diff --git a/src/extraction/synthid_bypass.py b/src/extraction/synthid_bypass.py
index f3180aa..5ac2b2f 100644
--- a/src/extraction/synthid_bypass.py
+++ b/src/extraction/synthid_bypass.py
@@ -1975,44 +1975,114 @@ class SpectralCodebook:
     # Save / Load
     # ------------------------------------------------------------------
 
+    # ------------------------------------------------------------------
+    # rfft symmetry helpers
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _rfft_to_full_sym(rfft_half, H, W):
+        """(H, W//2+1, C) → (H, W, C) via conjugate symmetry."""
+        rw = W // 2 + 1
+        full = np.zeros((H, W) + rfft_half.shape[2:], dtype=rfft_half.dtype)
+        full[:, :rw] = rfft_half
+        if W > 2:
+            sky = (H - np.arange(H)) % H
+            skx = W - np.arange(rw, W)
+            full[:, rw:] = full[sky[:, None], skx[None, :]]
+        return full
+
+    @staticmethod
+    def _rfft_to_full_anti(rfft_half, H, W):
+        """Anti-symmetric variant (for phase)."""
+        rw = W // 2 + 1
+        full = np.zeros((H, W) + rfft_half.shape[2:], dtype=rfft_half.dtype)
+        full[:, :rw] = rfft_half
+        if W > 2:
+            sky = (H - np.arange(H)) % H
+            skx = W - np.arange(rw, W)
+            full[:, rw:] = -full[sky[:, None], skx[None, :]]
+        return full
+
+    # ------------------------------------------------------------------
+    # Save / Load — compact format
+    #
+    # Reduces codebook size ~25x through:
+    #   1. rfft2 half-spectrum (conjugate symmetry → 2x)
+    #   2. Drop derivable arrays (content_baseline, white_phase_*)
+    #   3. float16 for magnitudes (log2-encoded) and phase
+    #   4. uint8 for consistency and agreement ([0,1] → 0..255)
+    #   5. Sparse storage for profiles where <50% of bins are active
+    #      (bins with consistency < 0.15 have zero bypass contribution)
+    # ------------------------------------------------------------------
+
+    _CONS_THRESHOLD = 0.15  # minimum cons_floor across all strength levels
+
     def save(self, path: str):
-        """Save all profiles to a single .npz file."""
-        data = {'resolutions': np.array(list(self.profiles.keys()))}
+        """Save in compact format (~20 MB for typical dual-resolution codebook)."""
+        data = {
+            'format_version': np.array(2),
+            'resolutions': np.array(list(self.profiles.keys())),
+        }
+
         for (h, w), prof in self.profiles.items():
             pfx = f'{h}x{w}/'
-            for key in self._PROFILE_ARRAYS:
-                if prof.get(key) is not None:
-                    data[pfx + key] = prof[key]
+            rw = w // 2 + 1
+
             for key in self._PROFILE_SCALARS:
                 data[pfx + key] = np.array(prof.get(key, 0))
-        np.savez_compressed(path, **data)
+
+            mag = prof['magnitude_profile']
+            phase = prof['phase_template']
+            cons = prof['phase_consistency']
+
+            mag_r = mag[:, :rw, :]
+            phase_r = phase[:, :rw, :]
+            cons_r = cons[:, :rw, :]
+
+            active_frac = float(np.mean(cons_r > self._CONS_THRESHOLD))
+            use_sparse = active_frac < 0.50
+            data[pfx + 'sparse'] = np.array(int(use_sparse))
+
+            if use_sparse:
+                for ch in range(3):
+                    mask = cons_r[:, :, ch] >= self._CONS_THRESHOLD
+                    idx = np.where(mask.ravel())[0].astype(np.uint32)
+                    vals_m = mag_r[:, :, ch].ravel()[idx]
+                    vals_p = phase_r[:, :, ch].ravel()[idx]
+                    vals_c = cons_r[:, :, ch].ravel()[idx]
+                    data[pfx + f'idx_{ch}'] = idx
+                    data[pfx + f'mag_{ch}'] = np.log2(1.0 + vals_m).astype(np.float16)
+                    data[pfx + f'phase_{ch}'] = vals_p.astype(np.float16)
+                    data[pfx + f'cons_{ch}'] = np.round(vals_c * 255).clip(0, 255).astype(np.uint8)
+            else:
+                data[pfx + 'mag'] = np.log2(1.0 + mag_r).astype(np.float16)
+                data[pfx + 'phase'] = phase_r.astype(np.float16)
+                data[pfx + 'cons'] = np.round(cons_r * 255).clip(0, 255).astype(np.uint8)
+
+                wmag = prof.get('white_magnitude_profile')
+                if wmag is not None:
+                    data[pfx + 'wmag'] = np.log2(1.0 + wmag[:, :rw, :]).astype(np.float16)
+
+                agree = prof.get('black_white_agreement')
+                if agree is not None:
+                    data[pfx + 'agree'] = np.round(agree[:, :rw, :] * 255).clip(0, 255).astype(np.uint8)
+
+        np.savez(path, **data)
+        sz = os.path.getsize(path) / 1e6
         res_str = ', '.join(f'{h}x{w}' for h, w in self.profiles)
-        print(f"Codebook saved → {path}  [{res_str}]")
+        print(f"Codebook saved → {path}  [{res_str}]  {sz:.1f} MB")
 
