETENSORS = None
_WAN = None
_GENERATE_VIDEO = None
_SCAIL_CONFIGS = None
_SCAIL_CONFIG_PATHS = None


@dataclass(frozen=True)
class ReferencePair:
    label: str
    image: str
    mask_image: str


@dataclass(frozen=True)
class PreparedExample:
    label: str
    image: str
    mask_image: str
    pose: str
    mask_video: str
    prompt: str
    replace_flag: bool = False
    additional_refs: tuple[ReferencePair, ...] = ()


PREPARED_EXAMPLES = {
    "Animation 001 - end-to-end": PreparedExample(
        label="Animation 001 - end-to-end",
        image="examples/animation_001/ref.jpg",
        mask_image="examples/animation_001/ref_mask.jpg",
        pose="examples/animation_001/rendered_v2.mp4",
        mask_video="examples/animation_001/rendered_mask_v2.mp4",
        prompt="A young woman is dancing with energetic body movement.",
    ),
    "Animation 001 - pose-driven": PreparedExample(
        label="Animation 001 - pose-driven",
        image="examples/animation_001_posedriven/ref.jpg",
        mask_image="examples/animation_001_posedriven/ref_mask.jpg",
        pose="examples/animation_001_posedriven/rendered_v2.mp4",
        mask_video="examples/animation_001_posedriven/rendered_mask_v2.mp4",
        prompt="A young woman is dancing with energetic body movement.",
    ),
    "Animation 002 - end-to-end": PreparedExample(
        label="Animation 002 - end-to-end",
        image="examples/animation_002/ref.jpg",
        mask_image="examples/animation_002/ref_mask.jpg",
        pose="examples/animation_002/rendered_v2.mp4",
        mask_video="examples/animation_002/rendered_mask_v2.mp4",
        prompt="A character performs the motion from the driving video.",
    ),
    "Animation 003 - multi-reference": PreparedExample(
        label="Animation 003 - multi-reference",
        image="examples/animation_003_multi_ref/ref.png",
        mask_image="examples/animation_003_multi_ref/ref_mask.jpg",
        pose="examples/animation_003_multi_ref/rendered_v2.mp4",
        mask_video="examples/animation_003_multi_ref/rendered_mask_v2.mp4",
        prompt="A character performs the motion from the driving video.",
        additional_refs=(
            ReferencePair(
                label="Background",
                image="examples/animation_003_multi_ref/background.png",
                mask_image="examples/animation_003_multi_ref/background_mask.png",
            ),
            ReferencePair(
                label="Reference view 1",
                image="examples/animation_003_multi_ref/character_0.png",
                mask_image="examples/animation_003_multi_ref/character_0_mask.png",
            ),
            ReferencePair(
                label="Reference view 2",
                image="examples/animation_003_multi_ref/character_1.png",
                mask_image="examples/animation_003_multi_ref/character_1_mask.png",
            ),
        ),
    ),
    "Replacement 001": PreparedExample(
        label="Replacement 001",
        image="examples/replace_001/ref.png",
        mask_image="examples/replace_001/ref_mask.png",
        pose="examples/replace_001/rendered_v2.mp4",
        mask_video="examples/replace_001/replace_mask.mp4",
        prompt=(
            "A blond white male wearing a black suit, trousers, and leather shoes "
            "is playing the violin on the street while pedestrians walk past him."
        ),
        replace_flag=True,
    ),
}


def _abs(path: str | Path) -> str:
    path = Path(path)
    if not path.is_absolute():
        path = ROOT / path
    return str(path)


def _example_paths(example: PreparedExample) -> list[str]:
    paths = [example.image, example.mask_image, example.pose, example.mask_video]
    for ref in example.additional_refs:
        paths.extend([ref.image, ref.mask_image])
    return paths


def _existing_examples() -> dict[str, PreparedExample]:
    available = {}
    for name, example in PREPARED_EXAMPLES.items():
        if all(Path(_abs(path)).exists() for path in _example_paths(example)):
            available[name] = example
    return available


IMAGE_EXTS = (".png", ".jpg", ".jpeg", ".webp")
VIDEO_EXTS = (".mp4", ".mov", ".webm", ".mkv")
PRIMARY_STEMS = ("front", "main", "ref", "reference")


def _safe_extract_zip(zip_path: str | Path) -> Path:
    zip_path = Path(zip_path)
    if not zip_path.exists():
        raise RuntimeError(f"Pack does not exist: {zip_path}")
    if zip_path.suffix.lower() != ".zip":
        raise RuntimeError("Advanced Pack expects a .zip file.")

    extract_root = Path(tempfile.gettempdir()) / "scail2_input_packs" / uuid.uuid4().hex
    extract_root.mkdir(parents=True, exist_ok=True)
    with zipfile.ZipFile(zip_path) as zf:
        for item in zf.infolist():
            item_path = Path(item.filename)
            if item_path.is_absolute() or ".." in item_path.parts:
                raise RuntimeError(f"Unsafe path in zip: {item.filename}")
        zf.extractall(extract_root)

    visible = [p for p in extract_root.iterdir() if p.name not in {".DS_Store", "__MACOSX"}]
    if len(visible) == 1 and visible[0].is_dir():
        return visible[0]
    return extract_root


def _read_metadata(pack_root: Path) -> dict:
    metadata_path = pack_root / "metadata.json"
    if not metadata_path.exists():
        return {}
    try:
        return json.loads(metadata_path.read_text(encoding="utf-8"))
    except Exception as exc:
        raise RuntimeError(f"Invalid metadata.json: {exc}") from exc


def _read_pack_prompt(pack_root: Path, metadata: dict) -> str:
    if isinstance(metadata.get("prompt"), str):
        return metadata["prompt"]
    prompt_path = pack_root / "prompt.txt"
    if prompt_path.exists():
        return prompt_path.read_text(encoding="utf-8").strip()
    return ""


def _find_stem_file(directory: Path, stems: tuple[str, ...], exts: tuple[str, ...]) -> Path | None:
    for stem in stems:
        stem_path = Path(stem)
        if stem_path.suffix:
            candidate = directory / stem
            if candidate.exists() and candidate.is_file():
                return candidate
            continue
        for ext in exts:
            candidate = directory / f"{stem}{ext}"
            if candidate.exists() and candidate.is_file():
                return candidate
    return None


def _mask_for_image(image_path: Path) -> Path | None:
    for ext in IMAGE_EXTS:
        candidate = image_path.with_name(f"{image_path.stem}_mask{ext}")
        if candidate.exists() and candidate.is_file():
            return candidate
    return None


def _rel(path: Path, root: Path) -> str:
    try:
        return path.relative_to(root).as_posix()
    except ValueError:
        return path.name


def _collect_pairs_from_dir(directory: Path, root: Path, identity: str) -> list[ReferencePair]:
    if not directory.exists() or not directory.is_dir():
        return []

    pairs = []
    for image_path in sorted(directory.iterdir(), key=lambda p: p.name.lower()):
        if not image_path.is_file() or image_path.suffix.lower() not in IMAGE_EXTS:
            continue
        if image_path.stem.endswith("_mask"):
            continue
        mask_path = _mask_for_image(image_path)
        if mask_path is None:
            raise RuntimeError(f"Missing mask for `{_rel(image_path, root)}`.")
        label = f"{identity}: {image_path.stem}"
        pairs.append(ReferencePair(label=label, image=str(image_path), mask_image=str(mask_path)))
    return pairs


