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genshin-stable-inversion

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stable-diffusion
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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "source": [
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- "# Trinket! Stable Inversion\n",
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- "A cheap alternative to finetuning stable diffusion, by [crumb](https://twitter.com/aicrumb)\n",
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- "\n",
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- "Finetunes the embedding layer like Textual-Inversion does, but on CLIP Text/Image pairs instead of reconstruction loss from Stable Diffusion. Lower memory requirements + (sometimes) faster than finetuning the traditional way."
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- ],
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- "metadata": {
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- "id": "mSXDq5-qTSF6"
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- },
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- "id": "mSXDq5-qTSF6"
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "!pip install git+https://github.com/openai/CLIP -q\n",
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- "!pip install bitsandbytes -q"
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- ],
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- "metadata": {
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- "id": "yB-XeS51NmI2"
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- },
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- "id": "yB-XeS51NmI2",
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- "execution_count": 1,
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- "outputs": []
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "import bitsandbytes as bnb\n",
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- "import torchvision\n",
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- "from torchvision import transforms\n",
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- "from tqdm.auto import *\n",
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- "from torch import nn, optim\n",
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- "from torch.nn import functional as F\n",
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- "from PIL import Image\n",
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- "import requests\n",
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- "from io import BytesIO\n",
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- "import torch\n",
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- "import random\n",
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- "import clip\n",
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- "import pandas as pd"
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- ],
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- "metadata": {
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- "id": "x1OogszooEi6",
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "outputId": "a55a628d-aafc-407e-a2a9-fb03b8a0896a"
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- },
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- "id": "x1OogszooEi6",
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- "execution_count": 2,
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
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- "\n",
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- "===================================BUG REPORT===================================\n",
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- "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
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- "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
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- "================================================================================\n",
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- "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
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- "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
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- "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
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- "CUDA SETUP: Detected CUDA version 111\n",
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- "CUDA SETUP: Loading binary /usr/local/lib/python3.7/dist-packages/bitsandbytes/libbitsandbytes_cuda111.so...\n"
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- ]
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- },
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- {
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- "output_type": "stream",
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- "name": "stderr",
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- "text": [
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- "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:99: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
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- " f'{candidate_env_vars[\"LD_LIBRARY_PATH\"]} did not contain '\n",
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- "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:21: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('{\"kernelManagerProxyPort\"'), PosixPath('\"/usr/local/bin/dap_multiplexer\",\"enableLsp\"'), PosixPath('\"172.28.0.3\",\"jupyterArgs\"'), PosixPath('[\"--ip=172.28.0.2\"],\"debugAdapterMultiplexerPath\"'), PosixPath('true}'), PosixPath('6000,\"kernelManagerProxyHost\"')}\n",
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- " \"WARNING: The following directories listed in your path were found to \"\n",
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- "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:21: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
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- " \"WARNING: The following directories listed in your path were found to \"\n",
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- "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:21: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
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- " \"WARNING: The following directories listed in your path were found to \"\n"
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- ]
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- }
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 3,
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- "id": "22b6861e-289f-4779-bbf1-40bfd1327823",
