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README.md

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+---
+license: creativeml-openrail-m
+library_name: diffusers
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- controlnet
+inference: true
+base_model: runwayml/stable-diffusion-v1-5
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# controlnet-camaro/test
+
+These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
+You can find some example images below.
+prompt: validation_prompt
+![images_0)](./images_0.png)
+prompt: validation_prompt
+![images_1)](./images_1.png)
+prompt: validation_prompt
+![images_2)](./images_2.png)
+
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

test_model_card.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!git clone https://github.com/bamps53/diffusers\n",
+        "%cd diffusers\n",
+        "!git checkout 2b8f0991f46749f22aa1394f19a80275d27d3fb9\n",
+        "!pip install -e \".[dev]\""
+      ],
+      "metadata": {
+        "id": "ZNgWSJQ-BViA",
+        "outputId": "3956d806-fc52-467d-e73f-a09961ed147d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "ZNgWSJQ-BViA",
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/diffusers\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_0.png\n",
+        "!wget https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_1.png\n",
+        "!wget https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_2.png"
+      ],
+      "metadata": {
+        "id": "pdpDJAul40ZB",
+        "outputId": "d6dbe2fa-c611-48dc-ed28-af4341f0e33e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "pdpDJAul40ZB",
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-02-08 13:23:49--  https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_1.png\n",
+            "Resolving huggingface.co (huggingface.co)... 18.67.181.126, 18.67.181.36, 18.67.181.124, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|18.67.181.126|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 386612 (378K) [image/png]\n",
+            "Saving to: ‘image_1.png’\n",
+            "\n",
+            "image_1.png         100%[===================>] 377.55K   501KB/s    in 0.8s    \n",
+            "\n",
+            "2024-02-08 13:23:50 (501 KB/s) - ‘image_1.png’ saved [386612/386612]\n",
+            "\n",
+            "--2024-02-08 13:23:51--  https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_2.png\n",
+            "Resolving huggingface.co (huggingface.co)... 18.67.181.126, 18.67.181.36, 18.67.181.124, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|18.67.181.126|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 386612 (378K) [image/png]\n",
+            "Saving to: ‘image_2.png’\n",
+            "\n",
+            "image_2.png         100%[===================>] 377.55K   503KB/s    in 0.8s    \n",
+            "\n",
+            "2024-02-08 13:23:52 (503 KB/s) - ‘image_2.png’ saved [386612/386612]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from diffusers.utils import load_image\n",
+        "from examples.controlnet.train_controlnet import save_model_card\n",
+        "\n",
+        "images = [load_image(f\"image_{i}.png\") for i in range(3)]\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",
+        "save_model_card(\n",
+        "    repo_id=\"camaro/test\",\n",
+        "    image_logs=image_logs,\n",
+        "    base_model=\"runwayml/stable-diffusion-v1-5\",\n",
+        "    repo_folder=\".\",\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "v7vs7eQyDeX1"
+      },
+      "id": "v7vs7eQyDeX1",
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "b19ff19c-fff0-469f-b004-dd731edbffa9",
+      "metadata": {
+        "id": "b19ff19c-fff0-469f-b004-dd731edbffa9",
+        "outputId": "b8682622-e0ce-4015-91a3-2e5b018f1c4f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "---\n",
+            "license: creativeml-openrail-m\n",
+            "library_name: diffusers\n",
+            "tags:\n",
+            "- stable-diffusion\n",
+            "- stable-diffusion-diffusers\n",
+            "- text-to-image\n",
+            "- diffusers\n",
+            "- controlnet\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",
+            "# controlnet-camaro/test\n",
+            "\n",
+            "These are controlnet 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]"
+          ]
+        }
+      ],
+      "source": [
+        "!cat README.md"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "Y3uTj1bv5Amw"
+      },
+      "id": "Y3uTj1bv5Amw",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.17"
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}