fix export case download with non-ascii names (#24100)

* run lpr on more vehicle types by default

before, a config change to attribute_map was required

* logging tweaks

* remove arg

* use attributes for frontend check

* docs

* only check thumbnail attributes the object can have

docs/docs/configuration/license_plate_recognition.md

@@ -8,7 +8,7 @@ import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 import FaqItem from "@site/src/components/FaqItem";
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car`, `motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
 
@@ -24,7 +24,7 @@ When a plate is recognized, the details are:
 - Viewable in the Details pane in Review/History.
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
 - Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
 
 ## Model Requirements
@@ -35,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
 
 :::note
 
-In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
 
 :::
 
@@ -86,7 +86,7 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
 
 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
 
@@ -158,7 +158,7 @@ lpr:
 
 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
 
-- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
+- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
 
 </TabItem>
@@ -316,7 +316,7 @@ lpr:
 
 :::note
 
-If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -456,7 +456,7 @@ With this setup:
 - Snapshots will have license plate bounding boxes on them.
 - The `frigate/events` MQTT topic will publish tracked object updates.
 - Debug view will display `license_plate` bounding boxes.
-- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
 
 ### Using the Secondary LPR Pipeline (Without Frigate+)
 
@@ -611,9 +611,9 @@ If you are still having issues detecting plates, start with a basic configuratio
 
 </FaqItem>
 
-<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
+<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting vehicle objects?</>}>
 
-In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
 
 </FaqItem>
 
@@ -699,7 +699,7 @@ lpr:
 4. Ensure the characters on detected plates are being _recognized_.
    - Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
    - Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
-   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
+   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the vehicle's label will change to the recognized plate when LPR is enabled and working.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 
 </FaqItem>
@@ -714,13 +714,13 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
 
 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
 
-If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
 
 </FaqItem>
 
 <FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
 
-This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
+This could happen if vehicles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
 
 If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
 

docs/docs/configuration/record.md

@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
 
 Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
 
-New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
+New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
 
 :::tip
 

docs/docs/troubleshooting/common_errors.md

@@ -66,17 +66,19 @@ An FFmpeg message meaning it probed the stream but never saw enough decodable vi
 
 ## Recording
 
-<FaqItem id="no-new-recording-segments" question="No new recording segments were created for <camera> in the last 120s">
+<FaqItem id="no-new-recording-segments" question="No new recording segments were created (or: No new valid recording segments were created / No valid segments created since last invalid segment) for <camera> in the last 120s">
 
-Frigate's record watchdog is restarting the record FFmpeg process because no valid segment has reached the cache. This means the record stream is not connecting or the segments are being rejected (see the audio-codec entry below).
+Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
 
-See [Recordings: the record stream isn't connecting](/troubleshooting/recordings#the-record-stream-isnt-connecting).
+See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
 
 </FaqItem>
 
-<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding.">
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding. / Discarding a corrupt recording segment / Failed to probe corrupt segment / Invalid recording segment detected">
 
-A cached recording segment failed validation (no readable video stream) and was deleted. The most common cause is a segment that was truncated because the record FFmpeg process was killed mid-write, so this often appears alongside, and as a consequence of, the record-stream restarts above. A segment containing only audio triggers it too.
+A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
+
+See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
 
 </FaqItem>
 

docs/docs/troubleshooting/recordings.md

@@ -209,6 +209,50 @@ If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parame
 
 </FaqItem>
 
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="I see the message: WARNING : Invalid or missing video stream in segment ... Discarding.">
+
+Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
+
+- `Invalid or missing video stream in segment <path>. Discarding.` The segment holds no video, or could not be read at all.
+- `Failed to probe corrupt segment <path>` followed by `Discarding a corrupt recording segment: <path>`. The segment was read, but its length could not be determined.
+- `Discarding a corrupt recording segment: <path>` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
+
+For each one, the camera watchdog also logs `Invalid recording segment detected for <camera> at <timestamp>`.
+
+:::warning
+
+This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
+
+:::
+
+#### Start with the camera and the network
+
+- **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
+- **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+- **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
+- **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
+
+Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from <camera> in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
+
+#### If the camera and network check out
+
+- **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
+- **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
+- **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
+
+</FaqItem>
+
+<FaqItem id="no-new-recording-segments-were-created" question="I see the message: ERROR : No new recording segments were created for <camera> in the last 120s. Restarting the ffmpeg record process...">
+
+When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
+
+- **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
+- **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
+
+The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
+
+</FaqItem>
+
 <FaqItem id="i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest" question="I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...">
 
 This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.

docs/static/frigate-api.yaml

@@ -1476,7 +1476,7 @@ paths:
         - Classification
       summary: Get custom classification attributes
       description: |-
-        **Access:** Admin role required.
+        **Access:** Authenticated user with access to all cameras.
 
