diff --git a/.gitignore b/.gitignore
index e0bd1ccf..14bbc054 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,8 @@
-
 .DS_Store
 gen.bat
 gen_ref.bat
 train.bat
 __pycache__
 */**/__pycache__
-logs
\ No newline at end of file
+logs
+.ipynb_checkpoints
\ No newline at end of file
diff --git a/dreambooth_colab_joepenna.ipynb b/dreambooth_colab_joepenna.ipynb
index 6396450c..2623fb68 100644
--- a/dreambooth_colab_joepenna.ipynb
+++ b/dreambooth_colab_joepenna.ipynb
@@ -1,32 +1,17 @@
 {
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
-  "colab": {
-   "provenance": [],
-   "collapsed_sections": []
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
  "cells": [
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "#@title Load repo (if needed)\n",
     "!git clone https://github.com/JoePenna/Dreambooth-Stable-Diffusion\n",
     "%cd Dreambooth-Stable-Diffusion"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -65,31 +50,36 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "#@title # Required - Navigate back to the directory.\n",
     "%cd Dreambooth-Stable-Diffusion"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "6tjx0HcjesFo"
+   },
+   "outputs": [],
    "source": [
     "#@markdown Hugging Face Login\n",
     "from huggingface_hub import notebook_login\n",
     "\n",
     "notebook_login()"
-   ],
-   "metadata": {
-    "id": "6tjx0HcjesFo"
-   },
-   "execution_count": 1,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "O15vMMhCevib"
+   },
+   "outputs": [],
    "source": [
     "#@markdown Download the 1.4 sd model\n",
     "from IPython.display import clear_output\n",
@@ -106,15 +96,15 @@
     "!mv {actual_locations_of_model_blob[-1]} model.ckpt\n",
     "clear_output()\n",
     "print(\"✅ model.ckpt successfully downloaded\")\n"
-   ],
-   "metadata": {
-    "id": "O15vMMhCevib"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "N96aedTtfBjO"
+   },
+   "outputs": [],
    "source": [
     "#@title # Download Regularization Images\n",
     "#@markdown We’ve created the following image sets\n",
@@ -129,15 +119,15 @@
     "\n",
     "!mkdir -p regularization_images/{dataset}\n",
     "!mv -v Stable-Diffusion-Regularization-Images-{dataset}/{dataset}/*.* regularization_images/{dataset}"
-   ],
-   "metadata": {
-    "id": "N96aedTtfBjO"
-   },
-   "execution_count": 2,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "A7hmdOdOfGzs"
+   },
+   "outputs": [],
    "source": [
     "#@title # Training Images\n",
     "#@markdown ## Upload your training images\n",
@@ -166,15 +156,15 @@
     " print(\"❌ no training images found. Please upload images to training_images\")\n",
     "else:\n",
     " print(\"✅ \" + str(len(training_images_file_paths)) + \" training images found\")\n"
-   ],
-   "metadata": {
-    "id": "A7hmdOdOfGzs"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "m2o_fFFvfxHi"
+   },
+   "outputs": [],
    "source": [
     "#@title # Training\n",
     "\n",
@@ -205,15 +195,15 @@
     " --class_word \"{class_word}\" \\\n",
     " --token \"{token}\" \\\n",
     " --no-test"
-   ],
-   "metadata": {
-    "id": "m2o_fFFvfxHi"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Ll_ZIFNUulKJ"
+   },
+   "outputs": [],
    "source": [
     "#@title # Copy and name the checkpoint file\n",
     "\n",
@@ -229,27 +219,44 @@
     "!mv \"{last_checkpoint_file}\" \"trained_models/{file_name}\"\n",
     "\n",
     "print(\"Download your trained model file from trained_models/\" + file_name + \" and use in your favorite Stable Diffusion repo!\")"
-   ],
-   "metadata": {
-    "id": "Ll_ZIFNUulKJ"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mkidEm4evn1J"
+   },
+   "outputs": [],
    "source": [
     "#@title Save model in google drive\n",
     "from google.colab import drive\n",
     "drive.mount('/content/drive')\n",
     "\n",
     "!cp trained_models/{file_name} /content/drive/MyDrive/{file_name}"
-   ],
-   "metadata": {
-    "id": "mkidEm4evn1J"
-   },
-   "execution_count": null,
-   "outputs": []
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.0 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "0f8486a44be3db540a44064ef4647f290d210aa4269e54f181de788486e3e134"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/dreambooth_runpod_joepenna.ipynb b/dreambooth_runpod_joepenna.ipynb
index 4e369153..707d42e9 100644
--- a/dreambooth_runpod_joepenna.ipynb
+++ b/dreambooth_runpod_joepenna.ipynb
@@ -1,475 +1,552 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "aa2c1ada",
-   "metadata": {
-    "id": "aa2c1ada"
-   },
-   "source": [
-    "# Dreambooth\n",
-    "### Notebook implementation by Joe Penna (@MysteryGuitarM on Twitter) - Improvements by David Bielejeski\n",
-    "\n",
-    "### Instructions\n",
-    "- Sign up for RunPod here: https://runpod.io/?ref=n8yfwyum\n",
-    "    - Note: That's my personal referral link. Please don't use it if we are mortal enemies.\n",
-    "\n",
-    "- Click *Deploy* on either `SECURE CLOUD` or `COMMUNITY CLOUD`\n",
-    "\n",
-    "- Follow the rest of the instructions in this video: https://www.youtube.com/watch?v=7m__xadX0z0#t=5m33.1s\n",
-    "\n",
-    "Latest information on:\n",
-    "https://github.com/JoePenna/Dreambooth-Stable-Diffusion"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "aa2c1ada",