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "id": "22b6861e-289f-4779-bbf1-40bfd1327823",
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- "outputId": "fd96d25d-8cbf-43d2-8d5b-983856bf2a3a"
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- },
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
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- "Loaded CLIP\n"
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- ]
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- }
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- ],
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- "source": [
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- "# the stable diffusion model uses L/14 by default\n",
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- "clip_model, _ = clip.load(\"ViT-L/14\", jit=False)\n",
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- "clip_model = clip_model.cuda()\n",
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- "print(\"Loaded CLIP\")"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 4,
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- "id": "eaa9bde8-3fdd-48df-aa36-8b88cec285be",
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- "metadata": {
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- "id": "eaa9bde8-3fdd-48df-aa36-8b88cec285be"
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- },
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- "outputs": [],
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- "source": [
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- "bs = 16 # need a large batch size for the contrastive loss to work properly\n",
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- "steps = 64\n",
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- "epochs = 4\n",
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- "lr = 1e-3"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 5,
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- "id": "0667d187-2c3e-40b8-a2fd-b408d5f144c5",
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/",
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- "height": 206
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- },
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- "id": "0667d187-2c3e-40b8-a2fd-b408d5f144c5",
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- "outputId": "b8f6056a-c2ac-4b89-e222-0d47705617e9"
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- },
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- "outputs": [
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- {
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- "output_type": "execute_result",
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- "data": {
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- "text/plain": [
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- " Unnamed: 0 url \\\n",
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- "0 0 https://cdn.donmai.us/original/51/bb/51bb44d69... \n",
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- "1 1 https://cdn.donmai.us/original/4f/8e/4f8e5eaba... \n",
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- "2 2 https://cdn.donmai.us/original/c8/54/c85428d89... \n",
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- "3 3 https://cdn.donmai.us/original/e9/fc/e9fcc788e... \n",
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- "4 4 https://cdn.donmai.us/original/8a/63/8a639d21b... \n",
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- "\n",
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- " prompt \n",
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- "0 genshin_impact boo_tao_(genshin_impact) hu_tao... \n",
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- "1 genshin_impact arlecchino_(genshin_impact) cap... \n",
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- "2 genshin_impact fischl_(ein_immernachtstraum)_(... \n",
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- "3 genshin_impact eula_(genshin_impact) 1girl :o ... \n",
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- "4 genshin_impact kuki_shinobu 1girl breasts brid... "
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- ],
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- "text/html": [
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- "\n",
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- " <div id=\"df-b1359ba3-d298-4260-9864-de09e0a61fd0\">\n",
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- " <div class=\"colab-df-container\">\n",
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- " <div>\n",
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- "<style scoped>\n",
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- " .dataframe tbody tr th:only-of-type {\n",
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- " vertical-align: middle;\n",
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- " }\n",
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- "\n",
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- " .dataframe tbody tr th {\n",
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- " vertical-align: top;\n",
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- " }\n",
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- "\n",
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- " .dataframe thead th {\n",
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- " text-align: right;\n",
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- " }\n",
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- "</style>\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
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- " <thead>\n",
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- " <tr style=\"text-align: right;\">\n",
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- " <th></th>\n",
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- " <th>Unnamed: 0</th>\n",
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- " <th>url</th>\n",
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- " <th>prompt</th>\n",
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- " </tr>\n",
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- " </thead>\n",
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- " <tbody>\n",
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- " <tr>\n",
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- " <th>0</th>\n",
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- " <td>0</td>\n",
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- " <td>https://cdn.donmai.us/original/51/bb/51bb44d69...</td>\n",
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- " <td>genshin_impact boo_tao_(genshin_impact) hu_tao...</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>1</th>\n",
