{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Jpeb3w3R1Bxx" }, "source": [ "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/williamyang1991/StyleGANEX/blob/master/inference_playground.ipynb)" ] }, { "cell_type": "markdown", "source": [ "This colab contains three parts\n", "\n", "- PART I: Build a web demo with Gradio UI for easy use\n", "- PART II: Face manipulation with Colab UI where you can look into the code details and easily modify the code" ], "metadata": { "id": "r1x_6TZ-b1jf" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "TRW3eNYd1Bx0", "outputId": "cd8e4abd-1bc3-4ffe-a088-e12fc93afb49", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting gradio\n", " Downloading 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charset-normalizer, async-timeout, aiofiles, yarl, uvicorn, markdown-it-py, linkify-it-py, huggingface_hub, anyio, aiosignal, starlette, mdit-py-plugins, httpcore, aiohttp, httpx, fastapi, gradio\n", "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 anyio-3.6.2 async-timeout-4.0.2 charset-normalizer-3.1.0 fastapi-0.94.1 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.21.0 h11-0.14.0 httpcore-0.16.3 httpx-0.23.3 huggingface_hub-0.13.2 linkify-it-py-2.0.0 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 mdurl-0.1.2 multidict-6.0.4 orjson-3.8.7 pydub-0.25.1 python-multipart-0.0.6 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.26.1 uc-micro-py-1.0.1 uvicorn-0.21.0 websockets-10.4 yarl-1.8.2\n" ] } ], "source": [ "!pip install gradio huggingface_hub" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "ey7bK3OJ1Bx1" }, "outputs": [], "source": [ "import os\n", "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n", "os.chdir('../')\n", "CODE_DIR = 'StyleGANEX'\n", "device = 'cuda'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "_I_TDhFG1Bx2", "outputId": "9efccfe0-afdb-4d8d-d46a-4451ba7e2f1d", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'StyleGANEX'...\n", "remote: Enumerating objects: 220, done.\u001b[K\n", "remote: Counting objects: 100% (100/100), done.\u001b[K\n", "remote: Compressing objects: 100% (53/53), done.\u001b[K\n", "remote: Total 220 (delta 52), reused 89 (delta 46), pack-reused 120\u001b[K\n", "Receiving objects: 100% (220/220), 15.57 MiB | 15.98 MiB/s, done.\n", "Resolving deltas: 100% (61/61), done.\n" ] } ], "source": [ "!git clone https://github.com/williamyang1991/StyleGANEX.git $CODE_DIR\n", "os.chdir(f'./{CODE_DIR}')" ] }, { "cell_type": "code", "source": [ "!wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip\n", "!sudo unzip ninja-linux.zip -d /usr/local/bin/\n", "!sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force " ], "metadata": { "id": "lvstV2wr1uRt", "outputId": "ebedb646-7273-41a0-99d8-2d77866e08cf", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2023-03-15 13:26:14-- https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip\n", "Resolving github.com (github.com)... 20.205.243.166\n", "Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/1335132/d2f252e2-9801-11e7-9fbf-bc7b4e4b5c83?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230315%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230315T132615Z&X-Amz-Expires=300&X-Amz-Signature=a187ac33868ff7b1c71276752679789475f8beb5d870db3e2fe0e9fad94a1b3f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=1335132&response-content-disposition=attachment%3B%20filename%3Dninja-linux.zip&response-content-type=application%2Foctet-stream [following]\n", "--2023-03-15 13:26:15-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/1335132/d2f252e2-9801-11e7-9fbf-bc7b4e4b5c83?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230315%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230315T132615Z&X-Amz-Expires=300&X-Amz-Signature=a187ac33868ff7b1c71276752679789475f8beb5d870db3e2fe0e9fad94a1b3f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=1335132&response-content-disposition=attachment%3B%20filename%3Dninja-linux.zip&response-content-type=application%2Foctet-stream\n", "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 77854 (76K) [application/octet-stream]\n", "Saving to: ‘ninja-linux.zip’\n", "\n", "ninja-linux.zip 100%[===================>] 76.03K --.