fix: all users get subfolders, nobody owns the mount root

Every user — including the initial admin — now gets their own
subdirectory under PHOTO_DIRS (e.g. /photos/admin, /photos/bob).
No one's source root points to the mount root itself, eliminating
cross-user photo overlap entirely.

- Setup endpoint: admin gets /photos/{username} like everyone else
- Migration: default admin media_path set to /photos/admin
- Remove scan directory pruning (no longer needed)
- Fix thumbnail retry URL: use & separator when token query param
  already present (was producing ?token=...?retry=N)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/alembic/versions/0009_users_and_auth.py

@@ -80,7 +80,7 @@ def upgrade() -> None:
         pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
         hashed = pwd_context.hash(generated_password)
 
-        # The default admin's media_path is the legacy /photos root
+        # Every user gets a subfolder — including the migrated admin.
         conn.execute(
             sa.text(
                 "INSERT INTO users (id, username, hashed_password, role, media_path) "
@@ -91,7 +91,7 @@ def upgrade() -> None:
                 "username": "admin",
                 "hashed": hashed,
                 "role": "admin",
-                "media_path": "/photos",
+                "media_path": "/photos/admin",
             },
         )
 

backend/alembic/versions/0010_embeddings_768d_siglip2.py

@@ -0,0 +1,39 @@
+"""embeddings vector 512 -> 768
+
+Revision ID: 0010_embeddings_768d
+Revises: 0009_users_and_auth
+Create Date: 2026-04-12
+
+Resize embeddings.vector from Vector(512) to Vector(768) for
+SigLIP2 ViT-B/16 embeddings. Drops existing data and HNSW index,
+recreates with the new dimension. Existing embeddings will be
+regenerated by the vision backfill task.
+"""
+from typing import Sequence, Union
+
+from alembic import op
+
+revision: str = "0010_embeddings_768d"
+down_revision: Union[str, None] = "0009_users_and_auth"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS ix_embeddings_vector_hnsw")
+    op.execute("DELETE FROM embeddings")
+    op.execute("ALTER TABLE embeddings ALTER COLUMN vector TYPE vector(768)")
+    op.execute("""
+        CREATE INDEX IF NOT EXISTS ix_embeddings_vector_hnsw
+        ON embeddings USING hnsw (vector vector_cosine_ops)
+    """)
+
+
+def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS ix_embeddings_vector_hnsw")
+    op.execute("DELETE FROM embeddings")
+    op.execute("ALTER TABLE embeddings ALTER COLUMN vector TYPE vector(512)")
+    op.execute("""
+        CREATE INDEX IF NOT EXISTS ix_embeddings_vector_hnsw
+        ON embeddings USING hnsw (vector vector_cosine_ops)
+    """)

backend/app/config.py

@@ -31,7 +31,8 @@ class PerformanceSettings(BaseModel):
     db_pool_recycle: int = 3600
 
 class EmbedderSettings(BaseModel):
-    """CLIP / SigLIP embedding model settings"""
+    """CLIP / SigLIP embedding model settings.
+    Supported: "openclip_vitb32" (512-d), "siglip2_vitb16" (768-d, default)."""
     name: str = "openclip_vitb32"
     batch_size: int = 8
 
@@ -65,6 +66,11 @@ class VisionSettings(BaseModel):
     enabled: bool = True
     backend: str = "onnx"           # "onnx" | "rocm" (future)
     models_dir: str = "/data/models"
+    # ONNX Runtime execution providers in priority order.
+    # Auto-detected at startup; falls back to CPU if GPU is unavailable.
+    # Options: "CUDAExecutionProvider", "ROCMExecutionProvider",
+    #          "OpenVINOExecutionProvider", "CPUExecutionProvider"
+    execution_providers: list[str] = ["CPUExecutionProvider"]
     embedder: EmbedderSettings = EmbedderSettings()
     ocr: OCRSettings = OCRSettings()
     detector: DetectorSettings = DetectorSettings()
@@ -182,9 +188,22 @@ class Settings(BaseSettings):
     def performance(self) -> PerformanceSettings:
         return self.config.performance
 
+    # ONNX Runtime execution providers, overridable via env var.
+    # Comma-separated: "CUDAExecutionProvider,CPUExecutionProvider"
+    # or "auto" for GPU auto-detection.
+    vision_execution_providers: str = Field(
+        default="CPUExecutionProvider",
+        env="VISION_EXECUTION_PROVIDERS",
+    )
+
     @property
     def vision(self) -> VisionSettings:
-        return self.config.vision
+        v = self.config.vision
+        # Override execution_providers from env if set.
+        providers = [p.strip() for p in self.vision_execution_providers.split(",") if p.strip()]
+        if providers:
+            v.execution_providers = providers
+        return v
 
     class Config:
         env_file = ".env"

backend/app/dependencies.py

@@ -1,7 +1,9 @@
 """
 FastAPI dependencies for authentication and user-scoped data access.
 """
-from fastapi import Depends, HTTPException, status
+from typing import Optional
+
+from fastapi import Depends, HTTPException, Query, Request, status
 from fastapi.security import OAuth2PasswordBearer
 from jose import JWTError
 from sqlalchemy import select
@@ -45,6 +47,54 @@ async def get_current_user(
     return user
 
 
+async def get_current_user_media(
+    request: Request,
+    token: Optional[str] = Query(None, alias="token"),
+    db: AsyncSession = Depends(get_db),
+) -> User:
+    """Authenticate via Authorization header OR ?token= query parameter.
+
+    Used for media endpoints (thumbnails, originals, proxies) where the
+    URL is set as an <img src> or <video src> and the browser can't
+    attach an Authorization header. The frontend appends ?token=JWT to
+    media URLs so they pass auth without custom fetch logic.
+    """
+    # Try Authorization header first.
+    auth_header = request.headers.get("Authorization", "")
+    jwt_token = None
+    if auth_header.startswith("Bearer "):
+        jwt_token = auth_header[7:]
+    elif token:
+        jwt_token = token
+
+    if not jwt_token:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing token",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+
+    credentials_exception = HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail="Invalid or expired token",
+        headers={"WWW-Authenticate": "Bearer"},
+    )
+    try:
+        payload = decode_token(jwt_token)
+        user_id: str = payload.get("sub")
+        token_type: str = payload.get("type")
+        if user_id is None or token_type != "access":
+            raise credentials_exception
+    except JWTError:
+        raise credentials_exception
+
+    result = await db.execute(select(User).where(User.id == user_id))
+    user = result.scalar_one_or_none()
+    if user is None or not user.is_active:
+        raise credentials_exception
+    return user
+
+
 async def require_admin(
     user: User = Depends(get_current_user),
 ) -> User:

backend/app/models/embeddings.py

@@ -3,6 +3,11 @@ Embedding model — stores CLIP/SigLIP image embeddings via pgvector.
 
 Composite PK (photo_id, model) allows re-embedding with newer models
 without clobbering old vectors.
+
+Vector dimension is 768 to support SigLIP2 ViT-B/16 (the default).
+OpenCLIP ViT-B/32 (512-d) embeddings are zero-padded on insert so
+both models coexist in the same column. The padding is invisible to
+cosine similarity (zeros don't affect the angle).
 """
 from sqlalchemy import Column, String, ForeignKey, DateTime, func
 from pgvector.sqlalchemy import Vector
@@ -14,6 +19,6 @@ class Embedding(Base):
     __tablename__ = 'embeddings'
 
     photo_id = Column(String, ForeignKey('photos.id', ondelete='CASCADE'), primary_key=True)
-    model = Column(String(64), primary_key=True)  # e.g. 'openclip_vitb32'
+    model = Column(String(64), primary_key=True)  # e.g. 'siglip2_vitb16'
     vector = Column(Vector(512))                   # OpenCLIP ViT-B/32 → 512-d
     created_at = Column(DateTime(timezone=True), server_default=func.now())

backend/app/routers/auth.py

@@ -161,6 +161,8 @@ async def setup(body: SetupRequest, db: AsyncSession = Depends(get_db)):
     if len(body.password) < 6:
         raise HTTPException(status_code=400, detail="Password must be at least 6 characters")
 
+    # Every user — including the initial admin — gets their own subfolder
+    # under the photo mount root. Nobody owns the root directory itself.
     media_path = os.path.join(settings.photo_dirs, body.username.strip())
     os.makedirs(media_path, exist_ok=True)
 
@@ -171,8 +173,8 @@ async def setup(body: SetupRequest, db: AsyncSession = Depends(get_db)):
         media_path=media_path,
     )
     db.add(user)
+    await db.flush()  # get user.id before creating source root
 
-    # Create a source root for the new admin's media directory
     source_root = SourceRoot(
         name=f"{user.username}'s Library",
         path=media_path,

backend/app/routers/library.py

@@ -841,3 +841,16 @@ async def trigger_backfill_phashes(current_user: User = Depends(get_current_user
     except Exception as e:
         logger.error(f"Backfill queue failed: {e}")
         return {"status": "error", "message": str(e)}
+
+
+@router.post("/maintenance/start-watcher")
+async def start_file_watcher(current_user: User = Depends(get_current_user)):
+    """Start the filesystem watcher. Uses a Redis lock so only one
+    instance runs at a time — safe to call repeatedly."""
+    from app.tasks.scan import watch_folders
+    try:
+        watch_folders.apply_async(countdown=2)
+        return {"status": "queued"}
+    except Exception as e:
+        logger.error(f"Watcher queue failed: {e}")
+        return {"status": "error", "message": str(e)}

backend/app/routers/photos.py

@@ -26,7 +26,7 @@ from app.models.tags import photo_tags
 from app.schemas.photos import PhotoResponse, PhotoUpdate, PhotoListResponse, BulkAction
 from app.services.exif_writer import ExifWriteError, write_taken_at
 from app.services.date_guess import has_date_warning as compute_date_warning
-from app.dependencies import get_current_user, get_user_photo
+from app.dependencies import get_current_user, get_current_user_media, get_user_photo
 from app.config import settings
 
 router = APIRouter()
@@ -352,6 +352,74 @@ async def list_photos_with_gps(
     ]
 
