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            "PyTorch version: 2.3.0+cu121\n",
            "Torchvision version: 0.18.0+cu121\n",
            "CUDA is available: True\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.8.0.76)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from opencv-python) (1.25.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Collecting git+https://github.com/facebookresearch/segment-anything.git\n",
            "  Cloning https://github.com/facebookresearch/segment-anything.git to /tmp/pip-req-build-2ff7n66o\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/segment-anything.git /tmp/pip-req-build-2ff7n66o\n",
            "  Resolved https://github.com/facebookresearch/segment-anything.git to commit 6fdee8f2727f4506cfbbe553e23b895e27956588\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "--2024-06-17 10:15:25--  https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 108.157.254.124, 108.157.254.15, 108.157.254.102, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|108.157.254.124|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2564550879 (2.4G) [binary/octet-stream]\n",
            "Saving to: ‘sam_vit_h_4b8939.pth.1’\n",
            "\n",
            "sam_vit_h_4b8939.pt 100%[===================>]   2.39G   241MB/s    in 11s     \n",
            "\n",
            "2024-06-17 10:15:36 (231 MB/s) - ‘sam_vit_h_4b8939.pth.1’ saved [2564550879/2564550879]\n",
            "\n",
            "fatal: destination path 'ControlNetInpaint' already exists and is not an empty directory.\n"
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            "text_encoder/model.safetensors not found\n"
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            "An error occurred while trying to fetch /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/afeee10def38be19995784bcc811882409d066e5/unet: Error no file named diffusion_pytorch_model.safetensors found in directory /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/afeee10def38be19995784bcc811882409d066e5/unet.\n",
            "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\n",
            "An error occurred while trying to fetch /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/afeee10def38be19995784bcc811882409d066e5/vae: Error no file named diffusion_pytorch_model.safetensors found in directory /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/afeee10def38be19995784bcc811882409d066e5/vae.\n",
            "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\n",
            "text_encoder/model.safetensors not found\n"
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          "name": "stderr",
          "text": [
            "An error occurred while trying to fetch /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/51388a731f57604945fddd703ecb5c50e8e7b49d/unet: Error no file named diffusion_pytorch_model.safetensors found in directory /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/51388a731f57604945fddd703ecb5c50e8e7b49d/unet.\n",
            "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\n",
            "An error occurred while trying to fetch /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/51388a731f57604945fddd703ecb5c50e8e7b49d/vae: Error no file named diffusion_pytorch_model.safetensors found in directory /root/.cache/huggingface/hub/models--runwayml--stable-diffusion-inpainting/snapshots/51388a731f57604945fddd703ecb5c50e8e7b49d/vae.\n",
            "Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StableDiffusionControlNetInpaintPipeline {\n",
              "  \"_class_name\": \"StableDiffusionControlNetInpaintPipeline\",\n",
              "  \"_diffusers_version\": \"0.29.0\",\n",
              "  \"_name_or_path\": \"runwayml/stable-diffusion-inpainting\",\n",
              "  \"controlnet\": [\n",
              "    \"diffusers\",\n",
              "    \"ControlNetModel\"\n",
              "  ],\n",
              "  \"feature_extractor\": [\n",
              "    \"transformers\",\n",
              "    \"CLIPImageProcessor\"\n",
              "  ],\n",
              "  \"image_encoder\": [\n",
              "    null,\n",
              "    null\n",
              "  ],\n",
              "  \"requires_safety_checker\": true,\n",
              "  \"safety_checker\": [\n",
              "    \"stable_diffusion\",\n",
              "    \"StableDiffusionSafetyChecker\"\n",
              "  ],\n",
              "  \"scheduler\": [\n",
              "    \"diffusers\",\n",
              "    \"UniPCMultistepScheduler\"\n",
