Upload test_model_card_template_t2i_adapter_sdxl.ipynb

test_model_card_template_t2i_adapter_sdxl.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4mDGz9V6JC0a",
+        "outputId": "a52fc5ef-3095-4433-ab8f-3c9c80f96b51"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  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",
+            "  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install git+https://github.com/cosmo3769/diffusers@standardize-model-card-template-t2i-adapter-sdxl -q"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "from PIL import Image\n",
+        "from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card"
+      ],
+      "metadata": {
+        "id": "r0rK5JfUrAfc"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def image_grid(imgs, rows, cols):\n",
+        "    assert len(imgs) == rows * cols\n",
+        "\n",
+        "    w, h = imgs[0].size\n",
+        "    grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n",
+        "\n",
+        "    for i, img in enumerate(imgs):\n",
+        "        grid.paste(img, box=(i % cols * w, i // cols * h))\n",
+        "    return grid"
+      ],
+      "metadata": {
+        "id": "m68SWTnH4qGY"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def save_model_card(repo_id: str, image_logs: dict = None, base_model: str = None, repo_folder: str = None):\n",
+        "    img_str = \"\"\n",
+        "    if image_logs is not None:\n",
+        "        img_str = \"You can find some example images below.\\n\"\n",
+        "        for i, log in enumerate(image_logs):\n",
+        "            images = log[\"images\"]\n",
+        "            validation_prompt = log[\"validation_prompt\"]\n",
+        "            validation_image = log[\"validation_image\"]\n",
+        "            validation_image.save(os.path.join(repo_folder, \"image_control.png\"))\n",
+        "            img_str += f\"prompt: {validation_prompt}\\n\"\n",
+        "            images = [validation_image] + images\n",
+        "            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f\"images_{i}.png\"))\n",
+        "            img_str += f\"![images_{i})](./images_{i}.png)\\n\"\n",
+        "\n",
+        "    model_description = f\"\"\"\n",
+        "# t2iadapter-{repo_id}\n",
+        "\n",
+        "These are t2iadapter weights trained on {base_model} with new type of conditioning.\n",
+        "{img_str}\n",
+        "\"\"\"\n",
+        "    model_card = load_or_create_model_card(\n",
+        "        repo_id_or_path=repo_id,\n",
+        "        from_training=True,\n",
+        "        license=\"creativeml-openrail-m\",\n",
+        "        base_model=base_model,\n",
+        "        model_description=model_description,\n",
+        "        inference=True,\n",
+        "    )\n",
+        "\n",
+        "    tags = [\"stable-diffusion-xl\", \"stable-diffusion-xl-diffusers\", \"text-to-image\", \"diffusers\", \"t2iadapter\"]\n",
+        "    model_card = populate_model_card(model_card, tags=tags)\n",
+        "\n",
+        "    model_card.save(os.path.join(repo_folder, \"README.md\"))"
+      ],
+      "metadata": {
+        "id": "6mgnDhfzrTp4"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from diffusers.utils import load_image\n",
+        "\n",
+        "images = [\n",
+        "    load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/A%20mushroom%20in%20%5BV%5D%20style.png\")\n",
+        "    for _ in range(3)\n",
+        "]\n",
+        "\n",
+        "image_logs = [\n",
+        "    dict(\n",
+        "        images=[image],\n",
+        "        validation_prompt=\"validation_prompt\",\n",
+        "        validation_image=image,\n",
+        "    )\n",
+        "    for image in images\n",
+        "]\n",
+        "\n",
+        "save_model_card(\n",
+        "    repo_id=\"cosmo3769/t2i-adapter-sdxl\",\n",
+        "    image_logs=image_logs,\n",
+        "    base_model=\"runwayml/stable-diffusion-v1-5\",\n",
+        "    repo_folder=\".\",\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "JTEDsOd_rm7-"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!cat README.md"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NwCOmASdsUCT",
+        "outputId": "c1422ff2-17ec-4917-f23f-1ebdcd9eab9c"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "---\n",
+            "license: creativeml-openrail-m\n",
+            "library_name: diffusers\n",
+            "tags:\n",
+            "- stable-diffusion-xl\n",
+            "- stable-diffusion-xl-diffusers\n",
+            "- text-to-image\n",
+            "- diffusers\n",
+            "- t2iadapter\n",
+            "inference: true\n",
+            "base_model: runwayml/stable-diffusion-v1-5\n",
+            "---\n",
+            "\n",
+            "<!-- This model card has been generated automatically according to the information the training script had access to. You\n",
+            "should probably proofread and complete it, then remove this comment. -->\n",
+            "\n",
+            "\n",
+            "# t2iadapter-cosmo3769/t2i-adapter-sdxl\n",
+            "\n",
+            "These are t2iadapter weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.\n",
+            "You can find some example images below.\n",
+            "prompt: validation_prompt\n",
+            "![images_0)](./images_0.png)\n",
+            "prompt: validation_prompt\n",
+            "![images_1)](./images_1.png)\n",
+            "prompt: validation_prompt\n",
+            "![images_2)](./images_2.png)\n",
+            "\n",
+            "\n",
+            "\n",
+            "## Intended uses & limitations\n",
+            "\n",
+            "#### How to use\n",
+            "\n",
+            "```python\n",
+            "# TODO: add an example code snippet for running this diffusion pipeline\n",
+            "```\n",
+            "\n",
+            "#### Limitations and bias\n",
+            "\n",
+            "[TODO: provide examples of latent issues and potential remediations]\n",
+            "\n",
+            "## Training details\n",
+            "\n",
+            "[TODO: describe the data used to train the model]"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "w7IqDNR72RGf"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}