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{
"cells": [
{
"cell_type": "code",
"source": [
"!git clone https://github.com/bamps53/diffusers\n",
"%cd diffusers\n",
"!git checkout a7546b79994e1cea7dc9d5a288df758a76201ee8\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",
"\n",
"prompt: validation_prompt\n",
"\n",
"prompt: validation_prompt\n",
"\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
} |