image.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xBer9mrfv7bz"
      },
      "source": [
        "# Image test"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install diffusers transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asbKyzkhv8O8",
        "outputId": "2cab3020-29f2-4f42-b48f-53a51198b1ba"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting diffusers\n",
            "  Downloading diffusers-0.24.0-py3-none-any.whl (1.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from diffusers) (9.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from diffusers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.19.4 in /usr/local/lib/python3.10/dist-packages (from diffusers) (0.19.4)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.10/dist-packages (from diffusers) (7.0.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from diffusers) (1.23.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from diffusers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from diffusers) (2.31.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from diffusers) (0.4.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.4->diffusers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.4->diffusers) (4.5.0)\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) (2023.11.17)\n",
            "Installing collected packages: diffusers\n",
            "Successfully installed diffusers-0.24.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368,
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          ]
        },
        "id": "G-coYgyEv7b0",
        "outputId": "51ae90b1-7ec0-47ee-f580-49cb3b176bf9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30063aa5b85749dcadb127f7d679b4a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model_index.json:   0%|          | 0.00/181 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59cd73f019a14c308ae5e3685beeb076"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac9e937579714e21a1671cd4c4dcd492"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "unet/config.json:   0%|          | 0.00/852 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71460a96bc2d4d3ca0674c220a6cd5d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "scheduler/scheduler_config.json:   0%|          | 0.00/289 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01d99f09346c4ef490a3706186143efa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "diffusion_pytorch_model.safetensors:   0%|          | 0.00/455M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09f14aea0f8d4c029963716966692e98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62fde6842cbb4510999cbee27549b25c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from diffusers import DiffusionPipeline\n",
        "\n",
        "generator = DiffusionPipeline.from_pretrained(\"anton-l/ddpm-butterflies-128\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPgmF4V-v7b0",
        "outputId": "f28ccad4-921c-4153-c733-8eb4d92dd109"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DDPMPipeline {\n",
              "  \"_class_name\": \"DDPMPipeline\",\n",
              "  \"_diffusers_version\": \"0.24.0\",\n",
              "  \"_name_or_path\": \"anton-l/ddpm-butterflies-128\",\n",
              "  \"scheduler\": [\n",
              "    \"diffusers\",\n",
              "    \"DDPMScheduler\"\n",
              "  ],\n",
              "  \"unet\": [\n",
              "    \"diffusers\",\n",
              "    \"UNet2DModel\"\n",
              "  ]\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "generator.to(\"cuda\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "522ca211d0f04317ab3eee70f90885a1",
            "07a2a27169c549da92938bd3c70a187a",
            "05676079e57d4580a3c06028580b49ae",
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            "2b138491802341e6bf1a22b5e6047904",
            "93e48a3c952a4b2999cbf53d89a74192",
            "10497669184f4d8598eb4c5e1c593453",
            "135666816fd84b7793ac5a9a10b2973a",
            "233683176c98463e9d615390ea36e429",
            "d201dd6a95554d9c93077d065e3ce614",
            "5a7a2865a85947baae5baa7647e55a28"
          ]
        },
        "id": "Ainm0Jcjv7b0",
        "outputId": "72fafd36-1773-4048-b347-1da71be5e184"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "522ca211d0f04317ab3eee70f90885a1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "image = generator().images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "PTYyC56fv7b0",
        "outputId": "7b5c865f-21a6-462a-c133-b73b17e931e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128>"
            ],
            "image/png": 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