sd_textual_inversion_selfie_training.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/sd_textual_inversion_selfie_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAZq3vFDcFiT"
      },
      "source": [
        "# Textual-inversion fine-tuning for Stable Diffusion using d🧨ffusers from your webcam 📷 🤳\n",
        "\n",
        " ### This notebook is just a simple modification of the [sd-concepts-library's](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) one to add images from **your webcam**\n",
        "> by [mrm8488](https://twitter.com/mrm8488)\n",
        "\n",
        "The original notebook shows how to \"teach\" Stable Diffusion a new concept via textual-inversion using 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers). \n",
        "\n",
        "![Textual Inversion example](https://textual-inversion.github.io/static/images/editing/colorful_teapot.JPG)\n",
        "_By using just 3-5 images you can teach new concepts to Stable Diffusion and personalize the model on your own images_ \n",
        "\n",
        "For a general introduction to the Stable Diffusion model please refer to this [colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbzZ9xe6dWwf"
      },
      "source": [
        "## Initial setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "30lu8LWXmg5j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f36c540-650f-4c02-a215-b2cf3ec34b32"
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "#@title Install the required libs\n",
        "!pip install -qq diffusers[\"training\"]==0.3.0 transformers ftfy\n",
        "!pip install -qq \"ipywidgets>=7,<8\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "24zwrNSBm4A3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299,
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        "outputId": "7bb367d4-342f-418c-a229-47e30b12c0d6"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "#@title Login to the Hugging Face Hub\n",
        "#@markdown Add a token with the \"Write Access\" role to be able to add your trained concept to the [Library of Concepts](https://huggingface.co/sd-concepts-library)\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
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          "height": 104,
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        "outputId": "736e4283-4469-488b-97bc-35b7d8c07a49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving 0 files to the new cache system\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
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          "metadata": {}
        }
      ],
      "source": [
        "#@title Import required libraries\n",
        "import argparse\n",
        "import itertools\n",
        "import math\n",
        "import os\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.checkpoint\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "import PIL\n",
        "from accelerate import Accelerator\n",
        "from accelerate.logging import get_logger\n",
        "from accelerate.utils import set_seed\n",
        "from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel\n",
        "from diffusers.hub_utils import init_git_repo, push_to_hub\n",
        "from diffusers.optimization import get_scheduler\n",
        "from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker\n",
        "from PIL import Image\n",
        "from torchvision import transforms\n",
        "from tqdm.auto import tqdm\n",
        "from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n",
        "\n",
        "def image_grid(imgs, rows, cols):\n",
        "    assert len(imgs) == rows*cols\n",
        "\n",
        "    w, h = imgs[0].size\n",
        "    grid = Image.new('RGB', size=(cols*w, rows*h))\n",
        "    grid_w, grid_h = grid.size\n",
        "    \n",
        "    for i, img in enumerate(imgs):\n",
        "        grid.paste(img, box=(i%cols*w, i//cols*h))\n",
        "    return grid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yl3r7A_3ASxm"
      },
      "source": [
        "## Settings for teaching your new concept"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "cellView": "form",
        "id": "If5Jswe526QP"
      },
      "outputs": [],
      "source": [
        "#@markdown `pretrained_model_name_or_path` which Stable Diffusion checkpoint you want to use\n",
        "pretrained_model_name_or_path = \"CompVis/stable-diffusion-v1-4\" #@param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Take your selfies/pics from your webcam"
      ],
      "metadata": {
        "id": "OFcbQzOmpAyb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Number of pics (choose between 3-5)\n",
        "\n",
        "num_pics = 3 #@param {type:\"number\"}"
      ],
      "metadata": {
        "cellView": "form",
        "id": "PnGa4e-SpI-p"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Webcam settings\n",
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.9):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename"
      ],
      "metadata": {
