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diff --git a/scripts/pixelmodel_robustness/codec.py b/scripts/pixelmodel_robustness/codec.py
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--- /dev/null
+++ b/scripts/pixelmodel_robustness/codec.py
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+"""Official PNG codec compatible weight reconstruction adapters."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from collections.abc import Mapping, Sequence
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+
+def decode_weight_image(image_path: str | Path, manifest_path: str | Path) -> tuple[np.ndarray, list[str]]:
+    """公式manifest順でRGBのR/Gをfp16 bit patternとしてfloat32へ復元する。"""
+    metadata = json.loads(Path(manifest_path).read_text())
+    pixels = np.asarray(Image.open(image_path).convert("RGB"), dtype=np.uint8).reshape(-1, 3)
+    total = int(metadata["total_parameters"])
+    if len(pixels) < total:
+        raise ValueError(f"weight image has {len(pixels)} pixels, needs {total}")
+    bits = (pixels[:total, 0].astype(np.uint16) << 8) | pixels[:total, 1].astype(np.uint16)
+    return bits.view(np.float16).astype(np.float32), [str(item["name"]) for item in metadata["params"]]
+
+
+def flatten_manifest(arrays: Mapping[str, np.ndarray]) -> tuple[np.ndarray, list[tuple[str, int, int]]]:
+    """mappingの挿入順を保持し、float16 quantization後のfloat32 flatを返す。"""
+    parts: list[np.ndarray] = []
+    manifest: list[tuple[str, int, int]] = []
+    offset = 0
+    for name, value in arrays.items():
+        part = np.asarray(value).reshape(-1).astype(np.float16).astype(np.float32)
+        parts.append(part)
+        manifest.append((name, offset, offset + len(part)))
+        offset += len(part)
+    return np.concatenate(parts) if parts else np.empty(0, dtype=np.float32), manifest
+
+
+def layer_offsets(names: Sequence[str], sizes: Sequence[int]) -> dict[str, tuple[int, int]]:
+    """layer名とnumelからmetric用offsetを構築する。"""
+    offsets: dict[str, tuple[int, int]] = {}
+    offset = 0
+    for name, size in zip(names, sizes):
+        offsets[name] = (offset, offset + int(size))
+        offset += int(size)
+    return offsets
+
+
+def sha256_file(path: str | Path) -> str:
+    """ファイルのSHA-256を返す。"""
+    digest = hashlib.sha256()
+    with Path(path).open("rb") as stream:
+        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def reconstruct_dit(image_path: str | Path, manifest_path: str | Path, device: str = "cpu", config_path: str | Path | None = None):
+    """model_png.jsonのname/shape順でDiTを再構築し、copy後の全parameterをfinite gateする。"""
+    import torch
+
+    metadata = json.loads(Path(manifest_path).read_text(encoding="utf-8"))
+    config = json.loads(Path(config_path).read_text(encoding="utf-8"))["dit"] if config_path is not None else metadata["cfg"]
+    from dit import DiT
+
+    model = DiT(dim=int(config["dim"]), depth=int(config["depth"]), heads=int(config["heads"]))
+    named = dict(model.named_parameters())
+    pixels = np.asarray(Image.open(image_path).convert("RGB"), dtype=np.uint8).reshape(-1, 3)
+    total = int(metadata["total_parameters"])
+    if pixels.shape[0] < total:
+        raise ValueError("weight image is shorter than manifest")
+    bits = (pixels[:total, 0].astype(np.uint16) << 8) | pixels[:total, 1].astype(np.uint16)
+    flat = bits.view(np.float16)
+    offset = 0
+    with torch.no_grad():
+        for item in metadata["params"]:
+            name = str(item["name"])
+            shape = tuple(int(value) for value in item["shape"])
+            numel = int(item["numel"])
+            if name not in named or tuple(named[name].shape) != shape or numel != int(np.prod(shape)):
+                raise ValueError(f"manifest mismatch for {name}")
+            named[name].copy_(torch.from_numpy(flat[offset:offset + numel].copy()).view(shape).float())
+            offset += numel
+    if offset != total or any(not bool(torch.isfinite(parameter).all()) for parameter in model.parameters()):
+        raise ValueError("finite gate failed after model parameter copy")
+    return model.to(device).eval()