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- " <td>1</td>\n",
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- " <td>https://cdn.donmai.us/original/4f/8e/4f8e5eaba...</td>\n",
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- " <td>genshin_impact arlecchino_(genshin_impact) cap...</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>2</th>\n",
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- " <td>2</td>\n",
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- " <td>https://cdn.donmai.us/original/c8/54/c85428d89...</td>\n",
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- " <td>genshin_impact fischl_(ein_immernachtstraum)_(...</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>3</th>\n",
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- " <td>3</td>\n",
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- " <td>https://cdn.donmai.us/original/e9/fc/e9fcc788e...</td>\n",
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- " <td>genshin_impact eula_(genshin_impact) 1girl :o ...</td>\n",
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- " </tr>\n",
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- " <tr>\n",
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- " <th>4</th>\n",
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- " <td>4</td>\n",
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- " <td>https://cdn.donmai.us/original/8a/63/8a639d21b...</td>\n",
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- " <td>genshin_impact kuki_shinobu 1girl breasts brid...</td>\n",
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- " </tr>\n",
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- " </tbody>\n",
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- "</table>\n",
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- "</div>\n",
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- " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1359ba3-d298-4260-9864-de09e0a61fd0')\"\n",
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- " title=\"Convert this dataframe to an interactive table.\"\n",
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- " style=\"display:none;\">\n",
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- " \n",
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- " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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- " width=\"24px\">\n",
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- " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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- " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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- " </svg>\n",
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- " </button>\n",
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- " \n",
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- " <style>\n",
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- " .colab-df-container {\n",
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- " display:flex;\n",
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- " flex-wrap:wrap;\n",
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- " gap: 12px;\n",
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- " }\n",
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- "\n",
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- " .colab-df-convert {\n",
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- " background-color: #E8F0FE;\n",
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- " border: none;\n",
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- " border-radius: 50%;\n",
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- " cursor: pointer;\n",
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- " display: none;\n",
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- " fill: #1967D2;\n",
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- " height: 32px;\n",
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- " padding: 0 0 0 0;\n",
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- " width: 32px;\n",
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- " }\n",
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- "\n",
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- " .colab-df-convert:hover {\n",
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- " background-color: #E2EBFA;\n",
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- " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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- " fill: #174EA6;\n",
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- " }\n",
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- "\n",
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- " [theme=dark] .colab-df-convert {\n",
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- " background-color: #3B4455;\n",
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- " fill: #D2E3FC;\n",
261
- " }\n",
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- "\n",
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- " [theme=dark] .colab-df-convert:hover {\n",
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- " background-color: #434B5C;\n",
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- " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
266
- " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
267
- " fill: #FFFFFF;\n",
268
- " }\n",
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- " </style>\n",
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- "\n",
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- " <script>\n",
272
- " const buttonEl =\n",
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- " document.querySelector('#df-b1359ba3-d298-4260-9864-de09e0a61fd0 button.colab-df-convert');\n",
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- " buttonEl.style.display =\n",
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- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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- "\n",
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- " async function convertToInteractive(key) {\n",
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- " const element = document.querySelector('#df-b1359ba3-d298-4260-9864-de09e0a61fd0');\n",
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- " const dataTable =\n",
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- " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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- " [key], {});\n",
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- " if (!dataTable) return;\n",
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- "\n",