+      "metadata": {
+        "id": "aa2c1ada"
+      },
+      "source": [
+        "# Dreambooth\n",
+        "### Notebook implementation by Joe Penna (@MysteryGuitarM on Twitter) - Improvements by David Bielejeski\n",
+        "\n",
+        "### Instructions\n",
+        "- Sign up for RunPod here: https://runpod.io/?ref=n8yfwyum\n",
+        "    - Note: That's my personal referral link. Please don't use it if we are mortal enemies.\n",
+        "\n",
+        "- Click *Deploy* on either `SECURE CLOUD` or `COMMUNITY CLOUD`\n",
+        "\n",
+        "- Follow the rest of the instructions in this video: https://www.youtube.com/watch?v=7m__xadX0z0#t=5m33.1s\n",
+        "\n",
+        "Latest information on:\n",
+        "https://github.com/JoePenna/Dreambooth-Stable-Diffusion"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7b971cc0",
+      "metadata": {
+        "id": "7b971cc0"
+      },
+      "source": [
+        "## Build Environment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2AsGA1xpNQnb",
+      "metadata": {
+        "id": "2AsGA1xpNQnb"
+      },
+      "outputs": [],
+      "source": [
+        "# If running on Vast.AI, copy the code in this cell into a new notebook. Run it, then launch the `dreambooth_runpod_joepenna.ipynb` notebook from the jupyter interface.\n",
+        "!git clone https://github.com/JoePenna/Dreambooth-Stable-Diffusion"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9e1bc458-091b-42f4-a125-c3f0df20f29d",
+      "metadata": {
+        "id": "9e1bc458-091b-42f4-a125-c3f0df20f29d",
+        "scrolled": true
+      },
+      "outputs": [],
+      "source": [
+        "# BUILD ENV\n",
+        "!pip install omegaconf\n",
+        "!pip install einops\n",
+        "!pip install pytorch-lightning==1.6.5\n",
+        "!pip install test-tube\n",
+        "!pip install transformers\n",
+        "!pip install kornia\n",
+        "!pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers\n",
+        "!pip install -e git+https://github.com/openai/CLIP.git@main#egg=clip\n",
+        "!pip install setuptools==59.5.0\n",
+        "!pip install pillow==9.0.1\n",
+        "!pip install torchmetrics==0.6.0\n",
+        "!pip install -e .\n",
+        "!pip install protobuf==3.20.1\n",
+        "!pip install gdown\n",
+        "!pip install -qq diffusers[\"training\"]==0.3.0 transformers ftfy\n",
+        "!pip install -qq \"ipywidgets>=7,<8\"\n",
+        "!pip install huggingface_hub\n",
+        "!pip install ipywidgets==7.7.1\n",
+        "!pip install captionizer==1.0.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3478e5a5",
+      "metadata": {},
+      "source": [
+        "### Try downloading hugging face model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "16d69879",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Hugging Face Login - can try this\n",
+        "from huggingface_hub import notebook_login\n",
+        "\n",
+        "notebook_login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "4b65bd52",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Download the 1.4 sd model\n",
+        "from IPython.display import clear_output\n",
+        "\n",
+        "from huggingface_hub import hf_hub_download\n",
+        "downloaded_model_path = hf_hub_download(\n",
+        " repo_id=\"CompVis/stable-diffusion-v-1-4-original\",\n",
+        " filename=\"sd-v1-4.ckpt\",\n",
+        " use_auth_token=True\n",
+        ")\n",
+        "\n",
+        "# Move the sd-v1-4.ckpt to the root of this directory as \"model.ckpt\"\n",
+        "actual_locations_of_model_blob = !readlink -f {downloaded_model_path}\n",
+        "!mv {actual_locations_of_model_blob[-1]} model.ckpt\n",
+        "clear_output()\n",
+        "print(\"✅ model.ckpt successfully downloaded\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "789324c3",
+      "metadata": {},
+      "source": [
+        "If that doesn't work, download it from gdrive:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fb0e9d30",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get model from google drive\n",
+        "# Place drive ID at end of the line below\n",
+        "!gdown https://drive.google.com/uc?id=EXAMPLEID"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a3ca4a81",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Rename model file\n",
+        "import glob, os\n",
+        "os.rename(glob.glob(\"*.ckpt\")[0], \"model.ckpt\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "17d1d11a",
+      "metadata": {
+        "id": "17d1d11a"
+      },
+      "source": [
+        "# Regularization Images (Skip this section if you are uploading your own or using the provided images)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ed07a5df",
+      "metadata": {
+        "id": "ed07a5df"
+      },
+      "source": [
+        "Training teaches your new model both your token **but** re-trains your class simultaneously.\n",
+        "\n",
+        "From cursory testing, it does not seem like reg images affect the model too much. However, they do affect your class greatly, which will in turn affect your generations.\n",
+        "\n",