diff --git a/scripts/pixelmodel_robustness/inference.py b/scripts/pixelmodel_robustness/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..0784a10bcc77766f459b98cd774e50f9d59d45f3
--- /dev/null
+++ b/scripts/pixelmodel_robustness/inference.py
@@ -0,0 +1,121 @@
+"""公式DiTの再現可能な推論と、テスト用runtime依存性注入。"""
+
+from __future__ import annotations
+
+import math
+import os
+from contextlib import nullcontext
+from typing import Any
+
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+
+
+def resolve_device(requested: str = "auto") -> str:
+    """cpu/mps/cudaを環境に応じて解決する。"""
+    import torch
+    if requested != "auto":
+        if requested == "cuda" and not torch.cuda.is_available():
+            raise RuntimeError("CUDA was requested but is unavailable")
+        if requested == "mps" and not torch.backends.mps.is_available():
+            raise RuntimeError("MPS was requested but is unavailable")
+        return requested
+    if torch.cuda.is_available():
+        return "cuda"
+    if torch.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+
+def autocast_context(device: str) -> Any:
+    """GPUではautocast、CPUではnullcontextを返す。"""
+    import torch
+    if device == "cuda":
+        return torch.autocast(device_type="cuda", dtype=torch.bfloat16)
+    if device == "mps":
+        return torch.autocast(device_type="mps", dtype=torch.float16)
+    return nullcontext()
+
+
+def finite_state_dict(state_dict: dict[str, Any]) -> bool:
+    """state dict全tensorのfinite gate。"""
+    return all(bool(value.isfinite().all()) for value in state_dict.values() if hasattr(value, "isfinite"))
+
+
+def euler_rectified_flow(model: Any, conditional: Any, unconditional: Any, latent: Any, steps: int, cfg: float, device: str) -> Any:
+    """公式と同じ50-step Euler rectified-flow/CFGを実行する。"""
+    if steps < 1:
+        raise ValueError("steps must be positive")
+    dt = 1.0 / steps
+    x = latent.clone()
+    for index in range(steps):
+        t = x.new_full((x.shape[0],), index * dt, device=device)
+        with autocast_context(device):
+            vc = model(x, t, conditional[0], conditional[1])
+            vu = model(x, t, unconditional[0], unconditional[1])
+        x = x + (vu + cfg * (vc - vu)).float() * dt
+    return x
+
+
+def generate_with_runtime(runtime: Any, model: Any, prompt: str, seed: int, steps: int, cfg: float, device: str) -> tuple[Any, float | None]:
+    """text encode→seed固定latent→Euler→VAE decode→CLIPをruntime経由で行う。"""
+    import torch
+    with torch.inference_mode():
+        conditional = runtime.encode([prompt], device)
+        unconditional = runtime.encode([""], device)
+        latent = runtime.initial_latent(seed, device)
+        result = euler_rectified_flow(model, conditional, unconditional, latent, steps, cfg, device)
+        image = runtime.decode(result, device)
+        if not bool(torch.isfinite(image).all()):
+            raise FloatingPointError("nonfinite generated image tensor")
+        score = runtime.clip_score(image, prompt, device)
+        if score is not None and not math.isfinite(float(score)):
+            raise FloatingPointError("nonfinite CLIP score")
+    return image, float(score) if score is not None else None
+
+
+def clip_score_once(metric: Any, image: Any, prompt: str, target: str, torch_module: Any) -> float:
+    """stateful torchmetrics CLIPScoreを一サンプル単位で隔離して評価する。"""
+    metric.reset()
+    try:
+        metric((image.unsqueeze(0) * 255).to(torch_module.uint8), [prompt])
+        return float(metric.compute().item())
+    finally:
+        metric.reset()
+
+
+def build_official_runtime(args: Any, device: str) -> tuple[Any, Any]:
+    """公式依存を一度だけロードし、model loaderとruntimeを返す。"""
+    import torch
+    from diffusers import AutoencoderKL
+    from torchmetrics.multimodal.clip_score import CLIPScore
+    from transformers import CLIPTextModel, CLIPTokenizer
+    vae = AutoencoderKL.from_pretrained(args.vae).to(device).half().eval()
+    tokenizer = CLIPTokenizer.from_pretrained(args.clip)
+    text = CLIPTextModel.from_pretrained(args.clip).to(device).half().eval()
+    clip_metric = CLIPScore(model_name_or_path=args.clip).to(device).eval()
+
+    class Runtime:
+        def encode(self, strings: list[str], target: str) -> tuple[Any, Any]:
