examples/images/Generate_Image_Free.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/vishalmysore/AI/blob/main/examples/images/Generate_Image_Free.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "Fri Jan 12 19:40:45 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   54C    P8              12W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install --upgrade \\\n",
        "  diffusers \\\n",
        "  transformers \\\n",
        "  safetensors \\\n",
        "  sentencepiece \\\n",
        "  accelerate \\\n",
        "  bitsandbytes \\\n",
        "  torch \\\n",
        "  huggingface_hub --quiet"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "Vhs1JzDsgYN-",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m670.2/670.2 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m46.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m61.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m88.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m29.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.8/209.8 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m87.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler, AutoencoderKL\n",
        "import torch\n",
        "pipe = DiffusionPipeline.from_pretrained(\n",
        "    \"prompthero/openjourney\",\n",
        "    torch_dtype=torch.float16\n",
        ")\n",
        "vae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16).to(\"cuda\")\n",
        "pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n",
        "pipe.vae = vae\n",
        "pipe = pipe.to(\"cuda\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778,
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        "id": "eb59I_ZMghO_",
        "outputId": "9d3bf7ea-ba91-482e-a7bb-304815eaef3b"
      },
      "execution_count": null,
      "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"
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          "text": [
            "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n",
            "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"bos_token_id\"]` will be overriden.\n",
            "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"eos_token_id\"]` will be overriden.\n"
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    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "prompt = 'Robot holding a samosa in his hand. Leica lens. Hi-res. hd 8k --ar 2:3'\n",
        "num_steps = 20\n",
        "num_variations = 4\n",
        "prompt_guidance = 9\n",
        "dimensions = (400, 600) # (width, height) tuple\n",
        "random_seeds = [random.randint(0, 65000) for _ in range(num_variations)]"
      ],
      "metadata": {
        "id": "JcZdkywLgkv8"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "images = pipe(prompt= num_variations * [prompt],\n",
        "              num_inference_steps=num_steps,\n",
        "              guidance_scale=prompt_guidance,\n",
        "              height = dimensions[0],\n",
        "              width = dimensions[1],\n",
        "              generator = [torch.Generator('cuda').manual_seed(i) for i in random_seeds]\n",
        "             ).images"
      ],
      "metadata": {
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
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    {
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      "source": [
        "images[0]"
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      "metadata": {
        "id": "--IIh089gpyk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images[1]"
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      "metadata": {
        "id": "5krKzePpgq19"
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      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "images[2]"
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      "metadata": {
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    {
      "cell_type": "code",
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        "images[3]"
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "prompt = 'Architecture diagram of an applicaiton, Software code talking to Database. Leica lens. Hi-res. hd 8k --ar 2:3'\n",
        "num_steps = 20\n",
        "num_variations = 4\n",
        "prompt_guidance = 9\n",
        "dimensions = (400, 600) # (width, height) tuple\n",
        "random_seeds = [random.randint(0, 65000) for _ in range(num_variations)]"
      ],
      "metadata": {
        "id": "ARiMpiTWjmeR"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "images = pipe(prompt= num_variations * [prompt],\n",
        "              num_inference_steps=num_steps,\n",
        "              guidance_scale=prompt_guidance,\n",
        "              height = dimensions[0],\n",
        "              width = dimensions[1],\n",
        "              generator = [torch.Generator('cuda').manual_seed(i) for i in random_seeds]\n",
        "             ).images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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          "data": {
            "text/plain": [
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      "source": [
        "images[1]"
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      "metadata": {
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    {
      "cell_type": "code",
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        "images[2]"
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}