No tiling commit for S3 save url and name

handler.py

@@ -31,8 +31,8 @@ class EndpointHandler:
                            num_grow_ch=32,
                            scale=4
                            ),
-            tile=1000,
-            tile_pad=20,
+            tile=0,
+            tile_pad=0,
             half=True,
         )
         
@@ -43,7 +43,8 @@ class EndpointHandler:
                                )
         # Get the S3 bucket name from environment variables
         self.bucket_name = os.environ["S3_BUCKET_NAME"]
-
+        
+        
     def __call__(self, data: Any) -> Dict[str, List[float]]:
         
         try:

test_api_endpoint.ipynb

@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from handler import EndpointHandler\n",
     "import base64\n",
@@ -17,34 +26,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# helper decoder\n",
-    "def decode_base64_image(image_string):\n",
-    "  base64_image = base64.b64decode(image_string)\n",
-    "  buffer = BytesIO(base64_image)\n",
-    "  return  Image.open(buffer)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "out_image.size: (12000, 5170)\n",
-      "out_image.size: (3168, 3204)\n",
-      "out_image.size: (5797, 5863)\n"
+      "https://upscale-process-results.s3.amazonaws.com/1156995e-7c5f-4f0a-aa47-0dc8922f6f69.png 1156995e-7c5f-4f0a-aa47-0dc8922f6f69.png None\n"
      ]
     }
    ],
    "source": [
-    "API_URL = \"https://s3pbd8dbht1g91w4.us-east-1.aws.endpoints.huggingface.cloud\"\n",
+    "API_URL = \"https://po409l85y2ps6yo5.us-east-1.aws.endpoints.huggingface.cloud\"\n",
     "headers = {\n",
     "\t\"Accept\" : \"application/json\",\n",
     "\t\"Content-Type\": \"application/json\" \n",
@@ -63,9 +57,8 @@
     "\t\t\n",
     "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
     "\toutput_payload = response.json()\t\n",
-    "\tout_image = decode_base64_image(output_payload[\"out_image\"])\n",
-    "\tprint(f\"out_image.size: {out_image.size}\")\n",
-    "\tout_image.save(f\"test_data/{img_name.split('.')[0]}_outscale_{outscale}.png\")\n"
+    "\tprint(output_payload['image_url'], output_payload['image_key'], output_payload['error'])\n",
+    "\tbreak"
    ]
   },
   {
@@ -92,7 +85,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

test_handler.ipynb

@@ -28,19 +28,6 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# helper decoder\n",
-    "def decode_base64_image(image_string):\n",
-    "  base64_image = base64.b64decode(image_string)\n",
-    "  buffer = BytesIO(base64_image)\n",
-    "  return  Image.open(buffer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# init handler\n",
     "my_handler = EndpointHandler(path=\".\")"
@@ -48,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -62,11 +49,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\tTile 1/2\n",
-      "\tTile 2/2\n",
-      "\tTile 1/2\n",
-      "\tTile 2/2\n",
-      "output.shape: (5170, 12000, 4)\n"
+      "output.shape: (5170, 12000, 4)\n",
+      "https://upscale-process-results.s3.amazonaws.com/0da01291-5a39-40fd-b322-f5d83e6066f6.png 0da01291-5a39-40fd-b322-f5d83e6066f6.png\n",
+      "image.size: (1056, 1068), image.mode: RGB, outscale: 3.0\n",
+      "output.shape: (3204, 3168, 3)\n",
+      "https://upscale-process-results.s3.amazonaws.com/c1ba714d-50e2-45d0-ac9f-8a4a4f218320.png c1ba714d-50e2-45d0-ac9f-8a4a4f218320.png\n",
+      "image.size: (1056, 1068), image.mode: L, outscale: 5.49\n",
+      "output.shape: (5863, 5797, 3)\n",
+      "https://upscale-process-results.s3.amazonaws.com/e964c021-9169-49d8-9382-104f704a1d92.png e964c021-9169-49d8-9382-104f704a1d92.png\n"
      ]
     }
    ],
@@ -88,18 +78,9 @@
     "\n",
     "\n",
     "    output_payload = my_handler(payload)\n",
-    "    # out_image = decode_base64_image(output_payload[\"out_image\"])\n",
-    "    # print(f\"out_image.size: {out_image.size}\")\n",
-    "    # out_image.save(f\"test_data/outputs/{img_name.split('.')[0]}_outscale_{outscale}.png\")\n",
-    "    break"
+    "    print(output_payload[\"image_url\"], output_payload[\"image_key\"])\n",
+    "    "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {