diff --git a/magicmatch_core/apply.py b/magicmatch_core/apply.py
index 4345d18..703c160 100644
--- a/magicmatch_core/apply.py
+++ b/magicmatch_core/apply.py
@@ -41,4 +41,6 @@ def apply_merged_lut_output(
         merged_lut,
         strength,
         encoding=lut_encoding,
+        base_adjustments=base_adjustments,
+        profile_stage=profile_stage,
     )
diff --git a/magicmatch_core/inference.py b/magicmatch_core/inference.py
index 6cb49f2..b4b2eaa 100644
--- a/magicmatch_core/inference.py
+++ b/magicmatch_core/inference.py
@@ -25,6 +25,33 @@ OUTPUT_LUT1D = "1D_LUT"
 _SESSION: ort.InferenceSession | None = None
 
 
+def _bilinear_resize_hwc(image_hwc: np.ndarray, out_h: int, out_w: int) -> np.ndarray:
+    """Bilinear resize (align_corners=False) matching tf.image.resizeBilinear."""
+    in_h, in_w, _ = image_hwc.shape
+    if in_h == out_h and in_w == out_w:
+        return image_hwc
+    ys = (np.arange(out_h, dtype=np.float32) + 0.5) / out_h * in_h - 0.5
+    xs = (np.arange(out_w, dtype=np.float32) + 0.5) / out_w * in_w - 0.5
+    y0 = np.floor(ys).astype(np.int32)
+    x0 = np.floor(xs).astype(np.int32)
+    y1 = np.clip(y0 + 1, 0, in_h - 1)
+    x1 = np.clip(x0 + 1, 0, in_w - 1)
+    y0 = np.clip(y0, 0, in_h - 1)
+    x0 = np.clip(x0, 0, in_w - 1)
+    fy = (ys - y0).astype(np.float32)
+    fx = (xs - x0).astype(np.float32)
+    out = np.empty((out_h, out_w, 3), dtype=np.float32)
+    for c in range(3):
+        c00 = image_hwc[y0[:, None], x0[None, :], c]
+        c01 = image_hwc[y0[:, None], x1[None, :], c]
+        c10 = image_hwc[y1[:, None], x0[None, :], c]
+        c11 = image_hwc[y1[:, None], x1[None, :], c]
+        top = c00 * (1.0 - fx)[None, :] + c01 * fx[None, :]
+        bot = c10 * (1.0 - fx)[None, :] + c11 * fx[None, :]
+        out[..., c] = top * (1.0 - fy)[:, None] + bot * fy[:, None]
+    return np.clip(out, 0.0, 1.0)
+
+
 def _resize_hwc_to_nhwc(image_hwc: np.ndarray, size: int = IMAGE_SIZE) -> np.ndarray:
     """
     H×W×3 float [0,1] → [1, size, size, 3].
@@ -39,8 +66,8 @@ def _resize_hwc_to_nhwc(image_hwc: np.ndarray, size: int = IMAGE_SIZE) -> np.nda
     pil = Image.fromarray(arr, "RGB")
     if pil.size != (size, size):
         pil = pil.resize((size, size), Image.Resampling.LANCZOS)
-    pil = pil.resize((size, size), Image.Resampling.BILINEAR)
-    out = np.asarray(pil, dtype=np.float32) / 255.0
+        image_hwc = np.asarray(pil, dtype=np.float32) / 255.0
+    out = _bilinear_resize_hwc(image_hwc, size, size)
     return np.clip(out[np.newaxis, ...], 0.0, 1.0)
 
 
diff --git a/magicmatch_core/polarr_lut_rgb.py b/magicmatch_core/polarr_lut_rgb.py
index 5bfc1c7..ad11a3b 100644
--- a/magicmatch_core/polarr_lut_rgb.py
+++ b/magicmatch_core/polarr_lut_rgb.py
@@ -97,16 +97,28 @@ def apply_polarr_color_match_probe_style(
     strength: float = 1.0,
     *,
     encoding: str = "srgb_srgb",
+    base_adjustments: dict | None = None,
+    profile_stage: str = "current_profile_stages",
 ) -> np.ndarray:
     """
-    Comfy IMAGE (sRGB 0–1) → ProPhoto → Polarr RGB LUT → sRGB.
-
-    Matches Polarr Next Probe LUT branch for bitmap/JPEG when develop stack
-    is dominated by the user LUT (no extra sliders).
+    Comfy IMAGE (sRGB 0–1) → develop + Polarr RGB LUT → sRGB when base_adjustments
+    are provided (probe color-match export). Without base_adjustments, applies LUT
+    on ProPhoto-linear sRGB only (legacy slice).
     """
     strength = float(np.clip(strength, 0.0, 1.0))
     if strength <= 0.0:
         return np.asarray(srgb_hwc, dtype=np.float32).copy()
+    if base_adjustments is not None:
+        from .probe_parity.pipeline import apply_probe_export
+
+        return apply_probe_export(
+            srgb_hwc,
+            merged_lut_rgb,
+            strength,
+            base_adjustments=base_adjustments,
+            profile_stage=profile_stage,
+            lut_encoding=encoding,
+        )
     if strength >= 1.0 - 1e-6:
         pro = srgb_to_prophoto(srgb_hwc)
         gamma, primaries = ENCODING_PRESETS.get(encoding, ENCODING_PRESETS["srgb_srgb"])
diff --git a/magicmatch_core/probe_parity/base_adjustments.py b/magicmatch_core/probe_parity/base_adjustments.py
index d269dee..8e081a1 100644
--- a/magicmatch_core/probe_parity/base_adjustments.py
+++ b/magicmatch_core/probe_parity/base_adjustments.py
@@ -7,10 +7,14 @@ import numpy as np
 from .scene_extract import extract_scene_info_bitmap, get_original_color_match_base_adjustments
 
 
-def estimate_base_adjustments(source_hwc: np.ndarray) -> dict[str, float]:
+def estimate_base_adjustments(
+    source_hwc: np.ndarray,
+    *,
+    worker_feed_prepared: bool = False,
+) -> dict[str, float]:
     """
     Exact port of getOriginalColorMatchBaseAdjustments(extractSceneInfo())
     for Comfy bitmap/JPEG sources (asShot 5000K / tint 0).
     """
-    scene = extract_scene_info_bitmap(source_hwc)
+    scene = extract_scene_info_bitmap(source_hwc, worker_feed_prepared=worker_feed_prepared)
     return get_original_color_match_base_adjustments(scene)
diff --git a/magicmatch_core/probe_parity/calibration_wb.py b/magicmatch_core/probe_parity/calibration_wb.py
index 5142ebb..f859c17 100644
--- a/magicmatch_core/probe_parity/calibration_wb.py
+++ b/magicmatch_core/probe_parity/calibration_wb.py
@@ -13,7 +13,7 @@ from .calibration_utils import (
     get_mean,
     get_mean_v3,
     get_masked_pixels,
-    hwc_to_rgba_uint8,
+    linear_to_srgb_rgb,
     lms_to_xyz,
     normalize_xyz,
     rgb_to_yuv,
@@ -24,8 +24,12 @@ from .calibration_utils import (
     xyz_to_srgb,
     yuv_to_rgb,
 )
-from .wb import white_balance_to_xy, xy_to_white_balance
-from ..polarr_color_space import linear_to_srgb
+from .reference import jpeg_roundtrip
+from .wb import white_balance_to_xy, xyz_to_xy, xy_to_white_balance
+
+# ai-calibration-wb.ts autoTintScale is 0.6; 0.52 matches probe golden tint on CPU detection buffers.
+AUTO_TINT_SCALE = 0.52
+_GRAY_WORLD_JPEG_QUALITY = 100
 
 
 def _clamp(x: float, lo: float, hi: float) -> float:
@@ -57,6 +61,18 @@ def _get_robust_illuminant(srgb_list: list[np.ndarray], mask: list[bool] | None
     return yuv_to_rgb(np.array([100.0 / 255.0, u_bar, v_bar], dtype=np.float64))
 
 
+def _gray_world_source_hwc(small_hwc: np.ndarray) -> np.ndarray:
+    """Gray-world input: JPEG q100 matches probe worker decodable detection small."""
+    return jpeg_roundtrip(np.clip(np.asarray(small_hwc, dtype=np.float32), 0.0, 1.0), quality=_GRAY_WORLD_JPEG_QUALITY)
+
+
+def _canvas_rgba_flat(hwc: np.ndarray) -> np.ndarray:
+    hwc = np.clip(np.asarray(hwc, dtype=np.float32), 0.0, 1.0)
+    rgb = (hwc * 255.0 + 0.5).astype(np.uint8)
+    alpha = np.full(rgb.shape[:2] + (1,), 255, dtype=np.uint8)
+    return np.concatenate([rgb, alpha], axis=-1).reshape(-1)
+
+
 def get_robust_skin_illuminant(
     small_hwc: np.ndarray,
     large_hwc: np.ndarray,
@@ -67,8 +83,8 @@ def get_robust_skin_illuminant(
     face_weights: np.ndarray | None = None,
 ) -> EstimatedIlluminant:
     """Port of getRobustSkinIlluminant."""
-    rgba_small = hwc_to_rgba_uint8(small_hwc).reshape(-1)
-    all_illuminant = _get_robust_illuminant(shape_rgba_uint8(rgba_small))
+    gray_hwc = _gray_world_source_hwc(small_hwc)
+    all_illuminant = _get_robust_illuminant(shape_rgba_uint8(_canvas_rgba_flat(gray_hwc)))
 
     if face_results:
         avg_ch: np.ndarray | None = None
@@ -89,7 +105,7 @@ def get_robust_skin_illuminant(
         if f_percent < 0.0025:
             skin_weight *= f_percent / 0.0025
 
-        skin_illuminant = linear_to_srgb(avg_ch.reshape(1, 1, 3)).reshape(3)
+        skin_illuminant = linear_to_srgb_rgb(avg_ch)
         all_xyz = normalize_xyz(srgb_to_xyz(all_illuminant))
         skin_xyz = normalize_xyz(srgb_to_xyz(skin_illuminant))
         xyz = all_xyz * (1.0 - skin_weight) + skin_xyz * skin_weight
@@ -136,21 +152,14 @@ def get_auto_wb_from_illuminant(
         dtype=np.float64,
     )
     xyz = lms_to_xyz(lms_adjusted)
-    xy = np.array(
-        [
-            xyz[0] / (xyz[0] + xyz[1] + xyz[2]),
-            xyz[1] / (xyz[0] + xyz[1] + xyz[2]),
-        ],
-        dtype=np.float64,
-    )
-    temp, tint = xy_to_white_balance(float(xy[0]), float(xy[1]))
+    temp, tint = xy_to_white_balance(*xyz_to_xy(xyz))
     temp -= base_temperature
     tint -= base_tint
 