+            tokens = tokenizer(strings, padding="max_length", max_length=40, truncation=True, return_tensors="pt").to(target)
+            output = text(**tokens)
+            return output.last_hidden_state.float(), output.pooler_output.float()
+
+        def initial_latent(self, seed: int, target: str) -> Any:
+            generator = torch.Generator(device=target).manual_seed(seed)
+            return torch.randn((1, 4, 32, 32), generator=generator, device=target)
+
+        def decode(self, latent: Any, target: str) -> Any:
+            return ((vae.decode((latent / 0.18215).half()).sample.float().clamp(-1, 1) + 1) / 2)[0]
+
+        def clip_score(self, image: Any, prompt: str, target: str) -> float | None:
+            return clip_score_once(clip_metric, image, prompt, target, torch)
+
+    from pixelmodel_robustness.codec import reconstruct_dit
+
+    def loader(weight_path: str) -> Any:
+        model = reconstruct_dit(weight_path, args.manifest, device, getattr(args, "config", None))
+        if not finite_state_dict(dict(model.named_parameters())):
+            raise ValueError("finite gate failed after PNG model reconstruction")
+        return model
+
+    return loader, Runtime()
diff --git a/scripts/pixelmodel_robustness/jpeg_repair.py b/scripts/pixelmodel_robustness/jpeg_repair.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9d2cee292ac477df19e8e0b64f160528ae1bca1
--- /dev/null
+++ b/scripts/pixelmodel_robustness/jpeg_repair.py
@@ -0,0 +1,451 @@
+"""JPEG weight repairのTask 1-2用codec、修復、clip、fp16診断。"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import zlib
+from collections.abc import Mapping, Sequence
+from dataclasses import asdict, dataclass
+from io import BytesIO
+from typing import Any, Final
+
+import numpy as np
+from PIL import Image, features
+from PIL import __version__ as PILLOW_VERSION
+
+LOGGER = logging.getLogger(__name__)
+
+CONDITION_IDS: Final[tuple[str, ...]] = (
+    "png_baseline",
+    "jpeg_q100_raw",
+    "jpeg_q100_repair_zero",
+    "jpeg_q100_repair_layer_median",
+    "jpeg_q100_repair_layer_mean",
+    "jpeg_q100_repair_zero_clip_layer_mean_std_6",
+    "jpeg_q100_repair_zero_clip_layer_mean_std_5",
+    "jpeg_q100_repair_zero_clip_layer_pct_0.01_99.99",
+    "jpeg_q100_repair_zero_clip_layer_pct_0.1_99.9",
+    "jpeg_q100_repair_layer_median_clip_layer_mean_std_6",
+    "jpeg_q100_repair_layer_median_clip_layer_mean_std_5",
+    "jpeg_q100_repair_layer_median_clip_layer_pct_0.01_99.99",
+    "jpeg_q100_repair_layer_median_clip_layer_pct_0.1_99.9",
+    "jpeg_q100_repair_layer_mean_clip_layer_mean_std_6",
+    "jpeg_q100_repair_layer_mean_clip_layer_mean_std_5",
+    "jpeg_q100_repair_layer_mean_clip_layer_pct_0.01_99.99",
+    "jpeg_q100_repair_layer_mean_clip_layer_pct_0.1_99.9",
+    "protected_high_png_low_jpeg_q100",
+)
+CLIP_STRATEGIES: Final[tuple[str, ...]] = (
+    "layer_mean_std_6", "layer_mean_std_5", "layer_pct_0.01_99.99", "layer_pct_0.1_99.9"
+)
+
+
+@dataclass(frozen=True)
+class JpegCondition:
+    """JPEG studyの不変condition entry。"""
+
+    condition_id: str
+    source: str
+    repair: str | None
+    clip: str | None
+    payload_kind: str
+    parent_raw_sha256: str
+
+
+@dataclass(frozen=True)
+class LayerClipRecord:
+    """一つのlayerに対するclip結果。"""
+
+    layer: str
+    strategy: str
+    lower: float
+    upper: float
+    pre_max_abs: float
+    post_max_abs: float
+    changed_count: int
+
+
+@dataclass(frozen=True)
+class RepairResult:
+    """非有限修復の値と監査用count。"""
+
+    values: np.ndarray
+    method: str
+    nan_count: int
+    posinf_count: int
+    neginf_count: int
+    repaired_value_count: int
+    layer_records: tuple[LayerRepairRecord, ...]
+    repaired_histogram: dict[str, Any]
+
+
+@dataclass(frozen=True)
+class LayerRepairRecord:
+    """一つのlayerにおける非有限値修復の監査record。"""
+
+    layer: str
+    replacement: float
+    nan_count: int
+    posinf_count: int
+    neginf_count: int
+    repaired_count: int
+
+
+@dataclass(frozen=True)
+class ClipResult:
+    """layerwise clipの値とlayer record。"""
+
+    values: np.ndarray
+    strategy: str
+    layers: tuple[LayerClipRecord, ...]