        "cellView": "form",
        "id": "0CIZG58kqqOQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Take the pics\n",
        "from IPython.display import Image\n",
        "try:\n",
        "  for i in range(num_pics):\n",
        "    filename = take_photo('photo_' + str(i) +'.jpg')\n",
        "    print('Saved to {}'.format(filename))\n",
        "    \n",
        "    # Show the image which was just taken.\n",
        "    display(Image(filename))\n",
        "except Exception as err:\n",
        "  # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "  # grant the page permission to access it.\n",
        "  print(str(err))"
      ],
      "metadata": {
        "cellView": "form",
        "id": "bh9GXgeKrBRb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "60jVYSk0BGC8"
      },
      "outputs": [],
      "source": [
        "#@title Setup and check the images you have just added\n",
        "\n",
        "pics = [f\"/content/photo_{i}.jpg\" for i in range(num_pics)]\n",
        "\n",
        "def open_image(img_path):\n",
        "  return Image.open(img_path).convert(\"RGB\")\n",
        "\n",
        "images = list(filter(None,[open_image(path) for path in pics]))\n",
        "save_path = \"./my_concept\"\n",
        "if not os.path.exists(save_path):\n",
        "  os.mkdir(save_path)\n",
        "[image.save(f\"{save_path}/{i}.jpeg\") for i, image in enumerate(images)]\n",
        "image_grid(images, 1, len(images))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "cellView": "form",
        "id": "8i_vLTBxAXpE"
      },
      "outputs": [],
      "source": [
        "#@title Settings for your newly created concept\n",
        "#@markdown `what_to_teach`: what is it that you are teaching? `object` enables you to teach the model a new object to be used, `style` allows you to teach the model a new style one can use.\n",
        "what_to_teach = \"object\" #@param [\"object\", \"style\"]\n",
        "#@markdown `placeholder_token` is the token you are going to use to represent your new concept (so when you prompt the model, you will say \"A `<my-placeholder-token>` in an amusement park\"). We use angle brackets to differentiate a token from other words/tokens, to avoid collision.\n",
        "placeholder_token = \"<selfie-me>\" #@param {type:\"string\"}\n",
        "#@markdown `initializer_token` is a word that can summarise what your new concept is, to be used as a starting point\n",
        "initializer_token = \"selfie\" #@param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D633UIuGgs6M"
      },
      "source": [
        "## Teach the model a new concept (fine-tuning with textual inversion)\n",
        "Execute this this sequence of cells to run the training process. The whole process may take from 1-4 hours. (Open this block if you are interested in how this process works under the hood or if you want to change advanced training settings or hyperparameters)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EuFP688UEwQR"
      },
      "source": [
        "### Create Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "cellView": "form",
        "id": "u4c1vbVfnmLf"
      },
      "outputs": [],
      "source": [
        "#@title Setup the prompt templates for training \n",
        "imagenet_templates_small = [\n",
        "    \"a photo of a {}\",\n",
        "    \"a rendering of a {}\",\n",
        "    \"a cropped photo of the {}\",\n",
        "    \"the photo of a {}\",\n",
        "    \"a photo of a clean {}\",\n",
        "    \"a photo of a dirty {}\",\n",
        "    \"a dark photo of the {}\",\n",
        "    \"a photo of my {}\",\n",
        "    \"a photo of the cool {}\",\n",
        "    \"a close-up photo of a {}\",\n",
        "    \"a bright photo of the {}\",\n",
        "    \"a cropped photo of a {}\",\n",
        "    \"a photo of the {}\",\n",
        "    \"a good photo of the {}\",\n",
        "    \"a photo of one {}\",\n",
        "    \"a close-up photo of the {}\",\n",
        "    \"a rendition of the {}\",\n",
        "    \"a photo of the clean {}\",\n",
        "    \"a rendition of a {}\",\n",
        "    \"a photo of a nice {}\",\n",
        "    \"a good photo of a {}\",\n",
        "    \"a photo of the nice {}\",\n",
        "    \"a photo of the small {}\",\n",
        "    \"a photo of the weird {}\",\n",
        "    \"a photo of the large {}\",\n",
        "    \"a photo of a cool {}\",\n",
        "    \"a photo of a small {}\",\n",
        "]\n",
        "\n",
        "imagenet_style_templates_small = [\n",
        "    \"a painting in the style of {}\",\n",
        "    \"a rendering in the style of {}\",\n",
        "    \"a cropped painting in the style of {}\",\n",
        "    \"the painting in the style of {}\",\n",
        "    \"a clean painting in the style of {}\",\n",
        "    \"a dirty painting in the style of {}\",\n",
        "    \"a dark painting in the style of {}\",\n",
        "    \"a picture in the style of {}\",\n",
        "    \"a cool painting in the style of {}\",\n",
