{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Trinket! Stable Inversion\n",
        "A cheap alternative to finetuning stable diffusion, by [crumb](https://twitter.com/aicrumb)\n",
        "\n",
        "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."
      ],
      "metadata": {
        "id": "mSXDq5-qTSF6"
      },
      "id": "mSXDq5-qTSF6"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/openai/CLIP -q\n",
        "!pip install bitsandbytes -q"
      ],
      "metadata": {
        "id": "yB-XeS51NmI2"
      },
      "id": "yB-XeS51NmI2",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import bitsandbytes as bnb\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from tqdm.auto import *\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import torch\n",
        "import random\n",
        "import clip\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "x1OogszooEi6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a55a628d-aafc-407e-a2a9-fb03b8a0896a"
      },
      "id": "x1OogszooEi6",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
            "================================================================================\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 111\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.7/dist-packages/bitsandbytes/libbitsandbytes_cuda111.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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",
            "  f'{candidate_env_vars[\"LD_LIBRARY_PATH\"]} did not contain '\n",
            "/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",
            "  \"WARNING: The following directories listed in your path were found to \"\n",
            "/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",
            "  \"WARNING: The following directories listed in your path were found to \"\n",
            "/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",
            "  \"WARNING: The following directories listed in your path were found to \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "22b6861e-289f-4779-bbf1-40bfd1327823",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22b6861e-289f-4779-bbf1-40bfd1327823",
        "outputId": "fd96d25d-8cbf-43d2-8d5b-983856bf2a3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded CLIP\n"
          ]
        }
      ],
      "source": [
        "# the stable diffusion model uses L/14 by default\n",
        "clip_model, _ = clip.load(\"ViT-L/14\", jit=False)\n",
        "clip_model = clip_model.cuda()\n",
        "print(\"Loaded CLIP\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "eaa9bde8-3fdd-48df-aa36-8b88cec285be",
      "metadata": {
        "id": "eaa9bde8-3fdd-48df-aa36-8b88cec285be"
      },
      "outputs": [],
      "source": [
        "bs = 16 # need a large batch size for the contrastive loss to work properly\n",
        "steps = 64\n",
        "epochs = 4\n",
        "lr = 1e-3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "0667d187-2c3e-40b8-a2fd-b408d5f144c5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0667d187-2c3e-40b8-a2fd-b408d5f144c5",
        "outputId": "b8f6056a-c2ac-4b89-e222-0d47705617e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                                url  \\\n",
              "0           0  https://cdn.donmai.us/original/51/bb/51bb44d69...   \n",
              "1           1  https://cdn.donmai.us/original/4f/8e/4f8e5eaba...   \n",
              "2           2  https://cdn.donmai.us/original/c8/54/c85428d89...   \n",
              "3           3  https://cdn.donmai.us/original/e9/fc/e9fcc788e...   \n",
              "4           4  https://cdn.donmai.us/original/8a/63/8a639d21b...   \n",
              "\n",
              "                                              prompt  \n",
              "0  genshin_impact boo_tao_(genshin_impact) hu_tao...  \n",
              "1  genshin_impact arlecchino_(genshin_impact) cap...  \n",
              "2  genshin_impact fischl_(ein_immernachtstraum)_(...  \n",
              "3  genshin_impact eula_(genshin_impact) 1girl :o ...  \n",
              "4  genshin_impact kuki_shinobu 1girl breasts brid...  "
            ],
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              "\n",
              "  <div id=\"df-b1359ba3-d298-4260-9864-de09e0a61fd0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>url</th>\n",
              "      <th>prompt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>https://cdn.donmai.us/original/51/bb/51bb44d69...</td>\n",
