Datasets:

TheLastBen
/

PPS

@@ -1,3 +0,0 @@
-https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks/Fast-Dreambooth-v1.5.ipynb
-https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks/Fast-Dreambooth-v2.ipynb
-https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks/Fast-SD-A1111.ipynb

Notebooks/Fast-Dreambooth-v1.5.ipynb DELETED Viewed

@@ -1,400 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "494d5ce4-5843-4d70-ae96-c1983e21b6e8",
-   "metadata": {},
-   "source": [
-    "## Dreambooth v1.5 Paperspace Notebook From https://github.com/TheLastBen/fast-stable-diffusion, if you encounter any issues, feel free to discuss them. [Support](https://ko-fi.com/thelastben)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8afdca63-eff3-4a9d-b4d9-127c0f028033",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be74b2d5-da96-4bf4-ae82-4fe4b8abc04c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Install the dependencies\n",
-    "\n",
-    "force_reinstall= False\n",
-    "\n",
-    "# Set to true only if you want to install the dependencies again.\n",
-    "\n",
-    "\n",
-    "#--------------------\n",
-    "with open('/dev/null', 'w') as devnull:import requests, os, time, importlib;open('/notebooks/mainpaperspacev1.py', 'wb').write(requests.get('https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Scripts/mainpaperspacev1.py').content); os.chdir('/notebooks');time.sleep(3);import mainpaperspacev1;importlib.reload(mainpaperspacev1);from mainpaperspacev1 import *;Deps(force_reinstall)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a4ef4a2-6863-4603-9254-a1e2a547ee38",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Download the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1ba734e-515b-4761-8c88-ef7f165d7971",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#Leave everything EMPTY to use the original model\n",
-    "\n",
-    "Path_to_HuggingFace= \"\"\n",
-    "\n",
-    "# Load and finetune a model from Hugging Face, use the format \"profile/model\" like : runwayml/stable-diffusion-v1-5\n",
-    "\n",
-    "\n",
-    "CKPT_Path = \"\"\n",
-    "\n",
-    "# Load a CKPT model from the storage.\n",
-    "\n",
-    "\n",
-    "CKPT_Link = \"\"\n",
-    "\n",
-    "# A CKPT direct link, huggingface CKPT link or a shared CKPT from gdrive.\n",
-    "\n",
-    "\n",
-    "#----------------\n",
-    "MODEL_NAME=dl(Path_to_HuggingFace, CKPT_Path, CKPT_Link)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c6c4932-e614-4f5e-8d4a-4feca5ce54f5",
-   "metadata": {},
-   "source": [
-    "# Create/Load a Session"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b6595c37-8ad2-45ff-a055-fe58c6663d2f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "Session_Name = \"\"\n",
-    "\n",
-    "# Enter the session name, it if it exists, it will load it, otherwise it'll create an new session.\n",
-    "\n",
-    "\n",
-    "Session_Link_optional = \"\"\n",
-    "\n",
-    "# Import a session from another gdrive, the shared gdrive link must point to the specific session's folder that contains the trained CKPT, remove any intermediary CKPT if any.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "[PT, WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, CONCEPT_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAME, resume]=sess(Session_Name, Session_Link_optional, MODEL_NAME if 'MODEL_NAME' in locals() else \"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5698de61-08d3-4d90-83ef-f882ed956d01",
-   "metadata": {},
-   "source": [
-    "# Instance Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc2f8f28-226e-45b8-8257-804bbb711f56",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "Remove_existing_instance_images= True\n",
-    "\n",
-    "# Set to False to keep the existing instance images if any.\n",
-    "\n",
-    "\n",
-    "IMAGES_FOLDER_OPTIONAL=\"\"\n",
-    "\n",
-    "# If you prefer to specify directly the folder of the pictures instead of uploading, this will add the pictures to the existing (if any) instance images. Leave EMPTY to upload.\n",
-    "\n",
-    "\n",
-    "Smart_crop_images= True\n",
-    "\n",
-    "# Automatically crop your input images.\n",
-    "\n",
-    "\n",
-    "Crop_size = 512\n",
-    "\n",
-    "# Choices: \"512\", \"576\", \"640\", \"704\", \"768\", \"832\", \"896\", \"960\", \"1024\"\n",
-    "\n",
-    "# Check out this example for naming : https://i.imgur.com/d2lD3rz.jpeg\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "uplder(Remove_existing_instance_images, Smart_crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e93924f-a6bf-45d5-aa77-915ad7385dcd",
-   "metadata": {},
-   "source": [
-    "# Manual Captioning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c5dbcb29-b42f-4cfc-9be8-83355838d5a2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Open a tool to manually caption the instance images.\n",
-    "\n",
-    "#-----------------\n",
-    "caption(CAPTIONS_DIR, INSTANCE_DIR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c90140c1-6c91-4cae-a222-e1a746957f95",
-   "metadata": {},
-   "source": [
-    "# Concept Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55c27688-8601-4943-b61d-fc48b9ded067",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Remove_existing_concept_images= True\n",
-    "\n",
-    "# Set to False to keep the existing concept images if any.\n",
-    "\n",
-    "\n",
-    "IMAGES_FOLDER_OPTIONAL=\"\"\n",
-    "\n",
-    "# If you prefer to specify directly the folder of the pictures instead of uploading, this will add the pictures to the existing (if any) concept images. Leave EMPTY to upload.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "uplder(Remove_existing_concept_images, True, 512, IMAGES_FOLDER_OPTIONAL, CONCEPT_DIR, CAPTIONS_DIR, True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a4aa42a-fd68-41ad-9ba7-da99f834e2c1",