              "  ],\n",
              "  \"text_encoder\": [\n",
              "    \"transformers\",\n",
              "    \"CLIPTextModel\"\n",
              "  ],\n",
              "  \"tokenizer\": [\n",
              "    \"transformers\",\n",
              "    \"CLIPTokenizer\"\n",
              "  ],\n",
              "  \"unet\": [\n",
              "    \"diffusers\",\n",
              "    \"UNet2DConditionModel\"\n",
              "  ],\n",
              "  \"vae\": [\n",
              "    \"diffusers\",\n",
              "    \"AutoencoderKL\"\n",
              "  ]\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "!pip install gradio\n",
        "!pip install ultralytics\n",
        "! pip install diffusers xformers transformers scipy ftfy accelerate controlnet_aux\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import os\n",
        "import cv2\n",
        "import shutil\n",
        "import tqdm\n",
        "import glob\n",
        "import torch\n",
        "from ultralytics import YOLO\n",
        "from PIL import Image\n",
        "import torchvision\n",
        "print(\"PyTorch version:\", torch.__version__)\n",
        "print(\"Torchvision version:\", torchvision.__version__)\n",
        "print(\"CUDA is available:\", torch.cuda.is_available())\n",
        "import sys\n",
        "!{sys.executable} -m pip install opencv-python matplotlib\n",
        "!{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n",
        "\n",
        "!wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\n",
        "import sys\n",
        "sys.path.append(\"..\")\n",
        "from segment_anything import sam_model_registry, SamPredictor\n",
        "\n",
        "sam_checkpoint = \"sam_vit_h_4b8939.pth\"\n",
        "model_type = \"vit_h\"\n",
        "device = \"cuda\"\n",
        "\n",
        "sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\n",
        "sam.to(device=device)\n",
        "! git clone https://github.com/mikonvergence/ControlNetInpaint\n",
        "import sys\n",
        "sys.path.append('./ControlNetInpaint/')\n",
        "from diffusers import StableDiffusionInpaintPipeline, ControlNetModel, UniPCMultistepScheduler\n",
        "from ControlNetInpaint.src import *\n",
        "from diffusers.utils import load_image\n",
        "\n",
        "import cv2\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import torch\n",
        "from matplotlib import pyplot as plt\n",
        "pipe_sd = StableDiffusionInpaintPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-inpainting\",\n",
        "    revision=\"fp16\",\n",
        "    torch_dtype=torch.float16,\n",
        "  )\n",
        "# speed up diffusion process with faster scheduler and memory optimization\n",
        "pipe_sd.scheduler = UniPCMultistepScheduler.from_config(pipe_sd.scheduler.config)\n",
        "pipe_sd.enable_xformers_memory_efficient_attention()\n",
        "pipe_sd.to('cuda')\n",
        "# load control net and stable diffusion v1-5\n",
        "from diffusers import StableDiffusionControlNetInpaintPipeline\n",
        "controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n",
        "pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-inpainting\", controlnet=controlnet, torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "# speed up diffusion process with faster scheduler and memory optimization\n",
        "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n",
        "# remove following line if xformers is not installed\n",
        "pipe.enable_xformers_memory_efficient_attention()\n",
        "text_prompt=\"Transform this image into a work of art by changing its style and color palette. Apply a distinct artistic style, such as impressionism, cubism, or surrealism, to give the image a unique and visually striking appearance. Experiment with brush strokes, textures, and effects to achieve the desired artistic effect while maintaining the essence of the original scene. Additionally, adjust the color palette to evoke a specific mood or theme. For example, infuse warm, earthy tones for a rustic and cozy feel, or opt for vibrant, psychedelic colors for a surreal and otherworldly atmosphere. The goal is to reimagine the image in a creative and expressive way, transforming it into a captivating visual masterpiece.\"\n",
        "import os\n",
        "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n",
        "pipe.to('cuda')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def image_enhance(image, mask):\n",
        "  image = np.array(image)\n",