def _collect_character_pairs(pack_root: Path) -> list[ReferencePair]:
    characters_dir = pack_root / "characters"
    if not characters_dir.exists():
        return []

    pairs = []
    for identity_dir in sorted(characters_dir.iterdir(), key=lambda p: p.name.lower()):
        if identity_dir.is_dir():
            pairs.extend(_collect_pairs_from_dir(identity_dir, pack_root, identity_dir.name))
    return pairs


def _collect_environment_pairs(pack_root: Path) -> list[ReferencePair]:
    pairs = _collect_pairs_from_dir(pack_root / "environment", pack_root, "environment")
    for stem in ("background", "environment"):
        image_path = _find_stem_file(pack_root, (stem,), IMAGE_EXTS)
        if image_path is None:
            continue
        mask_path = _mask_for_image(image_path)
        if mask_path is None:
            raise RuntimeError(f"Missing mask for `{_rel(image_path, pack_root)}`.")
        pairs.append(ReferencePair(label=f"environment: {image_path.stem}", image=str(image_path), mask_image=str(mask_path)))
    return pairs


def _collect_legacy_flat_pairs(pack_root: Path) -> list[ReferencePair]:
    pairs = []
    for image_path in sorted(pack_root.iterdir(), key=lambda p: p.name.lower()):
        if not image_path.is_file() or image_path.suffix.lower() not in IMAGE_EXTS:
            continue
        if image_path.stem.endswith("_mask"):
            continue
        if not image_path.stem.startswith("character_"):
            continue
        mask_path = _mask_for_image(image_path)
        if mask_path is None:
            raise RuntimeError(f"Missing mask for `{_rel(image_path, pack_root)}`.")
        pairs.append(ReferencePair(label=f"reference: {image_path.stem}", image=str(image_path), mask_image=str(mask_path)))
    return pairs


def _primary_sort_key(ref: ReferencePair):
    stem = Path(ref.image).stem.lower()
    if stem in PRIMARY_STEMS:
        return (0, PRIMARY_STEMS.index(stem), ref.label.lower())
    return (1, ref.label.lower())


def _resolve_metadata_file(pack_root: Path, rel_path: str | None, label: str) -> Path | None:
    if not rel_path:
        return None
    path = pack_root / rel_path
    if not path.exists() or not path.is_file():
        raise RuntimeError(f"metadata.json references missing {label}: `{rel_path}`.")
    return path


def _select_pack_primary(pack_root: Path, metadata: dict, character_pairs: list[ReferencePair]) -> ReferencePair:
    primary = metadata.get("primary") if isinstance(metadata.get("primary"), dict) else {}
    primary_image = _resolve_metadata_file(pack_root, primary.get("image"), "primary image")
    primary_mask = _resolve_metadata_file(pack_root, primary.get("mask"), "primary mask")
    if primary_image is not None or primary_mask is not None:
        if primary_image is None or primary_mask is None:
            raise RuntimeError("metadata.json primary must include both `image` and `mask`.")
        return ReferencePair(label="metadata primary", image=str(primary_image), mask_image=str(primary_mask))

    ref_image = _find_stem_file(pack_root, ("ref", "main", "reference"), IMAGE_EXTS)
    if ref_image is not None:
        ref_mask = _mask_for_image(ref_image)
        if ref_mask is None:
            raise RuntimeError(f"Missing mask for primary reference `{_rel(ref_image, pack_root)}`.")
        return ReferencePair(label="primary reference", image=str(ref_image), mask_image=str(ref_mask))

    if not character_pairs:
        raise RuntimeError(
            "No primary reference found. Provide `ref.png` + `ref_mask.png`, "
            "or at least one pair under `characters/character_0/`."
        )
    character_0 = [ref for ref in character_pairs if "/character_0/" in Path(ref.image).as_posix()]
    candidates = character_0 or character_pairs
    selected = sorted(candidates, key=_primary_sort_key)[0]
    return ReferencePair(label=f"{selected.label} (auto primary)", image=selected.image, mask_image=selected.mask_image)


def _find_pack_video(pack_root: Path, metadata: dict) -> Path:
    driving = metadata.get("driving") if isinstance(metadata.get("driving"), dict) else {}
    metadata_video = _resolve_metadata_file(pack_root, driving.get("video"), "driving video")
    if metadata_video is not None:
        return metadata_video
    video = _find_stem_file(pack_root, ("rendered_v2", "driving", "pose"), VIDEO_EXTS)
    if video is None:
        raise RuntimeError("Missing driving video. Expected `rendered_v2.mp4`.")
    return video


def _find_pack_mask_video(pack_root: Path, metadata: dict) -> tuple[Path, bool]:
    driving = metadata.get("driving") if isinstance(metadata.get("driving"), dict) else {}
    metadata_mask = _resolve_metadata_file(pack_root, driving.get("mask_video"), "driving mask video")
    if metadata_mask is not None:
        return metadata_mask, metadata_mask.name == "replace_mask.mp4" or metadata.get("mode") == "replacement"

    rendered_mask = pack_root / "rendered_mask_v2.mp4"
    replace_mask = pack_root / "replace_mask.mp4"
    if rendered_mask.exists() and replace_mask.exists():
        raise RuntimeError("Found both `rendered_mask_v2.mp4` and `replace_mask.mp4`; keep only one or set metadata.json.")
    if replace_mask.exists():
        return replace_mask, True
    if rendered_mask.exists():
        return rendered_mask, False
    raise RuntimeError("Missing mask video. Expected `rendered_mask_v2.mp4` or `replace_mask.mp4`.")


def _same_pair(a: ReferencePair, b: ReferencePair) -> bool:
    return Path(a.image).resolve() == Path(b.image).resolve() and Path(a.mask_image).resolve() == Path(b.mask_image).resolve()


def parse_input_pack(pack_zip: str | Path) -> dict:
    pack_root = _safe_extract_zip(pack_zip)
    metadata = _read_metadata(pack_root)
    character_pairs = _collect_character_pairs(pack_root)
    environment_pairs = _collect_environment_pairs(pack_root)
    legacy_pairs = _collect_legacy_flat_pairs(pack_root)
    primary = _select_pack_primary(pack_root, metadata, character_pairs + legacy_pairs)
    driving_video = _find_pack_video(pack_root, metadata)
    mask_video, replace_flag = _find_pack_mask_video(pack_root, metadata)

    refs = sorted(character_pairs + legacy_pairs, key=lambda ref: ref.label.lower()) + environment_pairs
    additional_refs = [ref for ref in refs if not _same_pair(ref, primary)]
    if len(additional_refs) > MAX_ADDITIONAL_REFS:
        raise RuntimeError(f"Too many additional references: {len(additional_refs)}. Limit is {MAX_ADDITIONAL_REFS}.")