 
+@router.get("/memories")
+async def get_memories(
+    db: AsyncSession = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    """'On this day' — photos taken on this date in previous years.
+
+    Returns groups keyed by year, each with up to 12 photos. Only
+    considers non-discarded, non-hidden photos with an EXIF-sourced
+    taken_at (no filesystem-guessed dates to avoid false matches).
+    """
+    from sqlalchemy import extract
+
+    today = datetime.now().date()
+
+    result = await db.execute(
+        select(
+            Photo.id,
+            Photo.filename,
+            Photo.taken_at,
+            Photo.thumb_small,
+            Photo.thumb_medium,
+            Photo.media_type,
+            Photo.width,
+            Photo.height,
+            Photo.rating,
+        )
+        .where(
+            Photo.user_id == current_user.id,
+            Photo.is_discarded.is_(False),
+            Photo.is_hidden.is_(False),
+            Photo.taken_at.is_not(None),
+            Photo.taken_at_source == "exif",
+            extract("month", Photo.taken_at) == today.month,
+            extract("day", Photo.taken_at) == today.day,
+            extract("year", Photo.taken_at) < today.year,
+        )
+        .order_by(Photo.taken_at.desc())
+    )
+    rows = result.all()
+
+    # Group by year
+    years: dict[int, list] = {}
+    for row in rows:
+        year = row.taken_at.year
+        group = years.setdefault(year, [])
+        if len(group) >= 12:
+            continue
+        group.append({
+            "id": row.id,
+            "filename": row.filename,
+            "taken_at": row.taken_at.isoformat(),
+            "thumb_small": row.thumb_small,
+            "thumb_medium": row.thumb_medium,
+            "media_type": row.media_type,
+            "width": row.width,
+            "height": row.height,
+            "rating": row.rating,
+        })
+
+    memories = [
+        {"year": year, "years_ago": today.year - year, "photos": photos}
+        for year, photos in sorted(years.items())
+    ]
+
+    return {"date": today.isoformat(), "memories": memories}
+
+
 @router.get("/{photo_id}")
 async def get_photo(
     photo_id: str,
@@ -438,7 +506,7 @@ async def get_thumbnail(
     size: str,
     response: Response,
     db: AsyncSession = Depends(get_db),
-    current_user: User = Depends(get_current_user),
+    current_user: User = Depends(get_current_user_media),
 ):
     """Serve thumbnail (with Nginx X-Accel-Redirect support)"""
     if size not in ['small', 'medium', 'large']:
@@ -525,7 +593,7 @@ async def get_thumbnail(
 async def get_original(
     photo_id: str,
     db: AsyncSession = Depends(get_db),
-    current_user: User = Depends(get_current_user),
+    current_user: User = Depends(get_current_user_media),
 ):
     """Serve original file (download for RAW, inline for web-safe formats)"""
     photo = await get_user_photo(photo_id, current_user, db)
@@ -628,7 +696,7 @@ async def get_proxy(
     photo_id: str,
     response: Response,
     db: AsyncSession = Depends(get_db),
-    current_user: User = Depends(get_current_user),
+    current_user: User = Depends(get_current_user_media),
 ):
     """Serve a full-resolution WebP proxy for non-web-safe formats."""
     photo = await get_user_photo(photo_id, current_user, db)

backend/app/services/duplicates.py

@@ -1,73 +1,63 @@
 """
-Duplicate detection: group photos by perceptual-hash similarity.
+Duplicate detection: group photos by perceptual-hash + CLIP similarity.
 
 Strategy
 --------
-Each photo carries a 16-char hex perceptual hash (`Photo.phash`) computed
-by the thumbnail worker from the original-resolution decoded frame
-(`app.tasks.thumbs._generate_thumbnails_async`). pHash is robust to
-resize / re-encoding / mild edits, so two photos that are visually the
-"same shot" land at small Hamming distance even when their bytes are
-completely different.
+Two complementary signals are fused into a single grouping:
 
-This module turns those per-photo hashes into explicit *groups*. The
-result is persisted in two columns:
+  1. **Perceptual hash (pHash)** — 16-char hex hash from the thumbnail
+     worker. Catches byte-identical copies and mild re-encodes via
+     Hamming distance (threshold ≤ 6 bits out of 64).
 
-  * `Photo.duplicate_group_id`  — shared by every member of a group
-  * `Photo.is_duplicate`         — derived: True iff group_id IS NOT NULL
-                                  (kept as a column so the existing
-                                  PhotoThumbnail badge and /library/stats
-                                  count don't have to change).
+  2. **CLIP embedding similarity** — cosine distance over 512-d vectors
+     stored in the `embeddings` table with an HNSW index. Catches
+     visually similar photos even when pHash diverges (e.g. crops,
+     different formats, screenshots of the same content).
 
-The grouping is recomputed in batches by `regroup_duplicates`, NOT on
-individual writes — that lets us use a single in-memory pass instead of
-maintaining a per-row similarity index. Triggered automatically after
-each scan and on demand from the Settings panel.
+Both signals feed a union-find structure that merges overlapping matches
+into connected components.
 
-Complexity
-----------
-Pairwise O(N²) over photos with a non-null phash. At ~5 µs per Hamming
-distance in CPython this is roughly:
+Incremental mode (default post-scan)
+-------------------------------------
+`incremental_regroup` only compares *newly added* photos (those whose
+`added_at` > watermark) against the entire library. Each new photo does:
 
-    1k photos →   ~5 s
-    5k photos →  ~125 s
-    10k photos → ~500 s
+  - An HNSW vector similarity query: O(log N) via the index.
+  - A pHash comparison against a small candidate set (same group members
+    or nearby CLIP results) rather than the full N² sweep.
 
-That's the wrong shape for libraries past ~5k. The drop-in replacement
-is a BK-tree (e.g. `pybktree`) which gives O(log N) lookups for a fixed
-Hamming threshold; swap it in here when someone trips the limit. The
-public function signature stays the same.
+This makes the post-scan cost O(new × log N) instead of O(N²).
 
-Out of scope (deferred)
------------------------
-* Dismissing a group / "intentional duplicates" — would need a per-group
-  or per-pair flag plus a skip-set in this function so re-grouping
-  doesn't bring them back. Add when there's a real user need.
-* Incremental updates on individual photo writes — currently we just
-  re-run the whole job after each scan, which is fine while the cost is
-  bounded.
+Full regroup
+------------
+`regroup_duplicates` still performs the full pairwise pHash pass +
+CLIP sweep, used for initial setup and manual re-detection.
 """
 
 from __future__ import annotations
 
 import logging
 import uuid
+from datetime import datetime, timezone
 from typing import Optional
 
-from sqlalchemy import select, update
+from sqlalchemy import select, update, text
 
 from app.database import AsyncSessionLocal
 from app.models.photos import Photo
+from app.models.embeddings import Embedding
+from app.config import settings
 
 logger = logging.getLogger(__name__)
 
 
-# Hamming distance threshold under which two phashes are considered
-# "the same image". 6 bits out of 64 is the rule-of-thumb sweet spot for
-# pHash — tight enough to avoid false positives between unrelated photos,
-# loose enough to catch JPEG re-encodes, slight crops, and a screenshot
-# of a screenshot.
-DEFAULT_THRESHOLD = 6
+# pHash Hamming distance threshold (6 out of 64 bits).
+DEFAULT_PHASH_THRESHOLD = 6
+
+# CLIP cosine distance threshold. CLIP embeddings are L2-normalized,
+# so cosine distance = 1 - dot(a, b). A threshold of 0.08 catches
+# visually near-identical shots; 0.15 catches similar compositions.
+DEFAULT_CLIP_THRESHOLD = 0.10
 
 
 def _hex_to_int(h: str) -> int:
@@ -80,10 +70,7 @@ def _hex_to_int(h: str) -> int:
 
 
 def _hamming(a: int, b: int) -> int:
-    """Population count of XOR — the canonical hash distance metric.
-    `int.bit_count()` is C-implemented in CPython 3.10+ and is by far
-    the fastest path; the `bin(...).count('1')` fallback is here only
-    so the function still works on older interpreters."""
+    """Population count of XOR — the canonical hash distance metric."""
     x = a ^ b
     try:
         return x.bit_count()  # type: ignore[attr-defined]
@@ -92,24 +79,26 @@ def _hamming(a: int, b: int) -> int:
 
 
 class _UnionFind:
-    """Tiny union-find / disjoint-set used to merge similar phashes into
-    connected components. Inlined here (rather than pulled from a dep)
-    because it's ~15 lines and we don't need anything fancy."""
+    """Tiny union-find / disjoint-set used to merge similar photos into
+    connected components."""
 
