diff --git a/src/services/clipService.ts b/src/services/clipService.ts
index f9a6edcce..88ea17645 100644
--- a/src/services/clipService.ts
+++ b/src/services/clipService.ts
@@ -12,6 +12,7 @@ import fetch from 'node-fetch';
 import { writeNodeStream } from './fs';
 import { getPlatform } from '../utils/common/platform';
 import { CustomErrors } from '../constants/errors';
+const jpeg = require('jpeg-js');
 
 const CLIP_MODEL_PATH_PLACEHOLDER = 'CLIP_MODEL';
 const GGMLCLIP_PATH_PLACEHOLDER = 'GGML_PATH';
@@ -34,8 +35,7 @@ const TEXT_EMBEDDING_EXTRACT_CMD: string[] = [
 ];
 const ort = require('onnxruntime-node');
 import Tokenizer from '../utils/clip-bpe-ts/mod';
-
-const { createCanvas, Image } = require('canvas');
+import { readFile } from 'promise-fs';
 
 const TEXT_MODEL_DOWNLOAD_URL = {
     ggml: 'https://models.ente.io/clip-vit-base-patch32_ggml-text-model-f16.gguf',
@@ -261,7 +261,7 @@ export async function computeONNXImageEmbedding(
 ): Promise<Float32Array> {
     try {
         const imageSession = await getOnnxImageSession();
-        const rgbData = await getRgbData(inputFilePath);
+        const rgbData = await getRGBData(inputFilePath);
         const feeds = {
             input: new ort.Tensor('float32', rgbData, [1, 3, 224, 224]),
         };
@@ -350,53 +350,72 @@ export async function computeONNXTextEmbedding(
     }
 }
 
-async function getRgbData(inputFilePath: string) {
-    const width = 224;
-    const height = 224;
-    // let blob = await fetch(imgUrl, {referrer:""}).then(r => r.blob());
+async function getRGBData(inputFilePath: string) {
+    const jpegData = await readFile(inputFilePath);
+    const rawImageData = jpeg.decode(jpegData, {
+        useTArray: true,
+        formatAsRGBA: false,
+    });
 
-    const img = new Image();
-    img.src = inputFilePath;
+    const nx: number = rawImageData.width;
+    const ny: number = rawImageData.height;
+    const inputImage: Uint8Array = rawImageData.data;
 
-    const canvas = createCanvas(width, height);
-    const ctx = canvas.getContext('2d');
+    const nx2: number = 224;
+    const ny2: number = 224;
+    const totalSize: number = 3 * nx2 * ny2;
 
-    // scale img to fit the shorter side to the canvas size
-    const scale = Math.max(
-        canvas.width / img.width,
-        canvas.height / img.height
-    );
+    const result: number[] = Array(totalSize).fill(0);
+    const scale: number = Math.max(nx, ny) / 224;
 
-    // compute new image dimensions that would maintain the original aspect ratio
-    const scaledW = img.width * scale;
-    const scaledH = img.height * scale;
+    const nx3: number = Math.round(nx / scale);
+    const ny3: number = Math.round(ny / scale);
 
-    // compute position to center the image
-    const posX = (canvas.width - scaledW) / 2;
-    const posY = (canvas.height - scaledH) / 2;
+    const mean: number[] = [0.48145466, 0.4578275, 0.40821073];
+    const std: number[] = [0.26862954, 0.26130258, 0.27577711];
 
-    // draw the image centered and scaled on the canvas
-    ctx.drawImage(img, posX, posY, scaledW, scaledH);
+    for (let y = 0; y < ny3; y++) {
+        for (let x = 0; x < nx3; x++) {
+            for (let c = 0; c < 3; c++) {
+                // linear interpolation
+                const sx: number = (x + 0.5) * scale - 0.5;
+                const sy: number = (y + 0.5) * scale - 0.5;
 
-    const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
-    const rgbData: [number[][], number[][], number[][]] = [[], [], []]; // [r, g, b]
-    // remove alpha and put into correct shape:
-    const d = imageData.data;
-    for (let i = 0; i < d.length; i += 4) {
-        const x = (i / 4) % width;
-        const y = Math.floor(i / 4 / width);
-        if (!rgbData[0][y]) rgbData[0][y] = [];
-        if (!rgbData[1][y]) rgbData[1][y] = [];
-        if (!rgbData[2][y]) rgbData[2][y] = [];
-        rgbData[0][y][x] = d[i + 0] / 255;
-        rgbData[1][y][x] = d[i + 1] / 255;
-        rgbData[2][y][x] = d[i + 2] / 255;
-        // From CLIP repo: Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-        rgbData[0][y][x] = (rgbData[0][y][x] - 0.48145466) / 0.26862954;
-        rgbData[1][y][x] = (rgbData[1][y][x] - 0.4578275) / 0.26130258;
-        rgbData[2][y][x] = (rgbData[2][y][x] - 0.40821073) / 0.27577711;
+                const x0: number = Math.max(0, Math.floor(sx));
+                const y0: number = Math.max(0, Math.floor(sy));
+
+                const x1: number = Math.min(x0 + 1, nx - 1);
+                const y1: number = Math.min(y0 + 1, ny - 1);
+
+                const dx: number = sx - x0;
+                const dy: number = sy - y0;
+
+                const j00: number = 3 * (y0 * nx + x0) + c;
+                const j01: number = 3 * (y0 * nx + x1) + c;
+                const j10: number = 3 * (y1 * nx + x0) + c;
+                const j11: number = 3 * (y1 * nx + x1) + c;
+
+                const v00: number = inputImage[j00];
+                const v01: number = inputImage[j01];
+                const v10: number = inputImage[j10];
+                const v11: number = inputImage[j11];
+
+                const v0: number = v00 * (1 - dx) + v01 * dx;
+                const v1: number = v10 * (1 - dx) + v11 * dx;
+
+                const v: number = v0 * (1 - dy) + v1 * dy;
+
+                const v2: number = Math.min(Math.max(Math.round(v), 0), 255);
+
+                // createTensorWithDataList is dump compared to reshape and hence has to be given with one channel after another
+                const i: number = y * nx3 + x + (c % 3) * 224 * 224;
+
+                result[i] = (v2 / 255 - mean[c]) / std[c];
+            }
+        }
     }
-    return Float32Array.from(rgbData.flat().flat());
+
+    return result;
 }
 
 export const computeClipMatchScore = async (