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- " const docLinkHtml = 'Like what you see? Visit the ' +\n",
285
- " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
286
- " + ' to learn more about interactive tables.';\n",
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- " element.innerHTML = '';\n",
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- " dataTable['output_type'] = 'display_data';\n",
289
- " await google.colab.output.renderOutput(dataTable, element);\n",
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- " const docLink = document.createElement('div');\n",
291
- " docLink.innerHTML = docLinkHtml;\n",
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- " element.appendChild(docLink);\n",
293
- " }\n",
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- " </script>\n",
295
- " </div>\n",
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- " </div>\n",
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- " "
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- ]
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- },
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- "metadata": {},
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- "execution_count": 5
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- }
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- ],
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- "source": [
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- "# ultimately you just need >1000 prompt+url pairs in lists named prompts and urls, however you load it is fine\n",
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- "# this is how i scraped them so this is how i load them\n",
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- "# you can scrape danbooru/safebooru/others with pybooru with this script from waifu-diffusion https://github.com/harubaru/waifu-diffusion/blob/main/danbooru_data/scrape.py\n",
308
- "df = pd.read_csv(\"/content/genshin.csv\")\n",
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- "prompts = df['prompt']#[:steps*bs]\n",
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- "urls = df['url']#[:steps*bs\n",
311
- "df.head()"
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- ]
313
- },
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- {
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- "cell_type": "code",
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- "source": [
317
- "print(len(prompts))"
318
- ],
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- "metadata": {
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- "colab": {
321
- "base_uri": "https://localhost:8080/"
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- },
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- "id": "6lYQYqKEZKhv",
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- "outputId": "7d26b946-ce1e-41ed-abaf-a4efed5d02cc"
325
- },
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- "id": "6lYQYqKEZKhv",
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- "execution_count": 6,
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
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- "1200\n"
334
- ]
335
- }
336
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 7,
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- "id": "12fce080-73f3-430f-ba2c-99f265316b66",
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- "metadata": {
343
- "id": "12fce080-73f3-430f-ba2c-99f265316b66"
344
- },
345
- "outputs": [],
346
- "source": [
347
- "clip_model.token_embedding.weight.requires_grad = True\n",
348
- "# opt = optim.Adam([clip_model.token_embedding.weight], lr)\n",
349
- "opt = bnb.optim.AdamW8bit([clip_model.token_embedding.weight], lr)"
350
- ]
351
- },
352
- {
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- "cell_type": "code",
354
- "execution_count": 8,
355
- "id": "9a4b1696-a8d9-4eea-a8e0-ba372d450619",
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- "metadata": {
357
- "id": "9a4b1696-a8d9-4eea-a8e0-ba372d450619"
358
- },
359
- "outputs": [],
360
- "source": [
361
- "# functions from another project of mine that's a bit messy\n",
362
- "def fix_to_224(pil_image):\n",
363
- " width, height = pil_image.size\n",
364
- " if width < height:\n",
365
- " new_width = 224\n",
366
- " new_height = int(new_width * height / width)\n",
367
- " else:\n",
368
- " new_height = 224\n",
369
- " new_width = int(new_height * width / height)\n",
370
- " return pil_image.resize((new_width, new_height))\n",
371
- "def to_tensor_and_center_crop(pil_image):\n",
372
- " tensor = torchvision.transforms.functional.to_tensor(pil_image)\n",
373
- " center_crop = torchvision.transforms.functional.center_crop(tensor, (224, 224))\n",
374
- " return center_crop\n",
375
- "def fix(img):\n",
376
- " return to_tensor_and_center_crop(fix_to_224(img))\n",
377
- "\n",
378
- "iter_prompts = iter(prompts)\n",
379
- "iter_urls = iter(urls)\n",
380
- "def get_batch(size=8, step=1, steps_per_epoch=128, epoch=1, total_epochs=1):\n",
381
- " to_tensor = transforms.ToTensor()\n",
382
- " x = []\n",
383
- " y = []\n",
384
- " total_steps = total_epochs*steps_per_epoch\n",
385
- " current_fraction = (epoch*steps_per_epoch-steps_per_epoch+step) / total_steps\n",
386
- "\n",
387
- " while len(x) < size:\n",
388
- " # uncomment these and space the big block right one tab\n",
389
- " # if you're using a set that might have dead urls\n",
390
- " try:\n",
391
- " url = next(iter_urls)\n",
392
- " response = requests.get(url)\n",
393
- " img = Image.open(BytesIO(response.content))\n",
394
- " img = img.convert(\"RGB\")\n",
395
- " img = fix(img)\n",
396
- " x.append(img.unsqueeze(0))\n",
397
- "\n",
398
- " p = next(iter_prompts)\n",
399
- "\n",
400
- " # comment out these lines if you aren't using danbooru tags\n",
401
- " p = p.split(\" \")\n",
402
- " if current_fraction < 0.75: random.shuffle(p)\n",
403
- " if current_fraction > 0.5: # halfway through training, start dropping half of the tags\n",
404
- " p = p[:len(p)//2]\n",
405
- " if current_fraction > 0.75: # 3/4 through training, start dropping 3/4 of the tags\n",
406
- " p = p[:4]\n",
407
- " p = \" \".join(p)\n",
408
- " y.append(p)\n",
409
- " except KeyboardInterrupt:\n",
410
- " print('Interrupted')\n",
411
- " break\n",
412
- " except:\n",
413
- " pass\n",
414
- " \n",
415
- " x = torch.cat(x, 0)\n",
416
- " x = clip_model.encode_image(x.cuda())\n",
417
- " \n",
418
- " y = clip.tokenize(y, truncate=True)\n",
419
- " y = clip_model.encode_text(y.cuda())\n",
420
- " return x, y"
421
- ]
422
- },
423
- {
424
- "cell_type": "code",
425
- "execution_count": null,
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- "id": "203637dd-441e-4b43-944d-48ffa126e826",