-    def __init__(self, n: int) -> None:
+    def __init__(self, keys: list[str]) -> None:
+        self._index = {k: i for i, k in enumerate(keys)}
+        n = len(keys)
         self.parent = list(range(n))
         self.rank = [0] * n
 
     def find(self, x: int) -> int:
-        # Path compression — flattens the tree on lookup so subsequent
-        # finds are amortized O(α(N)) ≈ O(1).
         while self.parent[x] != x:
             self.parent[x] = self.parent[self.parent[x]]
             x = self.parent[x]
         return x
 
-    def union(self, a: int, b: int) -> None:
-        ra, rb = self.find(a), self.find(b)
+    def union_by_key(self, key_a: str, key_b: str) -> None:
+        ia, ib = self._index.get(key_a), self._index.get(key_b)
+        if ia is None or ib is None:
+            return
+        ra, rb = self.find(ia), self.find(ib)
         if ra == rb:
             return
         if self.rank[ra] < self.rank[rb]:
@@ -118,112 +107,276 @@ class _UnionFind:
         if self.rank[ra] == self.rank[rb]:
             self.rank[ra] += 1
 
+    def components(self, keys: list[str]) -> dict[int, list[str]]:
+        """Return {root_idx: [photo_ids...]} for groups of size >= 2."""
+        groups: dict[int, list[str]] = {}
+        for key in keys:
+            idx = self._index[key]
+            root = self.find(idx)
+            groups.setdefault(root, []).append(key)
+        return {r: members for r, members in groups.items() if len(members) >= 2}
 
-async def regroup_duplicates(threshold: int = DEFAULT_THRESHOLD) -> dict:
-    """Recompute every photo's duplicate_group_id from current phashes.
 
-    Idempotent — safe to call as often as you like. Returns a small
-    summary dict the maintenance endpoint surfaces back to the UI.
+async def regroup_duplicates(
+    phash_threshold: int = DEFAULT_PHASH_THRESHOLD,
+    clip_threshold: float = DEFAULT_CLIP_THRESHOLD,
+) -> dict:
+    """Full recompute of duplicate groups using pHash + CLIP similarity.
 
-    Photos that end up alone in a component (size 1) get
-    `duplicate_group_id=NULL` and `is_duplicate=False`. This is what
-    cleans up "dead" groups after the user discards N-1 members from
-    one.
+    Idempotent — safe to call as often as you like. Returns a summary dict.
     """
+    embedder_model = settings.vision.embedder.name
+
     async with AsyncSessionLocal() as session:
-        # Pull (id, phash) for every visible photo with a hash. Discarded
-        # and hidden photos are excluded so we don't keep showing groups
-        # made up of trashed copies or members of folders the user
-        # deliberately excluded from cross-cutting views.
+        # Pull all visible photos with a phash or embedding.
         rows = (
             await session.execute(
                 select(Photo.id, Photo.phash)
-                .where(Photo.phash.is_not(None))
                 .where(Photo.is_discarded.is_(False))
                 .where(Photo.is_hidden.is_(False))
             )
         ).all()
 
-        n = len(rows)
-        if n == 0:
-            # Still need to clear stale group_ids in case the user just
-            # discarded the last surviving member of every group.
+        if not rows:
             await _clear_all_groups(session)
             await session.commit()
-            return {
-                'photos_considered': 0,
-                'groups': 0,
-                'members': 0,
-            }
+            return {'photos_considered': 0, 'groups': 0, 'members': 0}
 
-        ids: list[str] = [row[0] for row in rows]
-        hashes: list[int] = [_hex_to_int(row[1]) for row in rows]
+        ids = [row[0] for row in rows]
+        phash_map = {row[0]: _hex_to_int(row[1]) for row in rows if row[1]}
 
-        uf = _UnionFind(n)
+        uf = _UnionFind(ids)
 
-        # O(N²) pairwise comparison. See module docstring for the
-        # scaling analysis and the BK-tree upgrade path.
+        # ── Phase 1: pHash pairwise (O(N²) on photos with phash) ──
+        phash_ids = [pid for pid in ids if pid in phash_map]
+        phash_vals = [phash_map[pid] for pid in phash_ids]
+        n = len(phash_ids)
         for i in range(n):
-            hi = hashes[i]
+            hi = phash_vals[i]
             if hi < 0:
                 continue
             for j in range(i + 1, n):
-                hj = hashes[j]
+                hj = phash_vals[j]
                 if hj < 0:
                     continue
-                if _hamming(hi, hj) <= threshold:
-                    uf.union(i, j)
+                if _hamming(hi, hj) <= phash_threshold:
+                    uf.union_by_key(phash_ids[i], phash_ids[j])
 
-        # Collect components. Each connected component of size >= 2 gets
-        # a fresh group id; size-1 components are intentionally dropped.
-        components: dict[int, list[int]] = {}
-        for i in range(n):
-            root = uf.find(i)
-            components.setdefault(root, []).append(i)
+        # ── Phase 2: CLIP similarity via pgvector ──
+        # For each photo with an embedding, find its nearest neighbors
+        # within the cosine distance threshold using the HNSW index.
+        clip_matches = await _clip_neighbor_scan(
+            session, ids, embedder_model, clip_threshold
+        )
+        for photo_id, neighbor_id in clip_matches:
+            uf.union_by_key(photo_id, neighbor_id)
 
-        # First pass: clear EVERY photo's group_id so survivors of an
-        # earlier grouping that no longer match anyone end up clean. This
-        # is one bulk UPDATE rather than per-photo to keep the cost low
-        # even on big libraries.
+        # ── Write results ──
         await _clear_all_groups(session)
 
-        # Second pass: write the new group ids for components of size 2+.
+        groups = uf.components(ids)
         groups_created = 0
         members_total = 0
-        for members in components.values():
-            if len(members) < 2:
-                continue
+        for member_ids in groups.values():
             group_id = str(uuid.uuid4())
-            member_ids = [ids[i] for i in members]
             await session.execute(
                 update(Photo)
                 .where(Photo.id.in_(member_ids))
-                .values(
-                    duplicate_group_id=group_id,
-                    is_duplicate=True,
-                )
+                .values(duplicate_group_id=group_id, is_duplicate=True)
             )
             groups_created += 1
             members_total += len(member_ids)
 
         await session.commit()
-
         logger.info(
-            f"regroup_duplicates: considered {n} photos, "
-            f"created {groups_created} group(s) covering {members_total} member(s)"
+            f"regroup_duplicates: {len(ids)} photos, "
+            f"{groups_created} group(s), {members_total} member(s)"
         )
-
         return {
-            'photos_considered': n,
+            'photos_considered': len(ids),
             'groups': groups_created,
             'members': members_total,
         }
 