         Returns custom classification attributes for a given object type.
             Only includes models with classification_type set to 'attribute'.
@@ -1510,8 +1510,8 @@ paths:
               schema:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
-        - frigateAdminAuth: []
+        - frigateUserAuth: []
-      x-required-role: admin
+      x-required-role: all_cameras
   /classification/{name}/train:
     get:
       tags:

frigate/api/auth.py

@@ -85,6 +85,7 @@ def require_admin_by_default():
         "/sub_labels",
         "/plus/models",
         "/recognized_license_plates",
+        "/classification/attributes",
         "/timeline",
         "/timeline/hourly",
         "/recordings/storage",

frigate/api/classification.py

@@ -14,7 +14,7 @@ from fastapi.responses import JSONResponse
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
-from frigate.api.auth import require_role
+from frigate.api.auth import require_full_camera_access, require_role
 from frigate.api.defs.request.classification_body import (
     AudioTranscriptionBody,
     DeleteFaceImagesBody,
@@ -741,6 +741,7 @@ def get_classification_dataset(name: str):
 
 @router.get(
     "/classification/attributes",
+    dependencies=[Depends(require_full_camera_access)],
     summary="Get custom classification attributes",
     description="""Returns custom classification attributes for a given object type.
     Only includes models with classification_type set to 'attribute'.

frigate/api/export.py

@@ -9,6 +9,7 @@ import zipfile
 from collections import deque
 from collections.abc import Iterator
 from pathlib import Path
+from urllib.parse import quote
 
 import psutil
 from fastapi import APIRouter, Depends, Query, Request
@@ -453,6 +454,22 @@ def _stream_case_archive(exports: list[Export]) -> Iterator[bytes]:
     yield from buffer.drain()
 
 
+def _content_disposition(filename: str, ascii_fallback: str) -> str:
+    """Build an attachment Content-Disposition that survives non-ASCII names.
+
+    Header values are encoded as latin-1, so a name outside that range cannot
+    go in filename at all. RFC 6266 handles this with a pair: a plain ASCII
+    filename for old clients, plus a percent-encoded UTF-8 filename* that
+    every current browser prefers.
+    """
+    ascii_name = filename if filename.isascii() else ascii_fallback
+
+    return (
+        f'attachment; filename="{ascii_name}"; '
+        f"filename*=UTF-8''{quote(filename, safe='')}"
+    )
+
+
 @router.get(
     "/cases/{case_id}/download",
     dependencies=[Depends(allow_any_authenticated())],
@@ -495,7 +512,9 @@ def download_export_case(
         _stream_case_archive(exports),
         media_type="application/zip",
         headers={
-            "Content-Disposition": f'attachment; filename="{archive_base}.zip"',
+            "Content-Disposition": _content_disposition(
+                f"{archive_base}.zip", f"{case_id}.zip"
+            ),
         },
     )
 

frigate/camera/state.py

@@ -60,6 +60,11 @@ class CameraState:
         # face/LPR pipelines when using a model without built-in detection.
         self.face_recognition_min_obj_area: int = 0
         self.lpr_min_obj_area: int = 0
+        self.lp_objects = {
+            label
+            for label, attributes in config.model.attributes_map.items()
+            if "license_plate" in attributes
+        }
 
         if (
             self.camera_config.face_recognition.enabled
@@ -452,7 +457,7 @@ class CameraState:
                 and obj_area >= self.face_recognition_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
             ) or (
-                obj_label in ("car", "motorcycle")
+                obj_label in self.lp_objects
                 and self.lpr_min_obj_area > 0
                 and obj_area >= self.lpr_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
@@ -548,7 +553,7 @@ class CameraState:
                     current_best.thumbnail_data is not None
                     and obj.thumbnail_data is not None
                     and is_better_thumbnail(
-                        object_type,
+                        obj.thumbnail_attributes,
                         current_best.thumbnail_data,
                         obj.thumbnail_data,
                         self.camera_config.frame_shape,

frigate/comms/webpush.py

@@ -421,6 +421,7 @@ class WebPushClient(Communicator):
         # Don't notify if message is an update and important fields don't have an update
         if (
             state == "update"
+            and payload["before"]["severity"] == payload["after"]["severity"]
             and len(payload["before"]["data"]["objects"])
             == len(payload["after"]["data"]["objects"])
             and len(payload["before"]["data"]["zones"])