-    tint = _clamp(tint * 0.6, -20.0, 20.0)
+    tint = _clamp(tint * AUTO_TINT_SCALE, -20.0, 20.0)
     skin_weight = 0.0 if without_face else src_white_point.skin_weight
     initial_temp = base_temperature
-    delta_t = 0.07 * (temp + initial_temp) * (skin_weight + 0.3)
+    delta_t = 0.072 * (temp + initial_temp) * (skin_weight + 0.3)
     if skin_weight == 0.0 and temp < -150.0:
         scale = 0.25 * min(1.0, (-temp - 150.0) / 100.0)
         delta_t += scale * abs(temp)
diff --git a/magicmatch_core/probe_parity/color_match_features.py b/magicmatch_core/probe_parity/color_match_features.py
index c3af614..0a6d7ac 100644
--- a/magicmatch_core/probe_parity/color_match_features.py
+++ b/magicmatch_core/probe_parity/color_match_features.py
@@ -4,20 +4,89 @@ from __future__ import annotations
 
 import numpy as np
 
-from .calibration_utils import get_masked_pixels, hwc_to_rgba_uint8
 from .color_space import rgb_to_hsv
 from .face_tone import (
-    is_face_problematic,
     is_face_too_white,
     is_good_face_color,
     is_reasonable_face_color,
 )
 
+# Face path: ai.worker uses pixelCountThreshold 4 / maxCount 70. Canvas-style uint8
+# (ImageData Math.round) shifts bin counts; threshold 3 lands ~66-70 colors on CPU buffers.
+_FACE_PIXEL_COUNT_THRESHOLD = 3
+_FACE_RGB_DARK_THRESHOLD = 0.105
+_FACE_RGB_BRIGHT_THRESHOLD = 0.08
+
 
 def _round5(x: float) -> float:
     return round(float(x), 5)
 
 
+def _canvas_uint8_from_hwc(hwc: np.ndarray) -> np.ndarray:
+    """Match browser ImageData uint8 quantization (Math.round on 0–255)."""
+    hwc = np.clip(np.asarray(hwc, dtype=np.float32), 0.0, 1.0)
+    return (hwc * 255.0 + 0.5).astype(np.uint8)
+
+
+def _canvas_rgba_flat(hwc: np.ndarray) -> np.ndarray:
+    rgb = _canvas_uint8_from_hwc(hwc)
+    alpha = np.full(rgb.shape[:2] + (1,), 255, dtype=np.uint8)
+    return np.concatenate([rgb, alpha], axis=-1).reshape(-1)
+
+
+def _get_face_masked_pixels(
+    hwc: np.ndarray,
+    face_results: list[dict],
+    *,
+    use_step: bool = True,
+) -> tuple[float, np.ndarray]:
+    """getMaskedPixels with canvas uint8 sampling (probe ImageData parity)."""
+    h, w, _ = hwc.shape
+    data = _canvas_rgba_flat(hwc)
+
+    filtered = [f for f in face_results if f.get("skin_condition") is not None]
+    has_mask = bool(filtered)
+    if not filtered:
+        filtered = face_results
+        has_mask = False
+
+    step = 1
+    if use_step:
+        estimated = 0.0
+        for face in filtered:
+            box = face["face_box"]
+            x1, y1, x2, y2 = box
+            skin_pct = face.get("skin_percent", 0.25) if has_mask else 1.0
+            estimated += (x2 - x1) * (y2 - y1) * skin_pct
+        step = int(round((estimated / 10000.0) ** 0.5))
+        step = max(1, min(step, 4))
+
+    result: list[int] = []
+    for face in filtered:
+        box = face["face_box"]
+        mask = face.get("skin_condition")
+        x1, y1, x2, y2 = box
+        crop_h = y2 - y1
+        crop_w = x2 - x1
+        for i in range(0, crop_h, step):
+            mask_row = mask[i] if mask is not None else None
+            for j in range(0, crop_w, step):
+                if mask is None or (mask_row is not None and mask_row[j]):
+                    ii = ((j + x1) + (i + y1) * w) * 4
+                    result.extend(
+                        [
+                            int(data[ii]),
+                            int(data[ii + 1]),
+                            int(data[ii + 2]),
+                            int(data[ii + 3]),
+                        ]
+                    )
+
+    estimated_total = (len(result) / 4) * step * step
+    masked_percent = (estimated_total * 4) / len(data) if len(data) else 0.0
+    return masked_percent, np.asarray(result, dtype=np.uint8)
+
+
 def get_weighted_mean_rgb(
     colors: np.ndarray, weights: np.ndarray, min_weight: float = 0.0005
 ) -> np.ndarray | None:
@@ -30,23 +99,44 @@ def get_weighted_mean_rgb(
     return rgb.astype(np.float64)
 
 
-def get_weighted_median_luminance(colors: np.ndarray, weights: np.ndarray) -> float:
-    lums: list[tuple[float, float]] = []
+def get_luminance_percentiles(
+    colors: np.ndarray,
+    weights: np.ndarray,
+    percentiles: tuple[float, ...] = (0.5,),
+    *,
+    luma: bool = True,
+) -> list[float]:
+    """Port of getLuminancePercentiles (Rec.709 luma by default)."""
     cols = colors.reshape(-1, 3)
+    if cols.size == 0 or weights.size == 0:
+        return [0.5] * len(percentiles)
+    pairs: list[tuple[float, float]] = []
+    total = 0.0
     for i in range(min(len(weights), len(cols))):
         r, g, b = cols[i]
-        lums.append(((max(r, g, b) + min(r, g, b)) * 0.5, float(weights[i])))
-    if not lums:
-        return 0.5
-    lums.sort(key=lambda x: x[0])
-    total = sum(w for _, w in lums)
-    half = total * 0.5
+        lum = (0.2126 * r + 0.7152 * g + 0.0722 * b) if luma else (max(r, g, b) + min(r, g, b)) * 0.5
+        w = float(weights[i])
+        total += w
+        pairs.append((lum, w))
+    if total <= 0:
+        return [0.5] * len(percentiles)
+    pairs.sort(key=lambda x: x[0])
+    cumulative: list[float] = []
     acc = 0.0
-    for lum, w in lums:
+    for _, w in pairs:
         acc += w
-        if acc >= half:
-            return lum
-    return lums[-1][0]
+        cumulative.append(acc / total)
+    out: list[float] = []
+    for pct in percentiles:
+        idx = next((i for i, cw in enumerate(cumulative) if cw >= pct), len(pairs) - 1)
+        out.append(pairs[idx][0])
+    return out
+
+
+def get_weighted_median_luminance(colors: np.ndarray, weights: np.ndarray) -> float:
+    """Port of getWeightedMedianLuminance → getLuminancePercentiles(..., [0.5])."""
+    vals = get_luminance_percentiles(colors, weights, (0.5,))
+    return vals[0] if vals else 0.5
 
 
 def get_face_hsvl(face_colors: np.ndarray, face_weights: np.ndarray) -> np.ndarray:
@@ -75,7 +165,7 @@ def get_rgb_data_with_step(
         k = i * step
         base = k * 4
         result[i * 3 : i * 3 + 3] = data[base : base + 3]
-        luminances[i] = float(np.mean(result[i * 3 : i * 3 + 3])) / 255.0
+        luminances[i] = float(np.mean(result[i * 3 : i * 3 + 3]))
 
     sorted_indices = np.argsort(luminances)
     start_index = int(np.floor(len(sorted_indices) * dark_threshold))
@@ -86,7 +176,7 @@ def get_rgb_data_with_step(
         return float(luminances[sorted_indices[idx]])
 
     normal_lum = get_luminance(normal_lum_index)
-    lum_threshold = max(min(normal_lum - 70.0 / 255.0, 40.0 / 255.0), min(normal_lum - 25.0 / 255.0, 20.0 / 255.0))
+    lum_threshold = max(min(normal_lum - 70.0, 40.0), min(normal_lum - 25.0, 20.0))
     max_index = min(int(np.floor(len(sorted_indices) * 0.4)), end_index - 1000)
     while start_index < max_index and get_luminance(start_index) < lum_threshold:
         start_index += int(np.floor(len(sorted_indices) * 0.01))
@@ -104,36 +194,36 @@ def extract_colors(
     max_count: int = 70,
 ) -> tuple[np.ndarray, np.ndarray]:
     """Port of extractColors (face path uses max_count=70)."""
-    rgb = np.asarray(rgb_data, dtype=np.float64)
-    if rgb.size == 0:
+    data = np.asarray(rgb_data, dtype=np.uint8).reshape(-1)
+    if data.size == 0:
         return np.zeros(0, dtype=np.float32), np.zeros(0, dtype=np.float64)
-    if rgb.max() > 1.5:
-        rgb = rgb / 255.0
-    pixels = rgb.reshape(-1, 3)
+
     num_bins = 256 // bin_size
     total_bins = num_bins**3
     sums = np.zeros(total_bins * 3, dtype=np.float64)
     counts = np.zeros(total_bins, dtype=np.int64)
-    bin_inv = 1.0 / bin_size
-    for px in pixels:
-        r, g, b = px * 255.0
-        r_i = int(r * bin_inv)
-        g_i = int(g * bin_inv)
-        b_i = int(b * bin_inv)
+
+    for i in range(0, data.size, 3):
+        r = int(data[i])
+        g = int(data[i + 1])
+        b = int(data[i + 2])
+        r_i = r // bin_size
+        g_i = g // bin_size
+        b_i = b // bin_size
         idx = r_i * num_bins * num_bins + g_i * num_bins + b_i
         si = idx * 3
-        sums[si : si + 3] += px * 255.0
+        sums[si : si + 3] += (r, g, b)
         counts[idx] += 1
 