              "      <td>genshin_impact boo_tao_(genshin_impact) hu_tao...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>https://cdn.donmai.us/original/4f/8e/4f8e5eaba...</td>\n",
              "      <td>genshin_impact arlecchino_(genshin_impact) cap...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>https://cdn.donmai.us/original/c8/54/c85428d89...</td>\n",
              "      <td>genshin_impact fischl_(ein_immernachtstraum)_(...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>https://cdn.donmai.us/original/e9/fc/e9fcc788e...</td>\n",
              "      <td>genshin_impact eula_(genshin_impact) 1girl :o ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>https://cdn.donmai.us/original/8a/63/8a639d21b...</td>\n",
              "      <td>genshin_impact kuki_shinobu 1girl breasts brid...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b1359ba3-d298-4260-9864-de09e0a61fd0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# ultimately you just need >1000 prompt+url pairs in lists named prompts and urls, however you load it is fine\n",
        "# this is how i scraped them so this is how i load them\n",
        "# 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",
        "df = pd.read_csv(\"/content/genshin.csv\")\n",
        "prompts = df['prompt']#[:steps*bs]\n",
        "urls = df['url']#[:steps*bs\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(prompts))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lYQYqKEZKhv",
        "outputId": "7d26b946-ce1e-41ed-abaf-a4efed5d02cc"
      },
      "id": "6lYQYqKEZKhv",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "12fce080-73f3-430f-ba2c-99f265316b66",
      "metadata": {
        "id": "12fce080-73f3-430f-ba2c-99f265316b66"
      },
      "outputs": [],
      "source": [
        "clip_model.token_embedding.weight.requires_grad = True\n",
        "# opt = optim.Adam([clip_model.token_embedding.weight], lr)\n",
        "opt = bnb.optim.AdamW8bit([clip_model.token_embedding.weight], lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "9a4b1696-a8d9-4eea-a8e0-ba372d450619",
      "metadata": {
        "id": "9a4b1696-a8d9-4eea-a8e0-ba372d450619"
      },
      "outputs": [],
      "source": [
        "# functions from another project of mine that's a bit messy\n",
        "def fix_to_224(pil_image):\n",
        "    width, height = pil_image.size\n",
        "    if width < height:\n",
        "        new_width = 224\n",
        "        new_height = int(new_width * height / width)\n",
        "    else:\n",
        "        new_height = 224\n",
        "        new_width = int(new_height * width / height)\n",
        "    return pil_image.resize((new_width, new_height))\n",
        "def to_tensor_and_center_crop(pil_image):\n",
        "    tensor = torchvision.transforms.functional.to_tensor(pil_image)\n",
        "    center_crop = torchvision.transforms.functional.center_crop(tensor, (224, 224))\n",
        "    return center_crop\n",
        "def fix(img):\n",
        "    return to_tensor_and_center_crop(fix_to_224(img))\n",
        "\n",
        "iter_prompts = iter(prompts)\n",
        "iter_urls = iter(urls)\n",
        "def get_batch(size=8, step=1, steps_per_epoch=128, epoch=1, total_epochs=1):\n",
        "    to_tensor = transforms.ToTensor()\n",
        "    x = []\n",
        "    y = []\n",
        "    total_steps = total_epochs*steps_per_epoch\n",
        "    current_fraction = (epoch*steps_per_epoch-steps_per_epoch+step) / total_steps\n",
        "\n",
        "    while len(x) < size:\n",
        "        # uncomment these and space the big block right one tab\n",
        "        # if you're using a set that might have dead urls\n",
        "        try:\n",
        "            url = next(iter_urls)\n",
        "            response = requests.get(url)\n",
        "            img = Image.open(BytesIO(response.content))\n",
        "            img = img.convert(\"RGB\")\n",
        "            img = fix(img)\n",
        "            x.append(img.unsqueeze(0))\n",
        "\n",
        "            p = next(iter_prompts)\n",
        "\n",
        "            # comment out these lines if you aren't using danbooru tags\n",
        "            p = p.split(\" \")\n",
        "            if current_fraction < 0.75: random.shuffle(p)\n",
        "            if current_fraction > 0.5: # halfway through training, start dropping half of the tags\n",
        "                p = p[:len(p)//2]\n",