-   "metadata": {},
-   "source": [
-    "# Dreambooth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "612d8335-b984-4f34-911d-5457ff98e507",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Resume_Training = False\n",
-    "\n",
-    "# If you're not satisfied with the result, Set to True, run again the cell and it will continue training the current model.\n",
-    "\n",
-    "\n",
-    "UNet_Training_Steps=1500\n",
-    "\n",
-    "UNet_Learning_Rate = \"4e-6\"\n",
-    "\n",
-    "# If you use 10 images, use 1500 steps, if you're not satisfied with the result, resume training for another 200 steps, and so on ...\n",
-    "\n",
-    "\n",
-    "Text_Encoder_Training_Steps=300\n",
-    "\n",
-    "Text_Encoder_Learning_Rate= \"1e-6\"\n",
-    "\n",
-    "# 350-600 steps is enough for a small dataset, keep this number small to avoid overfitting, set to 0 to disable, set it to 0 before resuming training if it is already trained.\n",
-    "\n",
-    "\n",
-    "Text_Encoder_Concept_Training_Steps=0\n",
-    "\n",
-    "# Suitable for training a style/concept as it acts as regularization, with a minimum of 300 steps, 1 step/image is enough to train the concept(s), set to 0 to disable, set both the settings above to 0 to fintune only the text_encoder on the concept, set it to 0 before resuming training if it is already trained.\n",
-    "\n",
-    "\n",
-    "External_Captions= False\n",
-    "\n",
-    "# Get the captions from a text file for each instance image.\n",
-    "\n",
-    "\n",
-    "Style_Training=False\n",
-    "\n",
-    "# Further reduce overfitting, suitable when training a style or a general theme, don't check the box at the beginning, check it after training for at least 800 steps. (Has no effect when using External Captions)\n",
-    "\n",
-    "\n",
-    "Resolution = 512\n",
-    "\n",
-    "# Choices : \"512\", \"576\", \"640\", \"704\", \"768\", \"832\", \"896\", \"960\", \"1024\"\n",
-    "# Higher resolution = Higher quality, make sure the instance images are cropped to this selected size (or larger).\n",
-    "\n",
-    "#---------------------------------------------------------------\n",
-    "\n",
-    "Save_Checkpoint_Every_n_Steps = False\n",
-    "\n",
-    "Save_Checkpoint_Every=500\n",
-    "\n",
-    "# Minimum 200 steps between each save.\n",
-    "\n",
-    "\n",
-    "Start_saving_from_the_step=500\n",
-    "\n",
-    "# Start saving intermediary checkpoints from this step.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "resume=dbtrain(Resume_Training, UNet_Training_Steps, UNet_Learning_Rate, Text_Encoder_Training_Steps, Text_Encoder_Concept_Training_Steps, Text_Encoder_Learning_Rate, Style_Training, Resolution, MODEL_NAME, SESSION_DIR, INSTANCE_DIR, CONCEPT_DIR, CAPTIONS_DIR, External_Captions,  INSTANCE_NAME, Session_Name, OUTPUT_DIR, PT, resume, Save_Checkpoint_Every_n_Steps, Start_saving_from_the_step, Save_Checkpoint_Every)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf6f2232-60b3-41c5-bea6-b0dcc4aef937",
-   "metadata": {},
-   "source": [
-    "# Test the Trained Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1263a084-b142-4e63-a0aa-2706673a4355",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Previous_Session_Name=\"\"\n",
-    "\n",
-    "# Leave empty if you want to use the current trained model.\n",
-    "\n",
-    "\n",
-    "Custom_Path = \"\"\n",
-    "\n",
-    "# Input the full path to a desired model.\n",
-    "\n",
-    "\n",
-    "User = \"\"\n",
-    "\n",
-    "Password= \"\"\n",
-    "\n",
-    "# Add credentials to your Gradio interface (optional).\n",
-    "\n",
-    "\n",
-    "Use_localtunnel = False\n",
-    "\n",
-    "# If you have trouble using Gradio server, use this one.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "configf=test(Custom_Path, Previous_Session_Name, Session_Name, User, Password, Use_localtunnel) if 'Session_Name' in locals() else test(Custom_Path, Previous_Session_Name, \"\", User, Password, Use_localtunnel)\n",
-    "!python /notebooks/sd/stable-diffusion-webui/webui.py $configf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53ccbcaf-3319-44f5-967b-ecbdfa9d0e78",
-   "metadata": {},
-   "source": [
-    "# Upload The Trained Model to Hugging Face"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c9cb205-d828-4e51-9943-f337bd410ea8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Save it to your personal profile or collaborate to the public [library of concepts](https://huggingface.co/sd-dreambooth-library)\n",
-    "\n",
-    "Name_of_your_concept = \"\"\n",
-    "\n",
-    "# Leave empty if you want to name your concept the same as the current session.\n",
-    "\n",
-    "\n",
-    "Save_concept_to = \"My_Profile\"\n",
-    "\n",
-    "# Choices : \"Public_Library\", \"My_Profile\".\n",
-    "\n",
-    "\n",
-    "hf_token_write = \"\"\n",
-    "\n",
-    "# Create a write access token here : https://huggingface.co/settings/tokens, go to \"New token\" -> Role : Write, a regular read token won't work here.\n",
-    "\n",
-    "\n",
-    "#---------------------------------\n",
-    "hf(Name_of_your_concept, Save_concept_to, hf_token_write, INSTANCE_NAME, OUTPUT_DIR, Session_Name, MDLPTH)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "881d80a3-4ebf-41bc-b68f-ac1cacb080f3",
-   "metadata": {},
-   "source": [
-    "# Free up space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7403744d-cc45-419f-88ac-5475fa0f7f45",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Display a list of sessions from which you can remove any session you don't need anymore\n",
-    "\n",
-    "#-------------------------\n",
-    "clean()"
-   ]
-  }
- ],
- "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": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

