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{
"cells": [
{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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