-KB/s in 0.001s \n", "\n", "2023-03-15 13:26:15 (63.4 MB/s) - ‘ninja-linux.zip’ saved [77854/77854]\n", "\n", "Archive: ninja-linux.zip\n", " inflating: /usr/local/bin/ninja \n", "update-alternatives: using /usr/local/bin/ninja to provide /usr/bin/ninja (ninja) in auto mode\n" ] } ] }, { "cell_type": "markdown", "source": [ "# PART I - Face Manipulation with Gradio UI" ], "metadata": { "id": "Ni8brc4yb-gf" } }, { "cell_type": "code", "source": [ "from webUI.app_task import *\n", "from webUI.styleganex_model import Model\n", "import torch" ], "metadata": { "id": "lri38alScB3A" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/234_sketch.jpg',\n", " '234_sketch.jpg')\n", "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/output/ILip77SbmOE_inversion.pt',\n", " 'ILip77SbmOE_inversion.pt')\n", "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE.png',\n", " 'ILip77SbmOE.png')\n", "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE_mask.png',\n", " 'ILip77SbmOE_mask.png')\n", "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/pexels-daniel-xavier-1239291.jpg',\n", " 'pexels-daniel-xavier-1239291.jpg')\n", "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/529_2.mp4',\n", " '529_2.mp4')\n", "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/684.mp4',\n", " '684.mp4')\n", "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/pexels-anthony-shkraba-production-8136210.mp4',\n", " 'pexels-anthony-shkraba-production-8136210.mp4')" ], "metadata": { "id": "1V-HpMsOcUHI", "outputId": "32cb8f1e-094e-4b70-e052-c29809ed6678", "colab": { "base_uri": "https://localhost:8080/", "height": 273, "referenced_widgets": [ "a561b0a49ee343aaaf568cfa0e501736", "e21e48fe3cc0461a9749b9ced08f78cb", "8b290003bbb14f87a2925523e8f08a1d", "10bcb07fd2844b62a8f852515a2bc141", "5810ca4c2561480bb7adf991edbb7f4f", "71d12c80831646a7b47530c4f2b3075d", "66c0d29da18e4b84b0b7b0b42474f570", "5500dd4e879a4e39bf8d1fd3b1442335", "5227887d34974d6f924da62b8c3fd9a8", "eb4d2d9266264ae7b9176c60ac76f7a9", "45881539cc1f470dadd7e5cce27f76f3", "345327215e8c4526810e5452e16e9cbd", "032cf8b008af4faf862fde474f73cb21", "c0fd24844b764dca81eab021bfc54c47", "2d96b70d175a46c68858750a2699c43f", "7c556891b7924fd985111f43b237e1d5", "36a225763038429f8a966ec7808f3f11", "5822df9ee28a407ba6607891ffc80a55", "1ba44dc5e2c44979ad4c973a1f6a82c7", "1e45c73aa8f84b6aaeb00675b44b2d9d", "ef99cbc4ef3443a584432578a4b20f0c", "ab0a334e99574ad2b9c2f886c007755e", "521b35d9e90d4284b75a275274f2ef8b", "c140e6eececd4f99871356d091dbe9a5", "94188bfc121f4b0dbbdba76ce9043c88", "467966270a194bc5a3e5647fc20a2c8d", "e9fe9f2cf9e74f2c92b2a753af19cc96", "cdd183503416458f91e0973efc4373e1", "df83abe677de4d93bf2800b2b518336a", "57c3e1a52ff447fabbb793e1447e26d9", "5199409e3c1943a5a7702e323c516c04", "350d8992615d4d34b607eea600bf3b21", "b5f62d17be014ac5aa45a7854cce03c3", "83b575ea01c44283a851806469919578", "cc60e4dd42e04854b38c5d192b24ad93", "4670e779dd7f4acaa9181bcadd2ec307", "89be777acdb948d09e9f9a1202901138", "80432ca1678e4bf48563dc282dd6d1e5", "0491235ed3e342bebd8450e1ae6e754a", "94382af2d7044366b3b7b13d4933945b", "730bd1ca1d21420a8deb87029c2d049d", "47bc36a72f6649e4a6f97ee9320c5a76", "2269d333b2b94be78d33eb745185940c", "ab38e1c7666c4cbc8dc54fb1fdd9581f", "85c36f8ad7284e13a6cc0dd5b8550d76", "0e7fa9ecd1e74031ba2e77e9a33ce410", "72a364bdb374435380d271bcf56a51e2", "46284c3f335b4b5ca4cd238bad6cfc26", "7a818a2341d54db29e607282f63ea4fc", "03e6d97add5543f09a405185f2f09766", "53a91bad55454385b73f8a006ea997fa", "ae169323aeb24f988672501e9ecea9ca", "77e31b3388a743c6abf81cfa270e6c5d", "d93c3ed097d04de294fec9ec173bae61", "482fba6f70ec4af7bc4721550659c950", "6cdbd860b3fb491da369111209cd856e", "49ae9a1958f24cc2987e771aa9e47d64", "c8d3aed3c47b4913bf4e180f73007593", "fff063675cdd40f69972f8c5e708c090", "ffcfc6d71df34d329fb04a4dc4342b87", "add09ced3f9940a4ad47bed4840c8eb4", "aa829d3a596a4750a51087ec27f377d8", "9a2c277ba6c84313b783cc1f5ed8edf0", "415bdc6c4d4a44c2aaef706a22e08e66", "0f33024ce728483e8aa2a78e6650f343", "fdd99552b3de4aae86cfc17a24041353", "3164fedc134640048c0cc2808df9326d", "41ade26d98284f8bbbc65666fd636c24", "3a620aef448a449c8e8e0cecfa91458c", "ee7faeb0420a45c0aade9ce8d60d0490", "525e79af704c44c881069fd92f76e0e2", "c7e9e1ecb46d463e83d4b4852e3db506", "f83b16540df14f04ba3cb0f39002225a", "176df07167eb47ac8dffac24222b786b", "182c8c1fd4a445df988cc08c59210378", "84a8839d2a6d45eda9c7a9ec2fa8568e", "1d6263793ffc484ab0d2db482d314890", "372b7a6f9862430f8a5ac7167daf16fb", 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?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "3164fedc134640048c0cc2808df9326d" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0.00/901k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "372b7a6f9862430f8a5ac7167daf16fb" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "DESCRIPTION = '''\n", "
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. \n",
"