frigate/const.py

@@ -44,7 +44,11 @@ DEFAULT_ATTRIBUTE_LABEL_MAP = {
         "ups",
         "usps",
     ],
+    "truck": ["license_plate"],
+    "garbage_truck": ["license_plate"],
     "motorcycle": ["license_plate"],
+    "bus": ["license_plate"],
+    "school_bus": ["license_plate"],
 }
 ATTRIBUTE_LABEL_DISPLAY_MAP = {
     "amazon": "Amazon",

frigate/data_processing/common/license_plate/mixin.py

@@ -1290,7 +1290,7 @@ class LicensePlateProcessingMixin:
                 and obj_data.get("label") != "license_plate"
             ):
                 logger.debug(
-                    f"{camera}: Not a processing license plate for non car/motorcycle object."
+                    f"{camera}: Not a processing license plate for {obj_data.get('label', 'unknown')}."
                 )
                 return
 
@@ -1367,7 +1367,7 @@ class LicensePlateProcessingMixin:
 
                 if not license_plate:
                     logger.debug(
-                        f"{camera}: Detected no license plates for car/motorcycle object."
+                        f"{camera}: Detected no license plates for {obj_data.get('label', 'unknown')} object."
                     )
                     return
 

frigate/detectors/detection_runners.py

@@ -25,26 +25,32 @@ def is_arm64_platform() -> bool:
     return machine in ("aarch64", "arm64", "armv8", "armv7l")
 
 
-def get_ort_session_options(
+def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
-    is_complex_model: bool = False,
-) -> ort.SessionOptions | None:
     """Get ONNX Runtime session options with appropriate settings.
 
     Args:
-        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
+        model_type: Model being loaded, used to pin its graph optimization level.
 
     Returns:
-        SessionOptions with appropriate optimization level, or None for default settings.
+        SessionOptions with a pinned optimization level, or None for default settings.
     """
-    if is_complex_model:
+    # Import here to avoid circular imports
-        sess_options = ort.SessionOptions()
+    from frigate.embeddings.types import EnrichmentModelTypeEnum
-        sess_options.graph_optimization_level = (
-            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
-        )
-        return sess_options
 
+    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
+        # below EXTENDED the CUDA EP returns an identical vector for every image,
+        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
+    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
+        # aggressive optimizations create or expect nodes that don't exist
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    else:
         return None
 
+    sess_options = ort.SessionOptions()
+    sess_options.graph_optimization_level = level
+    return sess_options
+
 
 # Import OpenVINO only when needed to avoid circular dependencies
 try:
@@ -115,21 +121,6 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
     """Run ONNX models using ONNX Runtime."""
 
-    @staticmethod
-    def is_cpu_complex_model(model_type: str) -> bool:
-        """Check if model needs basic optimization level to avoid graph fusion issues.
-
-        Some models (like Jina-CLIP) have issues with aggressive optimizations like
-        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
-        """
-        # Import here to avoid circular imports
-        from frigate.embeddings.types import EnrichmentModelTypeEnum
-
-        return model_type in [
-            EnrichmentModelTypeEnum.jina_v1.value,
-            EnrichmentModelTypeEnum.jina_v2.value,
-        ]
-
     @staticmethod
     def is_migraphx_complex_model(model_type: str) -> bool:
         # Import here to avoid circular imports
@@ -323,6 +314,12 @@ class OpenVINOModelRunner(BaseModelRunner):
         if device in ["GPU", "AUTO", "NPU"]:
             self.ov_core.set_property(device, {"PERFORMANCE_HINT": "LATENCY"})
 
+        if device in ["GPU", "AUTO"]:
+            try:
+                self.ov_core.set_property("GPU", {"GPU_QUEUE_THROTTLE": "LOW"})
+            except Exception as e:
+                logger.debug(f"GPU_QUEUE_THROTTLE not supported: {e}")
+
         if device == "NPU" and OpenVINOModelRunner.is_detection_model(model_type):
             try:
                 self.ov_core.set_property(device, {"NPU_TURBO": "YES"})
@@ -626,9 +623,7 @@ def get_optimized_runner(
     return ONNXModelRunner(
         ort.InferenceSession(
             model_path,
-            sess_options=get_ort_session_options(
+            sess_options=get_ort_session_options(model_type),
-                ONNXModelRunner.is_cpu_complex_model(model_type)
-            ),
             providers=providers,
             provider_options=options,
         ),

frigate/test/http_api/test_http_export.py

@@ -1,5 +1,7 @@
+import io
 import os
 import tempfile
+import zipfile
 from unittest.mock import patch
 
