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{
"nbformat": 4,
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"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
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"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
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"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QV8xk7HmMX-M",
"outputId": "f92c1174-5e29-43fa-a54a-4dac3bfe6d59"
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'diffusers'...\n",
"remote: Enumerating objects: 52829, done.\u001b[K\n",
"remote: Counting objects: 100% (1298/1298), done.\u001b[K\n",
"remote: Compressing objects: 100% (852/852), done.\u001b[K\n",
"remote: Total 52829 (delta 594), reused 966 (delta 418), pack-reused 51531\u001b[K\n",
"Receiving objects: 100% (52829/52829), 38.59 MiB | 24.11 MiB/s, done.\n",
"Resolving deltas: 100% (37517/37517), done.\n",
"/content/diffusers\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
" Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m257.9/257.9 kB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building editable for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
],
"source": [
"!git clone https://github.com/Bhavay-2001/diffusers\n",
"%cd diffusers\n",
"!pip install -q -e \".[dev]\""
]
},
{
"cell_type": "code",
"source": [
"!pwd"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7tNQHp0MascO",
"outputId": "0ac02733-6a0f-484f-fd1b-ee58370e5bd8"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/diffusers\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"# /content/diffusers/src/diffusers/utils/hub_utils.py\n",
"from diffusers.src.diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card"
],
"metadata": {
"id": "kmQMzKuIXFvS"
},
"execution_count": 3,
"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",
" 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\"\\n\"\n",
"\n",
" model_description = f\"\"\"\n",
" # Textual inversion text2image fine-tuning - {repo_id}\n",
" These are textual inversion adaption weights for {base_model}. You can find some example images in the following. \\n\n",
" {img_str}\n",
" \"\"\"\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\", \"textual_inversion\"]\n",
" model_card = populate_model_card(model_card, tags=tags)\n",
"\n",
" model_card.save(os.path.join(repo_folder, \"README.md\"))"
],
"metadata": {
"id": "LiA0ILIdVp91"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from diffusers.src.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=\"Bhavay-2001/textual-inversion\",\n",
" image_logs=image_logs,\n",
" base_model=\"runwayml/stable-diffusion-v1-5\",\n",
" repo_folder=\".\",\n",
")"
],
"metadata": {
"id": "5UN8yQmYXEYQ"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!cat README.md"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yzaVaH8qfacW",
"outputId": "fed30f61-1e39-4d5d-93cf-c31e92bb5950"
},
"execution_count": 6,
"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",
"- textual_inversion\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",
" # Textual inversion text2image fine-tuning - Bhavay-2001/textual-inversion\n",
" These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. \n",
"\n",
" \n",
"\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]"
]
}
]
}
]
} |