+        "You can either generate your images here, or use the repos below to quickly download 1500 images."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "67f9ff0c-b529-4c7c-8e26-8388d70a5d91",
+      "metadata": {
+        "id": "67f9ff0c-b529-4c7c-8e26-8388d70a5d91"
+      },
+      "outputs": [],
+      "source": [
+        "# GENERATE 200 images - Optional\n",
+        "self_generated_files_prompt = \"person\" #@param {type:\"string\"}\n",
+        "self_generated_files_count = 200 #@param {type:\"integer\"}\n",
+        "\n",
+        "!python scripts/stable_txt2img.py \\\n",
+        " --seed 10 \\\n",
+        " --ddim_eta 0.0 \\\n",
+        " --n_samples 1 \\\n",
+        " --n_iter {self_generated_files_count} \\\n",
+        " --scale 10.0 \\\n",
+        " --ddim_steps 50 \\\n",
+        " --ckpt model.ckpt \\\n",
+        " --prompt {self_generated_files_prompt}\n",
+        "\n",
+        "dataset=self_generated_files_prompt\n",
+        "\n",
+        "!mkdir -p regularization_images/{dataset}\n",
+        "!mv outputs/txt2img-samples/*.png regularization_images/{dataset}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3d1c7e1c",
+      "metadata": {
+        "id": "3d1c7e1c"
+      },
+      "outputs": [],
+      "source": [
+        "# Zip up the files for downloading and reuse.\n",
+        "# Download this file locally so you can reuse during another training on this dataset\n",
+        "!apt-get install -y zip\n",
+        "!zip -r regularization_images.zip regularization_images/{dataset}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false
+      },
+      "source": [
+        "# Download pre-generated regularization images\n",
+        "We've created the following image sets\n",
+        "\n",
+        "`man_euler` - provided by Niko Pueringer (Corridor Digital) - euler @ 40 steps, CFG 7.5\n",
+        "`man_unsplash` - pictures from various photographers\n",
+        "`person_ddim`\n",
+        "`woman_ddim` - provided by David Bielejeski - ddim @ 50 steps, CFG 10.0\n",
+        "`person_ddim` is recommended"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "e7EydXCjOV1v",
+      "metadata": {
+        "id": "e7EydXCjOV1v"
+      },
+      "outputs": [],
+      "source": [
+        "#Download Regularization Images\n",
+        "\n",
+        "dataset=\"person_ddim\" #@param [\"man_euler\", \"man_unsplash\", \"person_ddim\", \"woman_ddim\", \"blonde_woman\"]\n",
+        "!git clone https://github.com/djbielejeski/Stable-Diffusion-Regularization-Images-{dataset}.git\n",
+        "\n",
+        "!mkdir -p regularization_images/{dataset}\n",
+        "!mv -v Stable-Diffusion-Regularization-Images-{dataset}/{dataset}/*.* regularization_images/{dataset}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Upload your training images\n",
+        "Upload 10-20 images of someone to\n",
+        "\n",
+        "```\n",
+        "/workspace/Dreambooth-Stable-Diffusion/training_images\n",
+        "```\n",
+        "\n",
+        "WARNING: Be sure to upload an *even* amount of images, otherwise the training inexplicably stops at 1500 steps.\n",
+        "\n",
+        "*   2-3 full body\n",
+        "*   3-5 upper body\n",
+        "*   5-12 close-up on face\n",
+        "\n",
+        "The images should be as close as possible to the kind of images you're trying to make (most of the time, that means no selfies).\n",
+        "\n",
+        "Images can be uploaded directly to the workspace directory, or they can be provided from URLs.\n",
+        "\n",
+        "**To upload images from URLs, set, run the block below only.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b242f027",
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "#@markdown Add here the URLs to the images of the subject you are adding\n",
+        "image_from_url = True\n",
+        "urls = [\n",
+        " \"https://i.imgur.com/test1.png\",\n",
+        " \"https://i.imgur.com/test2.png\",\n",
+        " \"https://i.imgur.com/test3.png\",\n",
+        " \"https://i.imgur.com/test4.png\",\n",
+        " \"https://i.imgur.com/test5.png\",\n",
+        " # You can add additional images here -- about 20-30 images in different\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e9c8b1d6",
+      "metadata": {},
+      "source": [
+        "**If you want to upload images directly to the workspace, change `path_to_images_folder` to the folder path where the images are stored and run the below block only.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1706511c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import glob\n",
+        "\n",
+        "image_from_url = False\n",
+        "path_to_images_folder = \"my_images_folder/\"\n",
+        "\n",
+        "imagePaths = list()\n",
+        "for path in glob.glob(path_to_images_folder + \"*\"):\n",
+        "    imagePaths.append(path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2cc02cce",
+      "metadata": {},
+      "source": [
+        "Run the below cell to finalise image imports and check the images that were just added."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0cfd0355",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#@title Download and check the images you have just added\n",
+        "import os\n",
+        "import requests\n",
+        "from io import BytesIO\n",
