diff --git a/src/models.js b/src/models.js
index 1a9b021f1..9c65ce5d8 100644
--- a/src/models.js
+++ b/src/models.js
@@ -3779,11 +3779,7 @@ export class VitMattePreTrainedModel extends PreTrainedModel { }
  * import { Tensor, cat } from '@xenova/transformers';
  * 
  * // Visualize predicted alpha matte
- * const imageTensor = new Tensor(
- *   'uint8',
- *   new Uint8Array(image.data),
- *   [image.height, image.width, image.channels]
- * ).transpose(2, 0, 1);
+ * const imageTensor = image.toTensor();
  * 
  * // Convert float (0-1) alpha matte to uint8 (0-255)
  * const alphaChannel = alphas
diff --git a/src/utils/image.js b/src/utils/image.js
index 2d12cb876..1ee77d900 100644
--- a/src/utils/image.js
+++ b/src/utils/image.js
@@ -10,6 +10,7 @@
 
 import { getFile } from './hub.js';
 import { env } from '../env.js';
+import { Tensor } from './tensor.js';
 
 // Will be empty (or not used) if running in browser or web-worker
 import sharp from 'sharp';
@@ -166,7 +167,7 @@ export class RawImage {
 
     /**
      * Helper method to create a new Image from a tensor
-     * @param {import('./tensor.js').Tensor} tensor 
+     * @param {Tensor} tensor 
      */
     static fromTensor(tensor, channel_format = 'CHW') {
         if (tensor.dims.length !== 3) {
@@ -586,6 +587,23 @@ export class RawImage {
         return await canvas.convertToBlob({ type, quality });
     }
 
+    toTensor(channel_format = 'CHW') {
+        let tensor = new Tensor(
+            'uint8',
+            new Uint8Array(this.data),
+            [this.height, this.width, this.channels]
+        );
+
+        if (channel_format === 'HWC') {
+            // Do nothing
+        } else if (channel_format === 'CHW') { // hwc -> chw
+            tensor = tensor.permute(2, 0, 1);
+        } else {
+            throw new Error(`Unsupported channel format: ${channel_format}`);
+        }
+        return tensor;
+    }
+
     toCanvas() {
         if (!BROWSER_ENV) {
             throw new Error('toCanvas() is only supported in browser environments.')