Notebooks/Fast-Dreambooth-v2.ipynb DELETED Viewed

@@ -1,409 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "494d5ce4-5843-4d70-ae96-c1983e21b6e8",
-   "metadata": {},
-   "source": [
-    "## Dreambooth v2 Paperspace Notebook From https://github.com/TheLastBen/fast-stable-diffusion, if you encounter any issues, feel free to discuss them. [Support](https://ko-fi.com/thelastben)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8afdca63-eff3-4a9d-b4d9-127c0f028033",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be74b2d5-da96-4bf4-ae82-4fe4b8abc04c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Install the dependencies\n",
-    "\n",
-    "force_reinstall= False\n",
-    "\n",
-    "# Set to true only if you want to install the dependencies again.\n",
-    "\n",
-    "\n",
-    "#--------------------\n",
-    "with open('/dev/null', 'w') as devnull:import requests, os, time, importlib;open('/notebooks/mainpaperspacev2.py', 'wb').write(requests.get('https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Scripts/mainpaperspacev2.py').content);os.chdir('/notebooks');time.sleep(3);import mainpaperspacev2;importlib.reload(mainpaperspacev2);from mainpaperspacev2 import *;Deps(force_reinstall)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a4ef4a2-6863-4603-9254-a1e2a547ee38",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Download the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1ba734e-515b-4761-8c88-ef7f165d7971",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "Model_Version = \"768\"\n",
-    "\n",
-    "# Choices are :  \"512\", \"768\"\n",
-    "\n",
-    "#-----------------------------------------------------------------------------------------------------------------------------------\n",
-    "\n",
-    "Custom_Model_Version = \"768\"\n",
-    "\n",
-    "# Choices are :  \"512\", \"768\"\n",
-    "\n",
-    "Path_to_HuggingFace= \"\"\n",
-    "\n",
-    "# Load and finetune a model from Hugging Face, use the format \"profile/model\" like : runwayml/stable-diffusion-v1-5.\n",
-    "\n",
-    "CKPT_Path = \"\"\n",
-    "\n",
-    "# Load a CKPT model from the storage.\n",
-    "\n",
-    "CKPT_Link = \"\"\n",
-    "\n",
-    "# A CKPT direct link, huggingface CKPT link or a shared CKPT from gdrive.\n",
-    "\n",
-    "\n",
-    "#-------------\n",
-    "MODEL_NAMEv2=dlv2(Path_to_HuggingFace, CKPT_Path, CKPT_Link, Model_Version, Custom_Model_Version)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c6c4932-e614-4f5e-8d4a-4feca5ce54f5",
-   "metadata": {},
-   "source": [
-    "# Create/Load a Session"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b6595c37-8ad2-45ff-a055-fe58c6663d2f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "Session_Name = \"\"\n",
-    "\n",
-    "# Enter the session name, it if it exists, it will load it, otherwise it'll create an new session.\n",
-    "\n",
-    "Session_Link_optional = \"\"\n",
-    "\n",
-    "# Import a session from another gdrive, the shared gdrive link must point to the specific session's folder that contains the trained CKPT, remove any intermediary CKPT if any.\n",
-    "\n",
-    "Model_Version = \"768\"\n",
-    "\n",
-    "# Ignore this if you're not loading a previous session that contains a trained model, choices are :  \"512\", \"768\"\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "[PT, WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, CONCEPT_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAMEv2, resumev2]=sessv2(Session_Name, Session_Link_optional, Model_Version, MODEL_NAMEv2 if 'MODEL_NAMEv2' in locals() else \"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5698de61-08d3-4d90-83ef-f882ed956d01",
-   "metadata": {},
-   "source": [
-    "# Instance Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bc2f8f28-226e-45b8-8257-804bbb711f56",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "Remove_existing_instance_images= True\n",
-    "\n",