+        "from PIL import Image\n",
+        "\n",
+        "def image_grid(imgs, rows, cols):\n",
+        "    assert len(imgs) == rows*cols\n",
+        "\n",
+        "    w, h = imgs[0].size\n",
+        "    grid = Image.new('RGB', size=(cols*w, rows*h))\n",
+        "    grid_w, grid_h = grid.size\n",
+        "\n",
+        "    for i, img in enumerate(imgs):\n",
+        "        grid.paste(img, box=(i%cols*w, i//cols*h))\n",
+        "    return grid\n",
+        "\n",
+        "def download_image(url):\n",
+        "    try:\n",
+        "        response = requests.get(url)\n",
+        "    except:\n",
+        "        return None\n",
+        "    return Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
+        "\n",
+        "def read_image(path):\n",
+        "    return Image.open(path).convert('RGB')\n",
+        "\n",
+        "# Assemble list of Image objects\n",
+        "if image_from_url:\n",
+        "    images = list(filter(None,[download_image(url) for url in urls]))\n",
+        "else:\n",
+        "    images = list(filter(None,[read_image(path) for path in imagePaths]))\n",
+        "\n",
+        "save_path = \"./training_samples\"\n",
+        "if not os.path.exists(save_path):\n",
+        "    os.mkdir(save_path)\n",
+        "[image.save(f\"{save_path}/{i}.png\", format=\"png\") for i, image in enumerate(images)]\n",
+        "image_grid(images, 1, len(images))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ad4e50df",
+      "metadata": {
+        "id": "ad4e50df"
+      },
+      "source": [
+        "## Training\n",
+        "\n",
+        "If training a person or subject, keep an eye on your project's `logs/{folder}/images/train/samples_scaled_gs-00xxxx` generations.\n",
+        "\n",
+        "If training a style, keep an eye on your project's `logs/{folder}/images/train/samples_gs-00xxxx` generations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6fa5dd66-2ca0-4819-907e-802e25583ae6",
+      "metadata": {
+        "id": "6fa5dd66-2ca0-4819-907e-802e25583ae6",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Training\n",
+        "\n",
+        "# This isn't used for training, just to help you remember what your trained into the model.\n",
+        "project_name = \"project_name\"\n",
+        "\n",
+        "# MAX STEPS\n",
+        "# How many steps do you want to train for?\n",
+        "max_training_steps = 2000\n",
+        "\n",
+        "# Match class_word to the category of the regularization images you chose above.\n",
+        "class_word = \"person\" # typical uses are \"man\", \"person\", \"woman\"\n",
+        "\n",
+        "# This is the unique token you are incorporating into the stable diffusion model.\n",
+        "token = \"firstNameLastName\"\n",
+        "\n",
+        "\n",
+        "reg_data_root = \"/workspace/Dreambooth-Stable-Diffusion/regularization_images/\" + dataset\n",
+        "\n",
+        "!rm -rf training_images/.ipynb_checkpoints\n",
+        "!python \"main.py\" \\\n",
+        " --base configs/stable-diffusion/v1-finetune_unfrozen.yaml \\\n",
+        " -t \\\n",
+        " --actual_resume \"model.ckpt\" \\\n",
+        " --reg_data_root \"{reg_data_root}\" \\\n",
+        " -n \"{project_name}\" \\\n",
+        " --gpus 0, \\\n",
+        " --data_root \"/workspace/Dreambooth-Stable-Diffusion/training_images\" \\\n",
+        " --max_training_steps {max_training_steps} \\\n",
+        " --class_word \"{class_word}\" \\\n",
+        " --token \"{token}\" \\\n",
+        " --no-test"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "dc49d0bd",
+      "metadata": {},
+      "source": [
+        "## Copy and name the checkpoint file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dd160680",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Copy the checkpoint into our `trained_models` folder\n",
+        "\n",
+        "directory_paths = !ls -d logs/*\n",
+        "last_checkpoint_file = directory_paths[-1] + \"/checkpoints/last.ckpt\"\n",
+        "training_images = !find training_images/*\n",
+        "date_string = !date +\"%Y-%m-%dT%H-%M-%S\"\n",
+        "file_name = date_string[-1] + \"_\" + project_name + \"_\" + str(len(training_images)) + \"_training_images_\" +  str(max_training_steps) + \"_max_training_steps_\" + token + \"_token_\" + class_word + \"_class_word.ckpt\"\n",
+        "\n",
+        "file_name = file_name.replace(\" \", \"_\")\n",
+        "\n",
+        "!mkdir -p trained_models\n",
+        "!mv \"{last_checkpoint_file}\" \"trained_models/{file_name}\"\n",
+        "\n",
+        "print(\"Download your trained model file from trained_models/\" + file_name + \" and use in your favorite Stable Diffusion repo!\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": false
+      },
+      "source": [
+        "# Optional - Upload to google drive\n",
+        "* run the following commands in a new `terminal` in the `Dreambooth-Stable-Diffusion` directory\n",
+        "* `chmod +x ./gdrive`\n",
+        "* `./gdrive about`\n",
+        "* `paste your token here after navigating to the link`\n",
+        "* `./gdrive upload trained_models/{file_name.ckpt}`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9a90ac5c",
+      "metadata": {},