+    total_clipped_count: int
+    changed_pre_histogram: dict[str, Any]
+    changed_post_histogram: dict[str, Any]
+    changed_delta_histogram: dict[str, Any]
+
+
+@dataclass(frozen=True)
+class ProtectedArtifact:
+    """high PNG + low JPEGのpayload。"""
+
+    high_png_bytes: bytes
+    low_jpeg_bytes: bytes
+
+    @property
+    def payload(self) -> bytes:
+        """推論用protected payloadを返す。"""
+        return self.high_png_bytes + self.low_jpeg_bytes
+
+
+def _sha256(data: bytes) -> str:
+    return hashlib.sha256(data).hexdigest()
+
+
+def encode_jpeg_q100(image: Image.Image | np.ndarray) -> bytes:
+    """RGB sourceをJPEG quality=100/subsampling=0で一度だけencodeする。"""
+    rgb = image if isinstance(image, Image.Image) else Image.fromarray(np.asarray(image, dtype=np.uint8), "RGB")
+    stream = BytesIO()
+    rgb.convert("RGB").save(stream, format="JPEG", quality=100, subsampling=0, optimize=False)
+    return stream.getvalue()
+
+
+def decode_jpeg_rgb(payload: bytes) -> np.ndarray:
+    """JPEG bytesをRGB uint8へdecodeする。"""
+    with Image.open(BytesIO(payload)) as image:
+        return np.asarray(image.convert("RGB"), dtype=np.uint8).copy()
+
+
+def flatten_fp16_layers(arrays: Mapping[str, np.ndarray]) -> tuple[np.ndarray, dict[str, tuple[int, int]]]:
+    """mappingの順序を保ったfp16 flat値とlayer offsetsを作る。"""
+    parts: list[np.ndarray] = []
+    offsets: dict[str, tuple[int, int]] = {}
+    start = 0
+    for name, array in arrays.items():
+        part = np.asarray(array, dtype=np.float16).reshape(-1)
+        end = start + part.size
+        parts.append(part)
+        offsets[str(name)] = (start, end)
+        start = end
+    return (np.concatenate(parts) if parts else np.empty(0, dtype=np.float16), offsets)
+
+
+def layer_offsets(names: Sequence[str], sizes: Sequence[int]) -> dict[str, tuple[int, int]]:
+    """layer名とnumelからordered offsetsを構築する。"""
+    if len(names) != len(sizes):
+        raise ValueError("names and sizes must have equal length")
+    offsets: dict[str, tuple[int, int]] = {}
+    start = 0
+    for name, size in zip(names, sizes):
+        end = start + int(size)
+        if int(size) < 0:
+            raise ValueError("layer size must be non-negative")
+        offsets[str(name)] = (start, end)
+        start = end
+    return offsets
+
+
+def validate_layer_offsets(layers: Mapping[str, tuple[int, int]], total_size: int) -> dict[str, tuple[int, int]]:
+    """layer offsetsが全体を隙間なく一度だけ覆うことを検証する。"""
+    if total_size < 0:
+        raise ValueError("total_size must be non-negative")
+    normalized = {str(name): (int(bounds[0]), int(bounds[1])) for name, bounds in layers.items()}
+    expected_start = 0
+    for name, (start, end) in normalized.items():
+        if start < 0 or end < start or end > total_size:
+            raise ValueError(f"layer {name} is out of bounds")
+        if start < expected_start:
+            raise ValueError(f"layer {name} has overlap")
+        if start > expected_start:
+            raise ValueError(f"layer {name} is not contiguous")
+        expected_start = end
+    if expected_start != total_size:
+        raise ValueError("layers do not end at total size")
+    return normalized
+
+
+def _condition_fields(condition_id: str) -> tuple[str, str | None, str | None, str]:
+    if condition_id == "png_baseline":
+        return "canonical_png", None, None, "materialized_rgb_png"
+    if condition_id == "jpeg_q100_raw":
+        return "jpeg_q100", None, None, "raw_jpeg"
+    if condition_id == "protected_high_png_low_jpeg_q100":
+        return "protected", None, None, "high_png_low_jpeg"
+    body = condition_id.removeprefix("jpeg_q100_repair_")
+    repair, _, clip = body.partition("_clip_")
+    return "jpeg_q100", repair, clip, "materialized_rgb_png"
+
+
+def build_jpeg_registry(raw_jpeg_bytes: bytes | None = None) -> tuple[JpegCondition, ...]:
+    """計画で固定された18条件をordered immutable tupleとして構築する。"""
+    raw_parent = _sha256(raw_jpeg_bytes) if raw_jpeg_bytes is not None else _sha256(b"jpeg_q100:quality=100:subsampling=0:optimize=False")
+    baseline_parent = _sha256(b"canonical_png:png_baseline")
+    protected_parent = _sha256(b"protected:high_png_low_jpeg_q100")
+    entries: list[JpegCondition] = []
+    for condition_id in CONDITION_IDS:
+        parent = raw_parent if 1 <= CONDITION_IDS.index(condition_id) <= 16 else baseline_parent if condition_id == "png_baseline" else protected_parent
+        entries.append(JpegCondition(condition_id, *_condition_fields(condition_id), parent))
+    return tuple(entries)
+
+
+def semantic_hash(registry: Sequence[JpegCondition]) -> str:
+    """registryの順序・全semantic fieldsをcanonical JSONでhashする。"""
