{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/IDEFICS_inference_adapter_captioning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNCNPVi8iAgw"
      },
      "source": [
        "# Fine-tune IDEFICS for image captioning\n",
        "> By [mrm8488](https://twitter.com/mrm8488) with the support of [CliBrAIn](https://clibrain.com/)\n",
        "\n",
        "## IDEFICS: A Flamingo-based model, trained at scale for the community\n",
        "\n",
        "<div style=\"text-align: center;\">\n",
        "</div>\n",
        "<div style=\"display: flex; justify-content: center;\">\n",
        "    <img src=\"https://huggingface.co/HuggingFaceM4/idefics-80b/resolve/main/assets/Idefics_colab.png\" alt=\"Idefics image\" >\n",
        "</div>\n",
        "\n",
        "Credit: [Flamingo blog](https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model)\n",
        "\n",
        "[IDEFICS](https://huggingface.co/HuggingFaceM4/idefics-80b) is a multi-modal model based on the [Flamingo](https://arxiv.org/abs/2204.14198) architecture.\n",
        "\n",
        "IDEFICS elegantly integrates two specialized, open-access pre-trained models to harmonize both visual and textual modalities. Sophisticated Transformer blocks serve as the newly instituted parameters, acting as the linchpin between the vision encoder and the linguistic model. This groundbreaking model is cultivated on a diverse training set, consisting of paired image-text instances as well as multifaceted multimedia web content.\n",
        "\n",
        "The code for this notebook is heavily based on this [colab](https://github.com/huggingface/notebooks/blob/main/examples/idefics/finetune_image_captioning_peft.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7m9zw1wcCC8e"
      },
      "source": [
        "# Install and import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8PtxGMg_beRq",
        "outputId": "a7769e51-82dd-4b9e-f89a-920b8c95da80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Sep 22 20:49:45 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\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  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P0    47W / 350W |  19343MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c0CN_O3xbhlI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prXRsUiXCII9",
        "outputId": "d6267483-654c-4c19-bcde-aa8100bd52c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m  WARNING: Did not find branch or tag 'add-model-idefics', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mgit checkout -q add-model-idefics\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m \u001b[32mgit checkout -q add-model-idefics\u001b[0m did not run successfully.\n",
            "\u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@add-model-idefics\n",
        "!pip install -q bitsandbytes sentencepiece accelerate loralib\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "9576fdf87e894c45a8503d8354e4bece",
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            "cfb80c7c5d8940ea8b0bd05327553c48"
          ]
        },
        "id": "ELu1W322bjkL",
        "outputId": "eedb47f6-98aa-4656-fd41-6146cab9b290"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9576fdf87e894c45a8503d8354e4bece",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MxoHmx-HfAgf"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig, get_peft_model\n",
        "from PIL import Image\n",
        "from transformers import IdeficsForVisionText2Text, AutoProcessor, Trainer, TrainingArguments, BitsAndBytesConfig\n",
        "import torchvision.transforms as transforms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP_ilre6jI6l"
      },
      "source": [
        "# Load quantized model\n",
        "First get the quantized version of the model. This will allow us to use the 9B version of Idefics with a single 16GB gpu\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BM4Tm4wwcGC8"
      },
      "outputs": [],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model_id = \"mrm8488/idefics-9b-ft-describe-diffusion-bf16\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IRiT0q0Ck-3Y"
      },
      "outputs": [],
      "source": [
        "# Here we skip some special modules that can't be quantized properly\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    llm_int8_skip_modules=[\"lm_head\", \"embed_tokens\"],\n",
        ")"
      ]
    },
    {
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        "colab": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModel(\n",
              "  (base_model): LoraModel(\n",
              "    (model): IdeficsForVisionText2Text(\n",
              "      (model): IdeficsModel(\n",
              "        (embed_tokens): IdeficsDecoupledEmbedding(\n",
              "          num_embeddings=32000, num_additional_embeddings=2, embedding_dim=4096, partially_freeze=False\n",
              "          (additional_embedding): Embedding(2, 4096)\n",
              "        )\n",
              "        (vision_model): IdeficsVisionTransformer(\n",
              "          (embeddings): IdeficsVisionEmbeddings(\n",
              "            (patch_embedding): Conv2d(3, 1280, kernel_size=(14, 14), stride=(14, 14), bias=False)\n",
              "            (position_embedding): Embedding(257, 1280)\n",
              "          )\n",
              "          (pre_layrnorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder): IdeficsVisionEncoder(\n",
              "            (layers): ModuleList(\n",
              "              (0-31): 32 x IdeficsVisionEncoderLayer(\n",
              "                (self_attn): IdeficsVisionAttention(\n",
              "                  (k_proj): Linear(\n",
              "                    in_features=1280, out_features=1280, bias=True\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.05, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=16, out_features=1280, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (v_proj): Linear(\n",
              "                    in_features=1280, out_features=1280, bias=True\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.05, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=16, out_features=1280, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (q_proj): Linear(\n",
              "                    in_features=1280, out_features=1280, bias=True\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.05, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=16, out_features=1280, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "                )\n",
              "                (layer_norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "                (mlp): IdeficsVisionMLP(\n",
              "                  (activation_fn): GELUActivation()\n",
              "                  (fc1): Linear(in_features=1280, out_features=5120, bias=True)\n",
              "                  (fc2): Linear(in_features=5120, out_features=1280, bias=True)\n",
              "                )\n",
              "                (layer_norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (post_layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (perceiver_resampler): IdeficsPerceiverResampler(\n",
              "          (blocks): ModuleList(\n",
              "            (0-5): 6 x ModuleList(\n",
              "              (0): IdeficsPerceiverAttention(\n",
