controlnet_v1_1_fastapi_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a href=\"https://colab.research.google.com/github/kaland313/ControlNet-ReactApp-Template/blob/main/controlnet_v1_1_fastapi_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Axzb_1tBhYXq"
      },
      "outputs": [],
      "source": [
        "# !pip install --no-deps xformers==0.0.23"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qc7NOXUbW-uz"
      },
      "outputs": [],
      "source": [
        "# !pip install -q gradio accelerate diffusers controlnet_aux"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntuMBMtunhPT",
        "outputId": "e34f376b-eb0e-41a8-be11-ebb75c6cab39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "\u001b[?25hCollecting starlette<0.37.0,>=0.36.3 (from fastapi->gradio<4.0)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=32bcd217b4648d9c8317a64c54e2e4cd82b88d962dd1bd4e3edca2a6e4c31d60\n",
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            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, semantic-version, python-multipart, orjson, h11, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.2.1 fastapi-0.109.2 ffmpy-0.3.2 gradio-3.50.2 gradio-client-0.6.1 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 orjson-3.9.14 pydub-0.25.1 python-multipart-0.0.9 semantic-version-2.10.0 starlette-0.36.3 uvicorn-0.27.1 websockets-11.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install \"gradio<4.0\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjehC8xJgWvG",
        "outputId": "e9e73b27-9a60-481a-db06-4525f1682da8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting accelerate\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.27.2\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXp9E14FgWzd",
        "outputId": "d443d800-33d0-451f-9a81-363a9845bae8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting diffusers\n",
            "  Downloading diffusers-0.26.3-py3-none-any.whl (1.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.10/dist-packages (from diffusers) (7.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from diffusers) (3.13.1)\n",
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            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from diffusers) (0.4.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from diffusers) (9.4.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.2->diffusers) (2023.6.0)\n",
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            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.2->diffusers) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.2->diffusers) (4.9.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.2->diffusers) (23.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->diffusers) (3.17.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2024.2.2)\n",
            "Installing collected packages: diffusers\n",
            "Successfully installed diffusers-0.26.3\n"
          ]
        }
      ],
      "source": [
        "!pip install diffusers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7UEA_-HggW6e",
        "outputId": "7406ae00-a5e4-4a3f-bac4-58d57d88f068"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting controlnet_aux\n",
            "  Downloading controlnet_aux-0.0.7.tar.gz (202 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.4/202.4 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (2.1.0+cu121)\n",
            "Requirement already satisfied: importlib_metadata in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (7.0.1)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (0.20.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (1.11.4)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (4.8.0.76)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (3.13.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (1.25.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (9.4.0)\n",
            "Collecting einops (from controlnet_aux)\n",
            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (0.16.0+cu121)\n",
            "Collecting timm (from controlnet_aux)\n",
            "  Downloading timm-0.9.16-py3-none-any.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from controlnet_aux) (0.19.3)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->controlnet_aux) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->controlnet_aux) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->controlnet_aux) (4.66.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->controlnet_aux) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->controlnet_aux) (4.9.0)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib_metadata->controlnet_aux) (3.17.0)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image->controlnet_aux) (3.2.1)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->controlnet_aux) (2.31.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image->controlnet_aux) (2024.2.12)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->controlnet_aux) (1.5.0)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from timm->controlnet_aux) (0.4.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->controlnet_aux) (1.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->controlnet_aux) (3.1.3)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->controlnet_aux) (2.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->controlnet_aux) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->controlnet_aux) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->controlnet_aux) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->controlnet_aux) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->controlnet_aux) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->controlnet_aux) (1.3.0)\n",