 from frigate.jobs.export import (
@@ -1431,3 +1433,79 @@ class TestHttpExport(BaseTestHttp):
             )
 
         assert response.status_code == 403
+
+    def test_download_export_case_with_multibyte_name(self):
+        """A case name outside latin-1 must not break the response headers."""
+        case = ExportCase.create(
+            id="case_multibyte",
+            name="テスト事案",
+            description="",
+            created_at=10,
+            updated_at=10,
+        )
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            video_path = os.path.join(tmpdir, "multibyte_export.mp4")
+            with open(video_path, "wb") as handle:
+                handle.write(b"video")
+
+            Export.create(
+                id="export_multibyte",
+                camera="front_door",
+                name="現場カメラ",
+                date=100,
+                video_path=video_path,
+                thumb_path=os.path.join(tmpdir, "multibyte_export.webp"),
+                in_progress=False,
+                export_case=case,
+            )
+
+            with AuthTestClient(self.app) as client:
+                response = client.get(f"/cases/{case.id}/download")
+
+        assert response.status_code == 200
+        # RFC 5987/6266: the UTF-8 name rides in filename*, and a latin-1 safe
+        # fallback stays in filename for old clients.
+        assert response.headers["content-disposition"] == (
+            'attachment; filename="case_multibyte.zip"; '
+            "filename*=UTF-8''%E3%83%86%E3%82%B9%E3%83%88%E4%BA%8B%E6%A1%88.zip"
+        )
+
+        archive = zipfile.ZipFile(io.BytesIO(response.content))
+        assert archive.namelist() == ["現場カメラ.mp4"]
+
+    def test_download_export_case_with_ascii_name(self):
+        """An ASCII case name still gets a plain, readable filename."""
+        case = ExportCase.create(
+            id="case_ascii",
+            name="Burglary 2026-08",
+            description="",
+            created_at=10,
+            updated_at=10,
+        )
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            video_path = os.path.join(tmpdir, "ascii_export.mp4")
+            with open(video_path, "wb") as handle:
+                handle.write(b"video")
+
+            Export.create(
+                id="export_ascii",
+                camera="front_door",
+                name="Front door",
+                date=100,
+                video_path=video_path,
+                thumb_path=os.path.join(tmpdir, "ascii_export.webp"),
+                in_progress=False,
+                export_case=case,
+            )
+
+            with AuthTestClient(self.app) as client:
+                response = client.get(f"/cases/{case.id}/download")
+
+        assert response.status_code == 200
+        assert (
+            response.headers["content-disposition"]
+            == 'attachment; filename="Burglary 2026-08.zip"; '
+            "filename*=UTF-8''Burglary%202026-08.zip"
+        )

frigate/test/test_detection_runners.py

@@ -0,0 +1,41 @@
+"""Tests for ONNX Runtime session option selection."""
+
+import unittest
+
+import onnxruntime as ort
+
+from frigate.detectors.detection_runners import get_ort_session_options
+from frigate.detectors.detector_config import ModelTypeEnum
+from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+
+class TestGetOrtSessionOptions(unittest.TestCase):
+    def test_jina_v2_uses_extended(self):
+        """jina-clip-v2 returns an identical vector for every image on the CUDA
+        execution provider at anything below EXTENDED."""
+        options = get_ort_session_options(EnrichmentModelTypeEnum.jina_v2.value)
+
+        self.assertIsNotNone(options)
+        self.assertEqual(
+            options.graph_optimization_level,
+            ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED,
+        )
+
+    def test_jina_v1_uses_basic(self):
+        options = get_ort_session_options(EnrichmentModelTypeEnum.jina_v1.value)
+
+        self.assertIsNotNone(options)
+        self.assertEqual(
+            options.graph_optimization_level,
+            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC,
+        )
+
+    def test_other_models_use_defaults(self):
+        for model_type in [
+            None,
+            EnrichmentModelTypeEnum.paddleocr.value,
+            EnrichmentModelTypeEnum.arcface.value,
+            ModelTypeEnum.rfdetr.value,
+        ]:
+            with self.subTest(model_type=model_type):
+                self.assertIsNone(get_ort_session_options(model_type))

frigate/track/tracked_object.py

@@ -54,6 +54,11 @@ class TrackedObject:
         self.obj_data = obj_data
         self.colormap = model_config.colormap
         self.logos = model_config.all_attribute_logos
+        self.thumbnail_attributes = [
+            attr
+            for attr in model_config.attributes_map.get(obj_data["label"], [])
+            if attr in model_config.non_logo_attributes
+        ]
         self.camera_config = camera_config
         self.ui_config = ui_config
         self.frame_cache = frame_cache
@@ -149,7 +154,7 @@ class TrackedObject:
         if not self.false_positive and has_valid_frame:
             # determine if this frame is a better thumbnail
             if self.thumbnail_data is None or is_better_thumbnail(
-                self.obj_data["label"],
+                self.thumbnail_attributes,
                 self.thumbnail_data,
                 obj_data,
                 self.camera_config.frame_shape,