    return {
        "root": str(pack_root),
        "prompt": _read_pack_prompt(pack_root, metadata),
        "mode": "replacement" if replace_flag else "animation",
        "replace_flag": replace_flag,
        "image": primary.image,
        "mask_image": primary.mask_image,
        "pose": str(driving_video),
        "mask_video": str(mask_video),
        "primary_label": primary.label,
        "additional_refs": [
            {"label": ref.label, "image": ref.image, "mask_image": ref.mask_image}
            for ref in additional_refs
        ],
    }


def _pack_gallery(pack: dict):
    items = [
        (pack["image"], f"Primary: {pack['primary_label']}"),
        (pack["mask_image"], "Primary mask"),
    ]
    for ref in pack["additional_refs"]:
        items.append((ref["image"], ref["label"]))
        items.append((ref["mask_image"], f"{ref['label']} mask"))
    return items


def _pack_summary(pack: dict) -> str:
    lines = [
        "### Pack validated",
        f"- Mode: `{pack['mode']}`",
        f"- Primary: `{pack['primary_label']}`",
        f"- Driving video: `{Path(pack['pose']).name}`",
        f"- Mask video: `{Path(pack['mask_video']).name}`",
        f"- Additional reference pairs: `{len(pack['additional_refs'])}`",
    ]
    for ref in pack["additional_refs"]:
        lines.append(f"  - `{ref['label']}`")
    return "\n".join(lines)


def validate_input_pack(pack_zip):
    if pack_zip is None:
        return None, "Upload a `.zip` pack first.", [], None, None, "", "animation"
    try:
        pack = parse_input_pack(pack_zip)
        return (
            pack,
            _pack_summary(pack),
            _pack_gallery(pack),
            pack["pose"],
            pack["mask_video"],
            pack["prompt"],
            pack["mode"],
        )
    except Exception:
        logging.exception("Advanced pack validation failed")
        return None, traceback.format_exc(), [], None, None, "", "animation"


def _require_repo_layout():
    missing = []
    for rel in ("wan/scail.py", "wan/modules/model_scail2.py", "generate.py", "configs/config-14b.json"):
        if not (ROOT / rel).exists():
            missing.append(rel)
    if missing:
        raise RuntimeError(
            "This app.py is meant to live at the root of the SCAIL-2 repository. "
            f"Missing: {', '.join(missing)}"
        )


def _download_safetensors_if_configured() -> Path | None:
    if not SAFETENSORS_REPO_ID:
        return None

    local_dir = Path(os.getenv("SCAIL_SAFETENSORS_CACHE", str(STORAGE_ROOT / "scail2_safetensors")))
    local_dir.mkdir(parents=True, exist_ok=True)
    local_path = local_dir / SAFETENSORS_FILENAME
    if local_path.exists():
        return local_path

    logging.info("Downloading converted SCAIL-2 safetensors from %s/%s", SAFETENSORS_REPO_ID, SAFETENSORS_FILENAME)
    downloaded = hf_hub_download(
        repo_id=SAFETENSORS_REPO_ID,
        filename=SAFETENSORS_FILENAME,
        local_dir=str(local_dir),
        local_dir_use_symlinks=False,
        resume_download=True,
    )
    return Path(downloaded)


def _find_converted_safetensors(ckpt_dir: Path | None) -> Path | None:
    candidates = []
    env_path = os.getenv("SCAIL_SAFETENSORS_PATH")
    if env_path:
        candidates.append(Path(env_path))
    if _LAST_CONVERTED_SAFETENSORS is not None:
        candidates.append(Path(_LAST_CONVERTED_SAFETENSORS))
    candidates += [
        ROOT / "SCAIL-2.safetensors",
        ROOT / "models" / "SCAIL-2.safetensors",
        ROOT / "model.safetensors",
        Path(os.getenv("SCAIL_CONVERTED_DIR", str(STORAGE_ROOT / "scail2_converted"))) / "SCAIL-2.safetensors",
    ]
    if ckpt_dir is not None:
        candidates += [ckpt_dir / "SCAIL-2.safetensors", ckpt_dir / "model.safetensors"]
    for candidate in candidates:
        if candidate.exists():
            return candidate
    return _download_safetensors_if_configured()


def _copy_file_with_progress(source: Path, dest: Path, description: str) -> Path:
    source_size = source.stat().st_size
    chunk_size = int(os.getenv("SCAIL_STAGE_COPY_CHUNK_MB", "64")) * 1024 * 1024
    log_every = int(os.getenv("SCAIL_STAGE_COPY_LOG_GB", "1")) * 1024 * 1024 * 1024
    dest.parent.mkdir(parents=True, exist_ok=True)

    if dest.exists() and dest.stat().st_size == source_size:
        return dest

    tmp_dest = dest.with_suffix(dest.suffix + ".tmp")
    copied = tmp_dest.stat().st_size if tmp_dest.exists() else 0
    if copied > source_size:
        tmp_dest.unlink()
        copied = 0

    logging.info("%s: %s -> %s", description, source, dest)
    next_log = ((copied // log_every) + 1) * log_every if log_every > 0 else source_size
    if copied:
        logging.info("Resuming copy at %.2f/%.2f GB", copied / 1024**3, source_size / 1024**3)

    with source.open("rb") as src, tmp_dest.open("ab") as dst:
        if copied:
            src.seek(copied)
        while copied < source_size:
            chunk = src.read(min(chunk_size, source_size - copied))
            if not chunk:
                raise RuntimeError(f"Unexpected EOF while copying {source}: {copied} of {source_size} bytes")
            dst.write(chunk)
            copied += len(chunk)
            if log_every > 0 and copied >= next_log:
                logging.info("%s: %.2f/%.2f GB", description, copied / 1024**3, source_size / 1024**3)
                next_log += log_every

    if tmp_dest.stat().st_size != source_size:
        raise RuntimeError(f"Copied file size mismatch: {tmp_dest.stat().st_size} != {source_size}")
    tmp_dest.replace(dest)
    logging.info("Finished %s: %s", description, dest)
    return dest


def _is_relative_to(path: Path, parent: Path) -> bool:
    try:
        path.resolve().relative_to(parent.resolve())
        return True
    except ValueError:
        return False


def _stage_safetensors_for_load(scail_path: Path) -> Path:
    if not STAGE_SAFETENSORS_FOR_LOAD:
        return scail_path

    source = Path(scail_path)
    if _is_relative_to(source, STAGING_ROOT):
        return source

    stage_dir = Path(os.getenv("SCAIL_MODEL_LOAD_CACHE", str(STAGING_ROOT / "scail2_model_load")))
    staged = stage_dir / source.name
    if staged.exists() and staged.stat().st_size == source.stat().st_size:
        return staged
    return _copy_file_with_progress(source, staged, "Staging SCAIL-2 safetensors for local load")


def _download_checkpoint_if_needed(include_original_dit: bool = False) -> Path:
    env_dir = os.getenv("SCAIL_CKPT_DIR")
    if env_dir:
        ckpt_dir = Path(env_dir)
        if not ckpt_dir.exists():
            raise RuntimeError(f"SCAIL_CKPT_DIR does not exist: {ckpt_dir}")
        return ckpt_dir

    local_dir = Path(os.getenv("SCAIL_CKPT_CACHE", str(STORAGE_ROOT / "scail2_ckpt")))
    has_base_assets = (
        (local_dir / "Wan2.1_VAE.pth").exists()
        and (local_dir / "umt5-xxl").exists()
        and (local_dir / CLIP_CKPT_NAME).exists()
    )
    if has_base_assets:
        return local_dir

    if include_original_dit:
        logging.warning("Original DiT staging uses SCAIL_ORIGINAL_DIT_CACHE; base download will stay narrow.")
    logging.info("Downloading SCAIL-2 base checkpoint assets from %s", MODEL_REPO_ID)
    snapshot_download(
        repo_id=MODEL_REPO_ID,
        local_dir=str(local_dir),
        local_dir_use_symlinks=False,
        resume_download=True,
        allow_patterns=BASE_ALLOW_PATTERNS,
    )
    return local_dir