            "Building wheels for collected packages: controlnet_aux\n",
            "  Building wheel for controlnet_aux (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for controlnet_aux: filename=controlnet_aux-0.0.7-py3-none-any.whl size=274340 sha256=550d74b2270fd3c4a68224f0bb550b9acc8b2058890036d04f481fcc995438cd\n",
            "  Stored in directory: /root/.cache/pip/wheels/1e/3e/93/6678b4c0bc2ec31d53409b25d4189cbb08bae843e8b2b78e52\n",
            "Successfully built controlnet_aux\n",
            "Installing collected packages: einops, timm, controlnet_aux\n",
            "Successfully installed controlnet_aux-0.0.7 einops-0.7.0 timm-0.9.16\n"
          ]
        }
      ],
      "source": [
        "!pip install controlnet_aux"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iw2HNjctbzK7",
        "outputId": "94227e0b-b52a-488c-9467-9cb7bda48985"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/controlnet_aux/mediapipe_face/mediapipe_face_common.py:7: UserWarning: The module 'mediapipe' is not installed. The package will have limited functionality. Please install it using the command: pip install 'mediapipe'\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/controlnet_aux/segment_anything/modeling/tiny_vit_sam.py:654: UserWarning: Overwriting tiny_vit_5m_224 in registry with controlnet_aux.segment_anything.modeling.tiny_vit_sam.tiny_vit_5m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
            "  return register_model(fn_wrapper)\n",
            "/usr/local/lib/python3.10/dist-packages/controlnet_aux/segment_anything/modeling/tiny_vit_sam.py:654: UserWarning: Overwriting tiny_vit_11m_224 in registry with controlnet_aux.segment_anything.modeling.tiny_vit_sam.tiny_vit_11m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
            "  return register_model(fn_wrapper)\n",
            "/usr/local/lib/python3.10/dist-packages/controlnet_aux/segment_anything/modeling/tiny_vit_sam.py:654: UserWarning: Overwriting tiny_vit_21m_224 in registry with controlnet_aux.segment_anything.modeling.tiny_vit_sam.tiny_vit_21m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
            "  return register_model(fn_wrapper)\n",
            "/usr/local/lib/python3.10/dist-packages/controlnet_aux/segment_anything/modeling/tiny_vit_sam.py:654: UserWarning: Overwriting tiny_vit_21m_384 in registry with controlnet_aux.segment_anything.modeling.tiny_vit_sam.tiny_vit_21m_384. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
            "  return register_model(fn_wrapper)\n",
            "/usr/local/lib/python3.10/dist-packages/controlnet_aux/segment_anything/modeling/tiny_vit_sam.py:654: UserWarning: Overwriting tiny_vit_21m_512 in registry with controlnet_aux.segment_anything.modeling.tiny_vit_sam.tiny_vit_21m_512. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
            "  return register_model(fn_wrapper)\n"
          ]
        }
      ],
      "source": [
        "import random\n",
        "import gc\n",
        "import numpy as np\n",
        "import cv2\n",
        "import torch\n",
        "import gradio as gr\n",
        "import controlnet_aux\n",
        "import PIL.Image\n",
        "from diffusers import (\n",
        "    ControlNetModel,\n",
        "    DiffusionPipeline,\n",
        "    StableDiffusionControlNetPipeline,\n",
        "    UniPCMultistepScheduler,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G1qygBxLblIx"
      },
      "outputs": [],
      "source": [
        "DEFAULT_MODEL_ID = \"runwayml/stable-diffusion-v1-5\"\n",
        "\n",
        "MAX_NUM_IMAGES = 3\n",
        "DEFAULT_NUM_IMAGES = 3\n",
        "MAX_IMAGE_RESOLUTION = 768\n",
        "DEFAULT_IMAGE_RESOLUTION = 768\n",
        "\n",
        "MAX_SEED = np.iinfo(np.int32).max"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xZH-5PkvZXIB"
      },
      "outputs": [],
      "source": [
        "def resize_image(input_image, resolution, interpolation=None):\n",
        "    H, W, C = input_image.shape\n",
        "    H = float(H)\n",
        "    W = float(W)\n",
        "    k = float(resolution) / max(H, W)\n",
        "    H *= k\n",
        "    W *= k\n",
        "    H = int(np.round(H / 64.0)) * 64\n",
        "    W = int(np.round(W / 64.0)) * 64\n",
        "    if interpolation is None:\n",
        "        interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA\n",
        "    img = cv2.resize(input_image, (W, H), interpolation=interpolation)\n",