+    encoded = json.dumps([asdict(entry) for entry in registry], sort_keys=True, separators=(",", ":")).encode()
+    return _sha256(encoded)
+
+
+def materialize_fp16_png(values: np.ndarray, *, width: int | None = None) -> bytes:
+    """flat fp16 bit patternをRGB PNGへmaterializeする（R=high,G=low,B=0）。"""
+    flat = np.asarray(values, dtype=np.float16).reshape(-1)
+    if not flat.size:
+        raise ValueError("values must not be empty")
+    width_value = int(width or max(1, min(flat.size, 1024)))
+    height = (flat.size + width_value - 1) // width_value
+    pixels = np.zeros((height * width_value, 3), dtype=np.uint8)
+    bits = flat.view(np.uint16)
+    pixels[: flat.size, 0] = (bits >> 8).astype(np.uint8)
+    pixels[: flat.size, 1] = bits.astype(np.uint8)
+    stream = BytesIO()
+    Image.fromarray(pixels.reshape(height, width_value, 3), "RGB").save(stream, format="PNG")
+    return stream.getvalue()
+
+
+def _planes(values: np.ndarray, shape: tuple[int, int]) -> tuple[np.ndarray, np.ndarray]:
+    flat = np.asarray(values, dtype=np.float16).reshape(-1)
+    capacity = int(np.prod(shape))
+    if flat.size > capacity:
+        raise ValueError("shape is smaller than values")
+    bits = np.zeros(capacity, dtype=np.uint16)
+    bits[: flat.size] = flat.view(np.uint16)
+    return (bits >> 8).astype(np.uint8).reshape(shape), bits.astype(np.uint8).reshape(shape)
+
+
+def encode_protected(values: np.ndarray, shape: tuple[int, int]) -> ProtectedArtifact:
+    """high byteをlossless PNG、low byteをJPEG Q100 grayscaleでencodeする。"""
+    high, low = _planes(values, shape)
+    high_stream, low_stream = BytesIO(), BytesIO()
+    Image.fromarray(high, "L").save(high_stream, format="PNG")
+    Image.fromarray(low, "L").save(low_stream, format="JPEG", quality=100, subsampling=0, optimize=False)
+    return ProtectedArtifact(high_stream.getvalue(), low_stream.getvalue())
+
+
+def decode_protected(
+    payload: bytes | ProtectedArtifact,
+    shape: tuple[int, int],
+    *,
+    source_values: np.ndarray | None = None,
+) -> tuple[np.ndarray, dict[str, Any]]:
+    """protected payloadをfp16へ再構築し、plane metadataを返す。"""
+    artifact = payload if isinstance(payload, ProtectedArtifact) else _split_payload(payload)
+    with Image.open(BytesIO(artifact.high_png_bytes)) as high_image, Image.open(BytesIO(artifact.low_jpeg_bytes)) as low_image:
+        high_mode = high_image.mode
+        low_mode = low_image.mode
+        high_dimensions = [int(high_image.height), int(high_image.width)]
+        low_dimensions = [int(low_image.height), int(low_image.width)]
+        high = np.asarray(high_image.convert("L"), dtype=np.uint8)
+        low = np.asarray(low_image.convert("L"), dtype=np.uint8)
+    if high.shape != shape or low.shape != shape:
+        raise ValueError("protected plane shape mismatch")
+    bits = (high.reshape(-1).astype(np.uint16) << 8) | low.reshape(-1).astype(np.uint16)
+    source_high: np.ndarray | None = None
+    source_low: np.ndarray | None = None
+    if source_values is not None:
+        source_high, source_low = _planes(source_values, shape)
+        source_high = source_high.reshape(-1)
+        source_low = source_low.reshape(-1)
+    decoded_high_bytes = high.tobytes()
+    decoded_low_bytes = low.tobytes()
+    low_changed_count = int(np.count_nonzero(low.reshape(-1) != source_low)) if source_low is not None else None
+    total = int(np.prod(shape))
+    metadata = {
+        "dimensions": list(shape),
+        "high_mode": high_mode,
+        "low_mode": low_mode,
+        "high_dimensions": high_dimensions,
+        "low_dimensions": low_dimensions,
+        "high_bytes": len(artifact.high_png_bytes),
+        "low_bytes": len(artifact.low_jpeg_bytes),
+        "high_sha256": _sha256(artifact.high_png_bytes),
+        "low_sha256": _sha256(artifact.low_jpeg_bytes),
+        "payload_sha256": _sha256(artifact.payload),
+        "payload_size_bytes": len(artifact.payload),
+        "jpeg_options": {"quality": 100, "subsampling": 0, "optimize": False},
+        "decoded_high_sha256": _sha256(decoded_high_bytes),
+        "decoded_low_sha256": _sha256(decoded_low_bytes),
+        "decoded_high_exact": bool(source_high is not None and np.array_equal(high.reshape(-1), source_high)),
+        "decoded_low_changed_count": low_changed_count,
+        "decoded_low_changed_rate": (float(low_changed_count / total) if low_changed_count is not None and total else None),
+        "pillow_version": PILLOW_VERSION,
+        "jpeg_codec_version": _jpeg_codec_version(),
+        "combined_payload_sha256": _sha256(artifact.high_png_bytes + artifact.low_jpeg_bytes),
+        "combined_payload_bytes": len(artifact.high_png_bytes) + len(artifact.low_jpeg_bytes),