        "    \"a close-up painting in the style of {}\",\n",
        "    \"a bright painting in the style of {}\",\n",
        "    \"a cropped painting in the style of {}\",\n",
        "    \"a good painting in the style of {}\",\n",
        "    \"a close-up painting in the style of {}\",\n",
        "    \"a rendition in the style of {}\",\n",
        "    \"a nice painting in the style of {}\",\n",
        "    \"a small painting in the style of {}\",\n",
        "    \"a weird painting in the style of {}\",\n",
        "    \"a large painting in the style of {}\",\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "cellView": "form",
        "id": "2ntpEpVfnd-0"
      },
      "outputs": [],
      "source": [
        "#@title Setup the dataset\n",
        "class TextualInversionDataset(Dataset):\n",
        "    def __init__(\n",
        "        self,\n",
        "        data_root,\n",
        "        tokenizer,\n",
        "        learnable_property=\"object\",  # [object, style]\n",
        "        size=512,\n",
        "        repeats=100,\n",
        "        interpolation=\"bicubic\",\n",
        "        flip_p=0.5,\n",
        "        set=\"train\",\n",
        "        placeholder_token=\"*\",\n",
        "        center_crop=False,\n",
        "    ):\n",
        "\n",
        "        self.data_root = data_root\n",
        "        self.tokenizer = tokenizer\n",
        "        self.learnable_property = learnable_property\n",
        "        self.size = size\n",
        "        self.placeholder_token = placeholder_token\n",
        "        self.center_crop = center_crop\n",
        "        self.flip_p = flip_p\n",
        "\n",
        "        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n",
        "\n",
        "        self.num_images = len(self.image_paths)\n",
        "        self._length = self.num_images\n",
        "\n",
        "        if set == \"train\":\n",
        "            self._length = self.num_images * repeats\n",
        "\n",
        "        self.interpolation = {\n",
        "            \"linear\": PIL.Image.LINEAR,\n",
        "            \"bilinear\": PIL.Image.BILINEAR,\n",
        "            \"bicubic\": PIL.Image.BICUBIC,\n",
        "            \"lanczos\": PIL.Image.LANCZOS,\n",
        "        }[interpolation]\n",
        "\n",
        "        self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n",
        "        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n",
        "\n",
        "    def __len__(self):\n",
        "        return self._length\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        example = {}\n",
        "        image = Image.open(self.image_paths[i % self.num_images])\n",
        "\n",
        "        if not image.mode == \"RGB\":\n",
        "            image = image.convert(\"RGB\")\n",
        "\n",
        "        placeholder_string = self.placeholder_token\n",
        "        text = random.choice(self.templates).format(placeholder_string)\n",
        "\n",
        "        example[\"input_ids\"] = self.tokenizer(\n",
        "            text,\n",
        "            padding=\"max_length\",\n",
        "            truncation=True,\n",
        "            max_length=self.tokenizer.model_max_length,\n",
        "            return_tensors=\"pt\",\n",
        "        ).input_ids[0]\n",
        "\n",
        "        # default to score-sde preprocessing\n",
        "        img = np.array(image).astype(np.uint8)\n",
        "\n",
        "        if self.center_crop:\n",
        "            crop = min(img.shape[0], img.shape[1])\n",
        "            h, w, = (\n",
        "                img.shape[0],\n",
        "                img.shape[1],\n",
        "            )\n",
        "            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n",
        "\n",
        "        image = Image.fromarray(img)\n",
        "        image = image.resize((self.size, self.size), resample=self.interpolation)\n",
        "\n",
        "        image = self.flip_transform(image)\n",
        "        image = np.array(image).astype(np.uint8)\n",
        "        image = (image / 127.5 - 1.0).astype(np.float32)\n",
        "\n",
        "        example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n",
        "        return example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmrXxJ-Eijwb"
      },
      "source": [
        "### Setting up the model"
      ]
    },
    {
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      "source": [
        "#@title Load the tokenizer and add the placeholder token as a additional special token.\n",
        "#@markdown Please read and if you agree accept the LICENSE [here](https://huggingface.co/CompVis/stable-diffusion-v1-4) if you see an error\n",
        "tokenizer = CLIPTokenizer.from_pretrained(\n",
        "    pretrained_model_name_or_path,\n",
        "    subfolder=\"tokenizer\",\n",
        "    use_auth_token=True,\n",
        ")\n",