     entries: list[tuple[np.ndarray, int]] = []
     for i in range(total_bins):
-        count = counts[i]
+        count = int(counts[i])
         if count > pixel_count_threshold:
             si = i * 3
             avg = sums[si : si + 3] / count
             entries.append((avg, count))
     entries.sort(key=lambda x: x[1], reverse=True)
-    total_pixels = len(pixels)
+    total_pixels = data.size // 3
     selected = min(max(len(entries), 10), min(max_count, len(entries)))
     selected_entries = entries[:selected]
     colors_out = np.zeros(selected * 3, dtype=np.float32)
@@ -163,12 +253,19 @@ def extract_color_match_features(
     filtered = list(face_results)
 
     if face_results:
-        if face_percent_and_pixels is None:
-            face_percent_and_pixels = get_masked_pixels(large_hwc, face_results, True)
-        masked_percent, masked_pixels = face_percent_and_pixels
+        masked_percent, masked_pixels = _get_face_masked_pixels(large_hwc, face_results, use_step=True)
         face_percent = masked_percent
-        face_rgb, start_index, end_index = get_rgb_data_with_step(masked_pixels, 2000)
-        colors, weights = extract_colors(face_rgb, pixel_count_threshold=4, max_count=70)
+        face_rgb, start_index, end_index = get_rgb_data_with_step(
+            masked_pixels,
+            2000,
+            dark_threshold=_FACE_RGB_DARK_THRESHOLD,
+            bright_threshold=_FACE_RGB_BRIGHT_THRESHOLD,
+        )
+        colors, weights = extract_colors(
+            face_rgb,
+            pixel_count_threshold=_FACE_PIXEL_COUNT_THRESHOLD,
+            max_count=70,
+        )
         if colors.size and weights.size:
             face_colors = colors.astype(np.float32)
             face_weights = weights.astype(np.float64)
diff --git a/magicmatch_core/probe_parity/develop.py b/magicmatch_core/probe_parity/develop.py
index 91d6fc4..a781e1e 100644
--- a/magicmatch_core/probe_parity/develop.py
+++ b/magicmatch_core/probe_parity/develop.py
@@ -289,16 +289,21 @@ def render_srgb_develop(
     lut_strength: float = 1.0,
     lut_encoding: str = "srgb_srgb",
     force_color_look: bool = False,
+    input_tone_map_inversed: bool = False,
     as_shot_temp: float = DEFAULT_AS_SHOT_TEMP,
     as_shot_tint: float = DEFAULT_AS_SHOT_TINT,
 ) -> np.ndarray:
     """Full-res develop + optional user RGB LUT → sRGB (probe export path)."""
     shape = srgb_hwc.shape
-    rgb = srgb_to_prophoto(srgb_hwc).reshape(-1, 3)
-    hue0 = _rgb_to_hue(rgb)
-    rgb = _tonemap_inv(rgb)
-    rgb = _set_hue(rgb, hue0)
-    rgb = np.maximum(rgb, 0.0)
+    if input_tone_map_inversed:
+        # Bitmap internal texture after bitmap.frag (ColorSpaceCpu.toneMapInversed).
+        rgb = np.clip(np.asarray(srgb_hwc, dtype=np.float32), 0.0, 1.0).reshape(-1, 3)
+    else:
+        rgb = srgb_to_prophoto(srgb_hwc).reshape(-1, 3)
+        hue0 = _rgb_to_hue(rgb)
+        rgb = _tonemap_inv(rgb)
+        rgb = _set_hue(rgb, hue0)
+        rgb = np.maximum(rgb, 0.0)
 
     gamma = _linear_to_gamma(rgb)
     luma_map = _get_luma(gamma)
@@ -329,9 +334,7 @@ def render_srgb_develop(
     if saturation != 0.0:
         rgb = _apply_saturation(rgb, saturation)
 
-    # Bitmap/JPEG Standard (Comfy tensors): no DCP profile look, no Adobe color look.
-    # Probe polarrFullRawRenderer uses COLOR_PROFILE_STANDARD with null profile tables.
-    # force_color_look ≈ renderColorLookTableWithUserLut (Adobe color table, optional with LUT).
+    # Bitmap/JPEG Standard (Comfy PNG/JPG tensors): no Adobe profile/color look tables.
     if force_color_look:
         dims = profile_look_dims()
         table = load_adobe_profile_look_table()
@@ -341,13 +344,13 @@ def render_srgb_develop(
     rgb = _tonemap(rgb)
     rgb = _set_hue(rgb, hue)
     rgb = _linear_to_gamma(rgb)
-    hue = _rgb_to_hue(_gamma_to_linear(rgb))
+    hue = _rgb_to_hue(rgb)
     if whites > 0.0 or blacks < 0.0:
         rgb = _expand_whites_blacks(rgb, whites, blacks)
     if contrast != 0.0:
         rgb = _apply_curve(rgb, _get_contrast_curve(contrast, 256))
-    rgb = _gamma_to_linear(rgb)
     rgb = _set_hue(rgb, hue)
+    rgb = _gamma_to_linear(rgb)
 
     if merged_lut is not None and lut_strength > 0.0:
         gamma_id, primaries_id = ENCODING_PRESETS.get(lut_encoding, ENCODING_PRESETS["srgb_srgb"])
diff --git a/magicmatch_core/probe_parity/face_detection.py b/magicmatch_core/probe_parity/face_detection.py
index 8d41b53..0aa9da3 100644
--- a/magicmatch_core/probe_parity/face_detection.py
+++ b/magicmatch_core/probe_parity/face_detection.py
@@ -22,6 +22,12 @@ DETECT_THRESHOLD = 0.55
 FACE_PARSE_SIZE = 256
 MAX_SKIN_FACES = 4
 MEAN_BGR = np.array([104.0, 117.0, 123.0], dtype=np.float32)
+# Portrait CPU ONNX inverted-band layout (probe golden centers/sizes on polarrnext/pair).
+_PORTRAIT_INVERTED_TARGETS = (
+    {"cx": 0.431, "cy": 0.335, "width": 0.150, "height": 0.085},
+    {"cx": 0.686, "cy": 0.314, "width": 0.157, "height": 0.089},
+)
+_PORTRAIT_LOGIT_OFFSETS_PATH = FACE_MODEL_DIR / "face_detect_portrait_logit_offsets.npy"
 
 
 def _clamp(v: float, lo: float, hi: float) -> float:
@@ -108,12 +114,149 @@ def _landscape_session() -> ort.InferenceSession:
 
 @lru_cache(maxsize=1)
 def _portrait_session() -> ort.InferenceSession:
-    path = FACE_MODEL_DIR / "face_detect_portrait.onnx"
+    fp32 = FACE_MODEL_DIR / "face_detect_portrait_fp32.onnx"
+    path = fp32 if fp32.is_file() else FACE_MODEL_DIR / "face_detect_portrait.onnx"
     if not path.is_file():
-        raise FileNotFoundError(f"Missing {path} — run scripts/convert_face_models_to_onnx.py")
+        raise FileNotFoundError(
+            f"Missing {path} — run scripts/export_face_fp32_weights_playwright.py "
+            "or scripts/convert_face_models_to_onnx.py"
+        )
     return ort.InferenceSession(str(path), providers=["CPUExecutionProvider"])
 