+      "source": [
+        "# Big Important Note!\n",
+        "\n",
+        "The way to use your token is `<token> <class>` ie `joepenna person` and not just `joepenna`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d28d0139",
+      "metadata": {},
+      "source": [
+        "## Generate Images With Your Trained Model!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "80ddb03b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!python scripts/stable_txt2img.py \\\n",
+        " --ddim_eta 0.0 \\\n",
+        " --n_samples 1 \\\n",
+        " --n_iter 4 \\\n",
+        " --scale 7.0 \\\n",
+        " --ddim_steps 50 \\\n",
+        " --ckpt \"/workspace/Dreambooth-Stable-Diffusion/trained_models/\" + {file_name} \\\n",
+        " --prompt \"joepenna person as a masterpiece portrait painting by John Singer Sargent in the style of Rembrandt\""
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.9.0 64-bit",
+      "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": "3.9.0"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "0f8486a44be3db540a44064ef4647f290d210aa4269e54f181de788486e3e134"
+      }
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "7b971cc0",
-   "metadata": {
-    "id": "7b971cc0"
-   },
-   "source": [
-    "## Build Environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2AsGA1xpNQnb",
-   "metadata": {
-    "id": "2AsGA1xpNQnb"
-   },
-   "outputs": [],
-   "source": [
-    "# If running on Vast.AI, copy the code in this cell into a new notebook. Run it, then launch the `dreambooth_runpod_joepenna.ipynb` notebook from the jupyter interface.\n",
-    "!git clone https://github.com/JoePenna/Dreambooth-Stable-Diffusion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9e1bc458-091b-42f4-a125-c3f0df20f29d",
-   "metadata": {
-    "id": "9e1bc458-091b-42f4-a125-c3f0df20f29d",
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# BUILD ENV\n",
-    "!pip install omegaconf\n",
-    "!pip install einops\n",
-    "!pip install pytorch-lightning==1.6.5\n",
-    "!pip install test-tube\n",
-    "!pip install transformers\n",
-    "!pip install kornia\n",
-    "!pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers\n",
-    "!pip install -e git+https://github.com/openai/CLIP.git@main#egg=clip\n",
-    "!pip install setuptools==59.5.0\n",
-    "!pip install pillow==9.0.1\n",
-    "!pip install torchmetrics==0.6.0\n",
-    "!pip install -e .\n",
-    "!pip install protobuf==3.20.1\n",
-    "!pip install gdown\n",
-    "!pip install -qq diffusers[\"training\"]==0.3.0 transformers ftfy\n",
-    "!pip install -qq \"ipywidgets>=7,<8\"\n",
-    "!pip install huggingface_hub\n",
-    "!pip install ipywidgets==7.7.1\n",
-    "!pip install captionizer==1.0.1\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dae11c10",
-   "metadata": {
-    "id": "dae11c10"
-   },
-   "outputs": [],
-   "source": [
-    "# Hugging Face Login\n",
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Download the 1.4 sd model\n",
-    "from IPython.display import clear_output\n",
-    "\n",
-    "from huggingface_hub import hf_hub_download\n",
-    "downloaded_model_path = hf_hub_download(\n",
-    " repo_id=\"CompVis/stable-diffusion-v-1-4-original\",\n",
-    " filename=\"sd-v1-4.ckpt\",\n",
-    " use_auth_token=True\n",
-    ")\n",
-    "\n",
-    "# Move the sd-v1-4.ckpt to the root of this directory as \"model.ckpt\"\n",
-    "actual_locations_of_model_blob = !readlink -f {downloaded_model_path}\n",
-    "!mv {actual_locations_of_model_blob[-1]} model.ckpt\n",
-    "clear_output()\n",
-    "print(\"✅ model.ckpt successfully downloaded\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17d1d11a",
-   "metadata": {
-    "id": "17d1d11a"
-   },
-   "source": [
-    "# Regularization Images (Skip this section if you are uploading your own or using the provided images)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed07a5df",
-   "metadata": {
-    "id": "ed07a5df"
-   },
-   "source": [
-    "Training teaches your new model both your token **but** re-trains your class simultaneously.\n",
-    "\n",
-    "From cursory testing, it does not seem like reg images affect the model too much. However, they do affect your class greatly, which will in turn affect your generations.\n",
-    "\n",
-    "You can either generate your images here, or use the repos below to quickly download 1500 images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "67f9ff0c-b529-4c7c-8e26-8388d70a5d91",
-   "metadata": {
-    "id": "67f9ff0c-b529-4c7c-8e26-8388d70a5d91"
-   },
-   "outputs": [],
-   "source": [
-    "# GENERATE 200 images - Optional\n",
-    "self_generated_files_prompt = \"person\" #@param {type:\"string\"}\n",
-    "self_generated_files_count = 200 #@param {type:\"integer\"}\n",
-    "\n",
-    "!python scripts/stable_txt2img.py \\\n",
-    " --seed 10 \\\n",
-    " --ddim_eta 0.0 \\\n",
-    " --n_samples 1 \\\n",
-    " --n_iter {self_generated_files_count} \\\n",
-    " --scale 10.0 \\\n",
-    " --ddim_steps 50 \\\n",
-    " --ckpt model.ckpt \\\n",
-    " --prompt {self_generated_files_prompt}\n",
-    "\n",
-    "dataset=self_generated_files_prompt\n",
-    "\n",
-    "!mkdir -p regularization_images/{dataset}\n",