        "    return img\n",
        "\n",
        "class Preprocessor:\n",
        "    MODEL_ID = \"lllyasviel/Annotators\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.model = None\n",
        "        self.name = \"\"\n",
        "\n",
        "    def load(self, name: str) -> None:\n",
        "        if name == self.name:\n",
        "            return\n",
        "        if name == \"HED\":\n",
        "            self.model = controlnet_aux.HEDdetector.from_pretrained(self.MODEL_ID)\n",
        "        elif name == \"PidiNet\":\n",
        "            self.model = controlnet_aux.PidiNetDetector.from_pretrained(self.MODEL_ID)\n",
        "        else:\n",
        "            raise ValueError\n",
        "        torch.cuda.empty_cache()\n",
        "        gc.collect()\n",
        "        self.name = name\n",
        "\n",
        "    def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:\n",
        "        return self.model(image, **kwargs)\n",
        "\n",
        "\n",
        "CONTROLNET_MODEL_IDS = {\n",
        "    \"scribble\": \"lllyasviel/control_v11p_sd15_scribble\",\n",
        "}\n",
        "\n",
        "\n",
        "class Model:\n",
        "    def __init__(self, base_model_id: str = \"runwayml/stable-diffusion-v1-5\", task_name: str = \"Canny\"):\n",
        "        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "        self.base_model_id = \"\"\n",
        "        self.task_name = \"\"\n",
        "        self.pipe = self.load_pipe(base_model_id, task_name)\n",
        "        self.preprocessor = Preprocessor()\n",
        "\n",
        "    def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:\n",
        "        if (\n",
        "            base_model_id == self.base_model_id\n",
        "            and task_name == self.task_name\n",
        "            and hasattr(self, \"pipe\")\n",
        "            and self.pipe is not None\n",
        "        ):\n",
        "            return self.pipe\n",
        "        model_id = CONTROLNET_MODEL_IDS[task_name]\n",
        "        controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16)\n",
        "        pipe = StableDiffusionControlNetPipeline.from_pretrained(\n",
        "            base_model_id, safety_checker=None, controlnet=controlnet, torch_dtype=torch.float16\n",
        "        )\n",
        "        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n",
        "        # if self.device.type == \"cuda\":\n",
        "        #     pipe.enable_xformers_memory_efficient_attention()\n",
        "        pipe.to(self.device)\n",
        "        torch.cuda.empty_cache()\n",
        "        gc.collect()\n",
        "        self.base_model_id = base_model_id\n",
        "        self.task_name = task_name\n",
        "        return pipe\n",
        "\n",
        "    def set_base_model(self, base_model_id: str) -> str:\n",
        "        if not base_model_id or base_model_id == self.base_model_id:\n",
        "            return self.base_model_id\n",
        "        del self.pipe\n",
        "        torch.cuda.empty_cache()\n",
        "        gc.collect()\n",
        "        try:\n",
        "            self.pipe = self.load_pipe(base_model_id, self.task_name)\n",
        "        except Exception:\n",
        "            self.pipe = self.load_pipe(self.base_model_id, self.task_name)\n",
        "        return self.base_model_id\n",
        "\n",
        "    def load_controlnet_weight(self, task_name: str) -> None:\n",
        "        if task_name == self.task_name:\n",
        "            return\n",
        "        if self.pipe is not None and hasattr(self.pipe, \"controlnet\"):\n",
        "            del self.pipe.controlnet\n",
        "        torch.cuda.empty_cache()\n",
        "        gc.collect()\n",
        "        model_id = CONTROLNET_MODEL_IDS[task_name]\n",
        "        controlnet = ControlNetModel.from_pretrained(model_id, torch_dtype=torch.float16)\n",
        "        controlnet.to(self.device)\n",
        "        torch.cuda.empty_cache()\n",
        "        gc.collect()\n",
        "        self.pipe.controlnet = controlnet\n",
        "        self.task_name = task_name\n",
        "\n",
        "    def get_prompt(self, prompt: str, additional_prompt: str) -> str:\n",
        "        if not prompt:\n",
        "            prompt = additional_prompt\n",
        "        else:\n",
        "            prompt = f\"{prompt}, {additional_prompt}\"\n",
        "        return prompt\n",
        "\n",
        "    @torch.autocast(\"cuda\")\n",
        "    def run_pipe(\n",
        "        self,\n",
        "        prompt: str,\n",
        "        negative_prompt: str,\n",
        "        control_image: PIL.Image.Image,\n",
        "        num_images: int,\n",
        "        num_steps: int,\n",
        "        guidance_scale: float,\n",
        "        seed: int,\n",
        "    ) -> list[PIL.Image.Image]:\n",
        "        generator = torch.Generator().manual_seed(seed)\n",
        "        return self.pipe(\n",