     def load(self, path: str):
-        """Load profiles.  Supports multi-res and legacy single-res files."""
+        """Load codebook (auto-detects format version)."""
         d = np.load(path)
-        if 'resolutions' in d:
-            for res in d['resolutions']:
-                h, w = int(res[0]), int(res[1])
-                pfx = f'{h}x{w}/'
-                prof = {}
-                for key in self._PROFILE_ARRAYS:
-                    fk = pfx + key
-                    prof[key] = d[fk] if fk in d else None
-                for key in self._PROFILE_SCALARS:
-                    fk = pfx + key
-                    prof[key] = int(d[fk]) if fk in d else 0
-                self.profiles[(h, w)] = prof
+        fmt = int(d['format_version']) if 'format_version' in d else 0
+
+        if fmt >= 2:
+            self._load_compact(d)
+        elif 'resolutions' in d:
+            self._load_v1(d)
         else:
-            h, w = int(d['ref_shape'][0]), int(d['ref_shape'][1])
-            prof = {}
-            for key in self._PROFILE_ARRAYS:
-                prof[key] = d[key] if key in d else None
-            prof['n_black_refs'] = int(d['n_black_refs']) if 'n_black_refs' in d else 0
-            prof['n_white_refs'] = int(d['n_white_refs']) if 'n_white_refs' in d else 0
-            prof['n_random_refs'] = int(d['n_random_refs']) if 'n_random_refs' in d else 0
-            self.profiles[(h, w)] = prof
+            self._load_legacy(d)
 
         res_str = ', '.join(f'{h}x{w}' for h, w in self.profiles)
         print(f"Codebook loaded: [{res_str}]")
@@ -2020,6 +2090,89 @@ class SpectralCodebook:
             nb, nw, nr = prof['n_black_refs'], prof['n_white_refs'], prof['n_random_refs']
             print(f"  {h}x{w}: {nb}b+{nw}w+{nr}r")
 