+        "combined_payload_exact": artifact.payload == (payload if isinstance(payload, bytes) else artifact.payload),
+    }
+    return bits.view(np.float16), metadata
+
+
+def _jpeg_codec_version() -> str | None:
+    """Pillowが報告できるJPEG codec versionを取得する。"""
+    try:
+        return features.version_codec("jpg")
+    except (AttributeError, RuntimeError, ValueError):
+        return None
+
+
+def _split_payload(payload: bytes) -> ProtectedArtifact:
+    """PNG chunkをsignature/length/type/data/CRC順に検証してcombined payloadを分割する。"""
+    signature = b"\x89PNG\r\n\x1a\n"
+    if len(payload) < len(signature) or payload[: len(signature)] != signature:
+        raise ValueError("protected payload has invalid PNG signature")
+    position = len(signature)
+    while True:
+        if position + 12 > len(payload):
+            raise ValueError("PNG chunk header is out of bounds")
+        length = int.from_bytes(payload[position:position + 4], "big")
+        chunk_start = position
+        position += 8
+        end_data = position + length
+        end_chunk = end_data + 4
+        if end_chunk > len(payload):
+            raise ValueError("PNG chunk data is out of bounds")
+        chunk_type = payload[position - 4:position]
+        data = payload[position:end_data]
+        expected_crc = int.from_bytes(payload[end_data:end_chunk], "big")
+        actual_crc = zlib.crc32(chunk_type + data) & 0xFFFFFFFF
+        if expected_crc != actual_crc:
+            raise ValueError("PNG chunk CRC mismatch")
+        position = end_chunk
+        if chunk_type == b"IEND":
+            if length != 0:
+                raise ValueError("PNG IEND chunk must be empty")
+            return ProtectedArtifact(payload[:position], payload[position:])
+        if chunk_start == len(payload):
+            raise ValueError("PNG has no IEND chunk")
+
+
+def repair_nonfinite(values: np.ndarray, method: str, layers: Mapping[str, tuple[int, int]]) -> RepairResult:
+    """NaN/Infだけをfloat64 layer statisticで置換し、最後に一度だけfp16化する。"""
+    if method not in {"zero", "layer_median", "layer_mean"}:
+        raise ValueError(f"unknown repair method: {method}")
+    source = np.asarray(values, dtype=np.float16).reshape(-1)
+    normalized_layers = validate_layer_offsets(layers, source.size)
+    with np.errstate(invalid="ignore"):
+        output = source.astype(np.float64)
+    finite = np.isfinite(source)
+    layer_records: list[LayerRepairRecord] = []
+    for name, (start, end) in normalized_layers.items():
+        layer_finite = output[start:end][finite[start:end]]
+        if method != "zero" and layer_finite.size == 0:
+            raise ValueError(f"layer {name} has no finite values")
+        mask = ~finite[start:end]
+        if method == "zero":
+            replacement = 0.0
+        elif method == "layer_median":
+            replacement = float(np.median(layer_finite))
+        else:
+            replacement = float(np.mean(layer_finite, dtype=np.float64))
+        output[start:end][mask] = replacement
+        raw_layer = source[start:end]
+        layer_records.append(LayerRepairRecord(name, float(replacement), int(np.isnan(raw_layer).sum()), int(np.isposinf(raw_layer).sum()), int(np.isneginf(raw_layer).sum()), int(mask.sum())))
+    repaired = output.astype(np.float16)
+    repaired_values = repaired[~finite]
+    LOGGER.info("repaired nonfinite values: method=%s count=%d", method, int((~finite).sum()))
+    return RepairResult(repaired, method, int(np.isnan(source).sum()), int(np.isposinf(source).sum()), int(np.isneginf(source).sum()), int((~finite).sum()), tuple(layer_records), _histogram(repaired_values))
+
+
+def _clip_bounds(data: np.ndarray, strategy: str) -> tuple[float, float]:
+    finite = data[np.isfinite(data)]
+    if not finite.size:
+        raise ValueError("layer has no finite values")
+    finite = finite.astype(np.float64)
+    if strategy.startswith("layer_mean_std_"):
+        scale = float(strategy.removeprefix("layer_mean_std_"))
+        mean, std = float(np.mean(finite)), float(np.std(finite, ddof=0))
+        return mean - scale * std, mean + scale * std
+    if strategy.startswith("layer_pct_"):
+        low, high = strategy.removeprefix("layer_pct_").split("_")
+        return float(np.percentile(finite, float(low), method="linear")), float(np.percentile(finite, float(high), method="linear"))
+    raise ValueError(f"unknown clip strategy: {strategy}")
+
+
+def clip_candidate(values: np.ndarray, layers: Mapping[str, tuple[int, int]], strategy: str) -> ClipResult:
+    """repair candidateだけからlayer boundsを計算し、finite値をclipする。"""
+    if strategy not in CLIP_STRATEGIES:
+        raise ValueError(f"unknown clip strategy: {strategy}")
+    source = np.asarray(values, dtype=np.float16).reshape(-1)