-    "# Set to False to keep the existing instance images if any.\n",
-    "\n",
-    "\n",
-    "IMAGES_FOLDER_OPTIONAL=\"\"\n",
-    "\n",
-    "# If you prefer to specify directly the folder of the pictures instead of uploading, this will add the pictures to the existing (if any) instance images. Leave EMPTY to upload.\n",
-    "\n",
-    "\n",
-    "Smart_crop_images= True\n",
-    "\n",
-    "# Automatically crop your input images.\n",
-    "\n",
-    "\n",
-    "Crop_size = 768\n",
-    "\n",
-    "# Choices: \"512\", \"576\", \"640\", \"704\", \"768\", \"832\", \"896\", \"960\", \"1024\"\n",
-    "\n",
-    "# Check out this example for naming : https://i.imgur.com/d2lD3rz.jpeg\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "uplder(Remove_existing_instance_images, Smart_crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e93924f-a6bf-45d5-aa77-915ad7385dcd",
-   "metadata": {},
-   "source": [
-    "# Manual Captioning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c5dbcb29-b42f-4cfc-9be8-83355838d5a2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Open a tool to manually caption the instance images.\n",
-    "\n",
-    "#-----------------\n",
-    "caption(CAPTIONS_DIR, INSTANCE_DIR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c90140c1-6c91-4cae-a222-e1a746957f95",
-   "metadata": {},
-   "source": [
-    "# Concept Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55c27688-8601-4943-b61d-fc48b9ded067",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Remove_existing_concept_images= True\n",
-    "\n",
-    "# Set to False to keep the existing concept images if any.\n",
-    "\n",
-    "\n",
-    "IMAGES_FOLDER_OPTIONAL=\"\"\n",
-    "\n",
-    "# If you prefer to specify directly the folder of the pictures instead of uploading, this will add the pictures to the existing (if any) concept images. Leave EMPTY to upload.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "uplder(Remove_existing_concept_images, True, 512, IMAGES_FOLDER_OPTIONAL, CONCEPT_DIR, CAPTIONS_DIR, True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a4aa42a-fd68-41ad-9ba7-da99f834e2c1",
-   "metadata": {},
-   "source": [
-    "# Dreambooth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "612d8335-b984-4f34-911d-5457ff98e507",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Resume_Training = False\n",
-    "\n",
-    "# If you're not satisfied with the result, Set to True, run again the cell and it will continue training the current model.\n",
-    "\n",
-    "\n",
-    "UNet_Training_Steps=850\n",
-    "\n",
-    "UNet_Learning_Rate = \"6e-6\"\n",
-    "\n",
-    "# If you use 10 images, use 650 steps, if you're not satisfied with the result, resume training for another 200 steps with a lower learning rate (8e-6), and so on ...\n",
-    "\n",
-    "\n",
-    "Text_Encoder_Training_Steps=300\n",
-    "\n",
-    "Text_Encoder_Learning_Rate= \"1e-6\"\n",
-    "\n",
-    "# 350-600 steps is enough for a small dataset, keep this number small to avoid overfitting, set to 0 to disable, set it to 0 before resuming training if it is already trained.\n",
-    "\n",
-    "\n",
-    "Text_Encoder_Concept_Training_Steps=0\n",
-    "\n",
-    "# Suitable for training a style/concept as it acts as regularization, with a minimum of 300 steps, 1 step/image is enough to train the concept(s), set to 0 to disable, set both the settings above to 0 to fintune only the text_encoder on the concept, set it to 0 before resuming training if it is already trained.\n",
-    "\n",
-    "\n",
-    "External_Captions= False\n",
-    "\n",
-    "# Get the captions from a text file for each instance image.\n",
-    "\n",
-    "\n",
-    "Style_Training=False\n",
-    "\n",
-    "# Further reduce overfitting, suitable when training a style or a general theme, don't check the box at the beginning, check it after training for at least 800 steps. (Has no effect when using External Captions)\n",
-    "\n",
-    "\n",
-    "Resolution = 768\n",