frigate/util/config.py

@@ -533,7 +533,7 @@ def migrate_018_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]
     genai = new_config.get("genai")
 
     if genai and genai.get("provider"):
-        genai["roles"] = ["embeddings", "descriptions", "chat"]
+        genai["roles"] = ["descriptions", "chat"]
         new_config["genai"] = {"default": genai}
 
     # Remove deprecated sync_recordings from global record config

frigate/util/image.py

@@ -67,7 +67,7 @@ def has_better_attr(current_thumb, new_obj, attr_label) -> bool:
 
 
 def is_better_thumbnail(
-    label: str,
+    label_attributes: list[str],
     current_thumb: dict[str, Any],
     new_obj: dict[str, Any],
     frame_shape: tuple[int, int],
@@ -76,20 +76,12 @@ def is_better_thumbnail(
     # cutoff images are less ideal, but they should also be smaller?
     # better scores are obviously better too
 
-    # check face on person
+    for attr_label in label_attributes:
-    if label == "person":
+        if has_better_attr(current_thumb, new_obj, attr_label):
-        if has_better_attr(current_thumb, new_obj, "face"):
             return True
-        # if the current thumb has a face attr, dont update unless it gets better
-        if any([a["label"] == "face" for a in current_thumb["attributes"]]):
-            return False
 
-    # check license_plate on car
+        # if the current thumb has the attr, dont update unless it gets better
-    if label in ["car", "motorcycle"]:
+        if any([a["label"] == attr_label for a in current_thumb["attributes"]]):
-        if has_better_attr(current_thumb, new_obj, "license_plate"):
-            return True
-        # if the current thumb has a license_plate attr, dont update unless it gets better
-        if any([a["label"] == "license_plate" for a in current_thumb["attributes"]]):
             return False
 
     # if the new_thumb is on an edge, and the current thumb is not

frigate/video/detect.py

@@ -216,20 +216,10 @@ def process_frames(
 
     # remove license_plate from attributes if this camera is a dedicated LPR cam
     if camera_config.type == CameraTypeEnum.lpr:
-        modified_attributes_map = model_config.attributes_map.copy()
+        attributes_map = {
-
+            label: [attr for attr in attributes if attr != "license_plate"]
-        if (
+            for label, attributes in model_config.attributes_map.items()
-            "car" in modified_attributes_map
+        }
-            and "license_plate" in modified_attributes_map["car"]
-        ):
-            modified_attributes_map["car"] = [
-                attr
-                for attr in modified_attributes_map["car"]
-                if attr != "license_plate"
-            ]
-
-            attributes_map = modified_attributes_map
-
         all_attributes = [
             attr for attr in model_config.all_attributes if attr != "license_plate"
         ]

web/public/locales/en/views/settings.json

@@ -1948,7 +1948,7 @@
     },
     "lpr": {
       "globalDisabled": "The license plate recognition enrichment must be enabled for LPR features to function on this camera.",
-      "vehicleNotTracked": "License plate recognition requires 'car' or 'motorcycle' to be tracked. Enable 'car' or 'motorcycle' in Objects for this camera.",
+      "vehicleNotTracked": "License plate recognition requires a vehicle to be tracked. Enable 'car' or another vehicle type in Objects for this camera.",
       "modelSizeLarge": "The 'large' model is optimized for multi-line license plates. The 'small' model provides better performance over 'large' and should be used unless your region uses multi-line plate formats."
     },
     "record": {

web/src/components/config-form/section-configs/lpr.ts

@@ -21,7 +21,12 @@ const lpr: SectionConfigOverrides = {
           if (ctx.level !== "camera" || !ctx.fullCameraConfig) return false;
           if (ctx.fullCameraConfig.type === "lpr") return false;
           const tracked = ctx.fullCameraConfig.objects?.track ?? [];
-          return !tracked.some((o) => ["car", "motorcycle"].includes(o));
+          const vehicles = Object.entries(
+            ctx.fullConfig.model?.attributes_map ?? {},
+          )
+            .filter(([, attributes]) => attributes.includes("license_plate"))
+            .map(([label]) => label);
+          return !tracked.some((o) => vehicles.includes(o));
         },
       },
     ],