-    "!mv outputs/txt2img-samples/*.png regularization_images/{dataset}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d1c7e1c",
-   "metadata": {
-    "id": "3d1c7e1c"
-   },
-   "outputs": [],
-   "source": [
-    "# Zip up the files for downloading and reuse.\n",
-    "# Download this file locally so you can reuse during another training on this dataset\n",
-    "!apt-get install -y zip\n",
-    "!zip -r regularization_images.zip regularization_images/{dataset}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Download pre-generated regularization images\n",
-    "We've created the following image sets\n",
-    "\n",
-    "`man_euler` - provided by Niko Pueringer (Corridor Digital) - euler @ 40 steps, CFG 7.5\n",
-    "`man_unsplash` - pictures from various photographers\n",
-    "`person_ddim`\n",
-    "`woman_ddim` - provided by David Bielejeski - ddim @ 50 steps, CFG 10.0\n",
-    "`person_ddim` is recommended"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "e7EydXCjOV1v",
-   "metadata": {
-    "id": "e7EydXCjOV1v"
-   },
-   "outputs": [],
-   "source": [
-    "#Download Regularization Images\n",
-    "\n",
-    "dataset=\"person_ddim\" #@param [\"man_euler\", \"man_unsplash\", \"person_ddim\", \"woman_ddim\", \"blonde_woman\"]\n",
-    "!git clone https://github.com/djbielejeski/Stable-Diffusion-Regularization-Images-{dataset}.git\n",
-    "\n",
-    "!mkdir -p regularization_images/{dataset}\n",
-    "!mv -v Stable-Diffusion-Regularization-Images-{dataset}/{dataset}/*.* regularization_images/{dataset}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Upload your training images\n",
-    "Upload 10-20 images of someone to\n",
-    "\n",
-    "```\n",
-    "/workspace/Dreambooth-Stable-Diffusion/training_images\n",
-    "```\n",
-    "\n",
-    "WARNING: Be sure to upload an *even* amount of images, otherwise the training inexplicably stops at 1500 steps.\n",
-    "\n",
-    "*   2-3 full body\n",
-    "*   3-5 upper body\n",
-    "*   5-12 close-up on face\n",
-    "\n",
-    "The images should be:\n",
-    "\n",
-    "- as close as possible to the kind of images you're trying to make"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "#@markdown Add here the URLs to the images of the subject you are adding\n",
-    "urls = [\n",
-    " \"https://i.imgur.com/test1.png\",\n",
-    " \"https://i.imgur.com/test2.png\",\n",
-    " \"https://i.imgur.com/test3.png\",\n",
-    " \"https://i.imgur.com/test4.png\",\n",
-    " \"https://i.imgur.com/test5.png\",\n",
-    " # You can add additional images here -- about 20-30 images in different\n",
-    "]"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "#@title Download and check the images you have just added\n",
-    "import os\n",
-    "import requests\n",
-    "from io import BytesIO\n",
-    "from PIL import Image\n",
-    "\n",
-    "\n",
-    "def image_grid(imgs, rows, cols):\n",
-    " assert len(imgs) == rows*cols\n",
-    "\n",
-    " w, h = imgs[0].size\n",
-    " grid = Image.new('RGB', size=(cols*w, rows*h))\n",
-    " grid_w, grid_h = grid.size\n",
-    "\n",
-    " for i, img in enumerate(imgs):\n",
-    "  grid.paste(img, box=(i%cols*w, i//cols*h))\n",
-    " return grid\n",
-    "\n",
-    "def download_image(url):\n",
-    " try:\n",
-    "  response = requests.get(url)\n",
-    " except:\n",
-    "  return None\n",
-    " return Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
-    "\n",
-    "images = list(filter(None,[download_image(url) for url in urls]))\n",
-    "save_path = \"./training_images\"\n",
-    "if not os.path.exists(save_path):\n",
-    " os.mkdir(save_path)\n",
-    "[image.save(f\"{save_path}/{i}.png\", format=\"png\") for i, image in enumerate(images)]\n",
-    "image_grid(images, 1, len(images))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad4e50df",
-   "metadata": {
-    "id": "ad4e50df"
-   },
-   "source": [
-    "## Training\n",
-    "\n",
-    "If training a person or subject, keep an eye on your project's `logs/{folder}/images/train/samples_scaled_gs-00xxxx` generations.\n",
-    "\n",
-    "If training a style, keep an eye on your project's `logs/{folder}/images/train/samples_gs-00xxxx` generations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fa5dd66-2ca0-4819-907e-802e25583ae6",
-   "metadata": {
-    "id": "6fa5dd66-2ca0-4819-907e-802e25583ae6",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Training\n",
-    "\n",
-    "# This isn't used for training, just to help you remember what your trained into the model.\n",
-    "project_name = \"project_name\"\n",
-    "\n",
-    "# MAX STEPS\n",
-    "# How many steps do you want to train for?\n",
-    "max_training_steps = 2000\n",
-    "\n",
-    "# Match class_word to the category of the regularization images you chose above.\n",
-    "class_word = \"person\" # typical uses are \"man\", \"person\", \"woman\"\n",
-    "\n",
-    "# This is the unique token you are incorporating into the stable diffusion model.\n",
-    "token = \"firstNameLastName\"\n",
-    "\n",
-    "\n",
-    "reg_data_root = \"/workspace/Dreambooth-Stable-Diffusion/regularization_images/\" + dataset\n",
-    "\n",
-    "!rm -rf training_images/.ipynb_checkpoints\n",
-    "!python \"main.py\" \\\n",