        "\n",
        "# Add the placeholder token in tokenizer\n",
        "num_added_tokens = tokenizer.add_tokens(placeholder_token)\n",
        "if num_added_tokens == 0:\n",
        "    raise ValueError(\n",
        "        f\"The tokenizer already contains the token {placeholder_token}. Please pass a different\"\n",
        "        \" `placeholder_token` that is not already in the tokenizer.\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "cellView": "form",
        "id": "5jgTNr7roCnV"
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      "outputs": [],
      "source": [
        "#@title Get token ids for our placeholder and initializer token. This code block will complain if initializer string is not a single token\n",
        "# Convert the initializer_token, placeholder_token to ids\n",
        "token_ids = tokenizer.encode(initializer_token, add_special_tokens=False)\n",
        "# Check if initializer_token is a single token or a sequence of tokens\n",
        "if len(token_ids) > 1:\n",
        "    raise ValueError(\"The initializer token must be a single token.\")\n",
        "\n",
        "initializer_token_id = token_ids[0]\n",
        "placeholder_token_id = tokenizer.convert_tokens_to_ids(placeholder_token)"
      ]
    },
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          "metadata": {}
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      ],
      "source": [
        "#@title Load the Stable Diffusion model\n",
        "# Load models and create wrapper for stable diffusion\n",
        "text_encoder = CLIPTextModel.from_pretrained(\n",
        "    pretrained_model_name_or_path, subfolder=\"text_encoder\", use_auth_token=True\n",
        ")\n",
        "vae = AutoencoderKL.from_pretrained(\n",
        "    pretrained_model_name_or_path, subfolder=\"vae\", use_auth_token=True\n",
        ")\n",
        "unet = UNet2DConditionModel.from_pretrained(\n",
        "    pretrained_model_name_or_path, subfolder=\"unet\", use_auth_token=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GD5MU6EzFe27"
      },
      "source": [
        "We have added the `placeholder_token` in the `tokenizer` so we resize the token embeddings here, this will a new embedding vector in the token embeddings for our `placeholder_token`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24-9I6mIoORT",
        "outputId": "c1021cdf-38b1-4dc6-a0a0-dfe481fda37a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embedding(49409, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "text_encoder.resize_token_embeddings(len(tokenizer))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IA3Xj7gBFv-6"
      },
      "source": [
        " Initialise the newly added placeholder token with the embeddings of the initializer token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "0mtxiZMNoQvE"
      },
      "outputs": [],
      "source": [
        "token_embeds = text_encoder.get_input_embeddings().weight.data\n",
        "token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQFUhImyFzoS"
      },
      "source": [
        "In Textual-Inversion we only train the newly added embedding vector, so lets freeze rest of the model parameters here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "vQOYhpPqoS4H"
      },
      "outputs": [],
      "source": [
        "def freeze_params(params):\n",
        "    for param in params:\n",
        "        param.requires_grad = False\n",
        "\n",
        "# Freeze vae and unet\n",
        "freeze_params(vae.parameters())\n",
        "freeze_params(unet.parameters())\n",
        "# Freeze all parameters except for the token embeddings in text encoder\n",
        "params_to_freeze = itertools.chain(\n",
        "    text_encoder.text_model.encoder.parameters(),\n",
        "    text_encoder.text_model.final_layer_norm.parameters(),\n",
        "    text_encoder.text_model.embeddings.position_embedding.parameters(),\n",
        ")\n",
        "freeze_params(params_to_freeze)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGfNa4dFF8Om"
      },
      "source": [
        "### Creating our training data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UG_cy1BrGDIo"
      },
      "source": [
        "Let's create the Dataset and Dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "bctHoiRPoWlY"
      },
      "outputs": [],
      "source": [
        "train_dataset = TextualInversionDataset(\n",
        "      data_root=save_path,\n",
        "      tokenizer=tokenizer,\n",
        "      size=512,\n",
        "      placeholder_token=placeholder_token,\n",
        "      repeats=100,\n",
        "      learnable_property=what_to_teach, #Option selected above between object and style\n",
        "      center_crop=False,\n",
        "      set=\"train\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "MEjOV1VPpB9U"
      },
      "outputs": [],
      "source": [
        "def create_dataloader(train_batch_size=1):\n",
        "    return torch.utils.data.DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmlMwmH5GOhJ"