+    def _load_compact(self, d):
+        """Load format_version >= 2 (rfft + mixed precision + optional sparse)."""
+        for res in d['resolutions']:
+            h, w = int(res[0]), int(res[1])
+            pfx = f'{h}x{w}/'
+            rw = w // 2 + 1
+            use_sparse = bool(int(d[pfx + 'sparse']))
+
+            if use_sparse:
+                mag_r = np.zeros((h, rw, 3), dtype=np.float64)
+                phase_r = np.zeros((h, rw, 3), dtype=np.float64)
+                cons_r = np.zeros((h, rw, 3), dtype=np.float64)
+                for ch in range(3):
+                    idx = d[pfx + f'idx_{ch}']
+                    rows, cols = np.unravel_index(idx, (h, rw))
+                    mag_r[rows, cols, ch] = np.power(2.0, d[pfx + f'mag_{ch}'].astype(np.float64)) - 1.0
+                    phase_r[rows, cols, ch] = d[pfx + f'phase_{ch}'].astype(np.float64)
+                    cons_r[rows, cols, ch] = d[pfx + f'cons_{ch}'].astype(np.float64) / 255.0
+            else:
+                mag_r = np.power(2.0, d[pfx + 'mag'].astype(np.float64)) - 1.0
+                phase_r = d[pfx + 'phase'].astype(np.float64)
+                cons_r = d[pfx + 'cons'].astype(np.float64) / 255.0
+
+            mag_full = self._rfft_to_full_sym(mag_r, h, w)
+            phase_full = self._rfft_to_full_anti(phase_r, h, w)
+            cons_full = self._rfft_to_full_sym(cons_r, h, w)
+
+            wmag_full = None
+            if pfx + 'wmag' in d:
+                wmag_r = np.power(2.0, d[pfx + 'wmag'].astype(np.float64)) - 1.0
+                wmag_full = self._rfft_to_full_sym(wmag_r, h, w)
+
+            agree_full = None
+            if pfx + 'agree' in d:
+                agree_r = d[pfx + 'agree'].astype(np.float64) / 255.0
+                agree_full = self._rfft_to_full_sym(agree_r, h, w)
+
+            # Reconstruct content_baseline for watermarked-only profiles
+            content_base = None
+            nb = int(d.get(pfx + 'n_black_refs', 0))
+            if nb == 0:
+                safe = np.maximum(cons_full ** 2, 1e-10)
+                content_base = mag_full / safe
+
+            self.profiles[(h, w)] = {
+                'magnitude_profile': mag_full,
+                'phase_template': phase_full,
+                'phase_consistency': cons_full,
+                'content_magnitude_baseline': content_base,
+                'white_magnitude_profile': wmag_full,
+                'white_phase_template': None,
+                'white_phase_consistency': None,
+                'black_white_agreement': agree_full,
+                'n_black_refs': nb,
+                'n_white_refs': int(d.get(pfx + 'n_white_refs', 0)),
+                'n_random_refs': int(d.get(pfx + 'n_random_refs', 0)),
+            }
+
+    def _load_v1(self, d):
+        """Load v1 multi-resolution format (full-precision arrays)."""
+        for res in d['resolutions']:
+            h, w = int(res[0]), int(res[1])
+            pfx = f'{h}x{w}/'
+            prof = {}
+            for key in self._PROFILE_ARRAYS:
+                fk = pfx + key
+                prof[key] = d[fk] if fk in d else None
+            for key in self._PROFILE_SCALARS:
+                fk = pfx + key
+                prof[key] = int(d[fk]) if fk in d else 0
+            self.profiles[(h, w)] = prof
+
+    def _load_legacy(self, d):
+        """Load original single-resolution format."""
+        h, w = int(d['ref_shape'][0]), int(d['ref_shape'][1])
+        prof = {}
+        for key in self._PROFILE_ARRAYS:
+            prof[key] = d[key] if key in d else None
+        prof['n_black_refs'] = int(d['n_black_refs']) if 'n_black_refs' in d else 0
+        prof['n_white_refs'] = int(d['n_white_refs']) if 'n_white_refs' in d else 0
+        prof['n_random_refs'] = int(d['n_random_refs']) if 'n_random_refs' in d else 0
+        self.profiles[(h, w)] = prof
+
 
 # ================================================================
 # CLI INTERFACE