stable-diffusion-paperspace/other/CodeFormer_Inference_Simpli...

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2022-09-07 19:33:03 -06:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ZjdQE0kKcqjA"
},
"source": [
"<p align=\"center\">\n",
" <img src=\"https://user-images.githubusercontent.com/14334509/179359809-bd45566a-486d-418f-83fa-67bbbba8c45c.png\" height=120>\n",
"</p>\n",
"\n",
"# CodeFormer Inference Demo \n",
"## Towards Robust Blind Face Restoration with Codebook Lookup Transformer"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_U5Bu-qie_aH",
"tags": []
},
"source": [
"# 1. Preparations"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6HnTAAlTfFCY",
"tags": []
},
"source": [
"### Initalize"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8SG9AcLQO_FQ"
},
"outputs": [],
"source": [
"# Clone CodeFormer and enter the CodeFormer folder\n",
"%cd /content\n",
"!rm -rf CodeFormer\n",
"!git clone https://github.com/sczhou/CodeFormer.git\n",
"%cd CodeFormer\n",
"\n",
"# Set up the environment\n",
"# Install python dependencies\n",
"!pip install -r requirements.txt\n",
"# Install basicsr\n",
"!python basicsr/setup.py develop\n",
"\n",
"# Download the pre-trained model\n",
"!python scripts/download_pretrained_models.py facelib\n",
"!python scripts/download_pretrained_models.py CodeFormer\n",
"\n",
"# Visualization function\n",
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"def display(img1, img2):\n",
" fig = plt.figure(figsize=(25, 10))\n",
" ax1 = fig.add_subplot(1, 2, 1) \n",
" plt.title('Input', fontsize=16)\n",
" ax1.axis('off')\n",
" ax2 = fig.add_subplot(1, 2, 2)\n",
" plt.title('CodeFormer', fontsize=16)\n",
" ax2.axis('off')\n",
" ax1.imshow(img1)\n",
" ax2.imshow(img2)\n",
"def imread(img_path):\n",
" img = cv2.imread(img_path)\n",
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
" return img\n",
"!mkdir /content/CodeFormer/inputs/user_upload"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ClNIcJqxSN2e"
},
"source": [
"# Run!\n",
"\n",
"Upload your images to `/content/CodeFormer/inputs/user_upload`. The AI will process them all in bulk and tell you where it put the results."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "Cj2YQGg3J0TQ"
},
"outputs": [],
"source": [
"# Config\n",
"\n",
"CODEFORMER_FIDELITY = 0.44 # min:0, max:1 (try steps of 0.01)\n",
"\n",
"BACKGROUND_ENHANCE = True # Enhance background image with Real-ESRGAN\n",
"\n",
"FACE_UPSAMPLE = True # Upsample restored faces for high-resolution AI-created\n",
"\n",
"# =====================================================================\n",
"\n",
"if BACKGROUND_ENHANCE:\n",
" if FACE_UPSAMPLE:\n",
" !python inference_codeformer.py --w $CODEFORMER_FIDELITY --test_path inputs/user_upload --bg_upsampler realesrgan --face_upsample\n",
" else:\n",
" !python inference_codeformer.py --w $CODEFORMER_FIDELITY --test_path inputs/user_upload --bg_upsampler realesrgan\n",
"else:\n",
" !python inference_codeformer.py --w $CODEFORMER_FIDELITY --test_path inputs/user_upload"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bn5EyOJJSgUa"
},
"source": [
"# Tools"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ddFQzqHWV1BM"
},
"source": [
"### Delete uploaded images and results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rcm30PlmSh4O"
},
"outputs": [],
"source": [
"!rm -rf /notebooks/CodeFormer/inputs/user_upload/*\n",
"!rm -rf /notebooks/CodeFormer/results/*"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"6HnTAAlTfFCY",
"kScNto3vV4qD",
"ddFQzqHWV1BM",
"OxiB23_sdG8m"
],
"private_outputs": true,
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 4
}