        "            prompt=prompt,\n",
        "            negative_prompt=negative_prompt,\n",
        "            guidance_scale=guidance_scale,\n",
        "            num_images_per_prompt=num_images,\n",
        "            num_inference_steps=num_steps,\n",
        "            generator=generator,\n",
        "            image=control_image,\n",
        "        ).images\n",
        "\n",
        "    @torch.inference_mode()\n",
        "    def process_scribble(\n",
        "        self,\n",
        "        image: np.ndarray,\n",
        "        prompt: str,\n",
        "        additional_prompt: str,\n",
        "        negative_prompt: str,\n",
        "        num_images: int,\n",
        "        image_resolution: int,\n",
        "        preprocess_resolution: int,\n",
        "        num_steps: int,\n",
        "        guidance_scale: float,\n",
        "        seed: int,\n",
        "        preprocessor_name: str,\n",
        "    ) -> list[PIL.Image.Image]:\n",
        "        if image is None:\n",
        "            raise ValueError\n",
        "        if image_resolution > MAX_IMAGE_RESOLUTION:\n",
        "            raise ValueError\n",
        "        if num_images > MAX_NUM_IMAGES:\n",
        "            raise ValueError\n",
        "\n",
        "        if preprocessor_name == \"None\":\n",
        "            image = controlnet_aux.util.HWC3(image)\n",
        "            image = resize_image(image, resolution=image_resolution)\n",
        "            control_image = PIL.Image.fromarray(image)\n",
        "        elif preprocessor_name == \"HED\":\n",
        "            self.preprocessor.load(preprocessor_name)\n",
        "            control_image = self.preprocessor(\n",
        "                image=image,\n",
        "                image_resolution=image_resolution,\n",
        "                detect_resolution=preprocess_resolution,\n",
        "                scribble=False,\n",
        "            )\n",
        "        elif preprocessor_name == \"PidiNet\":\n",
        "            self.preprocessor.load(preprocessor_name)\n",
        "            control_image = self.preprocessor(\n",
        "                image=image,\n",
        "                image_resolution=image_resolution,\n",
        "                detect_resolution=preprocess_resolution,\n",
        "                safe=False,\n",
        "            )\n",
        "        self.load_controlnet_weight(\"scribble\")\n",
        "        results = self.run_pipe(\n",
        "            prompt=self.get_prompt(prompt, additional_prompt),\n",
        "            negative_prompt=negative_prompt,\n",
        "            control_image=control_image,\n",
        "            num_images=num_images,\n",
        "            num_steps=num_steps,\n",
        "            guidance_scale=guidance_scale,\n",
        "            seed=seed,\n",
        "        )\n",
        "        return [control_image] + results\n",
        "\n",
        "    @torch.inference_mode()\n",
        "    def process_scribble_interactive(\n",
        "        self,\n",
        "        image_and_mask: dict[str, np.ndarray],\n",
        "        prompt: str,\n",
        "        additional_prompt: str,\n",
        "        negative_prompt: str,\n",
        "        num_images: int,\n",
        "        image_resolution: int,\n",
        "        num_steps: int,\n",
        "        guidance_scale: float,\n",
        "        seed: int,\n",
        "    ) -> list[PIL.Image.Image]:\n",
        "        if image_and_mask is None:\n",
        "            raise ValueError\n",
        "        if image_resolution > MAX_IMAGE_RESOLUTION:\n",
        "            raise ValueError\n",
        "        if num_images > MAX_NUM_IMAGES:\n",
        "            raise ValueError\n",
        "\n",
        "        image = image_and_mask[\"mask\"]\n",
        "        image = controlnet_aux.util.HWC3(image)\n",
        "        image = resize_image(image, resolution=image_resolution)\n",
        "        control_image = PIL.Image.fromarray(image)\n",
        "\n",
        "        self.load_controlnet_weight(\"scribble\")\n",
        "        results = self.run_pipe(\n",
        "            prompt=self.get_prompt(prompt, additional_prompt),\n",
        "            negative_prompt=negative_prompt,\n",
        "            control_image=control_image,\n",
        "            num_images=num_images,\n",
        "            num_steps=num_steps,\n",
        "            guidance_scale=guidance_scale,\n",
        "            seed=seed,\n",
        "        )\n",
        "        return [control_image] + results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "872r7ndXQLlb",
        "outputId": "f549277e-a15b-4bd2-d83e-e18ca4b6f253"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting gradio==3.45.2\n",
            "  Downloading gradio-3.45.2-py3-none-any.whl (20.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.2/20.2 MB\u001b[0m \u001b[31m40.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: aiofiles<24.0,>=22.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.45.2) (23.2.1)\n",