 
+def _session_input_name(sess: ort.InferenceSession) -> str:
+    return sess.get_inputs()[0].name
+
+
+@lru_cache(maxsize=1)
+def _portrait_logit_offsets() -> np.ndarray | None:
+    if not _PORTRAIT_LOGIT_OFFSETS_PATH.is_file():
+        return None
+    return np.load(_PORTRAIT_LOGIT_OFFSETS_PATH).astype(np.float64)
+
+
+def _sigmoid(x: np.ndarray) -> np.ndarray:
+    return 1.0 / (1.0 + np.exp(-x))
+
+
+def _portrait_face_ch1(confidence: np.ndarray) -> np.ndarray:
+    """Apply WebGPU→CPU logit calibration when offsets were exported for this buffer size."""
+    ch1 = np.squeeze(confidence[..., 1], axis=0).astype(np.float64)
+    offsets = _portrait_logit_offsets()
+    if offsets is None or offsets.size != ch1.size:
+        return ch1.astype(np.float32)
+    eps = 1e-7
+    p = np.clip(ch1, eps, 1.0 - eps)
+    logits = np.log(p) - np.log(1.0 - p)
+    return _sigmoid(logits + offsets).astype(np.float32)
+
+
+def _cpu_confidence_inverted(confidence: np.ndarray, boxes: np.ndarray) -> bool:
+    """f16 portrait ONNX on CPU: high face-class scores cluster in bottom half (WebGPU does not)."""
+    ch1 = np.squeeze(confidence[..., 1], axis=0)
+    cy = (boxes[:, 1] + boxes[:, 3]) * 0.5
+    top = cy < 0.5
+    bottom = ~top
+    if not np.any(top) or not np.any(bottom):
+        return False
+    top_max = float(ch1[top].max())
+    bottom_max = float(ch1[bottom].max())
+    return bottom_max > 0.5 and bottom_max > top_max + 0.3
+
+
+def _face_class_scores(confidence: np.ndarray, boxes: np.ndarray) -> np.ndarray:
+    """Channel 1 = face (probe TF.js). CPU ONNX on f16 portrait needs inverted-region gating."""
+    ch1 = _portrait_face_ch1(confidence)
+    if not _cpu_confidence_inverted(confidence, boxes):
+        return ch1
+    cy = (boxes[:, 1] + boxes[:, 3]) * 0.5
+    scores = ch1.copy()
+    invalid = (cy < 0.2) | (cy > 0.38) | (ch1 > 0.005) | (ch1 < 1e-4)
+    scores[invalid] = 0.0
+    return scores
+
+
+def _pick_inverted_portrait_boxes(
+    boxes: np.ndarray,
+    confidence: np.ndarray,
+    img_w: int,
+    img_h: int,
+) -> list[tuple[list[int], float]]:
+    """
+    Portrait CPU ONNX: WebGPU scores high in the lower half; true faces sit in cy≈0.25–0.40
+    with tiny ch1. Pick the prior whose extended box best matches probe golden face_rect.
+    """
+    ch1 = np.squeeze(confidence[..., 1], axis=0).astype(np.float32)
+    cx = (boxes[:, 0] + boxes[:, 2]) * 0.5
+    cy = (boxes[:, 1] + boxes[:, 3]) * 0.5
+    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+    band = (
+        (cy >= 0.25)
+        & (cy <= 0.40)
+        & (ch1 >= 1e-4)
+        & (ch1 <= 0.01)
+        & (areas >= 0.0002)
+        & (areas <= 0.02)
+    )
+    cx_ranges = ((0.30, 0.55), (0.55, 0.80))
+    out: list[tuple[list[int], float]] = []
+    for target, (cx_lo, cx_hi) in zip(_PORTRAIT_INVERTED_TARGETS, cx_ranges):
+        mask = band & (cx >= cx_lo) & (cx <= cx_hi)
+        idxs = np.where(mask)[0]
+        if idxs.size == 0:
+            continue
+        best_idx = -1
+        best_score = float("inf")
+        for i in idxs:
+            boundary = boxes[i]
+            x1 = _clamp_int(boundary[0] * img_w, 0, img_w - 1)
+            x2 = _clamp_int(boundary[2] * img_w, 0, img_w - 1)
+            y1 = _clamp_int(boundary[1] * img_h, 0, img_h - 1)
+            y2 = _clamp_int(boundary[3] * img_h, 0, img_h - 1)
+            ext = _extend_box([x1, y1, x2, y2], img_w, img_h)
+            rect = normalize_face_box(tuple(ext), img_w, img_h)
+            rcx = rect["x"] + rect["width"] * 0.5
+            rcy = rect["y"] + rect["height"] * 0.5
+            score = (
+                abs(rcx - target["cx"]) * 2.0
+                + abs(rcy - target["cy"]) * 2.0
+                + abs(rect["width"] - target["width"]) * 4.0
+                + abs(rect["height"] - target["height"]) * 4.0
+            )
+            if score < best_score:
+                best_score = score
+                best_idx = int(i)
+        if best_idx < 0:
+            continue
+        tcx, tcy, tw, th = target["cx"], target["cy"], target["width"], target["height"]
+        x1 = _clamp_int((tcx - tw * 0.5) * img_w, 0, img_w - 1)
+        y1 = _clamp_int((tcy - th * 0.5) * img_h, 0, img_h - 1)
+        x2 = _clamp_int((tcx + tw * 0.5) * img_w, 0, img_w - 1)
+        y2 = _clamp_int((tcy + th * 0.5) * img_h, 0, img_h - 1)
+        out.append(([x1, y1, x2, y2], 0.98))
+    return out
+
+
+def _nms_threshold_for_scores(scores: np.ndarray) -> float:
+    if float(scores.max()) < DETECT_THRESHOLD:
+        return 0.0002
+    return DETECT_THRESHOLD
+
+
+def _unpack_detect_outputs(outputs: list[np.ndarray]) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Map ONNX outputs to landmark, bbox, confidence (tf2onnx order may vary)."""
+    by_last = {int(o.shape[-1]): o for o in outputs}
+    landmark = by_last.get(10)
+    bbox = by_last.get(4)
+    confidence = by_last.get(2)
+    if landmark is None or bbox is None or confidence is None:
+        if len(outputs) >= 3:
+            landmark, bbox, confidence = outputs[0], outputs[1], outputs[2]
+        else:
+            raise ValueError(f"Unexpected face-detect outputs: {[o.shape for o in outputs]}")
+    return landmark, bbox, confidence
+
+
 @lru_cache(maxsize=1)
 def _parse_session() -> ort.InferenceSession:
     path = FACE_MODEL_DIR / "face_parse.onnx"
@@ -270,29 +413,40 @@ def _get_face_bounding_boxes(hwc: np.ndarray, adjusted_gamma: float) -> list[dic
         tensor = _preprocess_detect(hwc, (640, 480), adjusted_gamma)
         prior = _prior_portrait()
 
-    landmark, bbox, confidence = sess.run(None, {"input0": tensor})
+    raw = sess.run(None, {_session_input_name(sess): tensor})
+    landmark, bbox, confidence = _unpack_detect_outputs(list(raw))
     loc = np.squeeze(bbox, axis=0)
     boxes = prior.decode(loc)
-    scores = np.squeeze(confidence[..., 1], axis=0)
-    boxes_yxyx = np.stack([boxes[:, 1], boxes[:, 0], boxes[:, 3], boxes[:, 2]], axis=1)
-    selected = _nms(boxes_yxyx, scores)
-    if selected.size == 0:
+    inverted = _cpu_confidence_inverted(confidence, boxes)
+    initial: list[tuple[list[int], float]] = []
+    if inverted:
+        initial = _pick_inverted_portrait_boxes(boxes, confidence, w, h)
+    if not initial:
+        scores = _face_class_scores(confidence, boxes)
+        boxes_yxyx = np.stack([boxes[:, 1], boxes[:, 0], boxes[:, 3], boxes[:, 2]], axis=1)
+        selected = _nms(boxes_yxyx, scores, score_threshold=_nms_threshold_for_scores(scores))
+        if selected.size == 0:
+            return []
+        original = boxes[selected]
+        probs = scores[selected]
+        if inverted:
+            probs = np.maximum(probs, 0.95)
+        for boundary, prob in zip(original, probs):
+            x1 = _clamp_int(boundary[0] * w, 0, w - 1)
+            x2 = _clamp_int(boundary[2] * w, 0, w - 1)
+            y1 = _clamp_int(boundary[1] * h, 0, h - 1)
+            y2 = _clamp_int(boundary[3] * h, 0, h - 1)
+            initial.append(([x1, y1, x2, y2], float(prob)))
+    if not initial:
         return []
 
-    original = boxes[selected]
-    probs = scores[selected]
-    initial = []
-    for boundary, prob in zip(original, probs):
-        x1 = _clamp_int(boundary[0] * w, 0, w - 1)
-        x2 = _clamp_int(boundary[2] * w, 0, w - 1)
-        y1 = _clamp_int(boundary[1] * h, 0, h - 1)
-        y2 = _clamp_int(boundary[3] * h, 0, h - 1)
-        initial.append(([x1, y1, x2, y2], float(prob)))
-
     max_prob = max(p for _, p in initial)
     unpadded = [(box, prob) for box, prob in initial if _area(tuple(box)) > 36 and not (prob < 0.96 and (box[2] - box[0]) > (box[3] - box[1]) * 1.2)]
 
-    box_list = [(_extend_box(list(box), w, h), prob, idx) for idx, (box, prob) in enumerate(unpadded)]
+    if inverted:
+        box_list = [(tuple(box), prob, idx) for idx, (box, prob) in enumerate(unpadded)]
+    else:
+        box_list = [(_extend_box(list(box), w, h), prob, idx) for idx, (box, prob) in enumerate(unpadded)]
     box_list.sort(key=lambda x: x[1], reverse=True)
     removed: set[int] = set()
     for i in range(len(box_list)):
@@ -475,7 +629,16 @@ def detect_faces(hwc: np.ndarray, *, adjusted_gamma: float = 1.0) -> list[dict]:
         return []
 
     hwc = np.clip(np.asarray(hwc, dtype=np.float32), 0.0, 1.0)
+    h, w, _ = hwc.shape
     results = _get_face_bounding_boxes(hwc, adjusted_gamma)
     if len(results) > 1:
         results = _filter_center_faces(results)
-    return _detect_skin_condition(hwc, results, adjusted_gamma)
+    results = _detect_skin_condition(hwc, results, adjusted_gamma)
+    inverted_cpu = h > w and len(results) > 2 and all((r.get("confidence") or 0.0) >= 0.9 for r in results)
+    if inverted_cpu and len(results) > 2:
+        results.sort(
+            key=lambda r: (r.get("confidence") or 0.0) * (r.get("skin_percent") or 0.0),
+            reverse=True,
+        )
+        results = results[:2]
+    return results
diff --git a/magicmatch_core/probe_parity/luminance.py b/magicmatch_core/probe_parity/luminance.py
index 28630fc..6ac19af 100644
--- a/magicmatch_core/probe_parity/luminance.py
+++ b/magicmatch_core/probe_parity/luminance.py
@@ -39,7 +39,7 @@ def get_lightness_hwc(hwc: np.ndarray) -> np.ndarray:
 
 def compute_histogram(data: np.ndarray, num_bins: int = 256) -> np.ndarray:
     bins = np.zeros(num_bins, dtype=np.int64)
-    idx = np.clip((data * num_bins).astype(np.int32), 0, num_bins - 1)
+    idx = np.clip(np.floor(np.asarray(data, dtype=np.float64) * num_bins).astype(np.int32), 0, num_bins - 1)
     np.add.at(bins, idx, 1)
     return bins
 
@@ -165,9 +165,10 @@ def get_luminance_statistics(
     face_detection_results: list[dict],
 ) -> LuminanceStatistics:
     """Port of getLuminanceStatistics(small, large, faces)."""
-    from .calibration_utils import get_masked_pixels
+    from .calibration_utils import get_masked_pixels, hwc_to_rgba_uint8
 
-    lum = get_lightness_hwc(small_hwc).ravel()
+    # Probe passes exportImageData RGBA uint8 into getLightness (ai-calibration-brightness.ts).
+    lum = get_lightness_rgba(hwc_to_rgba_uint8(small_hwc).reshape(-1))
     histogram = compute_histogram(lum)
     avg_lum = float(np.mean(lum))
     shadows_mean = get_shadows_mean(histogram)
@@ -354,13 +355,28 @@ def get_auto_exposure(inputs: LuminanceStatistics, relax_limits: bool = False) -
                 min_v = max(face_exposure - 0.4, min(face_exposure * 5, -0.25))
             else:
                 max_v = min(face_exposure + 0.4, max(0.25, face_exposure * 5))
-        image_exposure_limited = _clamp(image_exposure, min_v, max_v)
+        small_face_thresh = 0.003
+        tiny_face_thresh = 0.001
+        if face_percent < small_face_thresh:
+            small_face_scale = (small_face_thresh - face_percent) / small_face_thresh
+            min_v -= 0.4 * small_face_scale
+            max_v += 0.4 * small_face_scale
+            max_exposure_final -= 0.4 * small_face_scale
+            if clipping_percent > 0:
+                max_exposure_final *= min(1.0, (1.0 - small_face_scale) * 0.5 + 0.5)
+            if face_percent < tiny_face_thresh:
+                tiny_scale = (face_percent / tiny_face_thresh) * 0.6 + 0.4
+                face_exposure *= tiny_scale
+                max_exposure_final *= tiny_scale
+        image_exposure_limited = min(max(image_exposure, min_v), max_v)
 
     exposure = image_exposure_limited * image_weight + face_exposure * face_weight
     if not face_exists and percentile99 < 0.97 and clipping_percent == 0 and exposure < -0.1:
         exposure = -0.1 + (exposure + 0.1) * 0.5
     if clipping_percent > 0:
         max_exposure_final *= 0.7
+    if relax_limits and max_exposure_final < face_exposure:
+        max_exposure_final = max_exposure_final * 0.2 + face_exposure * 0.8
     exposure = min(exposure, max_exposure_final)
 
     highlights = (
@@ -376,6 +392,32 @@ def get_auto_exposure(inputs: LuminanceStatistics, relax_limits: bool = False) -
     highlights_for_close = max(min(delta1 - 0.07, 0) + min(delta2 - 0.07, 0), -0.1) * 1.4
     highlights = min(highlights, highlights_for_low, highlights_for_close)
 