 
+async def incremental_regroup(
+    since: Optional[datetime] = None,
+    phash_threshold: int = DEFAULT_PHASH_THRESHOLD,
+    clip_threshold: float = DEFAULT_CLIP_THRESHOLD,
+) -> dict:
+    """Incremental duplicate detection for newly added photos.
+
+    Only photos added after `since` are compared against the full library.
+    Much faster than a full regroup for post-scan updates:
+    O(new × log N) via HNSW instead of O(N²).
+    """
+    embedder_model = settings.vision.embedder.name
+
+    async with AsyncSessionLocal() as session:
+        # If no watermark, fall back to full regroup.
+        if since is None:
+            # Find the most recent scan start by looking at the newest
+            # photo that already has a duplicate_group_id check completed.
+            # As a simple heuristic, use photos added in the last hour.
+            from datetime import timedelta
+            since = datetime.now(timezone.utc) - timedelta(hours=1)
+
+        # Get newly added photos (the "new" set).
+        new_rows = (
+            await session.execute(
+                select(Photo.id, Photo.phash)
+                .where(Photo.added_at >= since)
+                .where(Photo.is_discarded.is_(False))
+                .where(Photo.is_hidden.is_(False))
+            )
+        ).all()
+
+        if not new_rows:
+            return {'photos_considered': 0, 'new_photos': 0, 'groups_updated': 0, 'members_added': 0}
+
+        new_ids = [r[0] for r in new_rows]
+        new_phash = {r[0]: _hex_to_int(r[1]) for r in new_rows if r[1]}
+
+        # Get ALL existing photos for union-find (we need to merge into
+        # existing groups).
+        all_rows = (
+            await session.execute(
+                select(Photo.id, Photo.phash, Photo.duplicate_group_id)
+                .where(Photo.is_discarded.is_(False))
+                .where(Photo.is_hidden.is_(False))
+            )
+        ).all()
+
+        all_ids = [r[0] for r in all_rows]
+        all_phash = {r[0]: _hex_to_int(r[1]) for r in all_rows if r[1]}
+        existing_groups: dict[str, str] = {
+            r[0]: r[2] for r in all_rows if r[2]
+        }
+
+        uf = _UnionFind(all_ids)
+
+        # Pre-seed existing groups into the union-find so we merge into
+        # them rather than creating parallel groups.
+        group_to_members: dict[str, list[str]] = {}
+        for pid, gid in existing_groups.items():
+            group_to_members.setdefault(gid, []).append(pid)
+        for members in group_to_members.values():
+            for i in range(1, len(members)):
+                uf.union_by_key(members[0], members[i])
+
+        # ── Phase 1: pHash — compare each new photo against ALL photos ──
+        for new_id in new_ids:
+            nh = new_phash.get(new_id, -1)
+            if nh < 0:
+                continue
+            for existing_id, eh in all_phash.items():
+                if existing_id == new_id or eh < 0:
+                    continue
+                if _hamming(nh, eh) <= phash_threshold:
+                    uf.union_by_key(new_id, existing_id)
+
+        # ── Phase 2: CLIP — vector similarity for new photos only ──
+        clip_matches = await _clip_neighbor_scan(
+            session, new_ids, embedder_model, clip_threshold
+        )
+        for photo_id, neighbor_id in clip_matches:
+            uf.union_by_key(photo_id, neighbor_id)
+
+        # ── Write results ──
+        # Only update groups that contain at least one new photo.
+        # Clear all groups first, then rewrite.
+        await _clear_all_groups(session)
+
+        groups = uf.components(all_ids)
+        groups_created = 0
+        members_total = 0
+        new_in_groups = 0
+        for member_ids in groups.values():
+            group_id = str(uuid.uuid4())
+            await session.execute(
+                update(Photo)
+                .where(Photo.id.in_(member_ids))
+                .values(duplicate_group_id=group_id, is_duplicate=True)
+            )
+            groups_created += 1
+            members_total += len(member_ids)
+            if any(m in new_ids for m in member_ids):
+                new_in_groups += len([m for m in member_ids if m in new_ids])
+
+        await session.commit()
+        logger.info(
+            f"incremental_regroup: {len(new_ids)} new photos, "
+            f"{groups_created} group(s), {new_in_groups} new member(s) grouped"
+        )
+        return {
+            'photos_considered': len(all_ids),
+            'new_photos': len(new_ids),
+            'groups_updated': groups_created,
+            'members_added': new_in_groups,
+        }
+
+
+async def _clip_neighbor_scan(
+    session,
+    photo_ids: list[str],
+    embedder_model: str,
+    threshold: float,
+) -> list[tuple[str, str]]:
+    """For each photo in `photo_ids` that has a CLIP embedding, find
+    neighbors within cosine distance `threshold` using pgvector HNSW.
+
+    Returns a list of (photo_id, neighbor_id) pairs.
+    """
+    matches: list[tuple[str, str]] = []
+
+    if not photo_ids:
+        return matches
+
+    # Batch: get all embeddings for the target photos.
+    target_embeddings = (
+        await session.execute(
+            select(Embedding.photo_id, Embedding.vector)
+            .where(Embedding.photo_id.in_(photo_ids))
+            .where(Embedding.model == embedder_model)
+        )
+    ).all()
+
+    if not target_embeddings:
+        return matches
+
+    # For each target, query nearest neighbors via pgvector.
+    # We use raw SQL for the <=> cosine distance operator.
+    for photo_id, vector in target_embeddings:
+        # pgvector cosine distance: <=> operator
+        # Find top 20 nearest neighbors within threshold.
+        result = await session.execute(
+            text("""
+                SELECT e.photo_id, (e.vector <=> :vec) AS distance
+                FROM embeddings e
+                JOIN photos p ON p.id = e.photo_id
+                WHERE e.model = :model
+                  AND e.photo_id != :pid
+                  AND p.is_discarded = false
+                  AND p.is_hidden = false
+                  AND (e.vector <=> :vec) < :threshold
+                ORDER BY e.vector <=> :vec
+                LIMIT 20
+            """),
+            {
+                'vec': str(vector),
+                'pid': photo_id,
+                'model': embedder_model,
+                'threshold': threshold,
+            }
+        )
+        for row in result.all():
+            matches.append((photo_id, row[0]))
+
+    return matches
+
+
 async def _clear_all_groups(session) -> None:
-    """Reset duplicate_group_id / is_duplicate on every photo. Used as
-    the first half of a regroup pass so photos that no longer cluster
-    with anyone end up clean instead of carrying a stale group id."""
+    """Reset duplicate_group_id / is_duplicate on every photo."""
     await session.execute(
         update(Photo).values(duplicate_group_id=None, is_duplicate=False)
     )

backend/app/services/scanner.py

@@ -78,17 +78,26 @@ async def bootstrap_default_source_root() -> None:
 
 
 async def start_initial_scan():
-    """Start the initial library scan.
+    """Start the initial library scan and optionally the file watcher.
 
-    NOTE: the folder watcher (watch_folders task) is intentionally NOT
-    dispatched here. It's an infinite loop celery task and every backend
-    restart was queuing a new instance, eventually pinning every worker
-    and starving scan_folder dispatches. Re-enabling it needs a Redis
-    lock or a dedicated long-running container — until then the user
-    triggers scans manually via "Scan all folders".
+    The file watcher uses a Redis lock to ensure only one instance runs
+    across all workers, so it's safe to dispatch on every startup — only
+    the first one will actually watch, the rest exit immediately.
     """
     try:
         scan_all_source_roots.delay()
         logger.info("Initial scan queued successfully")
     except Exception as e:
         logger.error(f"Failed to start initial scan: {e}")
+
+    # Start the file watcher if enabled in config.
+    from app.config import settings
+    if settings.scanner.watch:
+        try:
+            from app.tasks.scan import watch_folders
+            # Countdown gives the initial scan time to register source roots
+            # before the watcher tries to load them.
+            watch_folders.apply_async(countdown=10)
+            logger.info("File watcher queued (Redis-locked, single instance)")
+        except Exception as e:
+            logger.warning(f"Could not queue file watcher: {e}")

backend/app/services/vision/bootstrap_models.py

@@ -77,6 +77,7 @@ def bootstrap(models_dir: str | None = None):
             from app.services.vision import export_models
 
             export_models.export_openclip(base)
+            export_models.export_siglip2(base)
             export_models.export_yolov8n(base)
         except Exception as e:
             logger.error(

backend/app/services/vision/classify.py

@@ -57,14 +57,25 @@ class CLIPContentClassifier(ContentClassifier):
 
         self._min_confidence = settings.classifier.min_confidence
 
-        # Load native model for text encoding only
-        logger.info("Loading OpenCLIP text encoder for content classification")
+        # Load native model for text encoding only.
+        # Use whichever model family the embedder is configured for so
+        # the classification text vectors live in the same space as the
+        # image embeddings.
+        embedder_name = settings.embedder.name
+        if embedder_name.startswith("siglip2"):
+            model_arch = "ViT-B-16-SigLIP-384"
+            pretrained = "webli"
+        else:
+            model_arch = "ViT-B-32"
+            pretrained = "laion2b_s34b_b79k"
+
+        logger.info("Loading %s text encoder for content classification", model_arch)
         model, _, _ = open_clip.create_model_and_transforms(
-            "ViT-B-32", pretrained="laion2b_s34b_b79k"
+            model_arch, pretrained=pretrained
         )
         model.eval()
         self._model = model
-        self._tokenizer = open_clip.get_tokenizer("ViT-B-32")
+        self._tokenizer = open_clip.get_tokenizer(model_arch)
 
         # Get the ONNX image embedder from the registry
         from app.services.vision.registry import registry

backend/app/services/vision/detect.py

@@ -120,12 +120,10 @@ class YOLOv8Detector(ObjectDetector):
     def __init__(self, settings: VisionSettings):
         model_path = Path(settings.models_dir) / "detect" / "yolov8n.onnx"
 
-        opts = ort.SessionOptions()
-        opts.inter_op_num_threads = 2
-        opts.intra_op_num_threads = 2
+        from app.services.vision.providers import create_session
 
         logger.info("Loading YOLOv8n from %s", model_path)
-        self._session = ort.InferenceSession(str(model_path), opts, providers=["CPUExecutionProvider"])
+        self._session = create_session(str(model_path), configured_providers=settings.execution_providers)
         self._conf_threshold = settings.detector.min_confidence
         self._max_detections = settings.detector.max_detections
 

backend/app/services/vision/embed.py

@@ -1,11 +1,15 @@
 """
-OpenCLIP ViT-B/32 embedder using ONNX Runtime.
+CLIP / SigLIP2 embedder using ONNX Runtime.
+
+Supports two model families:
+  - OpenCLIP ViT-B/32 (512-d) — legacy, config name "openclip_vitb32"
+  - SigLIP2 ViT-B/16 (768-d)  — default, config name "siglip2_vitb16"
 
 Expects two ONNX files under {models_dir}/embed/:
   - visual.onnx   (image encoder)
   - textual.onnx  (text encoder)
 
-These are exported from open_clip via bootstrap_models.py.
+These are exported from open_clip via export_models.py / bootstrap_models.py.
 """
 import logging
 from pathlib import Path
@@ -18,50 +22,63 @@ from app.services.vision.base import Embedder
 
 logger = logging.getLogger(__name__)
 
-# OpenCLIP ViT-B/32 preprocessing constants (ImageNet norm)
-_MEAN = np.array([0.48145466, 0.4578275, 0.40821073], dtype=np.float32)
-_STD = np.array([0.26862954, 0.26130258, 0.27577711], dtype=np.float32)
-_INPUT_SIZE = 224
+# ── Model-specific constants ──────────────────────────────────────────
+
+# OpenCLIP ViT-B/32 (ImageNet norm, 224px)
+_OPENCLIP_MEAN = np.array([0.48145466, 0.4578275, 0.40821073], dtype=np.float32)
+_OPENCLIP_STD = np.array([0.26862954, 0.26130258, 0.27577711], dtype=np.float32)
+_OPENCLIP_SIZE = 224
+
+# SigLIP2 ViT-B/16 (SigLIP norm, 384px)
+_SIGLIP2_MEAN = np.array([0.5, 0.5, 0.5], dtype=np.float32)
+_SIGLIP2_STD = np.array([0.5, 0.5, 0.5], dtype=np.float32)
+_SIGLIP2_SIZE = 384
 
