diff --git a/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx.dart b/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx.dart
index 81d1df75f4..a1258edbfb 100644
--- a/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx.dart
+++ b/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx.dart
@@ -4,6 +4,7 @@ import "package:onnxruntime/onnxruntime.dart";
 import 'package:photos/services/machine_learning/semantic_search/frameworks/ml_framework.dart';
 import 'package:photos/services/machine_learning/semantic_search/frameworks/onnx/onnx_image_encoder.dart';
 import 'package:photos/services/machine_learning/semantic_search/frameworks/onnx/onnx_text_encoder.dart';
+import "package:photos/utils/image_isolate.dart";
 
 class ONNX extends MLFramework {
   static const kModelBucketEndpoint = "https://models.ente.io/";
@@ -72,11 +73,11 @@ class ONNX extends MLFramework {
   Future<List<double>> getImageEmbedding(String imagePath) async {
     try {
       final startTime = DateTime.now();
-      final result = await _clipImage.inferByImage({
-        // TODO: add computer back later
-        "imagePath": imagePath,
-        "address": _imageEncoderAddress,
-      });
+      // TODO: properly integrate with other ml later (FaceMlService)
+      final result = await ImageIsolate.instance.inferClipImageEmbedding(
+        imagePath,
+        _imageEncoderAddress,
+      );
       final endTime = DateTime.now();
       _logger.info(
         "createImageEmbedding took: ${(endTime.millisecondsSinceEpoch - startTime.millisecondsSinceEpoch)}ms",
diff --git a/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx_image_encoder.dart b/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx_image_encoder.dart
index 8988913e3b..f4fe9c4c0f 100644
--- a/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx_image_encoder.dart
+++ b/mobile/lib/services/machine_learning/semantic_search/frameworks/onnx/onnx_image_encoder.dart
@@ -26,7 +26,7 @@ class OnnxImageEncoder {
     return -1;
   }
 
-  Future<List<double>> inferByImage(Map args) async {
+  static Future<List<double>> inferByImage(Map args) async {
     final imageData = await File(args["imagePath"]).readAsBytes();
     final image = await decodeImageFromData(imageData);
     final ByteData imgByteData = await getByteDataFromImage(image);
diff --git a/mobile/lib/utils/image_isolate.dart b/mobile/lib/utils/image_isolate.dart
index 9ebad2bb94..ce67c9fa9f 100644
--- a/mobile/lib/utils/image_isolate.dart
+++ b/mobile/lib/utils/image_isolate.dart
@@ -6,11 +6,13 @@ import 'dart:typed_data' show Uint8List;
 import "package:dart_ui_isolate/dart_ui_isolate.dart";
 import "package:logging/logging.dart";
 import "package:photos/face/model/box.dart";
+import "package:photos/services/machine_learning/semantic_search/frameworks/onnx/onnx_image_encoder.dart";
 import "package:photos/utils/image_ml_util.dart";
 import "package:synchronized/synchronized.dart";
 
 enum ImageOperation {
   generateFaceThumbnails,
+  clip,
 }
 
 class ImageIsolate {
@@ -88,6 +90,15 @@ class ImageIsolate {
               faceBoxes,
             );
             sendPort.send(List.from(results));
+          case ImageOperation.clip:
+            final imagePath = args['imagePath'] as String;
+            final address = args['address'] as int;
+            final result = await OnnxImageEncoder.inferByImage({
+              'imagePath': imagePath,
+              'address': address,
+            });
+            sendPort.send(List.from(result));
+            break;
         }
       } catch (e, stackTrace) {
         sendPort
@@ -175,4 +186,16 @@ class ImageIsolate {
       ),
     ).then((value) => value.cast<Uint8List>());
   }
+
+  Future<List<double>> inferClipImageEmbedding(String imagePath, int encoderAddress) async {
+    return await _runInIsolate(
+      (
+        ImageOperation.clip,
+        {
+          'imagePath': imagePath,
+          'address': encoderAddress,
+        },
+      ),
+    ).then((value) => value.cast<double>());
+  }
 }