              "                (context_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "                (latents_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "                (q_layer_norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
              "                (k_layer_norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
              "                (q_proj): Linear(\n",
              "                  in_features=1280, out_features=1536, bias=False\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.05, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=16, out_features=1536, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (k_proj): Linear(\n",
              "                  in_features=1280, out_features=1536, bias=False\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.05, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=16, out_features=1536, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (v_proj): Linear(\n",
              "                  in_features=1280, out_features=1536, bias=False\n",
              "                  (lora_dropout): ModuleDict(\n",
              "                    (default): Dropout(p=0.05, inplace=False)\n",
              "                  )\n",
              "                  (lora_A): ModuleDict(\n",
              "                    (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                  )\n",
              "                  (lora_B): ModuleDict(\n",
              "                    (default): Linear(in_features=16, out_features=1536, bias=False)\n",
              "                  )\n",
              "                  (lora_embedding_A): ParameterDict()\n",
              "                  (lora_embedding_B): ParameterDict()\n",
              "                )\n",
              "                (output_proj): Linear(in_features=1536, out_features=1280, bias=False)\n",
              "              )\n",
              "              (1): IdeficsMLP(\n",
              "                (ln): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "                (fc): Linear(in_features=1280, out_features=5120, bias=False)\n",
              "                (act): ReLU()\n",
              "                (c_proj): Linear(in_features=5120, out_features=1280, bias=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x IdeficsDecoderLayer(\n",
              "            (self_attn): IdeficsAttention(\n",
              "              (q_proj): Linear(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): Linear(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): Linear(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "              (rotary_emb): IdeficsEmbedding()\n",
              "            )\n",
              "            (mlp): IdeficsMLP(\n",
              "              (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "              (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
              "              (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "              (act_fn): SiLUActivation()\n",
              "            )\n",
              "            (input_layernorm): IdeficsRMSNorm()\n",
              "            (post_attention_layernorm): IdeficsRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (gated_cross_attn_layers): ModuleList(\n",
              "          (0-7): 8 x IdeficsGatedCrossAttentionLayer(\n",
              "            (cross_attn): IdeficsAttention(\n",
              "              (q_proj): Linear(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): Linear(\n",
              "                in_features=1280, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (v_proj): Linear(\n",
              "                in_features=1280, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=1280, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "              (rotary_emb): IdeficsEmbedding()\n",
              "              (q_layer_norm): IdeficsRMSNorm()\n",
              "              (k_layer_norm): IdeficsRMSNorm()\n",
              "            )\n",
              "            (mlp): IdeficsMLP(\n",
              "              (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "              (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
              "              (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "              (act_fn): SiLUActivation()\n",
              "            )\n",
              "            (input_layernorm): IdeficsRMSNorm()\n",
              "            (post_attention_layernorm): IdeficsRMSNorm()\n",
              "            (act_cross_attn): Tanh()\n",
              "            (act_dense): Tanh()\n",
              "          )\n",
              "        )\n",
              "        (norm): IdeficsRMSNorm()\n",
              "      )\n",
              "      (lm_head): IdeficsDecoupledLinear(\n",
              "        in_features=4096, out_features=32000, out_additional_features=2, bias=False, partially_freeze=False\n",
              "        (additional_fc): Linear(in_features=4096, out_features=2, bias=False)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from transformers import IdeficsForVisionText2Text, AutoProcessor\n",
        "from peft import PeftModel, PeftConfig\n",
        "\n",
        "peft_model_id = \"mrm8488/idefics-9b-ft-describe-diffusion-bf16-adapter\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = IdeficsForVisionText2Text.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.bfloat16)\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)\n",
        "processor = AutoProcessor.from_pretrained(config.base_model_name_or_path)\n",
        "model = model.to(device)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9tF080DCydpD"
      },
      "outputs": [],
      "source": [
        "url = \"https://framerusercontent.com/images/inZdRVn7eafZNvaVre2iW1a538.png\" # https://cdn.arstechnica.net/wp-content/uploads/2023/06/zoomout_2-1440x807.jpg\"\n",
        "image = processor.image_processor.fetch_images(url)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "PFcPhI9L0bvZ",
        "outputId": "3d88c337-6c8b-4135-9faa-8362ce3b72e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=P size=6133x4160>"
            ],
            "image/png": "[encoded data, 2520968 characters]\n"
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GvASOp7FyjPt"
      },
      "outputs": [],
      "source": [
        "prompts = [\n",
        "    [\n",
        "        image,\n",
        "        \"Describe the following image:\"\n",
        "    ]\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hsn4ePyjy7_q",
        "outputId": "2d6502e5-a7fd-43e4-ccf1-405eaa8b9ce6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Describe the following image: crypto logo, lynx, vector, white background, minimalistic, \n"
          ]
        }
      ],
      "source": [
        "inputs = processor(prompts[0], return_tensors=\"pt\").to(device)\n",
        "\n",
        "generated_ids = model.generate(**inputs, max_length=128)\n",
        "generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "print(generated_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oOEcg7O9NFP8"
      },
      "outputs": [],
      "source": []
    }
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    "accelerator": "GPU",
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      "provenance": [],
      "gpuType": "A100",
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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