-    " --base configs/stable-diffusion/v1-finetune_unfrozen.yaml \\\n",
-    " -t \\\n",
-    " --actual_resume \"model.ckpt\" \\\n",
-    " --reg_data_root \"{reg_data_root}\" \\\n",
-    " -n \"{project_name}\" \\\n",
-    " --gpus 0, \\\n",
-    " --data_root \"/workspace/Dreambooth-Stable-Diffusion/training_images\" \\\n",
-    " --max_training_steps {max_training_steps} \\\n",
-    " --class_word \"{class_word}\" \\\n",
-    " --token \"{token}\" \\\n",
-    " --no-test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc49d0bd",
-   "metadata": {},
-   "source": [
-    "## Copy and name the checkpoint file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Copy the checkpoint into our `trained_models` folder\n",
-    "\n",
-    "directory_paths = !ls -d logs/*\n",
-    "last_checkpoint_file = directory_paths[-1] + \"/checkpoints/last.ckpt\"\n",
-    "training_images = !find training_images/*\n",
-    "date_string = !date +\"%Y-%m-%dT%H-%M-%S\"\n",
-    "file_name = date_string[-1] + \"_\" + project_name + \"_\" + str(len(training_images)) + \"_training_images_\" +  str(max_training_steps) + \"_max_training_steps_\" + token + \"_token_\" + class_word + \"_class_word.ckpt\"\n",
-    "\n",
-    "file_name = file_name.replace(\" \", \"_\")\n",
-    "\n",
-    "!mkdir -p trained_models\n",
-    "!mv \"{last_checkpoint_file}\" \"trained_models/{file_name}\"\n",
-    "\n",
-    "print(\"Download your trained model file from trained_models/\" + file_name + \" and use in your favorite Stable Diffusion repo!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Optional - Upload to google drive\n",
-    "* run the following commands in a new `terminal` in the `Dreambooth-Stable-Diffusion` directory\n",
-    "* `chmod +x ./gdrive`\n",
-    "* `./gdrive about`\n",
-    "* `paste your token here after navigating to the link`\n",
-    "* `./gdrive upload trained_models/{file_name.ckpt}`"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a90ac5c",
-   "metadata": {},
-   "source": [
-    "# Big Important Note!\n",
-    "\n",
-    "The way to use your token is `<token> <class>` ie `joepenna person` and not just `joepenna`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d28d0139",
-   "metadata": {},
-   "source": [
-    "## Generate Images With Your Trained Model!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "80ddb03b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python scripts/stable_txt2img.py \\\n",
-    " --ddim_eta 0.0 \\\n",
-    " --n_samples 1 \\\n",
-    " --n_iter 4 \\\n",
-    " --scale 7.0 \\\n",
-    " --ddim_steps 50 \\\n",
-    " --ckpt \"/workspace/Dreambooth-Stable-Diffusion/trained_models/{file_name}\" \\\n",
-    " --prompt \"joepenna person as a masterpiece portrait painting by John Singer Sargent in the style of Rembrandt\""
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "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": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
diff --git a/scripts/stable_txt2img.py b/scripts/stable_txt2img.py
index 93642b4c..1fffb244 100644
--- a/scripts/stable_txt2img.py
+++ b/scripts/stable_txt2img.py
@@ -1,4 +1,4 @@
-import argparse, os, sys, glob
+import argparse, os, sys, glob, uuid
 import torch
 import numpy as np
 from omegaconf import OmegaConf
@@ -50,23 +50,23 @@ def main():
         type=str,
         nargs="?",
         default="a painting of a virus monster playing guitar",
-        help="the prompt to render"
+        help="the prompt to render",
     )
     parser.add_argument(
         "--outdir",
         type=str,
         nargs="?",
         help="dir to write results to",
-        default="outputs/txt2img-samples"
+        default="outputs/txt2img-samples",
     )
     parser.add_argument(
         "--skip_grid",
-        action='store_true',
+        action="store_true",
         help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
     )
     parser.add_argument(
         "--skip_save",
-        action='store_true',
+        action="store_true",
         help="do not save individual samples. For speed measurements.",
     )
     parser.add_argument(
@@ -77,17 +77,17 @@ def main():
     )
     parser.add_argument(
         "--plms",
-        action='store_true',
+        action="store_true",
         help="use plms sampling",
     )
     parser.add_argument(
         "--laion400m",
-        action='store_true',
+        action="store_true",
         help="uses the LAION400M model",
     )
     parser.add_argument(
         "--fixed_code",
-        action='store_true',
+        action="store_true",
         help="if enabled, uses the same starting code across samples ",
     )
     parser.add_argument(
@@ -160,7 +160,7 @@ def main():
         type=str,
         default="models/ldm/stable-diffusion-v1/model.ckpt",
         help="path to checkpoint of model",
-    )    
+    )
     parser.add_argument(
         "--seed",
         type=int,
@@ -172,14 +172,14 @@ def main():
         type=str,
         help="evaluate at this precision",
         choices=["full", "autocast"],
-        default="autocast"
+        default="autocast",
     )
 
-
     parser.add_argument(
-        "--embedding_path", 
-        type=str, 
-        help="Path to a pre-trained embedding manager checkpoint")
+        "--embedding_path",
