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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "qJDJLE3v0HNr"
},
"source": [
"# Fetch Codebase"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"cellView": "form",
"id": "JqiWKjpFa0ov"
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/content'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb Cell 2'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000001?line=0'>1</a>\u001b[0m \u001b[39m#@title\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000001?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000001?line=2'>3</a>\u001b[0m os\u001b[39m.\u001b[39;49mchdir(\u001b[39m'\u001b[39;49m\u001b[39m/content\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000001?line=3'>4</a>\u001b[0m CODE_DIR \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39msefa\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000001?line=4'>5</a>\u001b[0m \u001b[39m#!git clone https://github.com/genforce/sefa.git $CODE_DIR\u001b[39;00m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content'"
]
}
],
"source": [
"#@title\n",
"import os\n",
"CODE_DIR = 'sefa'\n",
"#!git clone https://github.com/genforce/sefa.git $CODE_DIR\n",
"os.chdir(f'..')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hQ_IXBZr8YcJ"
},
"source": [
"# Define Utility Functions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cellView": "form",
"id": "ijKTlG5GeTd3"
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'models'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb Cell 4'\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000003?line=6'>7</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mPIL\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mImage\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000003?line=8'>9</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000003?line=10'>11</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmodels\u001b[39;00m \u001b[39mimport\u001b[39;00m parse_gan_type\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000003?line=11'>12</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m to_tensor\n\u001b[1;32m <a href='vscode-notebook-cell:/home/johannes/Projects/Vision/sefa/docs/SeFa.ipynb#ch0000003?line=12'>13</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m postprocess\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'models'"
]
}
],
"source": [
"#@title\n",
"import os.path\n",
"import io\n",
"import IPython.display\n",
"import numpy as np\n",
"import cv2\n",
"import PIL.Image\n",
"\n",
"import torch\n",
"\n",
"from models import parse_gan_type\n",
"from utils import to_tensor\n",
"from utils import postprocess\n",
"from utils import load_generator\n",
"from utils import factorize_weight\n",
"\n",
"\n",
"def sample(generator, gan_type, num=1, seed=0):\n",
" \"\"\"Samples latent codes.\"\"\"\n",
" torch.manual_seed(seed)\n",
" codes = torch.randn(num, generator.z_space_dim).cuda()\n",
" if gan_type == 'pggan':\n",
" codes = generator.layer0.pixel_norm(codes)\n",
" elif gan_type == 'stylegan':\n",
" codes = generator.mapping(codes)['w']\n",
" codes = generator.truncation(codes, trunc_psi=0.7, trunc_layers=8)\n",
" elif gan_type == 'stylegan2':\n",
" codes = generator.mapping(codes)['w']\n",
" codes = generator.truncation(codes, trunc_psi=0.5, trunc_layers=18)\n",
" codes = codes.detach().cpu().numpy()\n",
" return codes\n",
"\n",
"\n",
"def synthesize(generator, gan_type, codes):\n",
" \"\"\"Synthesizes images with the give codes.\"\"\"\n",
" if gan_type == 'pggan':\n",
" images = generator(to_tensor(codes))['image']\n",
" elif gan_type in ['stylegan', 'stylegan2']:\n",
" images = generator.synthesis(to_tensor(codes))['image']\n",
" images = postprocess(images)\n",
" return images\n",
"\n",
"\n",
"def imshow(images, col, viz_size=256):\n",
" \"\"\"Shows images in one figure.\"\"\"\n",
" num, height, width, channels = images.shape\n",
" assert num % col == 0\n",
" row = num // col\n",
"\n",
" fused_image = np.zeros((viz_size * row, viz_size * col, channels), dtype=np.uint8)\n",
"\n",
" for idx, image in enumerate(images):\n",
" i, j = divmod(idx, col)\n",
" y = i * viz_size\n",
" x = j * viz_size\n",
" if height != viz_size or width != viz_size:\n",
" image = cv2.resize(image, (viz_size, viz_size))\n",
" fused_image[y:y + viz_size, x:x + viz_size] = image\n",
"\n",
" fused_image = np.asarray(fused_image, dtype=np.uint8)\n",
" data = io.BytesIO()\n",
" PIL.Image.fromarray(fused_image).save(data, 'jpeg')\n",
" im_data = data.getvalue()\n",
" disp = IPython.display.display(IPython.display.Image(im_data))\n",
" return disp"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q7gkmrVW8eR1"
},
"source": [
"# Select a Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NoWI4fPQ6Gnf"
},
"outputs": [],
"source": [
"#@title { display-mode: \"form\", run: \"auto\" }\n",
"model_name = \"stylegan_animeface512\" #@param ['stylegan_animeface512', 'stylegan_car512', 'stylegan_cat256', 'pggan_celebahq1024', 'stylegan_bedroom256']\n",
"\n",
"generator = load_generator(model_name)\n",
"gan_type = parse_gan_type(generator)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zDStH1O5t1KC"
},
"source": [
"# Sample Latent Codes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qlRGKZbJt9hA"
},
"outputs": [],
"source": [
"#@title { display-mode: \"form\", run: \"auto\" }\n",
"\n",
"num_samples = 3 #@param {type:\"slider\", min:1, max:8, step:1}\n",
"noise_seed = 0 #@param {type:\"slider\", min:0, max:1000, step:1}\n",
"\n",
"codes = sample(generator, gan_type, num_samples, noise_seed)\n",
"images = synthesize(generator, gan_type, codes)\n",
"imshow(images, col=num_samples)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MmRPN3xz8jCH"
},
"source": [
"# Factorize & Edit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ccONBF60mVir"
},
"outputs": [],
"source": [
"#@title { display-mode: \"form\", run: \"auto\" }\n",
"\n",
"layer_idx = \"0-1\" #@param ['all', '0-1', '2-5', '6-13']\n",
"semantic_1 = 0 #@param {type:\"slider\", min:-3.0, max:3.0, step:0.1}\n",
"semantic_2 = 0 #@param {type:\"slider\", min:-3.0, max:3.0, step:0.1}\n",
"semantic_3 = 0 #@param {type:\"slider\", min:-3.0, max:3.0, step:0.1}\n",
"semantic_4 = 0 #@param {type:\"slider\", min:-3.0, max:3.0, step:0.1}\n",
"semantic_5 = 0 #@param {type:\"slider\", min:-3.0, max:3.0, step:0.1}\n",
"\n",
"# Fast implementation to factorize the weight by SeFa.\n",
"layers, boundaries, _ = factorize_weight(generator, layer_idx)\n",
"\n",
"new_codes = codes.copy()\n",
"for sem_idx in range(5):\n",
" boundary = boundaries[sem_idx:sem_idx + 1]\n",
" step = eval(f'semantic_{sem_idx + 1}')\n",
" if gan_type == 'pggan':\n",
" new_codes += boundary * step\n",
" elif gan_type in ['stylegan', 'stylegan2']:\n",
" new_codes[:, layers, :] += boundary * step\n",
"new_images = synthesize(generator, gan_type, new_codes)\n",
"imshow(new_images, col=num_samples)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "SeFa",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|