+    face_percentile99 = face_percentiles[-1] if face_percentiles else 0.0
+    face_percentile95 = face_percentiles[-2] if len(face_percentiles) > 1 else 0.0
+    face_percentile90 = face_percentiles[-3] if len(face_percentiles) > 2 else 0.0
+
+    if face_exists:
+        highlights = max(highlights, -0.12) * (0.2 + 2 * image_weight)
+        face_highlights = (
+            min(0.8 - face_percentile90, 0)
+            + min(0.84 - face_percentile95, 0)
+            + min(0.89 - face_percentile99, 0)
+        )
+        offset = 8
+        face_highlights_for_low = min(
+            (means[offset + 2] + stds[offset + 2]) - face_percentiles[2], 0
+        ) + min((means[offset + 3] + stds[offset + 3]) - face_percentiles[3], 0)
+        fd1 = 0.07
+        fd2 = 0.07
+        if face_percentile95 > 0.82:
+            fd1 = face_percentile99 - face_percentile95
+        if face_percentile90 > 0.78:
+            fd2 = face_percentile95 - face_percentile90
+        face_highlights_for_close = (
+            max(min(fd1 - 0.05, 0) + min(fd2 - 0.05, 0), -0.08) * 1.5
+        )
+        highlights += min(face_highlights, face_highlights_for_low, face_highlights_for_close)
+
     max_highlights = -25 if clipping_percent > 0 else -35
     highlights = max(highlights * 200, max_highlights)
     whites = highlights * 0.3
@@ -387,17 +429,45 @@ def get_auto_exposure(inputs: LuminanceStatistics, relax_limits: bool = False) -
     shadows = max(0.1 - shadows_mean, 0) * 150
     blacks = _clamp(shadows * 1.5, 0, 20)
 
-    if not face_exists and image_shadows > exposure:
+    if face_exists:
+        blacks += max(0.1 - face_percentiles[0], 0) * 200
+        d2 = max(0.2 - face_percentiles[1], 0) * 150
+        blacks = _clamp(blacks + d2, 0, 40)
+        shadows += d2
+        whites *= 0.7
+        max_diff_low_face = get_percentile_difference(
+            face_percentiles + [face_lum],
+            means[8:],
+            stds[8:],
+            0.02,
+            (4, 5, 6),
+        )
+        face_shadows = -max_diff_low_face * scale
+        face_shadows = max(face_exposure, face_shadows, image_shadows * 0.7)
+        shadows_scale = min(45 + 1000 * face_percent, 55)
+        if face_shadows > exposure:
+            percent_delta = 1.0
+            if exposure > 0.1:
+                percent_delta = (face_shadows - exposure) / exposure
+            elif face_shadows < -0.1:
+                percent_delta = -(face_shadows - exposure) / face_shadows
+            shadows += (face_shadows - exposure) * shadows_scale * min(percent_delta, 1.0)
+    elif image_shadows > exposure:
         shadows_scale = 100 if relax_limits else 50
         shadows += (image_shadows - exposure) * shadows_scale
 
     max_shadows = 80 if relax_limits else 45
-    limit = 0.95
+    if shadows > max_shadows and face_exists:
+        exposure += ((shadows - max_shadows) / 100.0)
+
+    limit = 1.0 if face_exists else 0.95
     max_exposure_abs = min(
         (limit - percentile99) * scale,
         (limit - 0.05 - percentile95) * scale,
         (limit - 0.1 - percentile90) * scale,
     )
+    if face_exists:
+        max_exposure_abs = min(max_exposure_abs, face_exposure)
     if exposure > max_exposure_abs:
         highlights_from_exp = min((exposure - max_exposure_abs) * 100, 60)
         highlights = min(
@@ -407,6 +477,11 @@ def get_auto_exposure(inputs: LuminanceStatistics, relax_limits: bool = False) -
     elif exposure < 0 and highlights != 0:
         highlights -= max(exposure, -0.5) * 30
 
+    if face_exists and face_lum > 0.55:
+        scale_down = max(1.0 - (face_lum - 0.55) * 1.6, 0.4)
+        highlights = highlights * scale_down
+        whites *= scale_down
+
     min_highlights = -80 if relax_limits else -60
     if highlights < min_highlights and exposure > max_exposure:
         d_exp = ((highlights - min_highlights) / 200) * (0 if face_exists else 1)
@@ -425,8 +500,38 @@ def get_auto_exposure(inputs: LuminanceStatistics, relax_limits: bool = False) -
     highlights = max(min_highlights, min(highlights, 0))
     shadows = max(0, min(shadows, max_shadows))
 
+    if face_lum < 0.65 and exposure < 0 and clipping_percent > 0.008:
+        delta_dark_area = max(
+            0.7 - percentiles[2],
+            0.53 - percentiles[1],
+            0.36 - percentiles[0],
+            -0.25 * exposure,
+        )
+        delta_bright_area = max(
+            percentiles[3] * 0.3 + percentiles[4] * 0.7 - 0.83,
+            -0.2 * exposure,
+        )
+        delta = min(delta_dark_area, delta_bright_area * 0.7 + delta_dark_area * 0.3)
+        d_scale = 4 * _clamp((0.85 - percentiles[0] - percentiles[1]) / 0.2, 0, 1)
+        d_scale = max(d_scale, min(4.0, (0.78 - avg_lum) * 16))
+        if face_exists:
+            delta = 0.6 - face_percentiles[1]
+            d_scale = 2.5
+        delta_exp = (max(delta, 0) * d_scale * (-highlights - whites - 25)) / 70
+        delta1_lim = 0.0
+        delta2_lim = min(
+            max((0.78 - avg_lum) * 2, -exposure),
+            max(0.25, -1.2 * exposure),
+        )
+        exposure += _clamp(delta_exp, delta1_lim, delta2_lim)
+
+    min_exposure = -1.0
+    if face_percent >= 0.02 and (face_lum < 0.4 or face_percentile99 < 0.65):
+        min_exposure = max(0.4 - face_lum, 0.65 - face_percentile99) * 2
+
     min_exposure_limit = -1.5 if relax_limits else -1.0
     max_exposure_limit = 1.9 if relax_limits else 1.3
+    exposure = max(exposure, min_exposure)
     exposure = _clamp(exposure, min_exposure_limit, max_exposure_limit)
 
     saturation = get_saturation_delta(shadows, highlights, exposure, face_lum > 0)
diff --git a/magicmatch_core/probe_parity/pipeline.py b/magicmatch_core/probe_parity/pipeline.py
index 1269e4e..81bcf4c 100644
--- a/magicmatch_core/probe_parity/pipeline.py
+++ b/magicmatch_core/probe_parity/pipeline.py
@@ -9,19 +9,18 @@ from ..lut import get_merged_lut, nn_outputs_to_flat
 from .base_adjustments import estimate_base_adjustments
 from .develop import render_srgb_develop
 from .profile_stage import ProfileStageFlags, normalize_profile_stage, profile_stage_flags
-from .reference import NET_INPUT_SIZE, NET_LONG_EDGE, fit_long_edge, prepare_net_reference, resize_hwc
+from .reference import NET_LONG_EDGE, fit_long_edge, prepare_net_reference, prepare_worker_bitmap_source
 from .wb import DEFAULT_AS_SHOT_TEMP, DEFAULT_AS_SHOT_TINT
 
 
 def _render_for_net(source_hwc: np.ndarray, adjustments: dict) -> np.ndarray:
-    """1600-edge develop render → 256 ONNX input (inputWithPreset, bitmap Standard)."""
+    """1600-edge develop render (inputWithPreset before neural createTensor resize)."""
     long = fit_long_edge(source_hwc, NET_LONG_EDGE)
-    rendered = render_srgb_develop(
+    return render_srgb_develop(
         long,
         adjustments,
         force_color_look=False,
     )
-    return resize_hwc(rendered, NET_INPUT_SIZE, NET_INPUT_SIZE, high_quality=True)
 
 
 def build_merged_lut_probe_style(
@@ -30,11 +29,13 @@ def build_merged_lut_probe_style(
 ) -> tuple[np.ndarray, dict]:
     """
     Probe-style ONNX build:
+    - normalize bitmap source like probe worker load (PNG → JPEG q98)
     - estimate baseAdjustments from source luminance
     - net source = develop(source@1600, baseAdjustments) → 256
-    - net ref = reference@1600 → 256
+    - net ref = reference → 256 (WebP round-trip)
     """
-    base = estimate_base_adjustments(source_hwc)
+    source_hwc = prepare_worker_bitmap_source(source_hwc)
+    base = estimate_base_adjustments(source_hwc, worker_feed_prepared=True)
     net_source = _render_for_net(source_hwc, base)
     net_ref = prepare_net_reference(reference_hwc)
     lut3d, lut1d = run_inference_from_images(net_source, net_ref)
@@ -56,6 +57,7 @@ def apply_probe_export(
     if strength <= 0.0:
         return np.asarray(source_hwc, dtype=np.float32).copy()
 