-    "\n",
-    "# Choices : \"512\", \"576\", \"640\", \"704\", \"768\", \"832\", \"896\", \"960\", \"1024\"\n",
-    "# Higher resolution = Higher quality, make sure the instance images are cropped to this selected size (or larger).\n",
-    "\n",
-    "#---------------------------------------------------------------\n",
-    "\n",
-    "Save_Checkpoint_Every_n_Steps = False\n",
-    "\n",
-    "Save_Checkpoint_Every=500\n",
-    "\n",
-    "# Minimum 200 steps between each save.\n",
-    "\n",
-    "\n",
-    "Start_saving_from_the_step=500\n",
-    "\n",
-    "# Start saving intermediary checkpoints from this step.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "resumev2=dbtrainv2(Resume_Training, UNet_Training_Steps, UNet_Learning_Rate, Text_Encoder_Training_Steps, Text_Encoder_Concept_Training_Steps, Text_Encoder_Learning_Rate, Style_Training, Resolution, MODEL_NAMEv2, SESSION_DIR, INSTANCE_DIR, CONCEPT_DIR, CAPTIONS_DIR, External_Captions, INSTANCE_NAME, Session_Name, OUTPUT_DIR, PT, resumev2, Save_Checkpoint_Every_n_Steps, Start_saving_from_the_step, Save_Checkpoint_Every)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf6f2232-60b3-41c5-bea6-b0dcc4aef937",
-   "metadata": {},
-   "source": [
-    "# Test the Trained Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1263a084-b142-4e63-a0aa-2706673a4355",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Previous_Session_Name=\"\"\n",
-    "\n",
-    "# Leave empty if you want to use the current trained model.\n",
-    "\n",
-    "\n",
-    "Custom_Path = \"\"\n",
-    "\n",
-    "# Input the full path to a desired model.\n",
-    "\n",
-    "\n",
-    "User = \"\" \n",
-    "\n",
-    "Password= \"\"\n",
-    "\n",
-    "# Add credentials to your Gradio interface (optional).\n",
-    "\n",
-    "\n",
-    "Use_localtunnel = False\n",
-    "\n",
-    "# If you have trouble using Gradio server, use this one.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "configf=test(Custom_Path, Previous_Session_Name, Session_Name, User, Password, Use_localtunnel) if 'Session_Name' in locals() else test(Custom_Path, Previous_Session_Name, \"\", User, Password, Use_localtunnel)\n",
-    "!python /notebooks/sd/stable-diffusion-webui/webui.py $configf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53ccbcaf-3319-44f5-967b-ecbdfa9d0e78",
-   "metadata": {},
-   "source": [
-    "# Upload The Trained Model to Hugging Face"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c9cb205-d828-4e51-9943-f337bd410ea8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Save it to your personal profile or collaborate to the public [library of concepts](https://huggingface.co/sd-dreambooth-library)\n",
-    "\n",
-    "Name_of_your_concept = \"\"\n",
-    "\n",
-    "# Leave empty if you want to name your concept the same as the current session.\n",
-    "\n",
-    "\n",
-    "Save_concept_to = \"My_Profile\"\n",
-    "\n",
-    "# Choices : \"Public_Library\", \"My_Profile\".\n",
-    "\n",
-    "\n",
-    "hf_token_write = \"\"\n",
-    "\n",
-    "# Create a write access token here : https://huggingface.co/settings/tokens, go to \"New token\" -> Role : Write, a regular read token won't work here.\n",
-    "\n",
-    "\n",
-    "#---------------------------------\n",
-    "hfv2(Name_of_your_concept, Save_concept_to, hf_token_write, INSTANCE_NAME, OUTPUT_DIR, Session_Name, MDLPTH)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "881d80a3-4ebf-41bc-b68f-ac1cacb080f3",
-   "metadata": {},
-   "source": [
-    "# Free up space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7403744d-cc45-419f-88ac-5475fa0f7f45",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Display a list of sessions from which you can remove any session you don't need anymore\n",
-    "\n",
-    "#-------------------------\n",
-    "clean()"
-   ]
-  }
- ],
- "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": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