        "            if current_fraction > 0.75: # 3/4 through training, start dropping 3/4 of the tags\n",
        "                p = p[:4]\n",
        "            p = \" \".join(p)\n",
        "            y.append(p)\n",
        "        except KeyboardInterrupt:\n",
        "            print('Interrupted')\n",
        "            break\n",
        "        except:\n",
        "            pass\n",
        "        \n",
        "    x = torch.cat(x, 0)\n",
        "    x = clip_model.encode_image(x.cuda())\n",
        "    \n",
        "    y = clip.tokenize(y, truncate=True)\n",
        "    y = clip_model.encode_text(y.cuda())\n",
        "    return x, y"
      ]
    },
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          "name": "stderr",
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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",
            "  \"Palette images with Transparency expressed in bytes should be \"\n",
            "/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",
            "  warnings.warn(str(msg))\n"
          ]
        },
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      "source": [
        "losses = []\n",
        "\n",
        "for epoch in trange(epochs):\n",
        "    pbar = trange(steps)\n",
        "    iter_prompts = iter(prompts)\n",
        "    iter_urls = iter(urls)\n",
        "\n",
        "    for i in pbar:\n",
        "        x, y = get_batch(bs, i, steps, epoch, epochs)\n",
        "        loss = (x-y).pow(2).mean()\n",
        "        # loss = spherical_distance_loss(x,y).mean() # TODO: try whatever loss they use in the CLIP paper instead\n",
        "        loss.backward()\n",
        "        opt.step()\n",
        "        opt.zero_grad()\n",
        "\n",
        "        pbar.set_description(str(loss.item()))\n",
        "        losses.append(loss.item())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# hacky ema plot\n",
        "losses_df = pd.DataFrame(losses, columns=['loss'])\n",
        "losses_plot = losses_df.ewm(alpha=0.05).mean()['loss']\n",
        "plt.plot(losses_plot)\n",
        "plt.plot(losses)"
      ],
      "metadata": {
        "id": "J5QkXxh5PI04"
      },
      "id": "J5QkXxh5PI04",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(clip_model.token_embedding.weight, \"token_embeddings.pt\")"
      ],
      "metadata": {
        "id": "MdEjBt3hRIRB"
      },
      "id": "MdEjBt3hRIRB",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown 🔔\n",
        "from google.colab import output\n",
        "output.eval_js('new Audio(\"https://freesound.org/data/previews/80/80921_1022651-lq.ogg\").play()')"
      ],
      "metadata": {
        "cellView": "form",
        "id": "IU0mqpyVTQzt"
      },
      "id": "IU0mqpyVTQzt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Upload to 🤗"
      ],
      "metadata": {
        "id": "E5TP5qjAkTjL"
      },
      "id": "E5TP5qjAkTjL"
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Log in\n",
        "!pip install huggingface-hub -q\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "bUdMc0o4kBYx",
        "cellView": "form"
      },
      "id": "bUdMc0o4kBYx",
      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfApi\n",
        "api = HfApi()\n",
        "\n",
        "#@markdown go ahead and create the repository on 🤗 before running this\n",
        "my_repo = \"user/my-stable-inversion\" #@param {type:\"string\"}\n",
        "\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"token_embeddings.pt\",\n",
        "    path_in_repo=\"token_embeddings.pt\",\n",
        "    repo_id=my_repo,\n",
        "    repo_type=\"model\",\n",
        ")"
      ],
      "metadata": {
        "id": "WJ30dShA7koO"
      },
      "id": "WJ30dShA7koO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "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",
        "\n",
        "```\n",
        "from huggingface_hub import hf_hub_download\n",
        "\n",
        "stable_inversion = \"user/my-stable-inversion\" #@param {type:\"string\"}\n",
        "if len(stable_inversion)>1:\n",
        "    g = hf_hub_download(repo_id=stable_inversion, filename=\"token_embeddings.pt\")\n",
        "    text_encoder.text_model.embeddings.token_embedding.weight = torch.load(g)\n",
        "```"
      ],
      "metadata": {
        "id": "tIKiCVkHkFET"
      },
      "id": "tIKiCVkHkFET"
    }
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