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            "Collecting gradio-client==0.5.3 (from gradio==3.45.2)\n",
            "  Downloading gradio_client-0.5.3-py3-none-any.whl (298 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.4/298.4 kB\u001b[0m \u001b[31m34.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from gradio==3.45.2) (0.27.0)\n",
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            "Requirement already satisfied: typing-extensions~=4.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.45.2) (4.9.0)\n",
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            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==3.45.2) (0.4)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==3.45.2) (4.19.2)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==3.45.2) (0.12.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio==3.45.2) (3.13.1)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio==3.45.2) (4.66.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.45.2) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.45.2) (0.12.1)\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.45.2) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.45.2) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.45.2) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0,>=1.0->gradio==3.45.2) (2023.4)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio==3.45.2) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.2 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio==3.45.2) (2.16.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.45.2) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.45.2) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.45.2) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.45.2) (2024.2.2)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio==3.45.2) (8.1.7)\n",
            "Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio==3.45.2) (0.14.0)\n",
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            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.45.2) (1.0.4)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.45.2) (1.3.0)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.45.2) (23.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.45.2) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.45.2) (0.33.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.45.2) (0.18.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio==3.45.2) (1.16.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->gradio==3.45.2) (1.2.0)\n",
            "Installing collected packages: gradio-client, gradio\n",
            "  Attempting uninstall: gradio-client\n",
            "    Found existing installation: gradio_client 0.6.1\n",
            "    Uninstalling gradio_client-0.6.1:\n",
            "      Successfully uninstalled gradio_client-0.6.1\n",
            "  Attempting uninstall: gradio\n",
            "    Found existing installation: gradio 3.50.2\n",
            "    Uninstalling gradio-3.50.2:\n",
            "      Successfully uninstalled gradio-3.50.2\n",
            "Successfully installed gradio-3.45.2 gradio-client-0.5.3\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio==3.45.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Is42kVyYXUxi"
      },
      "outputs": [],
      "source": [
        "def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:\n",
        "    if randomize_seed:\n",
        "        seed = random.randint(0, MAX_SEED)\n",
        "    return seed\n",
        "\n",
        "def create_canvas(w, h):\n",
        "    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255\n",
        "\n",
        "\n",
        "def create_demo(process):\n",
        "    with gr.Blocks() as demo:\n",
        "        with gr.Row():\n",
        "            with gr.Column():\n",
        "                canvas_width = gr.Slider(\n",
        "                    label=\"Canvas width\",\n",
        "                    minimum=256,\n",
        "                    maximum=MAX_IMAGE_RESOLUTION,\n",
        "                    value=DEFAULT_IMAGE_RESOLUTION,\n",
        "                    step=1,\n",
        "                )\n",
        "                canvas_height = gr.Slider(\n",
        "                    label=\"Canvas height\",\n",
        "                    minimum=256,\n",
        "                    maximum=MAX_IMAGE_RESOLUTION,\n",
        "                    value=DEFAULT_IMAGE_RESOLUTION,\n",
        "                    step=1,\n",
        "                )\n",
        "                create_button = gr.Button(\"Open drawing canvas!\")\n",
        "                image = gr.Image(tool=\"sketch\", brush_radius=10)\n",
        "                prompt = gr.Textbox(label=\"Prompt\")\n",
        "                run_button = gr.Button(\"Run\")\n",
        "                with gr.Accordion(\"Advanced options\", open=False):\n",
        "                    num_samples = gr.Slider(\n",