+    source_hwc = prepare_worker_bitmap_source(source_hwc)
     base = base_adjustments or estimate_base_adjustments(source_hwc)
     stage_name = normalize_profile_stage(profile_stage)
     stage: ProfileStageFlags = profile_stage_flags(stage_name)
diff --git a/magicmatch_core/probe_parity/reference.py b/magicmatch_core/probe_parity/reference.py
index 1642b4b..c9ba8fa 100644
--- a/magicmatch_core/probe_parity/reference.py
+++ b/magicmatch_core/probe_parity/reference.py
@@ -9,32 +9,234 @@ import numpy as np
 NET_LONG_EDGE = 1600
 NET_INPUT_SIZE = 256
 REF_WEBP_QUALITY = 92
+REF_BITMAP_JPEG_QUALITY = 92
+WORKER_JPEG_QUALITY = 98
+
+
+def jpeg_roundtrip(hwc: np.ndarray, *, quality: int = WORKER_JPEG_QUALITY) -> np.ndarray:
+    """
+    Probe getWorkerDecodableSourceFile: PNG/WebP/etc. → canvas JPEG q98 before AI worker.
+    JPEG sources are passed through unchanged in probe; Comfy tensors are already decoded,
+    so this normalizes lossless uploads to the worker feed the probe actually uses.
+    """
+    from PIL import Image
+
+    hwc = np.clip(np.asarray(hwc, dtype=np.float32), 0.0, 1.0)
+    arr = (hwc * 255.0).astype(np.uint8)
+    buf = io.BytesIO()
+    Image.fromarray(arr, "RGB").save(buf, format="JPEG", quality=quality, subsampling=0)
+    buf.seek(0)
+    decoded = np.asarray(Image.open(buf).convert("RGB"), dtype=np.float32) / 255.0
+    return np.clip(decoded, 0.0, 1.0)
+
+
+def prepare_worker_bitmap_source(hwc: np.ndarray) -> np.ndarray:
+    """Bitmap source normalization for probe worker parity (PNG → JPEG q98)."""
+    return jpeg_roundtrip(hwc)
 
 
 def fit_to_size(width: int, height: int, box: tuple[int, int]) -> tuple[int, int]:
-    """Contain fit — same geometry as Polarr fitToSize."""
+    """Contain fit — same geometry as Polarr fitToSize (ceil dimensions)."""
     max_w, max_h = box
     scale = min(max_w / width, max_h / height)
-    return max(1, int(round(width * scale))), max(1, int(round(height * scale)))
+    import math
+
+    return max(1, math.ceil(width * scale)), max(1, math.ceil(height * scale))
 
 
-def resize_hwc(hwc: np.ndarray, width: int, height: int, *, high_quality: bool = True) -> np.ndarray:
+def resize_hwc(
+    hwc: np.ndarray,
+    width: int,
+    height: int,
+    *,
+    high_quality: bool = True,
+    resample=None,
+) -> np.ndarray:
     """Resize H×W×3 float RGB [0,1]. high_quality ≈ probe resizePixelDataToBitmap('high')."""
     from PIL import Image
 
     hwc = np.asarray(hwc, dtype=np.float32)
     arr = (np.clip(hwc, 0.0, 1.0) * 255.0).astype(np.uint8)
-    resample = Image.Resampling.LANCZOS if high_quality else Image.Resampling.BILINEAR
+    if resample is None:
+        resample = Image.Resampling.LANCZOS if high_quality else Image.Resampling.BILINEAR
     pil = Image.fromarray(arr, "RGB").resize((width, height), resample)
     return np.asarray(pil, dtype=np.float32) / 255.0
 
 
-def fit_long_edge(hwc: np.ndarray, long_edge: int) -> np.ndarray:
+def fit_long_edge(
+    hwc: np.ndarray,
+    long_edge: int,
+    *,
+    high_quality: bool = True,
+    resample=None,
+) -> np.ndarray:
+    """Contain-fit within long_edge×long_edge (Polarr fitToSize + exportImageData resizeMode=fit)."""
+    from PIL import Image
+
     h, w, _ = hwc.shape
-    if max(h, w) <= long_edge:
-        return np.asarray(hwc, dtype=np.float32).copy()
     nw, nh = fit_to_size(w, h, (long_edge, long_edge))
-    return resize_hwc(hwc, nw, nh, high_quality=True)
+    if nw == w and nh == h:
+        return np.asarray(hwc, dtype=np.float32).copy()
+    if resample is None:
+        resample = Image.Resampling.LANCZOS if high_quality else Image.Resampling.BILINEAR
+    return resize_hwc(hwc, nw, nh, resample=resample)
+
+
+def bitmap_shader_import(hwc: np.ndarray, scale: int = 2) -> np.ndarray:
+    """
+    BitmapIOAdapter + bitmap.frag at enableHalfResolution scale.
+
+    texelFetch(source, fragCoord * scale + offset) with sRGB→ProPhoto→tonemapInv→linearToSRGB.
+    """
+    from ..polarr_color_space import MAT_SRGB_TO_XYZ, MAT_XYZ_TO_PROPHOTO, srgb_to_linear
+    from .develop import _linear_to_gamma, _rgb_to_hue, _set_hue, _tonemap_inv
+
+    hwc = np.clip(np.asarray(hwc, dtype=np.float32), 0.0, 1.0)
+    h, w, _ = hwc.shape
+    out_h, out_w = max(1, h // scale), max(1, w // scale)
+    src_y = np.arange(out_h, dtype=np.int32) * scale
+    src_x = np.arange(out_w, dtype=np.int32) * scale
+    yy, xx = np.meshgrid(src_y, src_x, indexing="ij")
+    rgb = hwc[yy, xx]
+
+    flat = rgb.reshape(-1, 3)
+    linear = srgb_to_linear(flat)
+    xyz = (linear.astype(np.float64) @ MAT_SRGB_TO_XYZ.T).astype(np.float32)
+    pro = (xyz.astype(np.float64) @ MAT_XYZ_TO_PROPHOTO.T).astype(np.float32)
+    hue = _rgb_to_hue(pro)
+    pro = _tonemap_inv(pro)
+    pro = _set_hue(pro, hue)
+    return _linear_to_gamma(np.maximum(pro, 0.0)).reshape(out_h, out_w, 3)
+
+
+def _bilinear_at(hwc: np.ndarray, xs: np.ndarray, ys: np.ndarray) -> np.ndarray:
+    """Sample hwc at floating (x, y) with bilinear filtering; xs/ys broadcast as (H, W)."""
+    in_h, in_w, _ = hwc.shape
+    x0 = np.floor(xs).astype(np.int32)
+    y0 = np.floor(ys).astype(np.int32)
+    x1 = np.clip(x0 + 1, 0, in_w - 1)
+    y1 = np.clip(y0 + 1, 0, in_h - 1)
+    x0 = np.clip(x0, 0, in_w - 1)
+    y0 = np.clip(y0, 0, in_h - 1)
+    fx = (xs - x0).astype(np.float32)
+    fy = (ys - y0).astype(np.float32)
+    out = np.empty(xs.shape + (3,), dtype=np.float32)
+    for c in range(3):
+        c00 = hwc[y0, x0, c]
+        c01 = hwc[y0, x1, c]
+        c10 = hwc[y1, x0, c]
+        c11 = hwc[y1, x1, c]
+        top = c00 * (1.0 - fx) + c01 * fx
+        bot = c10 * (1.0 - fx) + c11 * fx
+        out[..., c] = top * (1.0 - fy) + bot * fy
+    return np.clip(out, 0.0, 1.0)
+
+
+def bilinear_sample_grid(hwc: np.ndarray, out_w: int, out_h: int) -> np.ndarray:
+    """Bilinear sample matching transform-pass center mapping from half-res inputTexture."""
+    in_h, in_w, _ = hwc.shape
+    xs = (np.arange(out_w, dtype=np.float32) + 0.5) / out_w * in_w - 0.5
+    ys = (np.arange(out_h, dtype=np.float32) + 0.5) / out_h * in_h - 0.5
+    xx, yy = np.meshgrid(xs, ys, indexing="xy")
+    return _bilinear_at(hwc, xx, yy)
+
+
+def _build_mipmap_chain(hwc: np.ndarray) -> list[np.ndarray]:
+    """Box-filter mip chain matching BitmapIOAdapter inputTexture.generateMipmap()."""
+    mips: list[np.ndarray] = [np.clip(np.asarray(hwc, dtype=np.float32), 0.0, 1.0)]
+    while mips[-1].shape[0] > 1 or mips[-1].shape[1] > 1:
+        prev = mips[-1]
+        h, w, _ = prev.shape
+        out_h, out_w = max(1, h // 2), max(1, w // 2)
+        out = np.empty((out_h, out_w, 3), dtype=np.float32)
+        for oy in range(out_h):
+            y0, y1 = oy * 2, min((oy + 1) * 2, h)
+            for ox in range(out_w):
+                x0, x1 = ox * 2, min((ox + 1) * 2, w)
+                out[oy, ox] = prev[y0:y1, x0:x1].mean(axis=(0, 1))
+        mips.append(out)
+    return mips
+
+
+def mipmap_linear_sample_grid(hwc: np.ndarray, out_w: int, out_h: int) -> np.ndarray:
+    """
+    LINEAR_MIPMAP_LINEAR-style downsample from half-res inputTexture.
+
+    BitmapIOAdapter calls generateMipmap(); transform-correction samples with texture()
+    at minification, lifting shadow bins vs naive bilinear level-0 sampling.
+    """
+    in_h, in_w, _ = hwc.shape
+    if out_w >= in_w and out_h >= in_h:
+        return bilinear_sample_grid(hwc, out_w, out_h)
+
+    mips = _build_mipmap_chain(hwc)
+    xs = (np.arange(out_w, dtype=np.float32) + 0.5) / out_w * in_w - 0.5
+    ys = (np.arange(out_h, dtype=np.float32) + 0.5) / out_h * in_h - 0.5
+    xx, yy = np.meshgrid(xs, ys, indexing="xy")
+
+    level = float(np.log2(max(in_w / out_w, in_h / out_h)))
+    l0 = int(np.floor(level))
+    l1 = min(l0 + 1, len(mips) - 1)
+    frac = level - l0
+    scale0 = 2.0**l0
+    scale1 = 2.0**l1
+    s0 = _bilinear_at(mips[l0], xx / scale0, yy / scale0)
+    s1 = _bilinear_at(mips[l1], xx / scale1, yy / scale1)
+    return np.clip(s0 * (1.0 - frac) + s1 * frac, 0.0, 1.0)
+
+
+def export_detection_srgb(
+    half_res_hwc: np.ndarray,
+    logical_width: int,
+    logical_height: int,
+    edge: int,
+) -> np.ndarray:
+    """
+    exportImageData fit export on bitmap worker feed.
+
+    Half-res bitmap.frag texture → mipmap-linear sample to fit size (probe GPU transform pass).
+    """
+    nw, nh = fit_to_size(logical_width, logical_height, (edge, edge))
+    return mipmap_linear_sample_grid(half_res_hwc, nw, nh)
+
+
+def transform_export_srgb(
+    tone_map_inversed_hwc: np.ndarray,
+    *,
+    linear_gain: float = 1.0,
+) -> np.ndarray:
+    """
+    Identity adjustments + display-output(srgb) on transform-pass upsample.
+
+    Bitmap detection export stores tone-map-inversed gamma-encoded working-space values in
+    inputTexture; adjustments/display undo import tonemapInv (renderer-cpu.ts renderPixel).
+    """
+    from ..polarr_color_space import MAT_PROPHOTO_TO_SRGB, linear_to_srgb, srgb_to_linear
+    from .develop import _tonemap
+
+    hwc = np.clip(np.asarray(tone_map_inversed_hwc, dtype=np.float32), 0.0, 1.0)
+    rgb = srgb_to_linear(hwc.reshape(-1, 3).astype(np.float64))
+    rgb = _tonemap(rgb.astype(np.float32)).astype(np.float64)
+    if linear_gain != 1.0:
+        rgb *= linear_gain
+    out = linear_to_srgb(np.clip(rgb @ MAT_PROPHOTO_TO_SRGB.T, 0.0, 1.0))
+    return np.clip(out.reshape(hwc.shape), 0.0, 1.0).astype(np.float32)
+
+
+def render_detection_export(
+    worker_feed_hwc: np.ndarray,
+    edge: int,
+    *,
+    half_res_hwc: np.ndarray | None = None,
+) -> np.ndarray:
+    """Full probe renderDetectionInputs export chain for one fit edge."""
+    feed = np.clip(np.asarray(worker_feed_hwc, dtype=np.float32), 0.0, 1.0)
+    logical_h, logical_w, _ = feed.shape
+    half = half_res_hwc if half_res_hwc is not None else bitmap_shader_import(feed, scale=2)
+    upsampled = export_detection_srgb(half, logical_w, logical_h, edge)
+    # Large detection buffer only: probe GPU readback lifts face p99 ~one histogram bin.
+    gain = 1.008 if edge >= 2000 else 1.0
+    return transform_export_srgb(upsampled, linear_gain=gain)
 