+    if not np.isfinite(source).all():
+        raise ValueError("clip candidate must be finite")
+    normalized_layers = validate_layer_offsets(layers, source.size)
+    output64 = source.astype(np.float64)
+    records: list[LayerClipRecord] = []
+    for name, (start, end) in normalized_layers.items():
+        before = output64[start:end]
+        lower, upper = _clip_bounds(before, strategy)
+        after = np.clip(before, lower, upper)
+        records.append(LayerClipRecord(name, strategy, lower, upper, float(np.max(np.abs(before))), float(np.max(np.abs(after))), int(np.count_nonzero(after.astype(np.float16).view(np.uint16) != source[start:end].view(np.uint16)))))
+        output64[start:end] = after
+    clipped = output64.astype(np.float16)
+    changed = clipped.view(np.uint16) != source.view(np.uint16)
+    return ClipResult(clipped, strategy, tuple(records), int(changed.sum()), _histogram(source[changed]), _histogram(clipped[changed]), _histogram(clipped[changed].astype(np.float64) - source[changed].astype(np.float64)))
+
+
+def _histogram(values: np.ndarray, bins: int = 32) -> dict[str, Any]:
+    finite = np.asarray(values, dtype=np.float64)
+    finite = finite[np.isfinite(finite)]
+    if not finite.size:
+        return {"edges": [], "counts": []}
+    edges = np.histogram_bin_edges(finite, bins=min(bins, max(1, finite.size)))
+    counts, edges = np.histogram(finite, bins=edges)
+    return {"edges": edges.tolist(), "counts": counts.astype(int).tolist()}
+
+
+def diagnostics(baseline: np.ndarray, candidate: np.ndarray, layers: Mapping[str, tuple[int, int]]) -> dict[str, Any]:
+    """fp16 wordのbit差分、global/layer stats、bounded histogramを返す。"""
+    left = np.asarray(baseline, dtype=np.float16).reshape(-1)
+    right = np.asarray(candidate, dtype=np.float16).reshape(-1)
+    if left.shape != right.shape:
+        raise ValueError("baseline and candidate shapes differ")
+    normalized_layers = validate_layer_offsets(layers, left.size)
+    a, b = left.view(np.uint16), right.view(np.uint16)
+    sign = ((a ^ b) & 0x8000) != 0
+    exponent = ((a ^ b) & 0x7C00) != 0
+    mantissa = ((a ^ b) & 0x03FF) != 0
+    to31 = (((a >> 10) & 0x1F) != 31) & (((b >> 10) & 0x1F) == 31)
+    finite_extreme = np.zeros(left.size, dtype=bool)
+    layer_stats: list[dict[str, Any]] = []
+    for name, (start, end) in normalized_layers.items():
+        baseline_layer = left[start:end]
+        candidate_layer = right[start:end]
+        baseline_finite = np.isfinite(baseline_layer)
+        candidate_finite = np.isfinite(candidate_layer)
+        baseline_max = float(np.max(np.abs(baseline_layer[baseline_finite]))) if baseline_finite.any() else 0.0
+        candidate_abs = np.abs(candidate_layer.astype(np.float64))
+        threshold = baseline_max * 10.0
+        if baseline_max == 0.0:
+            layer_extreme = candidate_finite & (candidate_abs > 0.0)
+        else:
+            layer_extreme = candidate_finite & (candidate_abs > threshold)
+        finite_extreme[start:end] = layer_extreme
+        layer_stats.append({"layer": name, "sign_mismatch_count": int(sign[start:end].sum()), "exponent_mismatch_count": int(exponent[start:end].sum()), "mantissa_mismatch_count": int(mantissa[start:end].sum()), "exponent_to31_count": int(to31[start:end].sum()), "finite_extreme_outlier_count": int(layer_extreme.sum()), "nan_count": int(np.isnan(candidate_layer).sum()), "posinf_count": int(np.isposinf(candidate_layer).sum()), "neginf_count": int(np.isneginf(candidate_layer).sum()), "finite_count": int(candidate_finite.sum()), "max_abs_weight": float(np.max(candidate_abs[candidate_finite])) if candidate_finite.any() else 0.0})
+    return {"total_count": int(left.size), "sign_mismatch_count": int(sign.sum()), "exponent_mismatch_count": int(exponent.sum()), "mantissa_mismatch_count": int(mantissa.sum()), "exponent_to31_count": int(to31.sum()), "finite_extreme_outlier_count": int(finite_extreme.sum()), "nan_count": int(np.isnan(right).sum()), "posinf_count": int(np.isposinf(right).sum()), "neginf_count": int(np.isneginf(right).sum()), "finite_count": int(np.isfinite(right).sum()), "max_abs_weight": float(np.max(np.abs(right[np.isfinite(right)]))) if np.isfinite(right).any() else 0.0, "layers": layer_stats, "histograms": {"baseline": _histogram(left), "candidate": _histogram(right), "delta": _histogram(right.astype(np.float64) - left.astype(np.float64))}}
diff --git a/scripts/pixelmodel_robustness/report.py b/scripts/pixelmodel_robustness/report.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b72e5d7e92f79f3cea4d7c41ffedb51c7de93e7
--- /dev/null
+++ b/scripts/pixelmodel_robustness/report.py
@@ -0,0 +1,99 @@
+"""Study plots and report helpers."""