Notebooks/Fast-SD-A1111.ipynb DELETED Viewed

@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6441563f-5e6b-4987-abe3-2a1b8c25a789",
-   "metadata": {},
-   "source": [
-    "## A1111 Paperspace Notebook From https://github.com/TheLastBen/fast-stable-diffusion, if you encounter any issues, feel free to discuss them. [Support](https://ko-fi.com/thelastben)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "840f2f6a-41d1-4938-a846-d51f76682b76",
-   "metadata": {},
-   "source": [
-    "# Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e22de7-f054-45a7-b7e3-b63b9a0188a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the dependencies\n",
-    "\n",
-    "force_reinstall= False\n",
-    "\n",
-    "# Set to true only if you want to install the dependencies again.\n",
-    "\n",
-    "\n",
-    "#--------------------\n",
-    "with open('/dev/null', 'w') as devnull:import requests, time, importlib;open('/notebooks/mainpaperspaceA1111.py', 'wb').write(requests.get('https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Scripts/mainpaperspaceA1111.py').content);os.chdir('/notebooks');time.sleep(3);import mainpaperspaceA1111;importlib.reload(mainpaperspaceA1111);from mainpaperspaceA1111 import *;Deps(force_reinstall)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e21f3583-2d0f-4218-9da2-303f9e202820",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Install/Update AUTOMATIC1111 repo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ed6cebed-8a4f-4a35-b5c3-36126be008b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Don't skip this cell to make sure the repo is up to date and functioning correctly\n",
-    "\n",
-    "#--------------------\n",
-    "repo()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ab0521c7-8c68-4ea2-915a-bc3f4b67f6e7",
-   "metadata": {},
-   "source": [
-    "## Model Download/Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a6f367e0-df08-41fd-91b5-e2afbcbd42e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Original_Model_Version = \"v1.5\"\n",
-    "\n",
-    "# Choices are \"v1.5\", \"v2-512\", \"v2-768\"\n",
-    "\n",
-    "\n",
-    "Path_to_MODEL = \"\"\n",
-    "\n",
-    "# Insert the full path of your trained model or to a folder containing multiple models.\n",
-    "\n",
-    "\n",
-    "MODEL_LINK = \"\"\n",
-    "\n",
-    "# A direct link to a Model or a shared gdrive link.\n",
-    "\n",
-    "safetensors= False\n",
-    "\n",
-    "# Set to True if the model from the link is in safetensors format.\n",
-    "\n",
-    "Temporary_Storage = True\n",
-    "\n",
-    "# Download the model to a temporary storage, bigger capacity but will be removed at session shutdown.\n",
-    "\n",
-    "\n",
-    "#--------------------\n",
-    "model=mdl(Original_Model_Version, Path_to_MODEL, MODEL_LINK, safetensors, Temporary_Storage)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0baf0c4-a410-432f-891b-975c7250c77d",
-   "metadata": {},
-   "source": [
-    "## Start Stable-Diffusion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0121ea1d-1aa0-4961-b916-c5dbb900e05f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "User = \"\"\n",
-    "\n",
-    "Password= \"\"\n",
-    "\n",
-    "# Add credentials to your Gradio interface (optional).\n",
-    "\n",
-    "Use_localtunnel = False\n",
-    "\n",
-    "# If you have trouble using Gradio server, use this one.\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "configf=sd(User, Password, Use_localtunnel)\n",
-    "!python /notebooks/sd/stable-diffusion-webui/webui.py --ckpt $model $configf"
-   ]
-  }
- ],
- "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": "3.9.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

