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{
"cells": [
{
"cell_type": "markdown",
"id": "acd7b15e",
"metadata": {},
"source": [
"# Dreambooth with OFT\n",
"This Notebook assumes that you already ran the train_dreambooth.py script to create your own adapter."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acab479f",
"metadata": {},
"outputs": [],
"source": [
"from diffusers import DiffusionPipeline\n",
"from diffusers.utils import check_min_version, get_logger\n",
"from peft import PeftModel\n",
"\n",
"# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n",
"check_min_version(\"0.10.0.dev0\")\n",
"\n",
"logger = get_logger(__name__)\n",
"\n",
"BASE_MODEL_NAME = \"stabilityai/stable-diffusion-2-1-base\"\n",
"ADAPTER_MODEL_PATH = \"INSERT MODEL PATH HERE\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pipe = DiffusionPipeline.from_pretrained(\n",
" BASE_MODEL_NAME,\n",
")\n",
"pipe.to(\"cuda\")\n",
"pipe.unet = PeftModel.from_pretrained(pipe.unet, ADAPTER_MODEL_PATH + \"/unet\", adapter_name=\"default\")\n",
"pipe.text_encoder = PeftModel.from_pretrained(\n",
" pipe.text_encoder, ADAPTER_MODEL_PATH + \"/text_encoder\", adapter_name=\"default\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prompt = \"A photo of a sks dog\"\n",
"image = pipe(\n",
" prompt,\n",
" num_inference_steps=50,\n",
" height=512,\n",
" width=512,\n",
").images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"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": "3.10.11"
},
"vscode": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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