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- "metadata": {
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- "colab": {
429
- "base_uri": "https://localhost:8080/",
430
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- "metadata": {}
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- },
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- {
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- "output_type": "stream",
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- "name": "stderr",
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- "text": [
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- "/usr/local/lib/python3.7/dist-packages/PIL/Image.py:960: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
504
- " \"Palette images with Transparency expressed in bytes should be \"\n",
505
- "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
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- " warnings.warn(str(msg))\n"
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- {
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- "output_type": "display_data",
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- "data": {
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- "text/plain": [
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- }
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- "metadata": {}
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- }
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- ],
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- "source": [
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- "losses = []\n",
526
- "\n",
527
- "for epoch in trange(epochs):\n",
528
- " pbar = trange(steps)\n",
529
- " iter_prompts = iter(prompts)\n",
530
- " iter_urls = iter(urls)\n",
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- "\n",
532
- " for i in pbar:\n",
533
- " x, y = get_batch(bs, i, steps, epoch, epochs)\n",
534
- " loss = (x-y).pow(2).mean()\n",
535
- " # loss = spherical_distance_loss(x,y).mean() # TODO: try whatever loss they use in the CLIP paper instead\n",
536
- " loss.backward()\n",
537
- " opt.step()\n",
538
- " opt.zero_grad()\n",
539
- "\n",
540
- " pbar.set_description(str(loss.item()))\n",
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- " losses.append(loss.item())"
542
- ]
543
- },
544
- {
545
- "cell_type": "code",
546
- "source": [
547
- "import matplotlib.pyplot as plt\n",
548
- "\n",
549
- "# hacky ema plot\n",
550
- "losses_df = pd.DataFrame(losses, columns=['loss'])\n",
551
- "losses_plot = losses_df.ewm(alpha=0.05).mean()['loss']\n",
552
- "plt.plot(losses_plot)\n",
553
- "plt.plot(losses)"
554
- ],
555
- "metadata": {
556
- "id": "J5QkXxh5PI04"
557
- },
558
- "id": "J5QkXxh5PI04",
559
- "execution_count": null,
560
- "outputs": []
561
- },
562
- {
563
- "cell_type": "code",
564
- "source": [
565
- "torch.save(clip_model.token_embedding.weight, \"token_embeddings.pt\")"
566
- ],
567
- "metadata": {
568
- "id": "MdEjBt3hRIRB"
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- },
570
- "id": "MdEjBt3hRIRB",
571
- "execution_count": null,
572
- "outputs": []
573
- },
574
- {
575
- "cell_type": "code",
576
- "source": [
577
- "#@markdown 🔔\n",
578
- "from google.colab import output\n",
579
- "output.eval_js('new Audio(\"https://freesound.org/data/previews/80/80921_1022651-lq.ogg\").play()')"
580
- ],
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- "metadata": {
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- "cellView": "form",
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- "id": "IU0mqpyVTQzt"
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- },
585
- "id": "IU0mqpyVTQzt",
586
- "execution_count": null,
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- "outputs": []
588
- },
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- {
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- "cell_type": "markdown",
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- "source": [
592
- "### Upload to 🤗"
593
- ],
594
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595
- "id": "E5TP5qjAkTjL"
596
- },
597
- "id": "E5TP5qjAkTjL"
598
- },
599
- {
600
- "cell_type": "code",
601
- "source": [
602
- "#@markdown Log in\n",
603
- "!pip install huggingface-hub -q\n",
604
- "from huggingface_hub import notebook_login\n",
605
- "notebook_login()"
606
- ],
607
- "metadata": {
608
- "id": "bUdMc0o4kBYx",
609
- "cellView": "form"
610
- },
611
- "id": "bUdMc0o4kBYx",
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- "execution_count": null,
613
- "outputs": []
614
- },
615
- {
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- "cell_type": "code",
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- "source": [
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- "from huggingface_hub import HfApi\n",
619
- "api = HfApi()\n",
620
- "\n",
621
- "#@markdown go ahead and create the repository on 🤗 before running this\n",
622
- "my_repo = \"user/my-stable-inversion\" #@param {type:\"string\"}\n",
623
- "\n",
624
- "api.upload_file(\n",
625
- " path_or_fileobj=\"token_embeddings.pt\",\n",
626
- " path_in_repo=\"token_embeddings.pt\",\n",
627
- " repo_id=my_repo,\n",
628
- " repo_type=\"model\",\n",
629
- ")"
630
- ],
631
- "metadata": {
632
- "id": "WJ30dShA7koO"
633
- },
634
- "id": "WJ30dShA7koO",
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- "execution_count": null,
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- "outputs": []
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- },
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- {
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- "cell_type": "markdown",
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- "source": [
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- "how you can load the stable inversions into a diffusers-based notebook like [Doohickey](https://github.com/aicrumb/doohickey) might look something like this\n",
642
- "\n",
643
- "```\n",
644
- "from huggingface_hub import hf_hub_download\n",
645
- "\n",
646
- "stable_inversion = \"user/my-stable-inversion\" #@param {type:\"string\"}\n",
647
- "if len(stable_inversion)>1:\n",
648
- " g = hf_hub_download(repo_id=stable_inversion, filename=\"token_embeddings.pt\")\n",
649
- " text_encoder.text_model.embeddings.token_embedding.weight = torch.load(g)\n",
650
- "```"
651
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652
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- "id": "tIKiCVkHkFET"
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- "display_name": "Python 3 (ipykernel)",
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- "nbconvert_exporter": "python",
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- "version": "3.10.4"
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