        "  mask_image = np.array(mask)\n",
        "  canny_image = cv2.Canny(image, 100, 200)\n",
        "  canny_image = canny_image[:, :, None]\n",
        "  canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)\n",
        "\n",
        "  image=Image.fromarray(image)\n",
        "  mask_image=Image.fromarray(mask_image)\n",
        "  canny_image = Image.fromarray(canny_image)\n",
        "\n",
        "  # generate image\n",
        "  generator = torch.manual_seed(42)\n",
        "  new_image = pipe(\n",
        "      text_prompt,\n",
        "      num_inference_steps=20,\n",
        "      generator=generator,\n",
        "      image=image,\n",
        "      control_image=canny_image,\n",
        "      controlnet_conditioning_scale = 0.5,\n",
        "      mask_image=mask_image\n",
        "  ).images[0]\n",
        "\n",
        "  return new_image\n",
        "def function(image):\n",
        "  original_image=image\n",
        "  model = YOLO('/content/drive/MyDrive/new Movie/best_yolo_2.pt')\n",
        "  image = np.array(image)\n",
        "  results = model.predict(image)\n",
        "  img=Image.fromarray(results[0].plot())\n",
        "  num_result=len(results[0].boxes.cls)\n",
        "  names_output=[]\n",
        "  for i in range(num_result):\n",
        "    name=results[0].names[int(results[0].boxes.cls[i])]\n",
        "    names_output.append(name)\n",
        "  boxes = results[0].boxes\n",
        "  boxes=boxes.xyxy\n",
        "  #image=Image.fromarray(image)\n",
        "  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  predictor = SamPredictor(sam)\n",
        "  predictor.set_image(image)\n",
        "  list_mask=[]\n",
        "  for box in boxes:\n",
        "    input_box = np.array(box.cpu())\n",
        "    masks, _, _ = predictor.predict(\n",
        "        point_coords=None,\n",
        "        point_labels=None,\n",
        "        box=input_box[None, :],\n",
        "        multimask_output=False,\n",
        "    )\n",
        "    for mask in masks:\n",
        "      list_mask.append(mask)\n",
        "  for i in range(len(list_mask)):\n",
        "    list_mask[i]=Image.fromarray(list_mask[i])\n",
        "    original_image=image_enhance(original_image,list_mask[i])\n",
        "  return original_image"
      ],
      "metadata": {
        "id": "-rh2-04gT6Al"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "VI4OHU84Vqo3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "demo=gr.Interface(fn=function,inputs=\"image\",outputs=[\"image\"])\n",
        "demo.launch(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833,
          "referenced_widgets": [
            "3f098b38b7e34902a1f39e266214d630",
            "18b4f0aef68c425ba7f3aaed773beadf",
            "fa23ebd5890f4aa8905f2de20393f314",
            "a9f3d5c126454c52a76e4a0b71bd14ab",
            "7f51f3d126a948b6a96b713b2f633110",
            "76dd708761e34060a1ab6b4f68319d4d",
            "9f9776a1810c4db6877132fc64df8cc5",
            "82fb4941c6994ab2948e78b4d4b8230b",
            "4547a05888ac4bcf9ca624bb112e86ff",
            "a20112975bbf4e0f81937d46badec443",
            "0694a01089b94b89b18ef5eb87b42fdc"
          ]
        },
        "id": "PVWDzcxWU-DX",
        "outputId": "db172447-492f-4a6c-e403-13dfc725ebdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "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://a4f5c5bdffdf975e61.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://a4f5c5bdffdf975e61.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "0: 640x448 1 jacket, 154.8ms\n",
            "Speed: 23.5ms preprocess, 154.8ms inference, 876.2ms postprocess per image at shape (1, 3, 640, 448)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (141 > 77). Running this sequence through the model will result in indexing errors\n",
            "The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['palette to evoke a specific mood or theme. for example, infuse warm, earthy tones for a rustic and cozy feel, or opt for vibrant, psychedelic colors for a surreal and otherworldly atmosphere. the goal is to reimagine the image in a creative and expressive way, transforming it into a captivating visual masterpiece.']\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return F.conv2d(input, weight, bias, self.stride,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f098b38b7e34902a1f39e266214d630"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://a4f5c5bdffdf975e61.gradio.live\n"
          ]
        }
      ]
    }
  ]
}