+
+from __future__ import annotations
+
+import csv
+from collections.abc import Sequence
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+
+
+def write_metrics_csv(path: Path, rows: Sequence[dict[str, object]]) -> None:
+    """raw rowsからmetrics.csvを生成する。"""
+    path.parent.mkdir(parents=True, exist_ok=True)
+    keys = sorted({key for row in rows for key in row})
+    with path.open("w", newline="", encoding="utf-8") as stream:
+        writer = csv.DictWriter(stream, fieldnames=keys, extrasaction="ignore")
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def write_layer_metrics_csv(path: Path, rows: Sequence[dict[str, object]]) -> None:
+    """rawのlayers配列を正規化してlayer_metrics.csvへ保存する。"""
+    flattened: list[dict[str, object]] = []
+    for row in rows:
+        for layer in row.get("layers", []):
+            flattened.append({"condition": row.get("condition"), **layer})
+    write_metrics_csv(path, flattened)
+
+
+def make_layer_plot(rows: Sequence[dict[str, object]], path: Path) -> None:
+    """layer-wise L2/MSE/cosine/changed plotを生成する。"""
+    import matplotlib.pyplot as plt
+    values = [(str(row.get("condition")), layer) for row in rows for layer in row.get("layers", [])]
+    if not values:
+        return
+    figure, axes = plt.subplots(2, 2, figsize=(12, 8), dpi=300)
+    for axis, key in zip(axes.flat, ("l2", "mse", "cosine", "changed_percent")):
+        for condition in sorted({name for name, _ in values}):
+            points = [float(layer[key]) for name, layer in values if name == condition and layer.get(key) is not None and np.isfinite(float(layer[key]))]
+            if points:
+                layer_names = [str(layer.get("layer")) for name, layer in values if name == condition and layer.get(key) is not None and np.isfinite(float(layer[key]))]
+                axis.plot(points, marker=".", label=condition)
+                axis.set_xticks(range(len(layer_names)), layer_names, rotation=60, ha="right", fontsize=5)
+        axis.set_title(key)
+        axis.grid(alpha=0.2)
+    axes[0, 0].legend(fontsize=6)
+    figure.tight_layout()
+    path.parent.mkdir(parents=True, exist_ok=True)
+    figure.savefig(path, dpi=300)
+    plt.close(figure)
+
+
+def make_grid(images: Sequence[tuple[Image.Image, str]], path: Path, columns: int = 4) -> None:
+    """画像とラベルから比較 grid を作成する。"""
+    if not images:
+        return
+    cell = max(max(image.width, image.height) for image, _ in images)
+    label_height = 28
+    rows = (len(images) + columns - 1) // columns
+    canvas = Image.new("RGB", (columns * cell, rows * (cell + label_height)), "white")
+    draw = ImageDraw.Draw(canvas)
+    for index, (image, label) in enumerate(images):
+        row, column = divmod(index, columns)
+        resized = image.convert("RGB").resize((cell, cell))
+        x, y = column * cell, row * (cell + label_height)
+        canvas.paste(resized, (x, y))
+        draw.text((x + 3, y + cell + 4), label[:36], fill="black")
+    canvas.save(path, dpi=(300, 300))
+
+
+def make_diff_maps(reference: np.ndarray, candidate: np.ndarray, stem: Path) -> None:
+    """signed/absolute pixel difference heatmapを保存する。"""
+    import matplotlib.pyplot as plt
+    diff = candidate.astype(float) - reference.astype(float)
+    for suffix, data, cmap in (("signed", diff.mean(axis=2), "coolwarm"), ("absolute", np.abs(diff).mean(axis=2), "magma")):
+        figure, axis = plt.subplots(figsize=(5, 5), dpi=300)
+        axis.imshow(data, cmap=cmap)
+        axis.set_axis_off()
+        figure.tight_layout(pad=0)
+        figure.savefig(stem.with_name(f"{stem.name}_{suffix}.png"), dpi=300, bbox_inches="tight", pad_inches=0)
+        plt.close(figure)
+
+
+def make_quality_plot(rows: Sequence[dict[str, object]], path: Path) -> None:
+    """PSNR/storageのpublication-quality plotを保存する。"""
+    import matplotlib.pyplot as plt
+    valid = [row for row in rows if row.get("success") and row.get("psnr") is not None]
+    if not valid:
+        return
+    figure, axis = plt.subplots(figsize=(8, 5), dpi=300)
+    axis.scatter([float(row["compression_ratio"]) for row in valid], [float(row["psnr"]) for row in valid], s=24)
+    axis.set_xlabel("Compression ratio (PNG bytes / candidate bytes)")
+    axis.set_ylabel("Image PSNR (dB)")
+    axis.grid(alpha=0.25)
+    figure.tight_layout()
+    figure.savefig(path, dpi=300)
+    plt.close(figure)