+        type=str,
+        help="Path to a pre-trained embedding manager checkpoint",
+    )
 
     opt = parser.parse_args()
 
@@ -193,7 +193,7 @@ def main():
 
     config = OmegaConf.load(f"{opt.config}")
     model = load_model_from_config(config, f"{opt.ckpt}")
-    #model.embedding_manager.load(opt.embedding_path)
+    # model.embedding_manager.load(opt.embedding_path)
 
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     model = model.to(device)
@@ -226,9 +226,11 @@ def main():
 
     start_code = None
     if opt.fixed_code:
-        start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
+        start_code = torch.randn(
+            [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device
+        )
 
-    precision_scope = autocast if opt.precision=="autocast" else nullcontext
+    precision_scope = autocast if opt.precision == "autocast" else nullcontext
     with torch.no_grad():
         with precision_scope("cuda"):
             with model.ema_scope():
@@ -243,24 +245,31 @@ def main():
                             prompts = list(prompts)
                         c = model.get_learned_conditioning(prompts)
                         shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
-                        samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
-                                                         conditioning=c,
-                                                         batch_size=opt.n_samples,
-                                                         shape=shape,
-                                                         verbose=False,
-                                                         unconditional_guidance_scale=opt.scale,
-                                                         unconditional_conditioning=uc,
-                                                         eta=opt.ddim_eta,
-                                                         x_T=start_code)
+                        samples_ddim, _ = sampler.sample(
+                            S=opt.ddim_steps,
+                            conditioning=c,
+                            batch_size=opt.n_samples,
+                            shape=shape,
+                            verbose=False,
+                            unconditional_guidance_scale=opt.scale,
+                            unconditional_conditioning=uc,
+                            eta=opt.ddim_eta,
+                            x_T=start_code,
+                        )
 
                         x_samples_ddim = model.decode_first_stage(samples_ddim)
-                        x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+                        x_samples_ddim = torch.clamp(
+                            (x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0
+                        )
 
                         if not opt.skip_save:
                             for x_sample in x_samples_ddim:
-                                x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                                x_sample = 255.0 * rearrange(
+                                    x_sample.cpu().numpy(), "c h w -> h w c"
+                                )
                                 Image.fromarray(x_sample.astype(np.uint8)).save(
-                                    os.path.join(sample_path, f"{base_count:05}.jpg"))
+                                    os.path.join(sample_path, f"{base_count:05}.jpg")
+                                )
                                 base_count += 1
 
                         if not opt.skip_grid:
@@ -269,23 +278,29 @@ def main():
                 if not opt.skip_grid:
                     # additionally, save as grid
                     grid = torch.stack(all_samples, 0)
-                    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
-                    
+                    grid = rearrange(grid, "n b c h w -> (n b) c h w")
+
+                    batch_uuid = uuid.uuid4()
                     for i in range(grid.size(0)):
-                        save_image(grid[i, :, :, :], os.path.join(outpath,opt.prompt[:180]+'_{}.png'.format(i)))
+                        file_name = f"{batch_uuid.hex[:10]}_{opt.prompt[:30]}_{i}.png"
+                        save_image(grid[i, :, :, :], os.path.join(outpath, file_name))
                     grid = make_grid(grid, nrow=n_rows)
 
                     # to image
-                    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt[:180].replace(" ", "-")}-{grid_count:04}.jpg'))
+                    grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
+                    Image.fromarray(grid.astype(np.uint8)).save(
+                        os.path.join(
+                            outpath,
+                            f'{batch_uuid.hex[:10]}-{prompt.replace(" ", "-")[:30]}-{grid_count:04}.jpg',
+                        )
+                    )
                     grid_count += 1
-                    
-                    
 
                 toc = time.time()
 
-    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
-          f" \nEnjoy.")
+    print(
+        f"Your samples are ready and waiting for you here: \n{outpath} \n" f" \nEnjoy."
+    )
 
 
 if __name__ == "__main__":