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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-n_eu6qpBH0W",
        "outputId": "61b11830-b940-4a3a-b25a-aec65fb45533"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'project'...\n",
            "remote: Enumerating objects: 33, done.\u001b[K\n",
            "remote: Counting objects: 100% (33/33), done.\u001b[K\n",
            "remote: Compressing objects: 100% (31/31), done.\u001b[K\n",
            "remote: Total 33 (delta 7), reused 0 (delta 0), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (33/33), 3.10 MiB | 7.23 MiB/s, done.\n",
            "Resolving deltas: 100% (7/7), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/adharv49/project.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install diffusers accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "bz93Abt3BM0i",
        "outputId": "9f3b87b6-c3f6-475c-89a2-430fe319cbb7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: diffusers in /usr/local/lib/python3.10/dist-packages (0.31.0)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (1.2.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.10/dist-packages (from diffusers) (8.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from diffusers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from diffusers) (0.27.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from diffusers) (1.26.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from diffusers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from diffusers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from diffusers) (0.4.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from diffusers) (11.0.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.2)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.5.1+cu121)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (2024.10.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (4.67.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.2->diffusers) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata->diffusers) (3.21.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->diffusers) (2024.12.14)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (3.0.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import AutoPipelineForInpainting, AutoencoderKL\n",
        "from diffusers.utils import load_image\n",
        "import torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
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      "execution_count": 3,
      "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": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
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              "version_major": 2,
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      "source": [
        "vae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16)\n",
        "pipeline = AutoPipelineForInpainting.from_pretrained(\"diffusers/stable-diffusion-xl-1.0-inpainting-0.1\", vae=vae, torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True).to(\"cuda\")\n",
        "pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\", low_cpu_mem_usage=True)"
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        },
        "id": "VutXRzkDBSqi",
        "outputId": "c8ef31c6-0a1a-47b5-8ada-43e009d16388"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The config attributes {'decay': 0.9999, 'inv_gamma': 1.0, 'min_decay': 0.0, 'optimization_step': 37000, 'power': 0.6666666666666666, 'update_after_step': 0, 'use_ema_warmup': False} were passed to UNet2DConditionModel, but are not expected and will be ignored. Please verify your config.json configuration file.\n"
          ]
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          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install albumentations\n",
        "!pip install iglovikov-helper-functions\n",
        "!pip install cloths_segmentation\n",
        "!pip install onnxruntime\n",
        "!pip install gradio\n",
        "!pip install insightface"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0MqEIiOKBUsi",
        "outputId": "c1a49455-8977-41d6-951f-ebf91a6efa46"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: albumentations in /usr/local/lib/python3.10/dist-packages (1.4.20)\n",
            "Requirement already satisfied: numpy>=1.24.4 in /usr/local/lib/python3.10/dist-packages (from albumentations) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from albumentations) (1.13.1)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from albumentations) (6.0.2)\n",
            "Requirement already satisfied: pydantic>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from albumentations) (2.10.3)\n",
            "Requirement already satisfied: albucore==0.0.19 in /usr/local/lib/python3.10/dist-packages (from albumentations) (0.0.19)\n",
            "Requirement already satisfied: eval-type-backport in /usr/local/lib/python3.10/dist-packages (from albumentations) (0.2.0)\n",
            "Requirement already satisfied: opencv-python-headless>=4.9.0.80 in /usr/local/lib/python3.10/dist-packages (from albumentations) (4.10.0.84)\n",
            "Requirement already satisfied: stringzilla>=3.10.4 in /usr/local/lib/python3.10/dist-packages (from albucore==0.0.19->albumentations) (3.11.1)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.7.0->albumentations) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.27.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.7.0->albumentations) (2.27.1)\n",
            "Requirement already satisfied: typing-extensions>=4.12.2 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.7.0->albumentations) (4.12.2)\n",
            "Collecting iglovikov-helper-functions\n",
            "  Downloading iglovikov_helper_functions-0.0.53-py2.py3-none-any.whl.metadata (1.5 kB)\n",
            "Collecting addict (from iglovikov-helper-functions)\n",
            "  Downloading addict-2.4.0-py3-none-any.whl.metadata (1.0 kB)\n",
            "Collecting imagecorruptions (from iglovikov-helper-functions)\n",
            "  Downloading imagecorruptions-1.1.2-py3-none-any.whl.metadata (3.5 kB)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (1.4.2)\n",
            "Collecting jpeg4py (from iglovikov-helper-functions)\n",
            "  Downloading jpeg4py-0.1.4.tar.gz (12 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (1.26.4)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (4.10.0.84)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (2.2.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (11.0.0)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (0.25.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (1.6.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (1.13.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from iglovikov-helper-functions) (2.5.1+cu121)\n",