      },
      "source": [
        "Create noise_scheduler for training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "0MTkwTNxpRnq"
      },
      "outputs": [],
      "source": [
        "noise_scheduler = DDPMScheduler(\n",
        "    beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000, tensor_format=\"pt\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJ4r0gzvGSg7"
      },
      "source": [
        "Define hyperparameters for our training\n",
        "If you are not happy with your results, you can tune the `learning_rate` and the `max_train_steps`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "pnq5vah7pabU"
      },
      "outputs": [],
      "source": [
        "hyperparameters = {\n",
        "    \"learning_rate\": 5e-04,\n",
        "    \"scale_lr\": True,\n",
        "    \"max_train_steps\": 3000,\n",
        "    \"train_batch_size\": 1,\n",
        "    \"gradient_accumulation_steps\": 4,\n",
        "    \"seed\": 42,\n",
        "    \"output_dir\": \"sd-concept-output\"\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YNuNDw0wNN5X"
      },
      "source": [
        "Train!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "djBS3343sIiY"
      },
      "outputs": [],
      "source": [
        "def training_function(text_encoder, vae, unet):\n",
        "    logger = get_logger(__name__)\n",
        "\n",
        "    train_batch_size = hyperparameters[\"train_batch_size\"]\n",
        "    gradient_accumulation_steps = hyperparameters[\"gradient_accumulation_steps\"]\n",
        "    learning_rate = hyperparameters[\"learning_rate\"]\n",
        "    max_train_steps = hyperparameters[\"max_train_steps\"]\n",
        "    output_dir = hyperparameters[\"output_dir\"]\n",
        "\n",
        "    accelerator = Accelerator(\n",
        "        gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    )\n",
        "\n",
        "    train_dataloader = create_dataloader(train_batch_size)\n",
        "\n",
        "    if hyperparameters[\"scale_lr\"]:\n",
        "        learning_rate = (\n",
        "            learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes\n",
        "        )\n",
        "\n",
        "    # Initialize the optimizer\n",
        "    optimizer = torch.optim.AdamW(\n",
        "        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings\n",
        "        lr=learning_rate,\n",
        "    )\n",
        "\n",
        "\n",
        "    text_encoder, optimizer, train_dataloader = accelerator.prepare(\n",
        "        text_encoder, optimizer, train_dataloader\n",
        "    )\n",
        "\n",
        "    # Move vae and unet to device\n",
        "    vae.to(accelerator.device)\n",
        "    unet.to(accelerator.device)\n",
        "\n",
        "    # Keep vae and unet in eval model as we don't train these\n",
        "    vae.eval()\n",
        "    unet.eval()\n",
        "\n",
        "    # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n",
        "    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)\n",
        "    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)\n",
        "\n",
        "    # Train!\n",
        "    total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps\n",
        "\n",
        "    logger.info(\"***** Running training *****\")\n",
        "    logger.info(f\"  Num examples = {len(train_dataset)}\")\n",
        "    logger.info(f\"  Instantaneous batch size per device = {train_batch_size}\")\n",
        "    logger.info(f\"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n",
        "    logger.info(f\"  Gradient Accumulation steps = {gradient_accumulation_steps}\")\n",
        "    logger.info(f\"  Total optimization steps = {max_train_steps}\")\n",
        "    # Only show the progress bar once on each machine.\n",
        "    progress_bar = tqdm(range(max_train_steps), disable=not accelerator.is_local_main_process)\n",
        "    progress_bar.set_description(\"Steps\")\n",
        "    global_step = 0\n",
        "\n",
        "    for epoch in range(num_train_epochs):\n",
        "        text_encoder.train()\n",
        "        for step, batch in enumerate(train_dataloader):\n",
        "            with accelerator.accumulate(text_encoder):\n",
        "                # Convert images to latent space\n",
        "                latents = vae.encode(batch[\"pixel_values\"]).latent_dist.sample().detach()\n",
        "                latents = latents * 0.18215\n",
        "\n",
        "                # Sample noise that we'll add to the latents\n",
        "                noise = torch.randn(latents.shape).to(latents.device)\n",
        "                bsz = latents.shape[0]\n",
        "                # Sample a random timestep for each image\n",
        "                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device).long()\n",
        "\n",
        "                # Add noise to the latents according to the noise magnitude at each timestep\n",