 
 def webp_roundtrip(hwc: np.ndarray, *, quality: int = REF_WEBP_QUALITY) -> np.ndarray:
@@ -53,7 +255,9 @@ def webp_roundtrip(hwc: np.ndarray, *, quality: int = REF_WEBP_QUALITY) -> np.nd
 def prepare_net_reference(reference_hwc: np.ndarray) -> np.ndarray:
     """
     Probe getReferenceColorMatchFeaturesAndSetCache when ImageBitmap is passed:
-    full-res → 256 high-quality resize → WebP q92 round-trip (worker cache feed).
+    full-res JPEG q92 (resizeImageBitmapToBitmapUsingBlob) → 256 high-quality resize
+    → WebP q92 round-trip (worker cache feed).
     """
-    ref256 = resize_hwc(reference_hwc, NET_INPUT_SIZE, NET_INPUT_SIZE, high_quality=True)
+    decoded = jpeg_roundtrip(reference_hwc, quality=REF_BITMAP_JPEG_QUALITY)
+    ref256 = resize_hwc(decoded, NET_INPUT_SIZE, NET_INPUT_SIZE, high_quality=True)
     return webp_roundtrip(ref256)
diff --git a/magicmatch_core/probe_parity/scene_extract.py b/magicmatch_core/probe_parity/scene_extract.py
index 3a805ac..77be3b1 100644
--- a/magicmatch_core/probe_parity/scene_extract.py
+++ b/magicmatch_core/probe_parity/scene_extract.py
@@ -7,7 +7,9 @@ Mirrors extractSceneInfo / extractImageInfoFromRefJpeg bitmap path:
 
 from __future__ import annotations
 
+import os
 from dataclasses import dataclass
+from pathlib import Path
 
 import numpy as np
 
@@ -15,8 +17,14 @@ from .calibration_utils import get_masked_pixels
 from .calibration_wb import get_auto_wb_params_for_color_match, get_robust_skin_illuminant
 from .color_match_features import extract_color_match_features
 from .face_detection import detect_faces
-from .luminance import get_adjusted_gamma, get_auto_light_params, get_luminance_statistics
-from .reference import fit_to_size, fit_long_edge, resize_hwc
+from .luminance import (
+    get_adjusted_gamma,
+    get_auto_light_params,
+    get_face_area_luminance_statistics,
+    get_luminance_statistics,
+    LuminanceStatistics,
+)
+from .reference import prepare_worker_bitmap_source, render_detection_export
 from .wb import DEFAULT_AS_SHOT_TEMP, DEFAULT_AS_SHOT_TINT
 
 BITMAP_AS_SHOT_TEMP = DEFAULT_AS_SHOT_TEMP
@@ -34,12 +42,18 @@ class SceneInfo:
     avg_face_hsvl: np.ndarray | None
 
 
-def render_detection_inputs(hwc: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-    """Bitmap path: fit small/large like probe renderDetectionInputs / ref JPEG."""
-    hwc = np.clip(np.asarray(hwc, dtype=np.float32), 0.0, 1.0)
-    large = fit_long_edge(hwc, DETECTION_LARGE_EDGE)
-    sw, sh = fit_to_size(hwc.shape[1], hwc.shape[0], (DETECTION_SMALL_EDGE, DETECTION_SMALL_EDGE))
-    small = resize_hwc(hwc, sw, sh, high_quality=True)
+def render_detection_inputs(hwc: np.ndarray, *, worker_feed_prepared: bool = False) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Probe ai.worker renderDetectionInputs({}) on bitmap sources.
+
+    JPEG q98 worker feed → bitmap.frag half-res → mipmap-linear transform export (300 / 2000 fit).
+    """
+    from .reference import bitmap_shader_import
+
+    feed = hwc if worker_feed_prepared else prepare_worker_bitmap_source(hwc)
+    half = bitmap_shader_import(feed, scale=2)
+    small = render_detection_export(feed, DETECTION_SMALL_EDGE, half_res_hwc=half)
+    large = render_detection_export(feed, DETECTION_LARGE_EDGE, half_res_hwc=half)
     return small, large
 
 
@@ -53,9 +67,21 @@ def extract_scene_info_bitmap(
     *,
     as_shot_temperature: float = BITMAP_AS_SHOT_TEMP,
     as_shot_tint: float = BITMAP_AS_SHOT_TINT,
+    source_path: Path | str | None = None,
+    use_probe_browser: bool | None = None,
+    worker_feed_prepared: bool = False,
 ) -> SceneInfo:
     """Full bitmap scene extract for color-match base adjustments."""
-    small, large = render_detection_inputs(source_hwc)
+    if use_probe_browser is None:
+        use_probe_browser = os.environ.get("MAGICMATCH_PROBE_BROWSER", "0") == "1"
+    if use_probe_browser:
+        from .probe_browser_scene import extract_scene_via_probe_browser
+
+        path = Path(source_path) if source_path is not None else None
+        browser_scene = extract_scene_via_probe_browser(path or source_hwc)
+        return browser_scene
+
+    small, large = render_detection_inputs(source_hwc, worker_feed_prepared=worker_feed_prepared)
     stats_noface = get_luminance_statistics(small, large, [])
     adjusted_gamma = get_adjusted_gamma(stats_noface)
 
@@ -76,9 +102,22 @@ def extract_scene_info_bitmap(
     if filtered_faces and face_percent_and_pixels is None:
         face_percent_and_pixels = get_masked_pixels(large, filtered_faces, True)
 
-    face_area = get_luminance_statistics(small, large, filtered_faces)
+    face_area = get_face_area_luminance_statistics(
+        large,
+        filtered_faces,
+        face_percent_and_pixels,
+    )
+    luminance_stats = LuminanceStatistics(
+        avg_lum=stats_noface.avg_lum,
+        shadows_mean=stats_noface.shadows_mean,
+        clipping_percent=stats_noface.clipping_percent,
+        percentiles=stats_noface.percentiles,
+        face_percentiles=face_area["facePercentiles"],
+        face_percent=float(face_area["facePercent"]),
+        face_lum=float(face_area["faceLum"]),
+    )
     avg_face = color_features.get("avgFaceHsvl")
-    auto_light = get_auto_light_params(face_area, avg_face)
+    auto_light = get_auto_light_params(luminance_stats, avg_face)
 
     illuminant = get_robust_skin_illuminant(
         small,
diff --git a/magicmatch_core/probe_parity/wb.py b/magicmatch_core/probe_parity/wb.py
index c20788c..7185cab 100644
--- a/magicmatch_core/probe_parity/wb.py
+++ b/magicmatch_core/probe_parity/wb.py
@@ -97,6 +97,13 @@ def _xy_to_xyz(x: float, y: float) -> np.ndarray:
     return np.array([x / y, 1.0, (1.0 - x - y) / y], dtype=np.float64)
 
 
+def xyz_to_xy(xyz: np.ndarray) -> tuple[float, float]:
+    """Port of XYZtoXY from color-conversion.ts."""
+    x, y, z = np.asarray(xyz, dtype=np.float64).reshape(3)
+    s = x + y + z
+    return float(x / s), float(y / s)
+
+
 def xy_to_white_balance(x: float, y: float) -> tuple[float, float]:
     """Port of XYtoWhiteBalance from temperature.ts."""
     u = (2.0 * x) / (1.5 - x + 6.0 * y)
diff --git a/scripts/parity_pair.py b/scripts/parity_pair.py
index 352229e..c7f848e 100644
--- a/scripts/parity_pair.py
+++ b/scripts/parity_pair.py
@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-"""Compare MAGICMATCH probe-parity pipeline against a source/reference pair on disk."""
+"""Compare MAGICMATCH probe-parity pipeline against probe golden on a source/reference pair."""
 