README.md DELETED Viewed

@@ -1,3 +0,0 @@
----
-license: cc-by-nc-4.0
----

Scripts/mainpaperspaceA1111.py DELETED Viewed

@@ -1,197 +0,0 @@
-import os
-from IPython.display import clear_output
-from subprocess import call, getoutput
-import time
-import sys
-import fileinput
-import ipywidgets as widgets
-def Deps(force_reinstall):
-    if not force_reinstall and os.path.exists('/usr/local/lib/python3.9/dist-packages/safetensors'):
-        os.chdir('/notebooks')
-        if not os.path.exists('Latest_Notebooks'):
-            call('mkdir Latest_Notebooks', shell=True)
-        else:
-            call('rm -r Latest_Notebooks', shell=True)
-            call('mkdir Latest_Notebooks', shell=True)
-        os.chdir('/notebooks/Latest_Notebooks')
-        call('wget -q -i https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks.txt', shell=True)
-        call('rm Notebooks.txt', shell=True)
-        os.chdir('/notebooks')
-        print('[1;32mModules and notebooks updated, dependencies already installed')
-    else:
-        print('[1;32mInstalling the dependencies...')
-        call("pip install --root-user-action=ignore --no-deps -q accelerate==0.12.0", shell=True, stdout=open('/dev/null', 'w'))
-        if not os.path.exists('/usr/local/lib/python3.9/dist-packages/safetensors'):
-            os.chdir('/usr/local/lib/python3.9/dist-packages')
-            call("rm -r torch torch-1.12.0+cu116.dist-info torchaudio* torchvision* PIL Pillow* transformers* numpy* gdown*", shell=True, stdout=open('/dev/null', 'w'))
-        os.chdir('/notebooks')
-        if not os.path.exists('Latest_Notebooks'):
-            call('mkdir Latest_Notebooks', shell=True)
-        else:
-            call('rm -r Latest_Notebooks', shell=True)
-            call('mkdir Latest_Notebooks', shell=True)
-        os.chdir('/notebooks/Latest_Notebooks')
-        call('wget -q -i https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks.txt', shell=True)
-        call('rm Notebooks.txt', shell=True)
-        os.chdir('/notebooks')
-        call('wget -q -i https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dependencies/aptdeps.txt', shell=True)
-        os.chdir('/notebooks')
-        if not os.path.exists('/models'):
-            call('mkdir /models', shell=True)
-        if not os.path.exists('/notebooks/models'):
-            call('ln -s /models /notebooks', shell=True)
-        if os.path.exists('/deps'):
-            call("rm -r /deps", shell=True)
-        call('mkdir /deps', shell=True)
-        if not os.path.exists('cache'):
-            call('mkdir cache', shell=True)
-        os.chdir('/deps')
-        call('wget -q -i https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dependencies/aptdeps.txt', shell=True)
-        call('dpkg -i *.deb', shell=True, stdout=open('/dev/null', 'w'))
-        call('wget -q https://huggingface.co/TheLastBen/dependencies/resolve/main/pps.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call('tar -C / --zstd -xf pps.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call("sed -i 's@~/.cache@/notebooks/cache@' /usr/local/lib/python3.9/dist-packages/transformers/utils/hub.py", shell=True)
-        os.chdir('/notebooks')
-        call("git clone --depth 1 -q --branch updt https://github.com/TheLastBen/diffusers /diffusers", shell=True, stdout=open('/dev/null', 'w'))
-        if not os.path.exists('/notebooks/diffusers'):
-            call('ln -s /diffusers /notebooks', shell=True)
-        call("rm -r /deps", shell=True)
-        os.chdir('/notebooks')
-        clear_output()
-        done()
-def repo():
-    print('[1;32mInstalling/Updating the repo...')
-    os.chdir('/notebooks')
-    if not os.path.exists('/notebooks/sd/stablediffusion'):
-       call('wget -q -O sd_rep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_rep.tar.zst', shell=True)
-       call('tar --zstd -xf sd_rep.tar.zst', shell=True)
-       call('rm sd_rep.tar.zst', shell=True)
-    os.chdir('/notebooks/sd')
-    if not os.path.exists('stable-diffusion-webui'):
-        call('git clone -q --depth 1 --branch master https://github.com/AUTOMATIC1111/stable-diffusion-webui', shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui/')
-    call('git reset --hard', shell=True, stdout=open('/dev/null', 'w'))
-    print('[1;32m')
-    call('git pull', shell=True, stdout=open('/dev/null', 'w'))
-    os.chdir('/notebooks')
-    clear_output()
-    done()
-def mdl(Original_Model_Version, Path_to_MODEL, MODEL_LINK, safetensors, Temporary_Storage):
-    import gdown
-    if Path_to_MODEL !='':
-      if os.path.exists(str(Path_to_MODEL)):
-        print('[1;32mUsing the trained model.')
-        model=Path_to_MODEL
-      else:
-          print('[1;31mWrong path, check that the path to the model is correct')
-    elif MODEL_LINK != "":
-      modelname="model.safetensors" if safetensors else "model.ckpt"