        "                # (this is the forward diffusion process)\n",
        "                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n",
        "\n",
        "                # Get the text embedding for conditioning\n",
        "                encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n",
        "\n",
        "                # Predict the noise residual\n",
        "                noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n",
        "\n",
        "                loss = F.mse_loss(noise_pred, noise, reduction=\"none\").mean([1, 2, 3]).mean()\n",
        "                accelerator.backward(loss)\n",
        "\n",
        "                # Zero out the gradients for all token embeddings except the newly added\n",
        "                # embeddings for the concept, as we only want to optimize the concept embeddings\n",
        "                if accelerator.num_processes > 1:\n",
        "                    grads = text_encoder.module.get_input_embeddings().weight.grad\n",
        "                else:\n",
        "                    grads = text_encoder.get_input_embeddings().weight.grad\n",
        "                # Get the index for tokens that we want to zero the grads for\n",
        "                index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id\n",
        "                grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)\n",
        "\n",
        "                optimizer.step()\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "            # Checks if the accelerator has performed an optimization step behind the scenes\n",
        "            if accelerator.sync_gradients:\n",
        "                progress_bar.update(1)\n",
        "                global_step += 1\n",
        "\n",
        "            logs = {\"loss\": loss.detach().item()}\n",
        "            progress_bar.set_postfix(**logs)\n",
        "\n",
        "            if global_step >= max_train_steps:\n",
        "                break\n",
        "\n",
        "        accelerator.wait_for_everyone()\n",
        "\n",
        "\n",
        "    # Create the pipeline using using the trained modules and save it.\n",
        "    if accelerator.is_main_process:\n",
        "        pipeline = StableDiffusionPipeline(\n",
        "            text_encoder=accelerator.unwrap_model(text_encoder),\n",
        "            vae=vae,\n",
        "            unet=unet,\n",
        "            tokenizer=tokenizer,\n",
        "            scheduler=PNDMScheduler(\n",
        "                beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", skip_prk_steps=True\n",
        "            ),\n",
        "            safety_checker=StableDiffusionSafetyChecker.from_pretrained(\"CompVis/stable-diffusion-safety-checker\"),\n",
        "            feature_extractor=CLIPFeatureExtractor.from_pretrained(\"openai/clip-vit-base-patch32\"),\n",
        "        )\n",
        "        pipeline.save_pretrained(output_dir)\n",
        "        # Also save the newly trained embeddings\n",
        "        learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]\n",
        "        learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}\n",
        "        torch.save(learned_embeds_dict, os.path.join(output_dir, \"learned_embeds.bin\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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        },
        "id": "jXi0NdsyBA4S",
        "outputId": "04c1a237-139d-47a2-f586-9a0e2c2dbe43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Launching training on one GPU.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ],
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              "model_id": "041ae67820714fa8b7514570237a525a"
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          "metadata": {}
        }
      ],
      "source": [
        "import accelerate\n",
        "accelerate.notebook_launcher(training_function, args=(text_encoder, vae, unet))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50JuJUM8EG1h"
      },
      "source": [
        "## Run the code with your newly trained model\n",
        "If you have just trained your model with the code above, use the block below to run it\n",
        "\n",
        "To save this concept for re-using, download the `learned_embeds.bin` file or save it on the library of concepts.\n",
        "\n",
        "Use the [Stable Conceptualizer notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) for inference with persistently saved pre-trained concepts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "gTlUJYB1QNSN"
      },
      "outputs": [],
      "source": [
        "#@title Save your newly created concept to the [library of concepts](https://huggingface.co/sd-concepts-library)?\n",
        "\n",
        "save_concept_to_public_library = True #@param {type:\"boolean\"}\n",
        "name_of_your_concept = \"Selfie Me\" #@param {type:\"string\"}\n",