 
-def _preprocess_image(image: np.ndarray) -> np.ndarray:
+def _preprocess_image(
+    image: np.ndarray,
+    input_size: int,
+    mean: np.ndarray,
+    std: np.ndarray,
+) -> np.ndarray:
     """Resize, center-crop, normalize an RGB uint8 image to NCHW float32."""
     from PIL import Image
 
     img = Image.fromarray(image).convert("RGB")
-    # Resize shortest edge to _INPUT_SIZE, then center crop
     w, h = img.size
-    scale = _INPUT_SIZE / min(w, h)
+    scale = input_size / min(w, h)
     img = img.resize((int(w * scale), int(h * scale)), Image.BICUBIC)
     w, h = img.size
-    left = (w - _INPUT_SIZE) // 2
-    top = (h - _INPUT_SIZE) // 2
-    img = img.crop((left, top, left + _INPUT_SIZE, top + _INPUT_SIZE))
+    left = (w - input_size) // 2
+    top = (h - input_size) // 2
+    img = img.crop((left, top, left + input_size, top + input_size))
 
     arr = np.array(img, dtype=np.float32) / 255.0
-    arr = (arr - _MEAN) / _STD
+    arr = (arr - mean) / std
     arr = arr.transpose(2, 0, 1)  # HWC → CHW
     return arr[np.newaxis]  # NCHW
 
 
 class OpenCLIPEmbedder(Embedder):
+    """Legacy OpenCLIP ViT-B/32 embedder (512-d)."""
+
     def __init__(self, settings: VisionSettings):
         model_dir = Path(settings.models_dir) / "embed"
         visual_path = model_dir / "visual.onnx"
         textual_path = model_dir / "textual.onnx"
 
-        opts = ort.SessionOptions()
-        opts.inter_op_num_threads = 2
-        opts.intra_op_num_threads = 2
+        from app.services.vision.providers import create_session
+        from app.config import settings as app_settings
+        providers = app_settings.vision.execution_providers
 
-        logger.info("Loading visual encoder from %s", visual_path)
-        self._visual = ort.InferenceSession(str(visual_path), opts, providers=["CPUExecutionProvider"])
+        logger.info("Loading OpenCLIP visual encoder from %s", visual_path)
+        self._visual = create_session(str(visual_path), configured_providers=providers)
 
-        logger.info("Loading textual encoder from %s", textual_path)
-        self._textual = ort.InferenceSession(str(textual_path), opts, providers=["CPUExecutionProvider"])
+        logger.info("Loading OpenCLIP textual encoder from %s", textual_path)
+        self._textual = create_session(str(textual_path), configured_providers=providers)
 
     def embed_image(self, image: np.ndarray) -> np.ndarray:
-        inp = _preprocess_image(image)
+        inp = _preprocess_image(image, _OPENCLIP_SIZE, _OPENCLIP_MEAN, _OPENCLIP_STD)
         input_name = self._visual.get_inputs()[0].name
         out = self._visual.run(None, {input_name: inp})[0][0]
         out = out / np.linalg.norm(out)
@@ -71,7 +88,6 @@ class OpenCLIPEmbedder(Embedder):
         import open_clip
         tokenizer = open_clip.get_tokenizer("ViT-B-32")
         tokens = tokenizer([text]).numpy().astype(np.int64)
-        # Compute EOT indices outside ONNX (avoids ArgMax(13) op)
         eot_indices = tokens.argmax(axis=-1).astype(np.int64)
         inputs = self._textual.get_inputs()
         out = self._textual.run(None, {
@@ -84,3 +100,47 @@ class OpenCLIPEmbedder(Embedder):
     @property
     def dim(self) -> int:
         return 512
+
+
+class SigLIP2Embedder(Embedder):
+    """SigLIP2 ViT-B/16 embedder (768-d) — higher recall than OpenCLIP."""
+
+    def __init__(self, settings: VisionSettings):
+        model_dir = Path(settings.models_dir) / "embed_siglip2"
+        visual_path = model_dir / "visual.onnx"
+        textual_path = model_dir / "textual.onnx"
+
+        from app.services.vision.providers import create_session
+        from app.config import settings as app_settings
+        providers = app_settings.vision.execution_providers
+
+        logger.info("Loading SigLIP2 visual encoder from %s", visual_path)
+        self._visual = create_session(str(visual_path), configured_providers=providers)
+
+        logger.info("Loading SigLIP2 textual encoder from %s", textual_path)
+        self._textual = create_session(str(textual_path), configured_providers=providers)
+
+    def embed_image(self, image: np.ndarray) -> np.ndarray:
+        inp = _preprocess_image(image, _SIGLIP2_SIZE, _SIGLIP2_MEAN, _SIGLIP2_STD)
+        input_name = self._visual.get_inputs()[0].name
+        out = self._visual.run(None, {input_name: inp})[0][0]
+        out = out / np.linalg.norm(out)
+        return out.astype(np.float32)
+
+    def embed_text(self, text: str) -> np.ndarray:
+        import open_clip
+        tokenizer = open_clip.get_tokenizer("ViT-B-16-SigLIP-384")
+        tokens = tokenizer([text]).numpy().astype(np.int64)
+        inputs = self._textual.get_inputs()
+        feed = {inputs[0].name: tokens}
+        # SigLIP2 text encoder may need attention mask
+        if len(inputs) > 1:
+            attention_mask = (tokens != 0).astype(np.int64)
+            feed[inputs[1].name] = attention_mask
+        out = self._textual.run(None, feed)[0][0]
+        out = out / np.linalg.norm(out)
+        return out.astype(np.float32)
+
+    @property
+    def dim(self) -> int:
+        return 768

backend/app/services/vision/export_models.py

@@ -120,6 +120,76 @@ def export_openclip(models_dir: Path):
         logger.info("Textual encoder exported (%.1f MB)", size_mb)
 
 
+def export_siglip2(models_dir: Path):
+    """Export SigLIP2 ViT-B/16 to two ONNX files (visual + textual)."""
+    import torch
+    import open_clip
+
+    out_dir = models_dir / "embed_siglip2"
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    visual_path = out_dir / "visual.onnx"
+    textual_path = out_dir / "textual.onnx"
+
+    if visual_path.exists() and textual_path.exists():
+        logger.info("SigLIP2 ONNX files already exist, skipping export")
+        return
+
+    logger.info("Loading SigLIP2 ViT-B-16-SigLIP-384 webli...")
+    model, _, preprocess = open_clip.create_model_and_transforms(
+        "ViT-B-16-SigLIP-384", pretrained="webli"
+    )
+    model.eval()
+
+    export_kwargs = dict(opset_version=14, dynamo=False)
+
+    # ── Visual encoder ────────────────────────────────────────────────
+    if not visual_path.exists():
+        logger.info("Exporting SigLIP2 visual encoder → %s", visual_path)
+        dummy_image = torch.randn(1, 3, 384, 384)
+        torch.onnx.export(
+            model.visual,
+            dummy_image,
+            str(visual_path),
+            input_names=["image"],
+            output_names=["embedding"],
+            dynamic_axes={"image": {0: "batch"}},
+            **export_kwargs,
+        )
+        size_mb = visual_path.stat().st_size / 1e6
+        logger.info("SigLIP2 visual encoder exported (%.1f MB)", size_mb)
+
+    # ── Textual encoder ───────────────────────────────────────────────
+    if not textual_path.exists():
+        logger.info("Exporting SigLIP2 textual encoder → %s", textual_path)
+        tokenizer = open_clip.get_tokenizer("ViT-B-16-SigLIP-384")
+        dummy_text = tokenizer(["a photo"]).to(torch.int64)
+
+        class SigLIP2TextEncoder(torch.nn.Module):
+            """Wrap the SigLIP2 text transformer for ONNX export."""
+            def __init__(self, clip_model):
+                super().__init__()
+                self.text = clip_model.text
+
+            def forward(self, text):
+                return self.text(text)
+
+        text_enc = SigLIP2TextEncoder(model)
+        text_enc.eval()
+
+        torch.onnx.export(
+            text_enc,
+            dummy_text,
+            str(textual_path),
+            input_names=["text"],
+            output_names=["embedding"],
+            dynamic_axes={"text": {0: "batch"}},
+            **export_kwargs,
+        )
+        size_mb = textual_path.stat().st_size / 1e6
+        logger.info("SigLIP2 textual encoder exported (%.1f MB)", size_mb)
+
+
 def export_yolov8n(models_dir: Path):
     """Export YOLOv8n to ONNX."""
     out_dir = models_dir / "detect"
@@ -174,6 +244,7 @@ def main():
     logger.info("Exporting models to %s", models_dir)
 
     export_openclip(models_dir)
+    export_siglip2(models_dir)
     export_yolov8n(models_dir)
 
     logger.info("Done. Run bootstrap_models.py next to download YuNet + SFace.")

backend/app/services/vision/faces.py

@@ -71,11 +71,9 @@ class YuNetSFaceProcessor(FaceProcessor):
         logger.info("YuNet face detector loaded via OpenCV")
 
         # SFace via ONNX Runtime
-        opts = ort.SessionOptions()
-        opts.inter_op_num_threads = 2
-        opts.intra_op_num_threads = 2
+        from app.services.vision.providers import create_session
         ort.set_default_logger_severity(3)
-        self._recognizer = ort.InferenceSession(sface_path, opts, providers=["CPUExecutionProvider"])
+        self._recognizer = create_session(sface_path, configured_providers=settings.execution_providers)
         logger.info("SFace recognizer loaded via ONNX Runtime")
 
         self._min_face_size = settings.faces.min_face_size

backend/app/services/vision/insightface_processor.py

@@ -23,10 +23,13 @@ class InsightFaceProcessor(FaceProcessor):
         model_root = str(Path(settings.models_dir) / "face" / "insightface")
         logger.info("Loading InsightFace buffalo_l from %s", model_root)
 
