{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "4f62bfd9-5396-48e2-aac7-bdf639cab345",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The config attributes {'block_out_channels': [128, 256, 512, 768, 768], 'force_upcast': False} were passed to AsymmetricAutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "from torchvision import transforms, utils\n",
    "\n",
    "import diffusers\n",
    "from diffusers import AsymmetricAutoencoderKL\n",
    "\n",
    "from diffusers.utils import load_image\n",
    "\n",
    "def crop_image_to_nearest_divisible_by_8(img):\n",
    "    # Check if the image height and width are divisible by 8\n",
    "    if img.shape[1] % 8 == 0 and img.shape[2] % 8 == 0:\n",
    "        return img\n",
    "    else:\n",
    "        # Calculate the closest lower resolution divisible by 8\n",
    "        new_height = img.shape[1] - (img.shape[1] % 8)\n",
    "        new_width = img.shape[2] - (img.shape[2] % 8)\n",
    "        \n",
    "        # Use CenterCrop to crop the image\n",
    "        transform = transforms.CenterCrop((new_height, new_width), interpolation=transforms.InterpolationMode.BILINEAR)\n",
    "        img = transform(img).to(torch.float32).clamp(-1, 1)\n",
    "        \n",
    "        return img\n",
    "        \n",
    "to_tensor = transforms.ToTensor()\n",
    "\n",
    "device = \"cuda\"\n",
    "dtype=torch.float16\n",
    "vae = AsymmetricAutoencoderKL.from_pretrained(\"vae\",torch_dtype=dtype).to(device).eval()\n",
    "\n",
    "image = load_image(\"generated.png\")\n",
    "\n",
    "image = crop_image_to_nearest_divisible_by_8(to_tensor(image)).unsqueeze(0).to(device,dtype=dtype)\n",
    "\n",
    "upscaled_image = vae(image).sample\n",
    "# Save the reconstructed image\n",
    "utils.save_image(upscaled_image, \"test.png\")\n",
    "print('ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e3ad326-c410-44b6-a738-15b7f7e15075",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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