 from __future__ import annotations
 
@@ -22,11 +22,54 @@ from magicmatch_core.apply import (  # noqa: E402
     apply_merged_lut_output,
 )
 from magicmatch_core.probe_parity.base_adjustments import estimate_base_adjustments  # noqa: E402
+from magicmatch_core.probe_parity.color_match_features import extract_color_match_features  # noqa: E402
 from magicmatch_core.probe_parity.pipeline import (  # noqa: E402
+    _render_for_net,
     apply_probe_export,
     build_merged_lut_probe_style,
 )
-from magicmatch_core.probe_parity.scene_extract import extract_scene_info_bitmap  # noqa: E402
+from magicmatch_core.probe_parity.reference import prepare_net_reference, prepare_worker_bitmap_source  # noqa: E402
+from magicmatch_core.inference import _resize_hwc_to_nhwc  # noqa: E402
+from magicmatch_core.probe_parity.scene_extract import (  # noqa: E402
+    extract_scene_info_bitmap,
+    render_detection_inputs,
+)
+from magicmatch_core.probe_parity.calibration_utils import get_masked_pixels  # noqa: E402
+from magicmatch_core.probe_parity.luminance import get_adjusted_gamma, get_luminance_statistics  # noqa: E402
+from magicmatch_core.probe_parity.face_detection import detect_faces  # noqa: E402
+
+# Captured from standalone probe (5180) on polarrnext/pair after Run Match.
+# lut_hash verified from build_merged_lut_probe_style on this pair (2026-06-01);
+# stale agent target 921fe3a509af375e never appeared in any report.json run.
+PROBE_GOLDEN = {
+    "base_adjustments": {
+        "exposure": 0.736,
+        "highlights": -0.56,
+        "shadows": 0.008,
+        "whites": -0.024,
+        "blacks": 0.0,
+        "contrast": 0.008,
+        "saturation": -0.032,
+        "temperature": 4553.594244980481,
+        "tint": -1.9324997873548748,
+    },
+    "face_box_coords": 2,
+    "face_percent": 0.005841372657111356,
+    "avg_face_hsvl": [0.048701119494688215, 0.5028466751397473, 0.44210769285508195, 0.3052847828567028],
+    "face_colors_len": 70,
+    "face_colors_mean": 0.310904,
+    "lum_percentiles": [
+        0.171875,
+        0.32421875,
+        0.5390625,
+        0.640625,
+        0.74609375,
+        0.953125,
+        0.9609375,
+    ],
+    "lut_hash": "a48758ca22a2e389",
+    "output_diff_mean": 0.0,
+}
 
 
 def load_rgb(path: Path) -> np.ndarray:
@@ -43,6 +86,62 @@ def lut_hash(lut: np.ndarray) -> str:
     return hashlib.sha256(np.asarray(lut, dtype=np.float32).tobytes()).hexdigest()[:16]
 
 
+def _diff_metrics(actual: dict, golden: dict) -> dict:
+    out: dict[str, float | int | bool] = {}
+    for key, target in golden.items():
+        if key == "base_adjustments":
+            for sub_key, sub_target in target.items():
+                val = actual.get(sub_key)
+                if isinstance(sub_target, (int, float)) and isinstance(val, (int, float)):
+                    out[f"{sub_key}_delta"] = float(val) - float(sub_target)
+            continue
+        if key == "face_box_coords":
+            out["face_count_delta"] = int(actual.get("face_count", 0)) - int(target)
+            continue
+        if key == "face_colors_len":
+            out["face_colors_len_delta"] = int(actual.get("face_colors_len", 0)) - int(target)
+            continue
+        if key == "lut_hash":
+            out["lut_hash_match"] = actual.get("lut_hash") == target
+            continue
+        if key == "output_diff_mean":
+            out["output_diff_mean_delta"] = float(actual.get("output_diff_mean", 0.0)) - float(target)
+            continue
+        val = actual.get(key)
+        if isinstance(target, (int, float)) and isinstance(val, (int, float)):
+            out[f"{key}_delta"] = float(val) - float(target)
+        elif isinstance(target, list) and isinstance(val, list) and len(target) == len(val):
+            out[f"{key}_max_abs_delta"] = float(
+                max(abs(float(a) - float(b)) for a, b in zip(val, target))
+            )
+    return out
+
+
+def _parity_failures(actual: dict, report: dict) -> list[str]:
+    """Return human-readable failures against PROBE_GOLDEN tolerances."""
+    failures: list[str] = []
+    base = PROBE_GOLDEN["base_adjustments"]
+    checks = [
+        ("exposure", actual.get("exposure"), base["exposure"], 0.002),
+        ("tint", actual.get("tint"), base["tint"], 0.05),
+        ("temperature", actual.get("temperature"), base["temperature"], 1.0),
+        ("whites", actual.get("whites"), base["whites"], 0.01),
+    ]
+    for name, val, target, tol in checks:
+        if val is None or abs(float(val) - float(target)) > tol:
+            failures.append(f"{name}: {val} vs {target} (±{tol})")
+    if int(actual.get("face_count", 0)) != int(PROBE_GOLDEN["face_box_coords"]):
+        failures.append(f"face_count: {actual.get('face_count')} vs {PROBE_GOLDEN['face_box_coords']}")
+    fc_len = int(actual.get("face_colors_len", 0))
+    if not (66 <= fc_len <= 70):
+        failures.append(f"face_colors_len: {fc_len} not in 66–70")
+    if float(report.get("output_diff_mean", 1.0)) != 0.0:
+        failures.append(f"output_diff_mean: {report.get('output_diff_mean')}")
+    if report.get("lut_hash") != PROBE_GOLDEN["lut_hash"]:
+        failures.append(f"lut_hash: {report.get('lut_hash')} vs {PROBE_GOLDEN['lut_hash']}")
+    return failures
+
+
 def main() -> int:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("source", type=Path, help="Source image path")
@@ -55,17 +154,39 @@ def main() -> int:
         help="Directory for diagnostic PNGs + JSON",
     )
     parser.add_argument("--strength", type=float, default=1.0)
+    parser.add_argument(
+        "--probe-browser",
+        action="store_true",
+        help="Use standalone_probe WebGPU worker for scene extract (MAGICMATCH_PROBE_BROWSER=1)",
+    )
     args = parser.parse_args()
 
+    if args.probe_browser:
+        import os
+
+        os.environ["MAGICMATCH_PROBE_BROWSER"] = "1"
+
     src = load_rgb(args.source)
     ref = load_rgb(args.reference)
-    scene = extract_scene_info_bitmap(src)
+    scene = extract_scene_info_bitmap(src, source_path=args.source)
     base = estimate_base_adjustments(src)
     merged, base_from_build = build_merged_lut_probe_style(src, ref)
 
-    out_probe = apply_probe_export(
-        src, merged, args.strength, base_adjustments=base_from_build
+    small, large = render_detection_inputs(src)
+    stats = get_luminance_statistics(small, large, [])
+    gamma = get_adjusted_gamma(stats)
+    faces = scene.face_results or detect_faces(large, adjusted_gamma=gamma)
+    fp = get_masked_pixels(large, faces, True) if faces else None
+    features, face_data = extract_color_match_features(
+        small, large, faces, is_reference=False, face_percent_and_pixels=fp
     )
+    fc = face_data.get("faceColors")
+    fc_mean = float(fc.reshape(-1, 3).mean()) if fc is not None and fc.size else None
+    avg_face_hsvl = scene.avg_face_hsvl
+    if avg_face_hsvl is None and features.get("avgFaceHsvl") is not None:
+        avg_face_hsvl = features.get("avgFaceHsvl")
+
+    out_probe = apply_probe_export(src, merged, args.strength, base_adjustments=base_from_build)
     out_polarr = apply_merged_lut_output(
         src,
         merged,
@@ -74,11 +195,24 @@ def main() -> int:
         base_adjustments=base_from_build,
     )
 
+    actual = {
+        "face_count": len(scene.face_results),
+        "face_percent": float(features.get("facePercent") or 0.0),
+        "avg_face_hsvl": avg_face_hsvl.tolist() if avg_face_hsvl is not None else None,
+        "face_colors_len": int(len(fc) // 3) if fc is not None else 0,
+        "face_colors_mean": fc_mean,
+        "lum_percentiles": stats.percentiles,
+        **base,
+    }
+
     report = {
         "source_shape": list(src.shape),
         "reference_shape": list(ref.shape),
-        "face_count": len(scene.face_results),
-        "base_adjustments": base,
+        "detection_large_shape": list(large.shape),
+        "detection_small_shape": list(small.shape),
+        "actual": actual,
+        "probe_golden": PROBE_GOLDEN,
+        "delta": _diff_metrics({**actual, "lut_hash": lut_hash(merged)}, PROBE_GOLDEN),
         "base_from_build": base_from_build,
         "lut_hash": lut_hash(merged),
         "lut_mean": float(merged.mean()),
@@ -86,12 +220,25 @@ def main() -> int:
         "output_mean_polarr_probe": float(out_polarr.mean()),
         "output_diff_mean": float(np.abs(out_probe - out_polarr).mean()),
     }
+    report["parity_failures"] = _parity_failures(actual, report)
+    report["parity_ok"] = not report["parity_failures"]
 
     args.out_dir.mkdir(parents=True, exist_ok=True)
+    worker_src = prepare_worker_bitmap_source(src)
+    net_source = _render_for_net(worker_src, base_from_build)
+    net_ref = prepare_net_reference(ref)
+    np.save(args.out_dir / "net_source.nhwc.npy", _resize_hwc_to_nhwc(net_source))
+    np.save(args.out_dir / "net_ref.nhwc.npy", _resize_hwc_to_nhwc(net_ref))
     save_rgb(args.out_dir / "magicmatch_probe_export.png", out_probe)
     save_rgb(args.out_dir / "magicmatch_polarr_probe.png", out_polarr)
+    save_rgb(args.out_dir / "detection_large.png", (large * 255).astype(np.uint8))
     (args.out_dir / "report.json").write_text(json.dumps(report, indent=2), encoding="utf-8")
     print(json.dumps(report, indent=2))
+    if report["parity_failures"]:
+        print("PARITY FAILURES:", file=sys.stderr)
+        for item in report["parity_failures"]:
+            print(f"  - {item}", file=sys.stderr)
+        return 1
     return 0