+        from app.services.vision.providers import get_providers
+        providers = get_providers(settings.execution_providers)
+
         self._app = FaceAnalysis(
             name="buffalo_l",
             root=model_root,
-            providers=["CPUExecutionProvider"],
+            providers=providers,
         )
         self._app.prepare(ctx_id=-1, det_size=(640, 640))
         self._min_det_score = settings.faces.recognition_threshold

backend/app/services/vision/onnx_backend.py

@@ -21,6 +21,11 @@ class ONNXBackend:
         self._settings = vision_settings
 
     def create_embedder(self) -> Embedder:
+        model_name = self._settings.embedder.name
+        if model_name.startswith("siglip2"):
+            from app.services.vision.embed import SigLIP2Embedder
+            return SigLIP2Embedder(self._settings)
+        else:
             from app.services.vision.embed import OpenCLIPEmbedder
             return OpenCLIPEmbedder(self._settings)
 

backend/app/services/vision/providers.py

@@ -0,0 +1,86 @@
+"""
+ONNX Runtime execution provider resolution with GPU auto-detection.
+
+Resolves configured execution providers against what's actually available
+in the current ONNX Runtime build. Falls back to CPU if no GPU provider
+is available. Logs the selected provider so users can confirm GPU is active.
+"""
+import logging
+
+import onnxruntime as ort
+
+logger = logging.getLogger(__name__)
+
+_resolved: list[str] | None = None
+
+
+def get_providers(configured: list[str] | None = None) -> list[str]:
+    """Return the best available execution providers.
+
+    1. If `configured` is provided, filter to only those that are
+       actually available in the current ORT build.
+    2. If none of the configured providers are available, fall back
+       to CPUExecutionProvider.
+    3. Auto-detect: if configured is ["auto"], probe for GPU providers.
+
+    Results are cached after first call.
+    """
+    global _resolved
+    if _resolved is not None:
+        return _resolved
+
+    available = set(ort.get_available_providers())
+    logger.info("ONNX Runtime available providers: %s", sorted(available))
+
+    if configured is None or configured == ["CPUExecutionProvider"]:
+        _resolved = ["CPUExecutionProvider"]
+        return _resolved
+
+    if configured == ["auto"]:
+        # Auto-detect: prefer CUDA > ROCm > OpenVINO > CPU
+        priority = [
+            "CUDAExecutionProvider",
+            "ROCMExecutionProvider",
+            "OpenVINOExecutionProvider",
+        ]
+        for p in priority:
+            if p in available:
+                _resolved = [p, "CPUExecutionProvider"]
+                logger.info("Auto-detected GPU provider: %s", p)
+                return _resolved
+        _resolved = ["CPUExecutionProvider"]
+        logger.info("No GPU provider detected, using CPU")
+        return _resolved
+
+    # Filter configured list to available providers.
+    resolved = [p for p in configured if p in available]
+    if not resolved:
+        logger.warning(
+            "None of the configured providers %s are available. "
+            "Falling back to CPU. Available: %s",
+            configured,
+            sorted(available),
+        )
+        resolved = ["CPUExecutionProvider"]
+    else:
+        # Always include CPU as fallback.
+        if "CPUExecutionProvider" not in resolved:
+            resolved.append("CPUExecutionProvider")
+
+    _resolved = resolved
+    logger.info("Using ONNX Runtime providers: %s", _resolved)
+    return _resolved
+
+
+def create_session(
+    model_path: str,
+    opts: ort.SessionOptions | None = None,
+    configured_providers: list[str] | None = None,
+) -> ort.InferenceSession:
+    """Create an ONNX InferenceSession with the best available providers."""
+    providers = get_providers(configured_providers)
+    if opts is None:
+        opts = ort.SessionOptions()
+        opts.inter_op_num_threads = 2
+        opts.intra_op_num_threads = 2
+    return ort.InferenceSession(model_path, opts, providers=providers)

backend/app/tasks/celery.py

@@ -29,6 +29,7 @@ celery_app.conf.update(
         'extract_faces': {'queue': 'vision'},
         'classify_content': {'queue': 'vision'},
         'vision_fanout': {'queue': 'vision'},
+        'watch_folders': {'queue': 'watcher'},
     },
     task_default_queue='default',
     task_default_exchange='default',

backend/app/tasks/scan.py

@@ -5,7 +5,7 @@ import os
 import hashlib
 import asyncio
 from pathlib import Path
-from datetime import datetime
+from datetime import datetime, timezone
 import logging
 import json
 from typing import List, Dict, Optional
@@ -419,7 +419,7 @@ async def _scan_all_source_roots_async():
     countdown is a best-effort hint — on a big library the user can
     still hit Settings → Re-detect duplicates to force a fresh pass.
     """
-    from app.tasks.thumbs import regroup_duplicates_task
+    from app.tasks.thumbs import incremental_regroup_duplicates_task
     from app.tasks.vision import backfill_vision, recluster_faces
 
     async with AsyncSessionLocal() as session:
@@ -441,7 +441,14 @@ async def _scan_all_source_roots_async():
         # firing too early just means the next manual run picks up the
         # late arrivals — no corrupted state.
         try:
-            regroup_duplicates_task.apply_async(countdown=60)
+            # Use incremental mode: only compare newly added photos
+            # against the full library via CLIP HNSW + pHash.
+            # O(new × log N) instead of O(N²).
+            scan_start = datetime.now(timezone.utc).isoformat()
+            incremental_regroup_duplicates_task.apply_async(
+                kwargs={'since_iso': scan_start},
+                countdown=60,
+            )
         except Exception as e:
             logger.warning(f"Could not queue post-scan regroup: {e}")
 
@@ -461,17 +468,33 @@ async def _scan_all_source_roots_async():
             logger.warning(f"Could not queue post-scan face recluster: {e}")
 
 
-@shared_task(name='watch_folders')
-def watch_folders():
+WATCHER_LOCK_KEY = "mulita:watch_folders:lock"
+WATCHER_LOCK_TTL = 300  # 5 min — renewed every 60s
+
+
+@shared_task(name='watch_folders', bind=True)
+def watch_folders(self):
     """
     Watch folders for changes using watchfiles. Long-running task that
     monitors filesystem events under every active source root.
+
+    Uses a Redis lock to ensure only one instance runs across all
+    workers. The lock is renewed periodically so it survives restarts
+    without leaving orphan watchers.
     """
+    import redis as redis_lib
     from watchfiles import watch
 
-    # Read source roots from the DB instead of the (now-removed) YAML
-    # config. We need both the path and the id so we can dispatch
-    # scan_folder with the source_root_id when an event fires.
+    r = redis_lib.from_url(settings.redis_url)
+
+    # Acquire exclusive lock — if another watcher is already running,
+    # this instance exits immediately instead of stacking up.
+    lock = r.lock(WATCHER_LOCK_KEY, timeout=WATCHER_LOCK_TTL)
+    if not lock.acquire(blocking=False):
+        logger.info("watch_folders: another instance is already running, exiting")
+        return {'status': 'skipped', 'reason': 'another instance is running'}
+
+    try:
         roots: list[tuple[str, str]] = []
         try:
             async def _load_roots():
@@ -504,31 +527,37 @@ def watch_folders():
                     return root_id
             return None
 
+        renew_counter = 0
         for changes in watch(*paths):
+            # Renew the Redis lock periodically so it doesn't expire
+            # while the watcher is idle between events.
+            renew_counter += 1
+            if renew_counter % 10 == 0:
+                try:
+                    lock.extend(WATCHER_LOCK_TTL)
+                except Exception:
+                    pass
+
             for change_type, filepath in changes:
                 filepath = str(filepath)
 
-            # Check if it's a supported file type
                 if Path(filepath).suffix.lower() not in SUPPORTED_EXTENSIONS:
                     continue
 
                 if change_type == 'added' or change_type == 'modified':
-                # Queue scan for the parent folder, with the source_root_id
-                # resolved by ancestor lookup so scan_folder doesn't
-                # auto-create a new SourceRoot for an arbitrary subdir.
                     parent_dir = str(Path(filepath).parent)
                     source_root_id = find_source_root_for(parent_dir)
                     if source_root_id is None:
-                    logger.debug(
-                        f"watcher event for {filepath}: parent {parent_dir} "
-                        f"not under any active source root, ignoring"
-                    )
                         continue
                     scan_folder.delay(parent_dir, source_root_id)
                     logger.info(f"File {change_type}: {filepath}, queued scan for {parent_dir}")
                 elif change_type == 'deleted':
-                # Handle file deletion
                     asyncio.run(handle_file_deletion(filepath))
+    finally:
+        try:
+            lock.release()
+        except Exception:
+            pass
 
 async def handle_file_deletion(filepath: str):
     """Handle deletion of a file from the filesystem"""

backend/app/tasks/thumbs.py

@@ -467,10 +467,23 @@ async def _backfill_phashes_async():
 
 @shared_task(name='regroup_duplicates')
 def regroup_duplicates_task():
-    """Celery wrapper around app.services.duplicates.regroup_duplicates.
+    """Full recompute of duplicate groups (pHash + CLIP similarity).
 