        "#@markdown `hf_token_write`: leave blank if you logged in with a token with `write access` in the [Initial Setup](#scrollTo=KbzZ9xe6dWwf). If not, [go to your tokens settings and create a write access token](https://huggingface.co/settings/tokens)\n",
        "hf_token_write = \"\" #@param {type:\"string\"}\n",
        "\n",
        "if(save_concept_to_public_library):\n",
        "  from slugify import slugify\n",
        "  from huggingface_hub import HfApi, HfFolder, CommitOperationAdd\n",
        "  from huggingface_hub import create_repo\n",
        "  repo_id = f\"sd-concepts-library/{slugify(name_of_your_concept)}\"\n",
        "  output_dir = hyperparameters[\"output_dir\"]\n",
        "  if(not hf_token_write):\n",
        "    with open(HfFolder.path_token, 'r') as fin: hf_token = fin.read();\n",
        "  else:\n",
        "    hf_token = hf_token_write\n",
        "  #Join the Concepts Library organization if you aren't part of it already\n",
        "  !curl -X POST -H 'Authorization: Bearer '$hf_token -H 'Content-Type: application/json' https://huggingface.co/organizations/sd-concepts-library/share/VcLXJtzwwxnHYCkNMLpSJCdnNFZHQwWywv\n",
        "  images_upload = os.listdir(\"my_concept\")\n",
        "  image_string = \"\"\n",
        "  repo_id = f\"sd-concepts-library/{slugify(name_of_your_concept)}\"\n",
        "  for i, image in enumerate(images_upload):\n",
        "      image_string = f'''{image_string}![{placeholder_token} {i}](https://huggingface.co/{repo_id}/resolve/main/concept_images/{image})\n",
        "'''\n",
        "  if(what_to_teach == \"style\"):\n",
        "      what_to_teach_article = f\"a `{what_to_teach}`\"\n",
        "  else:\n",
        "      what_to_teach_article = f\"an `{what_to_teach}`\"\n",
        "  readme_text = f'''---\n",
        "license: mit\n",
        "---\n",
        "### {name_of_your_concept} on Stable Diffusion\n",
        "This is the `{placeholder_token}` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).\n",
        "\n",
        "Here is the new concept you will be able to use as {what_to_teach_article}:\n",
        "{image_string}\n",
        "'''\n",
        "  #Save the readme to a file\n",
        "  readme_file = open(\"README.md\", \"w\")\n",
        "  readme_file.write(readme_text)\n",
        "  readme_file.close()\n",
        "  #Save the token identifier to a file\n",
        "  text_file = open(\"token_identifier.txt\", \"w\")\n",
        "  text_file.write(placeholder_token)\n",
        "  text_file.close()\n",
        "  #Save the type of teached thing to a file\n",
        "  type_file = open(\"type_of_concept.txt\",\"w\")\n",
        "  type_file.write(what_to_teach)\n",
        "  type_file.close()\n",
        "  operations = [\n",
        "    CommitOperationAdd(path_in_repo=\"learned_embeds.bin\", path_or_fileobj=f\"{output_dir}/learned_embeds.bin\"),\n",
        "    CommitOperationAdd(path_in_repo=\"token_identifier.txt\", path_or_fileobj=\"token_identifier.txt\"),\n",
        "    CommitOperationAdd(path_in_repo=\"type_of_concept.txt\", path_or_fileobj=\"type_of_concept.txt\"),\n",
        "    CommitOperationAdd(path_in_repo=\"README.md\", path_or_fileobj=\"README.md\"),\n",
        "  ]\n",
        "  create_repo(repo_id,private=True, token=hf_token)\n",
        "  api = HfApi()\n",
        "  api.create_commit(\n",
        "    repo_id=repo_id,\n",
        "    operations=operations,\n",
        "    commit_message=f\"Upload the concept {name_of_your_concept} embeds and token\",\n",
        "    token=hf_token\n",
        "  )\n",
        "  api.upload_folder(\n",
        "    folder_path=save_path,\n",
        "    path_in_repo=\"concept_images\",\n",
        "    repo_id=repo_id,\n",
        "    token=hf_token\n",
        "  )"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "id": "2CMlPbOeEC09",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Set up the pipeline \n",
        "pipe = StableDiffusionPipeline.from_pretrained(\n",
        "    hyperparameters[\"output_dir\"],\n",
        "    torch_dtype=torch.float16,\n",
        ").to(\"cuda\")"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "id": "E3UREGd7EkLh",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Run the Stable Diffusion pipeline\n",
        "#@markdown Don't forget to use the placeholder token in your prompt\n",
        "\n",
        "from torch import autocast\n",
        "prompt = \"a grafitti in a wall with a \\u003Cselfie-me> on it\" #@param {type:\"string\"}\n",
        "\n",
        "num_samples = 2 #@param {type:\"number\"}\n",
        "num_rows = 2 #@param {type:\"number\"}\n",
        "\n",
        "all_images = [] \n",
        "for _ in range(num_rows):\n",
        "    with autocast(\"cuda\"):\n",
        "        images = pipe([prompt] * num_samples, num_inference_steps=50, guidance_scale=7.5).images\n",
        "        all_images.extend(images)\n",
        "\n",
        "grid = image_grid(all_images, num_samples, num_rows)\n",
        "grid"
      ]
    }
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