def _download_original_dit_for_conversion() -> Path:
    env_dir = os.getenv("SCAIL_ORIGINAL_DIT_DIR")
    if env_dir:
        original_dir = Path(env_dir)
        original_path = original_dir / ORIGINAL_DIT_REL_PATH
        if not original_path.exists():
            raise RuntimeError(f"SCAIL_ORIGINAL_DIT_DIR is missing {ORIGINAL_DIT_REL_PATH}: {original_dir}")
        return original_dir

    local_dir = Path(os.getenv("SCAIL_ORIGINAL_DIT_CACHE", str(STAGING_ROOT / "scail2_original_dit")))
    original_path = local_dir / ORIGINAL_DIT_REL_PATH
    if original_path.exists():
        return local_dir

    logging.info("Downloading original SCAIL-2 DiT checkpoint for one-time conversion into %s", local_dir)
    snapshot_download(
        repo_id=MODEL_REPO_ID,
        local_dir=str(local_dir),
        local_dir_use_symlinks=False,
        resume_download=True,
        allow_patterns=[ORIGINAL_DIT_REL_PATH],
    )
    return local_dir


def _prepare_assets_for_runtime() -> str:
    global _ASSET_STATUS, _ASSET_ERROR
    try:
        ckpt_dir = _download_checkpoint_if_needed(include_original_dit=False)
        scail_path = _find_converted_safetensors(ckpt_dir)
        if scail_path is None and AUTO_CONVERT:
            original_dir = _download_original_dit_for_conversion()
            scail_path = _maybe_convert_checkpoint(original_dir, None)
        if scail_path is None:
            _ASSET_STATUS = (
                "Base checkpoint assets are present, but no converted safetensors file was found. "
                "Set SCAIL_SAFETENSORS_PATH or SCAIL_SAFETENSORS_REPO_ID."
            )
        else:
            _ASSET_STATUS = f"Assets ready. Base checkpoint: {ckpt_dir}. Converted DiT safetensors: {scail_path}."
        _ASSET_ERROR = None
    except Exception:
        _ASSET_ERROR = traceback.format_exc()
        _ASSET_STATUS = "Asset preparation failed. See the traceback below."
        logging.exception("Asset preparation failed")
    return _ASSET_STATUS if _ASSET_ERROR is None else _ASSET_STATUS + "\n\n" + _ASSET_ERROR


def _maybe_convert_checkpoint(ckpt_dir: Path, scail_path: Path | None) -> Path:
    global _LAST_CONVERTED_SAFETENSORS
    if scail_path is not None:
        return scail_path

    if not AUTO_CONVERT:
        raise RuntimeError(
            "Converted SCAIL-2 safetensors file was not found. Set SCAIL_SAFETENSORS_PATH, "
            "or enable SCAIL_AUTO_CONVERT=1 for one-time conversion."
        )

    persistent_dir = Path(os.getenv("SCAIL_CONVERTED_DIR", str(STORAGE_ROOT / "scail2_converted")))
    persistent_path = persistent_dir / "SCAIL-2.safetensors"
    if persistent_path.exists():
        return persistent_path

    save_dir = Path(os.getenv("SCAIL_CONVERSION_WORK_DIR", str(STAGING_ROOT / "scail2_converted_work")))
    if not CONVERT_TO_STAGING_FIRST:
        save_dir = persistent_dir
    save_dir.mkdir(parents=True, exist_ok=True)
    save_path = save_dir / "SCAIL-2.safetensors"
    if save_path.exists():
        _LAST_CONVERTED_SAFETENSORS = save_path
        if save_path != persistent_path:
            _copy_file_with_progress(save_path, persistent_path, "Persisting converted SCAIL-2 safetensors to storage")
        return save_path

    logging.info("Converting checkpoint to safetensors: %s", save_path)
    convert_env = os.environ.copy()
    convert_env["TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD"] = "1"
    subprocess.run(
        [
            sys.executable,
            str(ROOT / "convert.py"),
            "--scail-dir",
            str(ckpt_dir),
            "--save-path",
            str(save_path),
        ],
        check=True,
        cwd=str(ROOT),
        env=convert_env,
    )
    _LAST_CONVERTED_SAFETENSORS = save_path
    if save_path != persistent_path:
        _copy_file_with_progress(save_path, persistent_path, "Persisting converted SCAIL-2 safetensors to storage")
    return save_path


def _install_attention_patch():
    import wan.modules.attention as attention_mod

    hf_flash_attn2 = None
    try:
        from kernels import get_kernel

        hf_flash_attn2 = get_kernel("kernels-community/flash-attn2", version=2)
        logging.info("Using kernels-community/flash-attn2 through HF Kernels.")
    except Exception as exc:
        if torch.cuda.is_available():
            device_name = torch.cuda.get_device_name(0)
            capability = torch.cuda.get_device_capability(0)
        else:
            device_name = "no cuda"
            capability = None
        logging.warning("Could not initialize HF Kernels flash-attn2: %r", exc)
        logging.warning(
            "Attention fallback environment: torch=%s cuda=%s device=%s capability=%s",
            torch.__version__,
            torch.version.cuda,
            device_name,
            capability,
        )

    def patched_flash_attention(
        q,
        k,
        v,
        q_lens=None,
        k_lens=None,
        dropout_p=0.0,
        softmax_scale=None,
        q_scale=None,
        causal=False,
        window_size=(-1, -1),
        deterministic=False,
        dtype=torch.bfloat16,
        version=None,
    ):
        half_dtypes = (torch.float16, torch.bfloat16)
        b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype

        def half(x):
            return x if x.dtype in half_dtypes else x.to(dtype)

        if hf_flash_attn2 is not None and q.device.type == "cuda":
            if q_lens is None:
                q_var = half(q.flatten(0, 1))
                q_lens_t = torch.full((b,), lq, dtype=torch.int32, device=q.device)
            else:
                q_lens_t = q_lens.to(device=q.device, dtype=torch.int32)
                q_var = half(torch.cat([u[: int(n)] for u, n in zip(q, q_lens_t)]))

            if k_lens is None:
                k_var = half(k.flatten(0, 1))
                v_var = half(v.flatten(0, 1))
                k_lens_t = torch.full((b,), lk, dtype=torch.int32, device=k.device)
            else:
                k_lens_t = k_lens.to(device=k.device, dtype=torch.int32)
                k_var = half(torch.cat([u[: int(n)] for u, n in zip(k, k_lens_t)]))
                v_var = half(torch.cat([u[: int(n)] for u, n in zip(v, k_lens_t)]))

            q_var = q_var.to(v_var.dtype)
            k_var = k_var.to(v_var.dtype)
            if q_scale is not None:
                q_var = q_var * q_scale

            cu_q = torch.cat([q_lens_t.new_zeros([1]), q_lens_t]).cumsum(0, dtype=torch.int32)
            cu_k = torch.cat([k_lens_t.new_zeros([1]), k_lens_t]).cumsum(0, dtype=torch.int32)
            try:
                out = hf_flash_attn2.flash_attn_varlen_func(
                    q=q_var,
                    k=k_var,
                    v=v_var,
                    cu_seqlens_q=cu_q,
                    cu_seqlens_k=cu_k,
                    max_seqlen_q=lq,
                    max_seqlen_k=lk,
                    dropout_p=dropout_p,
                    softmax_scale=softmax_scale,
                    causal=causal,
                    window_size=window_size,
                    deterministic=deterministic,
                )
                if isinstance(out, tuple):
                    out = out[0]
                return out.unflatten(0, (b, lq)).type(out_dtype)
            except Exception as exc:
                logging.warning("HF Kernels flash-attn2 failed, falling back to SDPA: %s", exc)

        if q_lens is not None and not torch.all(q_lens == lq):
            logging.warning("SDPA fallback ignores variable q_lens; demo batch size should stay at 1.")
        if k_lens is not None and not torch.all(k_lens == lk):
            logging.warning("SDPA fallback ignores variable k_lens; demo batch size should stay at 1.")