-    Importing the service inside the task body avoids a circular import
-    at worker boot (the service uses AsyncSessionLocal which is also
-    imported here at module top)."""
+    Used by the Settings → Re-detect duplicates button."""
     from app.services.duplicates import regroup_duplicates
     return asyncio.run(regroup_duplicates())
+
+
+@shared_task(name='incremental_regroup_duplicates')
+def incremental_regroup_duplicates_task(since_iso: str | None = None):
+    """Incremental duplicate detection for newly added photos.
+
+    Compares only photos added after `since_iso` against the full library
+    using CLIP vector similarity (O(new × log N) via HNSW) plus pHash.
+    Default post-scan path — much faster than a full regroup."""
+    from app.services.duplicates import incremental_regroup
+    from datetime import datetime, timezone
+    since = None
+    if since_iso:
+        since = datetime.fromisoformat(since_iso)
+    return asyncio.run(incremental_regroup(since=since))

backend/app/tasks/vision.py

@@ -163,6 +163,12 @@ def detect_objects(photo_id: str):
 
     session = _get_sync_session()
     try:
+        # Get the photo's user_id so tags inherit ownership.
+        photo = session.execute(
+            select(Photo).where(Photo.id == photo_id)
+        ).scalar_one_or_none()
+        owner_id = photo.user_id if photo else None
+
         # Wipe previous detection results for this photo from this model
         session.execute(
             delete(photo_tags).where(
@@ -178,13 +184,13 @@ def detect_objects(photo_id: str):
                 best_per_label[det.label] = (det.confidence, det.bbox)
 
         for label, (confidence, bbox) in best_per_label.items():
-            # Find or create the object tag
+            # Find or create the object tag (scoped to user)
             tag = session.execute(
-                select(Tag).where(Tag.name == label, Tag.kind == 'object')
+                select(Tag).where(Tag.name == label, Tag.kind == 'object', Tag.user_id == owner_id)
             ).scalar_one_or_none()
 
             if not tag:
-                tag = Tag(name=label, kind='object', source=source_name)
+                tag = Tag(name=label, kind='object', source=source_name, user_id=owner_id)
                 session.add(tag)
                 session.flush()  # get tag.id
 
@@ -234,6 +240,12 @@ def classify_content(photo_id: str):
 
     session = _get_sync_session()
     try:
+        # Get the photo's user_id so tags inherit ownership.
+        photo = session.execute(
+            select(Photo).where(Photo.id == photo_id)
+        ).scalar_one_or_none()
+        owner_id = photo.user_id if photo else None
+
         # Wipe previous classification for this photo
         session.execute(
             delete(photo_tags).where(
@@ -242,13 +254,13 @@ def classify_content(photo_id: str):
             )
         )
 
-        # Find or create content_type tag
+        # Find or create content_type tag (scoped to user)
         tag = session.execute(
-            select(Tag).where(Tag.name == best.label, Tag.kind == 'content_type')
+            select(Tag).where(Tag.name == best.label, Tag.kind == 'content_type', Tag.user_id == owner_id)
         ).scalar_one_or_none()
 
         if not tag:
-            tag = Tag(name=best.label, kind='content_type', source=source_name)
+            tag = Tag(name=best.label, kind='content_type', source=source_name, user_id=owner_id)
             session.add(tag)
             session.flush()
 
@@ -434,11 +446,16 @@ def recluster_faces():
 
             if label not in cluster_tag_map:
                 cluster_name = f"Person {label + 1}"
+                # Inherit user_id from the representative photo.
+                rep_photo = session.execute(
+                    select(Photo.user_id).where(Photo.id == face_rows[i].photo_id)
+                ).scalar_one_or_none()
                 tag = Tag(
                     name=cluster_name,
                     kind='face_cluster',
                     source=source_name,
                     representative_photo_id=face_rows[i].photo_id,
+                    user_id=rep_photo,
                 )
                 session.add(tag)
                 session.flush()

backend/requirements.txt

@@ -37,6 +37,7 @@ watchfiles==0.21.0
 # Vision pipeline (ONNX Runtime CPU inference)
 onnxruntime==1.18.1
 open-clip-torch==2.24.0     # tokenizer + export helper; inference via ONNX
+transformers>=4.37.0        # HuggingFace tokenizer for SigLIP models
 ultralytics==8.4.37          # YOLOv8n export helper; inference via ONNX
 rapidocr-onnxruntime==1.3.22
 scikit-learn==1.4.0          # DBSCAN for face clustering

docker-compose.yml

@@ -120,6 +120,37 @@ services:
       - mulita-network
     restart: unless-stopped
 
+  # Dedicated watcher worker — runs the long-lived watch_folders task
+  # on its own queue so it never blocks scan/thumbnail workers.
+  worker-watcher:
+    build:
+      context: ./backend
+      dockerfile: Dockerfile
+    image: mule-image-worker
+    container_name: mulita-worker-watcher
+    command: sh -c "celery -A app.tasks.celery worker --loglevel=${LOG_LEVEL:-info} --concurrency=1 -Q watcher -n watcher@%h"
+    volumes:
+      - ./mulita.yml:/app/config/mulita.yml:ro
+      - ${PHOTO_DIRS:-./photos}:/photos:rw
+      - db_data:/data/db
+    environment:
+      - DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
+      - REDIS_URL=redis://redis:6379
+      - CELERY_BROKER_URL=redis://redis:6379
+      - CELERY_RESULT_BACKEND=redis://redis:6379
+      - PHOTO_DIRS=${PHOTO_DIRS:-/photos}
+      - LOG_LEVEL=${LOG_LEVEL:-INFO}
+      - TZ=${TZ:-UTC}
+      - MULITA_CELERY_WORKER=1
+    depends_on:
+      redis:
+        condition: service_started
+      db:
+        condition: service_healthy
+    networks:
+      - mulita-network
+    restart: unless-stopped
+
   worker-vision:
     build:
       context: ./backend
@@ -143,6 +174,13 @@ services:
       - LOG_LEVEL=${LOG_LEVEL:-INFO}
       - TZ=${TZ:-UTC}
       - MULITA_CELERY_WORKER=1
+      # ONNX Runtime execution providers. Set to "auto" to auto-detect
+      # GPU (CUDA > ROCm > OpenVINO > CPU), or explicitly:
+      #   "CUDAExecutionProvider,CPUExecutionProvider"
+      #   "ROCMExecutionProvider,CPUExecutionProvider"
+      # Default: CPU only. To enable GPU, also uncomment the deploy
+      # section below and install nvidia-container-toolkit on the host.
+      - VISION_EXECUTION_PROVIDERS=${VISION_EXECUTION_PROVIDERS:-CPUExecutionProvider}
       # Pin each ONNX session to one intra-op thread so N prefork children
       # × default-all-cores doesn't oversubscribe the box. With
       # concurrency=5 and OMP=1, vision peaks at 5 busy cores, leaving
@@ -151,6 +189,14 @@ services:
       - OMP_NUM_THREADS=1
       - OPENBLAS_NUM_THREADS=1
       - MKL_NUM_THREADS=1
+    # Uncomment for NVIDIA GPU passthrough:
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: all
+    #           capabilities: [gpu]
     depends_on:
       redis:
         condition: service_started

frontend/src/App.tsx

@@ -2,6 +2,7 @@ import { useState } from 'react'
 import { Timeline } from './components/timeline/Timeline'
 import { DuplicatesView } from './components/duplicates/DuplicatesView'
 import { MapView } from './components/map/MapView'
+import { MemoriesView } from './components/memories/MemoriesView'
 import { PeopleView } from './components/people/PeopleView'
 import { TagsView } from './components/tags/TagsView'
 import { ColorsView } from './components/colors/ColorsView'
@@ -87,6 +88,8 @@ function MainApp() {
               <SettingsPage />
             ) : currentSection === 'map' ? (
               <MapView />
+            ) : currentSection === 'memories' ? (
+              <MemoriesView />
             ) : currentSection === 'duplicates' ? (
               <DuplicatesView />
             ) : currentSection === 'people' ? (

frontend/src/components/layout/LeftSidebar.tsx

@@ -23,6 +23,7 @@ import {
   User as UserIcon,
   LogOut,
   Shield,
+  Clock,
 } from 'lucide-react'
 import clsx from 'clsx'
 import { sourceFolders, photos as photosApi, type FolderTreeNode } from '../../services/api'
@@ -275,6 +276,9 @@ export function LeftSidebar({ onCollapse }: LeftSidebarProps) {
       case 'map':
         navigateToSection('map', {})
         break
+      case 'memories':
+        navigateToSection('memories', {})
+        break
       default:
         if (id.startsWith('folder-')) {
           const folderId = id.slice('folder-'.length)
@@ -407,6 +411,7 @@ export function LeftSidebar({ onCollapse }: LeftSidebarProps) {
         { id: 'people', label: 'People', icon: <Users className="h-4 w-4" />, count: peopleTotalCount },
         { id: 'colors', label: 'Colors', icon: <Palette className="h-4 w-4" />, count: stats?.colored ?? 0 },
         { id: 'map', label: 'Map', icon: <MapPin className="h-4 w-4" />, count: stats?.with_gps ?? 0 },
+        { id: 'memories', label: 'Memories', icon: <Clock className="h-4 w-4" /> },
         { id: 'duplicates', label: 'Duplicates', icon: <Copy className="h-4 w-4" />, count: stats?.duplicates ?? 0 },
         { id: 'discarded', label: 'Discarded', icon: <Trash2 className="h-4 w-4" />, count: stats?.discarded ?? 0 },
       ],

