diff --git a/tfjs-backend-webgl/src/webgl_topixels_test.ts b/tfjs-backend-webgl/src/webgl_topixels_test.ts
new file mode 100644
index 00000000000..e0dc2bb9120
--- /dev/null
+++ b/tfjs-backend-webgl/src/webgl_topixels_test.ts
@@ -0,0 +1,41 @@
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import * as tf from '@tensorflow/tfjs-core';
+// tslint:disable-next-line: no-imports-from-dist
+import {describeWithFlags} from '@tensorflow/tfjs-core/dist/jasmine_util';
+
+import {WebGLMemoryInfo} from './backend_webgl';
+import {WEBGL_ENVS} from './backend_webgl_test_registry';
+
+describeWithFlags('toPixels', WEBGL_ENVS, () => {
+  it('does not leak memory', async () => {
+    const x = tf.tensor2d([[.1], [.2]], [2, 1]);
+    const startNumBytesInGPU = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
+    await tf.browser.toPixels(x);
+    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
+        .toEqual(startNumBytesInGPU);
+  });
+
+  it('does not leak memory given a tensor-like object', async () => {
+    const x = [[10], [20]];  // 2x1;
+    const startNumBytesInGPU = (tf.memory() as WebGLMemoryInfo).numBytesInGPU;
+    await tf.browser.toPixels(x);
+    expect((tf.memory() as WebGLMemoryInfo).numBytesInGPU)
+        .toEqual(startNumBytesInGPU);
+  });
+});
diff --git a/tfjs-core/src/ops/browser.ts b/tfjs-core/src/ops/browser.ts
index e060db10fa1..5589c3d3c92 100644
--- a/tfjs-core/src/ops/browser.ts
+++ b/tfjs-core/src/ops/browser.ts
@@ -24,8 +24,6 @@ import {convertToTensor} from '../tensor_util_env';
 import {PixelData, TensorLike} from '../types';
 
 import {cast} from './cast';
-import {max} from './max';
-import {min} from './min';
 import {op} from './operation';
 import {tensor3d} from './tensor3d';
 
@@ -196,60 +194,50 @@ export async function toPixels(
         `1, 3 or 4 but got ${depth}`);
   }
 
-  const data = await $img.data();
-  const minTensor = min($img);
-  const maxTensor = max($img);
-  const vals = await Promise.all([minTensor.data(), maxTensor.data()]);
-  const minVals = vals[0];
-  const maxVals = vals[1];
-  const minVal = minVals[0];
-  const maxVal = maxVals[0];
-  minTensor.dispose();
-  maxTensor.dispose();
-  if ($img.dtype === 'float32') {
-    if (minVal < 0 || maxVal > 1) {
-      throw new Error(
-          `Tensor values for a float32 Tensor must be in the ` +
-          `range [0 - 1] but got range [${minVal} - ${maxVal}].`);
-    }
-  } else if ($img.dtype === 'int32') {
-    if (minVal < 0 || maxVal > 255) {
-      throw new Error(
-          `Tensor values for a int32 Tensor must be in the ` +
-          `range [0 - 255] but got range [${minVal} - ${maxVal}].`);
-    }
-  } else {
+  if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {
     throw new Error(
         `Unsupported type for toPixels: ${$img.dtype}.` +
         ` Please use float32 or int32 tensors.`);
   }
+
+  const data = await $img.data();
   const multiplier = $img.dtype === 'float32' ? 255 : 1;
   const bytes = new Uint8ClampedArray(width * height * 4);
 
   for (let i = 0; i < height * width; ++i) {
-    let r, g, b, a;
-    if (depth === 1) {
-      r = data[i] * multiplier;
-      g = data[i] * multiplier;
-      b = data[i] * multiplier;
-      a = 255;
-    } else if (depth === 3) {
-      r = data[i * 3] * multiplier;
-      g = data[i * 3 + 1] * multiplier;
-      b = data[i * 3 + 2] * multiplier;
-      a = 255;
-    } else if (depth === 4) {
-      r = data[i * 4] * multiplier;
-      g = data[i * 4 + 1] * multiplier;
-      b = data[i * 4 + 2] * multiplier;
-      a = data[i * 4 + 3] * multiplier;
+    const rgba = [0, 0, 0, 255];
+
+    for (let d = 0; d < depth; d++) {
+      const value = data[i * depth + d];
+
+      if ($img.dtype === 'float32') {
+        if (value < 0 || value > 1) {
+          throw new Error(
+              `Tensor values for a float32 Tensor must be in the ` +
+              `range [0 - 1] but encountered ${value}.`);
+        }
+      } else if ($img.dtype === 'int32') {
+        if (value < 0 || value > 255) {
+          throw new Error(
+              `Tensor values for a int32 Tensor must be in the ` +
+              `range [0 - 255] but encountered ${value}.`);
+        }
+      }
+
+      if (depth === 1) {
+        rgba[0] = value * multiplier;
+        rgba[1] = value * multiplier;
+        rgba[2] = value * multiplier;
+      } else {
+        rgba[d] = value * multiplier;
+      }
     }
 
     const j = i * 4;
-    bytes[j + 0] = Math.round(r);
-    bytes[j + 1] = Math.round(g);
-    bytes[j + 2] = Math.round(b);
-    bytes[j + 3] = Math.round(a);
+    bytes[j + 0] = Math.round(rgba[0]);
+    bytes[j + 1] = Math.round(rgba[1]);
+    bytes[j + 2] = Math.round(rgba[2]);
+    bytes[j + 3] = Math.round(rgba[3]);
   }
 
   if (canvas != null) {