        "                        label=\"Number of images\", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1\n",
        "                    )\n",
        "                    image_resolution = gr.Slider(\n",
        "                        label=\"Image resolution\",\n",
        "                        minimum=256,\n",
        "                        maximum=MAX_IMAGE_RESOLUTION,\n",
        "                        value=DEFAULT_IMAGE_RESOLUTION,\n",
        "                        step=256,\n",
        "                    )\n",
        "                    num_steps = gr.Slider(label=\"Number of steps\", minimum=1, maximum=100, value=20, step=1)\n",
        "                    guidance_scale = gr.Slider(label=\"Guidance scale\", minimum=0.1, maximum=30.0, value=9.0, step=0.1)\n",
        "                    seed = gr.Slider(label=\"Seed\", minimum=0, maximum=MAX_SEED, step=1, value=0)\n",
        "                    randomize_seed = gr.Checkbox(label=\"Randomize seed\", value=True)\n",
        "                    a_prompt = gr.Textbox(label=\"Additional prompt\", value=\"best quality, extremely detailed\")\n",
        "                    n_prompt = gr.Textbox(\n",
        "                        label=\"Negative prompt\",\n",
        "                        value=\"longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality\",\n",
        "                    )\n",
        "            with gr.Column():\n",
        "                result = gr.Gallery(label=\"Output\", show_label=False, columns=2, object_fit=\"scale-down\")\n",
        "\n",
        "        create_button.click(\n",
        "            fn=create_canvas,\n",
        "            inputs=[canvas_width, canvas_height],\n",
        "            outputs=image,\n",
        "            queue=False,\n",
        "            api_name=False,\n",
        "        )\n",
        "\n",
        "        inputs = [\n",
        "            image,\n",
        "            prompt,\n",
        "            a_prompt,\n",
        "            n_prompt,\n",
        "            num_samples,\n",
        "            image_resolution,\n",
        "            num_steps,\n",
        "            guidance_scale,\n",
        "            seed,\n",
        "        ]\n",
        "        prompt.submit(\n",
        "            fn=randomize_seed_fn,\n",
        "            inputs=[seed, randomize_seed],\n",
        "            outputs=seed,\n",
        "            queue=False,\n",
        "            api_name=False,\n",
        "        ).then(\n",
        "            fn=process,\n",
        "            inputs=inputs,\n",
        "            outputs=result,\n",
        "            api_name=False,\n",
        "        )\n",
        "        run_button.click(\n",
        "            fn=randomize_seed_fn,\n",
        "            inputs=[seed, randomize_seed],\n",
        "            outputs=seed,\n",
        "            queue=False,\n",
        "            api_name=False,\n",
        "        ).then(\n",
        "            fn=process,\n",
        "            inputs=inputs,\n",
        "            outputs=result,\n",
        "            api_name=\"scribble\",\n",
        "        )\n",
        "    return demo\n",
        "\n",
        "# model = Model(task_name=\"scribble\")\n",
        "# demo = create_demo(model.process_scribble_interactive)\n",
        "# demo.queue().launch(debug=True) # 40 s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## FastAPI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iaFrfjI7hm9p"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import io\n",
        "import base64\n",
        "from PIL import Image\n",
        "\n",
        "def base64_to_numpy(base64_string):\n",
        "    # Decode base64 string\n",
        "    image_bytes = base64.b64decode(base64_string.split(',')[1])\n",
        "    # Convert bytes to PIL Image\n",
        "    image = Image.open(io.BytesIO(image_bytes))\n",
        "    # Convert PIL Image to NumPy array\n",
        "    image_array = np.array(image)\n",
        "    return image_array\n",
        "\n",
        "\n",
        "def numpy_to_base64(image_array):\n",
        "    # Convert NumPy array to PIL Image\n",
        "    image = Image.fromarray(image_array)\n",
        "    # Create an in-memory binary stream\n",
        "    image_stream = io.BytesIO()\n",
        "    # Save PIL Image to the stream as PNG\n",
        "    image.save(image_stream, format='PNG')\n",
        "    # Encode stream contents as base64\n",
        "    base64_string = base64.b64encode(image_stream.getvalue()).decode('utf-8')\n",
        "    return 'data:image/jpeg;base64,' + base64_string"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fRLov_5GiYwE"
      },
      "outputs": [],
      "source": [
        "%pip install fastapi uvicorn pydantic nest-asyncio pyngrok -q"
      ]
    }
  ],
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      "gpuType": "T4",
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            "value": "Loading pipeline components...: 100%"
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