        q_sdpa = q.transpose(1, 2).to(dtype)
        k_sdpa = k.transpose(1, 2).to(dtype)
        v_sdpa = v.transpose(1, 2).to(dtype)
        out = torch.nn.functional.scaled_dot_product_attention(
            q_sdpa,
            k_sdpa,
            v_sdpa,
            attn_mask=None,
            dropout_p=dropout_p,
            is_causal=causal,
            scale=softmax_scale,
        )
        return out.transpose(1, 2).contiguous().type(out_dtype)

    attention_mod.flash_attention = patched_flash_attention
    for module_name in (
        "wan.modules.clip",
        "wan.modules.model",
        "wan.modules.model_scail",
        "wan.modules.model_scail2",
    ):
        try:
            module = __import__(module_name, fromlist=["flash_attention"])
            if hasattr(module, "flash_attention"):
                module.flash_attention = patched_flash_attention
                logging.info("Patched %s.flash_attention", module_name)
        except Exception as exc:
            logging.warning("Could not patch %s.flash_attention: %s", module_name, exc)


def _import_runtime():
    global _WAN, _GENERATE_VIDEO, _SCAIL_CONFIGS, _SCAIL_CONFIG_PATHS
    if _WAN is not None:
        return

    _require_repo_layout()
    if str(ROOT) not in sys.path:
        sys.path.insert(0, str(ROOT))

    import wan
    from generate import generate_video
    from wan.configs import SCAIL_CONFIGS, SCAIL_CONFIG_PATHS

    _install_attention_patch()
    _WAN = wan
    _GENERATE_VIDEO = generate_video
    _SCAIL_CONFIGS = SCAIL_CONFIGS
    _SCAIL_CONFIG_PATHS = SCAIL_CONFIG_PATHS


def _prepare_runtime_for_startup() -> str:
    global _RUNTIME_STATUS, _RUNTIME_ERROR
    try:
        _import_runtime()
        _RUNTIME_STATUS = "Runtime ready. Attention backend has been initialized at startup."
        _RUNTIME_ERROR = None
    except Exception:
        _RUNTIME_ERROR = traceback.format_exc()
        _RUNTIME_STATUS = "Runtime preparation failed. See the traceback below."
        logging.exception("Runtime preparation failed")
    return _RUNTIME_STATUS if _RUNTIME_ERROR is None else _RUNTIME_STATUS + "\n\n" + _RUNTIME_ERROR


def _get_pipeline():
    global _PIPELINE, _PIPELINE_KEY
    _import_runtime()

    ckpt_dir = _download_checkpoint_if_needed(include_original_dit=False)
    scail_path = _find_converted_safetensors(ckpt_dir)
    if scail_path is None and AUTO_CONVERT:
        original_dir = _download_original_dit_for_conversion()
        scail_path = _maybe_convert_checkpoint(original_dir, None)
    else:
        scail_path = _maybe_convert_checkpoint(ckpt_dir, scail_path)

    scail_load_path = _stage_safetensors_for_load(scail_path)
    config_path = Path(os.getenv("SCAIL_CONFIG_PATH", _SCAIL_CONFIG_PATHS[MODEL_NAME]))
    if not config_path.is_absolute():
        config_path = ROOT / config_path

    lora_path = os.getenv("SCAIL_LORA_PATH") or None
    lora_alpha = float(os.getenv("SCAIL_LORA_ALPHA", "1.0"))
    key = (str(ckpt_dir), str(scail_load_path), str(config_path), lora_path, lora_alpha)
    if _PIPELINE is not None and _PIPELINE_KEY == key:
        return _PIPELINE

    logging.info("Loading SCAIL-2 pipeline.")
    cfg = _SCAIL_CONFIGS[MODEL_NAME]
    _PIPELINE = _WAN.SCAIL2Pipeline(
        config=cfg,
        checkpoint_dir=str(ckpt_dir),
        scail_safetensors_path=str(scail_load_path),
        scail_config_path=str(config_path),
        device_id=0,
        rank=0,
        t5_fsdp=False,
        dit_fsdp=False,
        use_usp=False,
        t5_cpu=False,
        lora_path=lora_path,
        lora_alpha=lora_alpha,
    )
    _PIPELINE_KEY = key
    return _PIPELINE


def _prepare_pipeline_for_startup() -> str:
    global _PIPELINE_STATUS, _PIPELINE_ERROR
    try:
        _get_pipeline()
        _PIPELINE_STATUS = "Pipeline preloaded at startup."
        _PIPELINE_ERROR = None
    except Exception:
        _PIPELINE_ERROR = traceback.format_exc()
        _PIPELINE_STATUS = "Pipeline preload failed. See the traceback below."
        logging.exception("Pipeline preload failed")
    return _PIPELINE_STATUS if _PIPELINE_ERROR is None else _PIPELINE_STATUS + "\n\n" + _PIPELINE_ERROR


def _is_gradio_native_file_path(path: Path) -> bool:
    path = path.resolve()
    native_roots = [ROOT.resolve(), Path(tempfile.gettempdir()).resolve()]
    return any(_is_relative_to(path, root) for root in native_roots)


def _prepare_output_for_gradio(path: str | Path) -> str:
    source = Path(path)
    if not source.exists():
        raise RuntimeError(f"Generated video was not found: {source}")
    if _is_gradio_native_file_path(source):
        return str(source)

    gradio_dir = Path(os.getenv("SCAIL_GRADIO_OUTPUT_CACHE", str(Path(tempfile.gettempdir()) / "scail2_gradio_outputs")))
    gradio_dir.mkdir(parents=True, exist_ok=True)
    dest = gradio_dir / source.name
    shutil.copy2(source, dest)
    logging.info("Copied generated video for Gradio display: %s -> %s", source, dest)
    return str(dest)


def _duration_for_job(*args, **kwargs):
    if _PIPELINE is None:
        return int(os.getenv("SCAIL_GPU_DURATION", str(GPU_DURATION_COLD)))
    return int(os.getenv("SCAIL_GPU_DURATION", str(GPU_DURATION_WARM)))


def _run_scail_job(
    image_path,
    mask_image_path,
    pose_path,
    mask_video_path,
    prompt,
    replace_flag,
    target_h,
    target_w,
    sample_steps,
    guide_scale,
    sample_shift,
    seed,
    segment_len,
    segment_overlap,
    additional_refs: tuple[ReferencePair, ...] = (),
    progress=None,
):
    if progress is not None:
        progress(0.02, desc="Loading SCAIL-2 pipeline")
    pipeline = _get_pipeline()
    cfg = _SCAIL_CONFIGS[MODEL_NAME]
    save_file = OUTPUT_DIR / f"scail2_{uuid.uuid4().hex}.mp4"

    if progress is not None:
        progress(0.12, desc="Preparing inputs")
    args = SimpleNamespace(
        target_h=int(target_h),
        target_w=int(target_w),
        sample_shift=float(sample_shift),
        sample_solver=DEFAULT_SOLVER,
        segment_len=int(segment_len),
        segment_overlap=int(segment_overlap),
        sample_steps=int(sample_steps),
        sample_guide_scale=float(guide_scale),
        base_seed=int(seed),
        offload_model=True,
        save_file=str(save_file),
        save_dir=str(OUTPUT_DIR),
        prompt=prompt or "",
    )

    additional_task_input = None
    if additional_refs:
        additional_task_input = {
            "additional_ref_image_paths": [str(ref.image) for ref in additional_refs],
            "additional_ref_mask_image_paths": [str(ref.mask_image) for ref in additional_refs],
        }

    if progress is not None:
        progress(0.15, desc="Generating video")
    _GENERATE_VIDEO(
        pipeline,
        prompt or "",
        str(image_path),
        str(mask_image_path),
        str(pose_path),
        str(mask_video_path),
        args,
        device=0,
        rank=0,
        cfg=cfg,
        input_idx=None,
        replace_flag=bool(replace_flag),
        additional_task_input=additional_task_input,
    )

    if progress is not None:
        progress(0.95, desc="Finalizing output")
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    if progress is not None:
        progress(0.98, desc="Preparing video for display")
    display_file = _prepare_output_for_gradio(save_file)
    if progress is not None:
        progress(1.0, desc="Done")
    return display_file


