{
 "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 24.13it/s]\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
    "pipe = DiffusionPipeline.from_pretrained(\n",
    "    BASE_MODEL_NAME,\n",
    ")\n",
    "pipe.to(device)\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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:11<00:00,  4.46it/s]\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "[encoded data, 61164 characters]",
      "image/png": "[encoded data, 594368 characters]",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=512x512>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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