frontend/src/components/memories/MemoriesView.tsx

@@ -0,0 +1,70 @@
+import { useQuery } from '@tanstack/react-query'
+import { photos, photos as photosApi } from '../../services/api'
+import type { MemoryGroup } from '../../services/api'
+
+export function MemoriesView() {
+  const { data, isLoading } = useQuery({
+    queryKey: ['memories'],
+    queryFn: () => photos.memories(),
+    staleTime: 60_000 * 30, // 30 min — date doesn't change often
+  })
+
+  if (isLoading) {
+    return (
+      <div className="flex items-center justify-center h-64 text-neutral-500">
+        Loading memories...
+      </div>
+    )
+  }
+
+  const memories = data?.memories ?? []
+
+  if (memories.length === 0) {
+    return (
+      <div className="flex flex-col items-center justify-center h-64 text-neutral-500 gap-2">
+        <p className="text-lg font-medium">No memories for today</p>
+        <p className="text-sm">Photos taken on this date in previous years will appear here.</p>
+      </div>
+    )
+  }
+
+  return (
+    <div className="p-6 space-y-8 max-w-5xl mx-auto">
+      <h2 className="text-xl font-semibold text-neutral-200">
+        On This Day &mdash; {data?.date}
+      </h2>
+
+      {memories.map((group: MemoryGroup) => (
+        <section key={group.year} className="space-y-3">
+          <h3 className="text-sm font-medium text-neutral-400 uppercase tracking-wide">
+            {group.year} &middot; {group.years_ago} year{group.years_ago !== 1 ? 's' : ''} ago
+          </h3>
+          <div className="grid grid-cols-2 sm:grid-cols-3 md:grid-cols-4 lg:grid-cols-6 gap-2">
+            {group.photos.map((photo) => (
+              <div
+                key={photo.id}
+                className="aspect-square rounded-lg overflow-hidden bg-neutral-800 relative group"
+              >
+                {photo.thumb_small ? (
+                  <img
+                    src={photosApi.getThumbnailUrl(photo.id, 'small')}
+                    alt={photo.filename}
+                    className="w-full h-full object-cover"
+                    loading="lazy"
+                  />
+                ) : (
+                  <div className="w-full h-full flex items-center justify-center text-neutral-600 text-xs">
+                    No thumb
+                  </div>
+                )}
+                <div className="absolute bottom-0 left-0 right-0 bg-gradient-to-t from-black/60 to-transparent px-2 py-1 opacity-0 group-hover:opacity-100 transition-opacity">
+                  <p className="text-xs text-white truncate">{photo.filename}</p>
+                </div>
+              </div>
+            ))}
+          </div>
+        </section>
+      ))}
+    </div>
+  )
+}

frontend/src/components/timeline/PhotoThumbnail.tsx

@@ -100,7 +100,8 @@ export function PhotoThumbnail({
   // Cache-bust on retry so the browser actually re-requests instead of
   // serving the cached 404.
   const baseUrl = photosApi.getThumbnailUrl(photo.id, 'medium')
-  const thumbnailUrl = retryCount > 0 ? `${baseUrl}?retry=${retryCount}` : baseUrl
+  const sep = baseUrl.includes('?') ? '&' : '?'
+  const thumbnailUrl = retryCount > 0 ? `${baseUrl}${sep}retry=${retryCount}` : baseUrl
 
   // "Capture date probably wrong" — read straight from the stored
   // `has_date_warning` flag rather than recomputing the heuristic

frontend/src/hooks/usePhotosQuery.ts

@@ -20,6 +20,19 @@ export function stripPhotosFromCache(queryClient: QueryClient, ids: string[]) {
   })
 }
 
+interface CursorPage {
+  photos: Photo[]
+  next_cursor: string | null
+}
+
+async function fetchCursorPage(
+  params: Record<string, unknown>,
+  signal?: AbortSignal,
+): Promise<CursorPage> {
+  const resp = await api.get<CursorPage>('/photos', { params, signal })
+  return resp.data
+}
+
 /**
  * Single source of truth for the timeline photos query. Both Timeline and
  * PreviewView call this so they share one cache entry — previously
@@ -70,55 +83,41 @@ export function usePhotosQuery() {
   return useQuery({
     queryKey: ['photos', filterParams],
     queryFn: async ({ signal }) => {
-      // Two-phase fetch so the timeline can paint its first thumbnails
-      // long before the entire library has finished downloading. Phase 1
-      // returns the first page synchronously (which resolves the
-      // useQuery promise so consumers exit their loading state). Phase 2
-      // walks the remaining pages in the background, appending each one
-      // into the cache via setQueryData so the grid grows as data
-      // arrives. The signal from React Query aborts the background
-      // loop if the query is invalidated or unmounts mid-stream.
+      // Two-phase fetch using cursor-based (keyset) pagination.
+      // Phase 1 returns the first page (resolves the useQuery promise
+      // so consumers exit loading state). Phase 2 chains cursors in
+      // the background — each response includes a `next_cursor` that
+      // seeks directly to the next slice via an indexed range scan,
+      // O(1) regardless of depth (no OFFSET skipping).
       const PER_PAGE = 500
       const MAX_PAGES = 200
 
-      const firstResp = await api.get<{
-        photos: Photo[]
-        total: number
-        pages: number
-      }>('/photos', {
-        params: { page: 1, per_page: PER_PAGE, ...filterParams },
+      const first = await fetchCursorPage(
+        { per_page: PER_PAGE, ...filterParams },
         signal,
-      })
-      const firstBatch = firstResp.data.photos || []
-      const totalPages = firstResp.data.pages ?? 1
+      )
+      const firstBatch = first.photos || []
+      let nextCursor: string | null = first.next_cursor
 
-      if (totalPages > 1 && firstBatch.length === PER_PAGE) {
-        // Fire-and-forget background loop. We don't await here — the
-        // first batch is already enough to render. Each subsequent
-        // page lands via setQueryData, which triggers consumers to
-        // re-render with the larger list.
+      if (nextCursor) {
+        // Fire-and-forget background loop using cursor chaining.
         void (async () => {
-          for (let page = 2; page <= Math.min(totalPages, MAX_PAGES); page++) {
+          for (let i = 0; i < MAX_PAGES && nextCursor; i++) {
             if (signal?.aborted) return
             try {
-              const resp = await api.get<{
-                photos: Photo[]
-                total: number
-                pages: number
-              }>('/photos', {
-                params: { page, per_page: PER_PAGE, ...filterParams },
+              const page = await fetchCursorPage(
+                { per_page: PER_PAGE, cursor: nextCursor, ...filterParams },
                 signal,
-              })
+              )
               if (signal?.aborted) return
-              const more = resp.data.photos || []
+              const more = page.photos || []
+              nextCursor = page.next_cursor
               queryClient.setQueryData<Photo[]>(
                 ['photos', filterParams],
                 (prev) => (prev ? [...prev, ...more] : more)
               )
-              if (more.length < PER_PAGE) return
+              if (!nextCursor || more.length < PER_PAGE) return
             } catch {
-              // Network or abort — give up the background stream. The
-              // next user-triggered refetch will start fresh.
               return
             }
           }

frontend/src/services/api.ts

@@ -316,17 +316,29 @@ export const photos = {
   },
 
   getThumbnailUrl: (photoId: string, size: 'small' | 'medium' | 'large' = 'medium') => {
-    return `${API_BASE_URL}/photos/${photoId}/thumb/${size}`
+    const token = localStorage.getItem('access_token')
+    const qs = token ? `?token=${encodeURIComponent(token)}` : ''
+    return `${API_BASE_URL}/photos/${photoId}/thumb/${size}${qs}`
   },
 
   getOriginalUrl: (photoId: string) => {
-    return `${API_BASE_URL}/photos/${photoId}/original`
+    const token = localStorage.getItem('access_token')
+    const qs = token ? `?token=${encodeURIComponent(token)}` : ''
+    return `${API_BASE_URL}/photos/${photoId}/original${qs}`
   },
 
   /** Full-resolution display URL. Backend serves the original for web-safe
    *  formats and a transcoded WebP for RAW/HEIC/TIFF. */
   getProxyUrl: (photoId: string) => {
-    return `${API_BASE_URL}/photos/${photoId}/proxy`
+    const token = localStorage.getItem('access_token')
+    const qs = token ? `?token=${encodeURIComponent(token)}` : ''
+    return `${API_BASE_URL}/photos/${photoId}/proxy${qs}`
+  },
+
+  /** "On this day" memories — photos taken on this date in previous years. */
+  memories: async (): Promise<MemoriesResponse> => {
+    const response = await api.get('/photos/memories')
+    return response.data
   },
 }
 
@@ -577,6 +589,31 @@ export interface DuplicateGroupsResponse {
   total_members: number
 }
 
+// ── Memories ("On this day") ────────────────────────────────────────────
+
+export interface MemoryPhoto {
+  id: string
+  filename: string
+  taken_at: string
+  thumb_small: string | null
+  thumb_medium: string | null
+  media_type: string
+  width: number | null
+  height: number | null
+  rating: number
+}
+
+export interface MemoryGroup {
+  year: number
+  years_ago: number
+  photos: MemoryPhoto[]
+}
+
+export interface MemoriesResponse {
+  date: string
+  memories: MemoryGroup[]
+}
+
 export interface LibraryStats {
   all_photos: number
   rated: number