@spaces.GPU(duration=_duration_for_job, size=GPU_SIZE)
def generate_from_example(
    example_name,
    prompt,
    sample_steps,
    guide_scale,
    sample_shift,
    seed,
    target_size,
    segment_len,
    segment_overlap,
    progress=gr.Progress(track_tqdm=True),
):
    try:
        progress(0.0, desc="Checking example inputs")
        examples = _existing_examples()
        if example_name not in examples:
            raise RuntimeError(f"Example is missing from this checkout: {example_name}")
        example = examples[example_name]
        target_w, target_h = [int(v) for v in str(target_size).split("x")]
        refs = tuple(
            ReferencePair(ref.label, _abs(ref.image), _abs(ref.mask_image))
            for ref in example.additional_refs
        )
        output = _run_scail_job(
            _abs(example.image),
            _abs(example.mask_image),
            _abs(example.pose),
            _abs(example.mask_video),
            prompt,
            example.replace_flag,
            target_h,
            target_w,
            sample_steps,
            guide_scale,
            sample_shift,
            seed,
            segment_len,
            segment_overlap,
            additional_refs=refs,
            progress=progress,
        )
        return output, "Done."
    except Exception:
        logging.exception("Generation failed")
        return None, traceback.format_exc()


@spaces.GPU(duration=_duration_for_job, size=GPU_SIZE)
def generate_from_pack(
    pack,
    prompt,
    sample_steps,
    guide_scale,
    sample_shift,
    seed,
    target_size,
    segment_len,
    segment_overlap,
    progress=gr.Progress(track_tqdm=True),
):
    try:
        progress(0.0, desc="Checking validated pack")
        if not pack:
            raise RuntimeError("Validate an Advanced Pack before generating.")
        target_w, target_h = [int(v) for v in str(target_size).split("x")]
        refs = tuple(
            ReferencePair(ref["label"], ref["image"], ref["mask_image"])
            for ref in pack.get("additional_refs", [])
        )
        output = _run_scail_job(
            pack["image"],
            pack["mask_image"],
            pack["pose"],
            pack["mask_video"],
            prompt,
            pack.get("replace_flag", False),
            target_h,
            target_w,
            sample_steps,
            guide_scale,
            sample_shift,
            seed,
            segment_len,
            segment_overlap,
            additional_refs=refs,
            progress=progress,
        )
        return output, "Done."
    except Exception:
        logging.exception("Generation failed")
        return None, traceback.format_exc()


@spaces.GPU(duration=_duration_for_job, size=GPU_SIZE)
def generate_from_uploads(
    image,
    mask_image,
    pose_video,
    mask_video,
    prompt,
    mode,
    sample_steps,
    guide_scale,
    sample_shift,
    seed,
    target_size,
    segment_len,
    segment_overlap,
    progress=gr.Progress(track_tqdm=True),
):
    try:
        progress(0.0, desc="Checking uploaded inputs")
        required = {
            "reference image": image,
            "reference mask": mask_image,
            "driving/rendered video": pose_video,
            "driving mask video": mask_video,
        }
        missing = [name for name, value in required.items() if value is None]
        if missing:
            raise RuntimeError("Missing required input(s): " + ", ".join(missing))

        target_w, target_h = [int(v) for v in str(target_size).split("x")]
        output = _run_scail_job(
            image,
            mask_image,
            pose_video,
            mask_video,
            prompt,
            mode == "replacement",
            target_h,
            target_w,
            sample_steps,
            guide_scale,
            sample_shift,
            seed,
            segment_len,
            segment_overlap,
            progress=progress,
        )
        return output, "Done."
    except Exception:
        logging.exception("Generation failed")
        return None, traceback.format_exc()


def _reference_gallery(example: PreparedExample):
    items = [
        (_abs(example.image), "Primary reference"),
        (_abs(example.mask_image), "Primary mask"),
    ]
    for ref in example.additional_refs:
        items.append((_abs(ref.image), ref.label))
        items.append((_abs(ref.mask_image), f"{ref.label} mask"))
    return items


def _reference_note(example: PreparedExample) -> str:
    if not example.additional_refs:
        return "Single-reference example."
    return f"Multi-reference example: {len(example.additional_refs)} additional reference pair(s) are passed to SCAIL-2."


def load_example_preview(example_name):
    examples = _existing_examples()
    if example_name not in examples:
        return None, None, None, None, "", "animation", [], "Example not available."
    example = examples[example_name]
    mode = "replacement" if example.replace_flag else "animation"
    return (
        _abs(example.image),
        _abs(example.pose),
        _abs(example.mask_image),
        _abs(example.mask_video),
        example.prompt,
        mode,
        _reference_gallery(example),
        _reference_note(example),
    )


def _startup_message():
    try:
        _require_repo_layout()
        examples = _existing_examples()
        if not examples:
            return "Repo layout detected, but no prepared examples were found."
        return (
            f"Ready. Found {len(examples)} prepared example(s). "
            f"Storage root: {STORAGE_ROOT}. Staging root: {STAGING_ROOT}. "
            f"Output dir: {OUTPUT_DIR}.\n\n"
            f"{_ASSET_STATUS}\n\n{_RUNTIME_STATUS}\n\n{_PIPELINE_STATUS}\n\n"
            "Attention backend: HF Kernels flash-attn2 when available, otherwise SDPA."
        )
    except Exception as exc:
        return str(exc)


def _sampling_controls(prefix: str = ""):
    with gr.Row():
        steps = gr.Slider(4, 40, value=8, step=1, label=f"{prefix}Steps".strip())
        cfg = gr.Slider(1.0, 8.0, value=DEFAULT_GUIDE_SCALE, step=0.1, label=f"{prefix}CFG".strip())
        shift = gr.Slider(1.0, 6.0, value=DEFAULT_SHIFT, step=0.1, label=f"{prefix}Shift".strip())
    with gr.Row():
        seed = gr.Number(value=42, precision=0, label=f"{prefix}Seed".strip())
        target_size = gr.Dropdown(
            ["896x512", "512x896", "1280x704", "704x1280"],
            value=f"{DEFAULT_TARGET_W}x{DEFAULT_TARGET_H}",
            label=f"{prefix}Target size".strip(),
        )
        segment_len = gr.Number(value=DEFAULT_SEGMENT_LEN, precision=0, label=f"{prefix}Segment length".strip())
        segment_overlap = gr.Number(value=DEFAULT_SEGMENT_OVERLAP, precision=0, label=f"{prefix}Segment overlap".strip())
    return steps, cfg, shift, seed, target_size, segment_len, segment_overlap