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            "\u001b[?25hBuilding wheels for collected packages: insightface\n",
            "  Building wheel for insightface (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for insightface: filename=insightface-0.7.3-cp310-cp310-linux_x86_64.whl size=1055392 sha256=02585ffd9134ef52664199e6661e5f14dfecabfc960c33eee92b6def66dc9731\n",
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            "Successfully built insightface\n",
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            "Successfully installed insightface-0.7.3 onnx-1.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd project"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IWf_2XyIbBg",
        "outputId": "ce5f80c2-9a53-41e5-dcc0-c20b95d2fa68"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/project\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from SegBody import segment_body\n",
        "from diffusers.utils import load_image\n",
        "import numpy as np\n",
        "import cv2\n",
        "import torch\n",
        "import albumentations as albu\n",
        "from PIL import Image\n",
        "from iglovikov_helper_functions.utils.image_utils import load_rgb, pad, unpad\n",
        "from iglovikov_helper_functions.dl.pytorch.utils import tensor_from_rgb_image\n",
        "#from cloths_segmentation.pre_trained_models import create_model\n",
        "from diffusers import AutoencoderKL,AutoPipelineForInpainting\n",
        "\n",
        "def generate_masked_image(cloth_path, model_path):\n",
        "    cloth_image = load_image(cloth_path).convert(\"RGB\")\n",
        "    cloth_image.resize((512, 512))\n",
        "    model_image = load_image(model_path).convert(\"RGB\")\n",
        "    model_image.resize((512, 512))\n",
        "    seg_image, mask_image = segment_body(model_path, face=False)\n",
        "    mask_image.resize((512, 512))\n",
        "    pipeline.set_ip_adapter_scale(1.0)\n",
        "    images = pipeline(\n",
        "    prompt=\"photorealistic, perfect body, beautiful skin, realistic skin, natural skin\",\n",
        "    negative_prompt=\"ugly, bad quality, bad anatomy, deformed body, deformed hands, deformed feet, deformed face, deformed clothing, deformed skin, bad skin, leggings, tights, stockings\",\n",
        "    image=model_image,\n",
        "    mask_image=mask_image,\n",
        "    ip_adapter_image=cloth_image,\n",
        "    strength=0.99,\n",
        "    guidance_scale=7.5,\n",
        "    num_inference_steps=100,\n",
        "    ).images\n",
        "    return images[0]"
      ],
      "metadata": {
        "id": "DmS7sW9LBcDP"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "from PIL import Image\n",
        "import os\n",
        "def run(cloth, model):\n",
        "    # Load the selected cloth and the uploaded model image\n",
        "    #cloth = Image.open(cloth_path)\n",
        "    model.save(\"temp_model.jpg\")\n",
        "    cloth.save(\"temp_cloth.jpg\")\n",
        "\n",
        "    # Call the masking function from mask.py\n",
        "    # Replace generate_masked_image with your actual function\n",
        "    masked_image = generate_masked_image(\"temp_cloth.jpg\", \"temp_model.jpg\")\n",
        "\n",
        "    # Return the masked image for display\n",
        "    return masked_image\n",
        "\n",
        "# Predefined cloth images\n",
        "CLOTH_OPTIONS = {\n",
        "    \"Cloth 1\": \"/content/project/samplecloth/01_upper.jpg\",\n",
        "    \"Cloth 2\": \"/content/project/samplecloth/06_upper.png\",\n",
        "    \"Cloth 3\": \"/content/project/samplecloth/09163_00.jpg\",\n",
        "    \"Cloth 4\": \"/content/project/samplecloth/09176_00.jpg\",\n",
        "    \"Cloth 5\": \"/content/project/samplecloth/09236_00.jpg\",\n",
        "    \"Cloth 6\": \"/content/project/samplecloth/09290_00.jpg\",\n",
        "    \"Cloth 7\": \"/content/project/samplecloth/boys-Puffer-Coat-With-Detachable-Hood-1.jpg\",\n",
        "    \"Cloth 8\": \"/content/project/samplecloth/brown-jacket.jpg\",\n",
        "    \"Cloth 9\": \"/content/project/samplecloth/clothing1.jpg\",\n",
        "    \"Cloth 10\": \"/content/project/samplecloth/kid-1-b.png\",\n",
        "\n",
        "}\n",
        "\n",
        "# Gradio app\n",
        "with gr.Blocks() as app:\n",
        "    with gr.Row():\n",
        "        gr.Markdown(\"<center><h1> DIGIDRAPE </h1><center>\")\n",
        "\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "            # Dropdown to select predefined cloths\n",
        "\n",
        "            cloth_input=gr.Image(sources='clipboard', type=\"pil\", label=\"Model\", value=CLOTH_OPTIONS[\"Cloth 1\"])\n",
        "            example = gr.Examples(inputs=cloth_input,\n",
        "                                  examples_per_page=4,\n",
        "                                  examples=[CLOTH_OPTIONS[\"Cloth 1\"], CLOTH_OPTIONS[\"Cloth 2\"],CLOTH_OPTIONS[\"Cloth 3\"],\n",
        "                                            CLOTH_OPTIONS[\"Cloth 4\"],CLOTH_OPTIONS[\"Cloth 5\"],CLOTH_OPTIONS[\"Cloth 6\"],\n",
        "                                            CLOTH_OPTIONS[\"Cloth 7\"],CLOTH_OPTIONS[\"Cloth 8\"],CLOTH_OPTIONS[\"Cloth 9\"],\n",
        "                                            CLOTH_OPTIONS[\"Cloth 10\"]])\n",
        "        with gr.Column():\n",
        "            model_input = gr.Image(sources=['upload'], type=\"pil\", label=\"Upload the Human Image\")\n",
        "\n",
        "    with gr.Row():\n",
        "        final_output = gr.Image(type=\"pil\", label=\"Final Prediction\")\n",
        "\n",
        "    with gr.Row():\n",
        "        submit_button = gr.Button(\"Submit\")\n",
        "\n",
        "\n",
        "\n",
        "    submit_button.click(fn=run, inputs=[cloth_input, model_input], outputs=[final_output])\n",
        "\n",
        "app.launch(debug=True, share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947,
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        "id": "4LzeOjw2Bdha",
        "outputId": "30e200a4-e434-4537-e124-b9af82cbd3fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "* Running on public URL: https://2b3222f4251b530b1d.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://2b3222f4251b530b1d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4\n"
          ]
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4\n"
          ]
        },
        {
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        {
          "metadata": {
            "tags": null
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4\n"
          ]
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/insightface/utils/transform.py:68: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  P = np.linalg.lstsq(X_homo, Y)[0].T # Affine matrix. 3 x 4\n"
          ]
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}