# ============================================================================
# NEW: generate_from_image_video — ทำ SAM3 mask บน GPU เอง (ส่งแค่รูป+วิดีโอ)
# ใช้ SCAIL-Pose e2e (process_one e2e_mode → SAM3 ผ่าน ultralytics) → _run_scail_job
# ============================================================================
import tempfile as _tempfile

_SCAIL_POSE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "SCAIL-Pose")
if _SCAIL_POSE_DIR not in sys.path:
    sys.path.insert(0, _SCAIL_POSE_DIR)

SAM3_MODEL_PATH = os.getenv(
    "SAM3_MODEL", os.path.join(_SCAIL_POSE_DIR, "pretrained_weights", "sam3.pt")
)

_sam3_predictor = None


def _get_sam3_predictor():
    global _sam3_predictor
    if _sam3_predictor is None:
        from ultralytics.models.sam import SAM3VideoSemanticPredictor
        overrides = dict(
            conf=0.25, task="segment", mode="predict", imgsz=640,
            model=SAM3_MODEL_PATH, half=True, save=False, verbose=False,
        )
        _sam3_predictor = SAM3VideoSemanticPredictor(overrides=overrides, new_det_thresh=1.0)
    return _sam3_predictor


def _make_masks_e2e(subdir, video_name="driving.mp4",
                    text=("human", "character"), max_persons=1):
    from NLFPoseExtract.process_animation_aio import process_one
    predictor = _get_sam3_predictor()
    process_one(
        subdir, video_name, e2e_mode=True, crop_kind=None,
        max_persons=int(max_persons), text=list(text),
        model_nlf=None, detector=None, predictor=predictor, image_predictor=None,
    )
    return (
        os.path.join(subdir, "ref_mask.jpg"),
        os.path.join(subdir, "rendered_mask_v2.mp4"),
        os.path.join(subdir, "rendered_v2.mp4"),
    )


def _ensure_sam3():
    if os.path.exists(SAM3_MODEL_PATH):
        return
    os.makedirs(os.path.dirname(SAM3_MODEL_PATH), exist_ok=True)
    tok = os.getenv("HF_TOKEN") or os.getenv("HUGGING_FACE_HUB_TOKEN")
    f = hf_hub_download("facebook/sam3", "sam3.pt", token=tok)
    try:
        os.symlink(f, SAM3_MODEL_PATH)
    except OSError:
        shutil.copyfile(f, SAM3_MODEL_PATH)


@spaces.GPU(duration=_duration_for_job, size=GPU_SIZE)
def generate_from_image_video(
    image, pose_video, prompt, mode,
    sample_steps, guide_scale, sample_shift, seed,
    target_size, segment_len, segment_overlap,
    progress=gr.Progress(track_tqdm=True),
):
    try:
        if image is None or pose_video is None:
            raise RuntimeError("Missing required input(s): reference image / pose video")
        target_w, target_h = [int(v) for v in str(target_size).split("x")]
        subdir = _tempfile.mkdtemp(prefix="scail_iv_")
        ref_path = os.path.join(subdir, "ref.png")
        drv_path = os.path.join(subdir, "driving.mp4")
        shutil.copyfile(image, ref_path)
        shutil.copyfile(pose_video, drv_path)
        progress(0.05, desc="Masking with SAM3 on GPU ...")
        ref_mask, drv_mask, rendered = _make_masks_e2e(
            subdir, "driving.mp4", text=("human", "character"), max_persons=1,
        )
        progress(0.35, desc="Running SCAIL-2 generation ...")
        output = _run_scail_job(
            ref_path, ref_mask, rendered, drv_mask,
            prompt, (mode == "replacement"), target_h, target_w,
            sample_steps, guide_scale, sample_shift, seed,
            segment_len, segment_overlap, progress=progress,
        )
        return output, "Done."
    except Exception:
        logging.exception("generate_from_image_video failed")
        return None, traceback.format_exc()


def build_ui():
    examples = _existing_examples()
    example_names = list(examples.keys())
    default_example = example_names[0] if example_names else None
    default_preview = (
        load_example_preview(default_example)
        if default_example
        else (None, None, None, None, "", "animation", [], "No prepared examples found.")
    )

    with gr.Blocks(title="SCAIL-2 Character Animation Demo") as demo:
        gr.Markdown(
            "# SCAIL-2 Character Animation Demo\n"
            "Try SCAIL-2 from curated examples or from already-prepared custom inputs. "
            "The multi-reference example from the repo is handled as a prepared example: "
            "its additional references are passed to the model automatically."
        )
        startup = gr.Textbox(value=_startup_message(), label="Startup status", interactive=False)

        with gr.Tab("Prepared Examples"):
            with gr.Row():
                example_dropdown = gr.Dropdown(choices=example_names, value=default_example, label="Example")
                mode_view = gr.Textbox(value=default_preview[5], label="Mode", interactive=False)
            reference_note = gr.Markdown(default_preview[7])
            with gr.Accordion("Input preview", open=False):
                with gr.Row():
                    ref_preview = gr.Image(value=default_preview[0], label="Primary reference", interactive=False)
                    driving_preview = gr.Video(value=default_preview[1], label="Driving / rendered video")
                with gr.Row():
                    ref_mask_preview = gr.Image(value=default_preview[2], label="Primary mask", interactive=False)
                    driving_mask_preview = gr.Video(value=default_preview[3], label="Driving mask")
                reference_gallery = gr.Gallery(
                    value=default_preview[6],
                    label="Reference set",
                    columns=4,
                    height=260,
                    selected_index=0,
                    preview=True,
                )

            prompt = gr.Textbox(value=default_preview[4], label="Prompt", lines=3)
            sample_steps, guide_scale, sample_shift, seed, target_size, segment_len, segment_overlap = _sampling_controls()

            run_example = gr.Button("Generate", variant="primary")
            output_video = gr.Video(label="Output")
            status = gr.Textbox(label="Run status", lines=8)

            example_dropdown.change(
                load_example_preview,
                inputs=[example_dropdown],
                outputs=[
                    ref_preview,
                    driving_preview,
                    ref_mask_preview,
                    driving_mask_preview,
                    prompt,
                    mode_view,
                    reference_gallery,
                    reference_note,
                ],
            )
            run_example.click(
                generate_from_example,
                inputs=[
                    example_dropdown,
                    prompt,
                    sample_steps,
                    guide_scale,
                    sample_shift,
                    seed,
                    target_size,
                    segment_len,
                    segment_overlap,
                ],
                outputs=[output_video, status],
            )

        with gr.Tab("Custom Uploads"):
            gr.Markdown(
                "Upload a prepared SCAIL-2 input set: reference image, reference mask, "
                "driving/rendered video, and driving mask video. This tab is intentionally "
                "single-reference; use prepared examples for the official multi-reference case."
            )
            with gr.Row():
                up_image = gr.Image(type="filepath", label="Reference image")
                up_mask_image = gr.Image(type="filepath", label="Reference mask")
            with gr.Row():
                up_pose_video = gr.Video(label="Driving / rendered video")
                up_mask_video = gr.Video(label="Driving mask / replace mask")
            up_mode = gr.Radio(["animation", "replacement"], value="animation", label="Mode")
            up_prompt = gr.Textbox(label="Prompt", lines=3)
            up_steps, up_cfg, up_shift, up_seed, up_target_size, up_segment_len, up_segment_overlap = _sampling_controls()

            run_upload = gr.Button("Generate from uploads", variant="primary")
            upload_output = gr.Video(label="Output")
            upload_status = gr.Textbox(label="Run status", lines=8)
            run_upload.click(
                generate_from_uploads,
                inputs=[
                    up_image,
                    up_mask_image,
                    up_pose_video,
                    up_mask_video,
                    up_prompt,
                    up_mode,
                    up_steps,
                    up_cfg,
                    up_shift,
                    up_seed,
                    up_target_size,
                    up_segment_len,
                    up_segment_overlap,
                ],
                outputs=[upload_output, upload_status],
            )

        with gr.Tab("Image + Video (auto-mask)"):
            gr.Markdown(
                "Send only a **reference image** + **driving video**. "
                "The Space runs SAM3 masking on the GPU (SCAIL-Pose e2e) and generates."
            )
            iv_image = gr.Image(type="filepath", label="Reference image")
            iv_video = gr.Video(label="Driving / pose video")
            iv_prompt = gr.Textbox(label="Prompt", value="A young woman with natural body movement.")
            iv_mode = gr.Radio(["animation", "replacement"], value="animation", label="Mode")
            with gr.Row():
                iv_steps = gr.Slider(1, 50, value=8, step=1, label="Steps")
                iv_cfg = gr.Slider(1, 10, value=1.0, step=0.5, label="CFG (guide_scale)")
                iv_shift = gr.Slider(1, 10, value=3.0, step=0.5, label="Shift")
            with gr.Row():
                iv_seed = gr.Number(value=42, precision=0, label="Seed")
                iv_target = gr.Dropdown(
                    ["512x896", "896x512", "704x1280", "1280x704"],
                    value="512x896", label="Target size",
                )
                iv_segment_len = gr.Number(value=81, precision=0, label="Segment length")
                iv_segment_overlap = gr.Number(value=5, precision=0, label="Segment overlap")
            run_iv = gr.Button("Generate (auto-mask)", variant="primary")
            iv_output = gr.Video(label="Output")
            iv_status = gr.Textbox(label="Run status")
            run_iv.click(
                generate_from_image_video,
                inputs=[
                    iv_image, iv_video, iv_prompt, iv_mode,
                    iv_steps, iv_cfg, iv_shift, iv_seed,
                    iv_target, iv_segment_len, iv_segment_overlap,
                ],
                outputs=[iv_output, iv_status],
                api_name="generate_from_image_video",
            )

        with gr.Tab("Advanced Pack"):
            gr.Markdown(
                "Upload a `.zip` pack for multi-reference or multi-character inputs. "
                "The app validates the file structure, selects one primary reference, and "
                "passes every other image/mask pair as additional references to SCAIL-2."
            )
            with gr.Accordion("Pack format", open=False):
                gr.Markdown(
                    "### Canonical zip structure\n"
                    "Use this layout when one or more characters have several reference views. "
                    "Each image must have a matching mask with the same stem plus `_mask`.\n\n"
                    "```text\n"
                    "scail2_input_pack/\n"
                    "|-- rendered_v2.mp4\n"
                    "|-- rendered_mask_v2.mp4\n"
                    "|-- prompt.txt                  # optional\n"
                    "|-- metadata.json               # optional\n"
                    "|-- characters/\n"
                    "|   |-- character_0/\n"
                    "|   |   |-- front.png\n"
                    "|   |   |-- front_mask.png\n"
                    "|   |   |-- back.png\n"
                    "|   |   `-- back_mask.png\n"
                    "|   `-- character_1/\n"
                    "|       |-- front.png\n"
                    "|       `-- front_mask.png\n"
                    "`-- environment/\n"
                    "    |-- background.png\n"
                    "    `-- background_mask.png\n"
                    "```\n\n"
                    "### Mask convention\n"
                    "Colors represent identity slots, not individual views. If `character_0` has "
                    "front, back, and close-up references, all masks for those views should use the "
                    "same identity color. A different character gets a different color. The driving "
                    "mask video should use the same color assignments.\n\n"
                    "### Mapping to SCAIL-2\n"
                    "SCAIL-2 receives one primary reference plus a list of additional refs. The parser "
                    "uses `metadata.json` when a primary is declared. Otherwise it uses "
                    "`characters/character_0/front.*` or the first available view from `character_0`. "
                    "All remaining image/mask pairs become additional references.\n\n"
                    "### Legacy repo-style pack\n"
                    "The official multi-reference example also uses this flat layout, which is supported:\n\n"
                    "```text\n"
                    "ref.png\n"
                    "ref_mask.jpg\n"
                    "rendered_v2.mp4\n"
                    "rendered_mask_v2.mp4\n"
                    "background.png\n"
                    "background_mask.png\n"
                    "character_0.png\n"
                    "character_0_mask.png\n"
                    "character_1.png\n"
                    "character_1_mask.png\n"
                    "```\n"
                )

            pack_state = gr.State(None)
            pack_file = gr.File(
                label="SCAIL-2 input pack (.zip)",
                file_types=[".zip"],
                type="filepath",
            )
            validate_pack = gr.Button("Validate pack")
            pack_summary = gr.Markdown("Upload a pack and validate it before generating.")
            pack_gallery = gr.Gallery(
                label="Parsed reference set",
                columns=4,
                height=260,
                selected_index=0,
                preview=True,
            )
            with gr.Row():
                pack_driving_preview = gr.Video(label="Driving / rendered video")
                pack_mask_preview = gr.Video(label="Driving mask / replace mask")
            pack_mode = gr.Textbox(value="animation", label="Mode", interactive=False)
            pack_prompt = gr.Textbox(label="Prompt", lines=3)
            pack_steps, pack_cfg, pack_shift, pack_seed, pack_target_size, pack_segment_len, pack_segment_overlap = _sampling_controls()
            run_pack = gr.Button("Generate from pack", variant="primary")
            pack_output = gr.Video(label="Output")
            pack_status = gr.Textbox(label="Run status", lines=8)

            validate_pack.click(
                validate_input_pack,
                inputs=[pack_file],
                outputs=[
                    pack_state,
                    pack_summary,
                    pack_gallery,
                    pack_driving_preview,
                    pack_mask_preview,
                    pack_prompt,
                    pack_mode,
                ],
            )
            run_pack.click(
                generate_from_pack,
                inputs=[
                    pack_state,
                    pack_prompt,
                    pack_steps,
                    pack_cfg,
                    pack_shift,
                    pack_seed,
                    pack_target_size,
                    pack_segment_len,
                    pack_segment_overlap,
                ],
                outputs=[pack_output, pack_status],
            )

    return demo


if __name__ == "__main__":
    if os.getenv("SCAIL_PRELOAD_ASSETS", "1") == "1":
        _prepare_assets_for_runtime()
    if os.getenv("SCAIL_PRELOAD_RUNTIME", "1") == "1":
        _prepare_runtime_for_startup()
    if PRELOAD_PIPELINE:
        _prepare_pipeline_for_startup()
    try:
        _ensure_sam3()   # โหลด sam3.pt (ครั้งแรก) — ต้องมี secret HF_TOKEN ที่ได้สิทธิ์ facebook/sam3
    except Exception as _e:
        logging.warning("SAM3 weights not ready: %s", _e)
    build_ui().queue(max_size=8).launch(
        allowed_paths=[str(OUTPUT_DIR.resolve())],
        show_error=True,
    )