-      if Temporary_Storage:
-         model=f'/models/{modelname}'
-      else:
-         model=f'/notebooks/sd/stable-diffusion-webui/models/Stable-diffusion/{modelname}'
-      if os.path.exists(model):
-        call('rm '+model, shell=True)
-      gdown.download(url=MODEL_LINK, output=model, quiet=False, fuzzy=True)
-      if os.path.exists(model) and os.path.getsize(model) > 1810671599:
-        clear_output()
-        print('[1;32mModel downloaded, using the trained model.')
-      else:
-        print('[1;31mWrong link, check that the link is valid')
-    else:
-        if Original_Model_Version == "v1.5":
-           model="/datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt"
-           print('[1;32mUsing the original V1.5 model')
-        elif Original_Model_Version == "v2-512":
-           model="dataset"
-           print('[1;32mUsing the original V2-512 model')
-        elif Original_Model_Version == "v2-768":
-           model="/datasets/stable-diffusion-v2-1/stable-diffusion-2-1/v2-1_768-nonema-pruned.safetensors"
-           print('[1;32mUsing the original V2-768 model')
-        else:
-            model=""
-            print('[1;31mWrong model version')
-    return model
-def sd(User, Password, Use_localtunnel):
-    auth=f"--gradio-auth {User}:{Password}"
-    if User =="" or Password=="":
-      auth=""
-    if not os.path.exists('/usr/lib/node_modules/localtunnel'):
-       call('npm install -g localtunnel  --silent', shell=True, stdout=open('/dev/null', 'w'))
-       clear_output()
-    share=''
-    call('wget -q -O /usr/local/lib/python3.9/dist-packages/gradio/blocks.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/blocks.py', shell=True)
-    if not Use_localtunnel:
-      share='--share'
-    else:
-      share=''
-      os.chdir('/notebooks')
-      call('nohup lt --port 7860 > srv.txt 2>&1 &', shell=True)
-      time.sleep(2)
-      call("grep -o 'https[^ ]*' /notebooks/srv.txt >srvr.txt", shell=True)
-      time.sleep(2)
-      srv= getoutput('cat /notebooks/srvr.txt')
-      for line in fileinput.input('/usr/local/lib/python3.9/dist-packages/gradio/blocks.py', inplace=True):
-        if line.strip().startswith('self.server_name ='):
-            line = f'            self.server_name = "{srv[8:]}"\n'
-        if line.strip().startswith('self.server_port ='):
-            line = '            self.server_port = 443\n'
-        if line.strip().startswith('self.protocol = "https"'):
-            line = '            self.protocol = "https"\n'
-        if line.strip().startswith('if self.local_url.startswith("https") or self.is_colab'):
-            line = ''
-        if line.strip().startswith('else "http"'):
-            line = ''
-        sys.stdout.write(line)
-      call('rm /notebooks/srv.txt', shell=True)
-      call('rm /notebooks/srvr.txt', shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui/modules')
-    call('wget -q -O paths.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/paths.py', shell=True)
-    call("sed -i 's@/content/gdrive/MyDrive/sd/stablediffusion@/notebooks/sd/stablediffusion@' /notebooks/sd/stable-diffusion-webui/modules/paths.py", shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui')
-    clear_output()
-    configf="--disable-console-progressbars --no-half-vae --disable-safe-unpickle --api --xformers --medvram --skip-version-check "+auth+" "+share
-    return configf, auth, share
-def done():
-    done = widgets.Button(
-        description='Done!',
-        disabled=True,
-        button_style='success',
-        tooltip='',
-        icon='check'
-    )
-    display(done)

Scripts/mainpaperspacev1.py DELETED Viewed

@@ -1,1271 +0,0 @@
-from IPython.display import clear_output
-from subprocess import call, getoutput
-from IPython.display import display
-import ipywidgets as widgets
-import io
-from PIL import Image, ImageDraw
-import fileinput
-import time
-import os
-from os import listdir
-from os.path import isfile
-from tqdm import tqdm
-import gdown
-import random
-import sys
-import cv2
-from io import BytesIO
-import requests
-from collections import defaultdict
-from math import log, sqrt
-import numpy as np
-def Deps(force_reinstall):
-    if not force_reinstall and os.path.exists('/usr/local/lib/python3.9/dist-packages/safetensors'):
-        os.chdir('/notebooks')
-        if not os.path.exists('Latest_Notebooks'):
-            call('mkdir Latest_Notebooks', shell=True)
-        else:
-            call('rm -r Latest_Notebooks', shell=True)
-            call('mkdir Latest_Notebooks', shell=True)
-        os.chdir('/notebooks/Latest_Notebooks')
-        call('wget -q -i https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks.txt', shell=True)
-        call('rm Notebooks.txt', shell=True)
-        os.chdir('/notebooks')
-        print('[1;32mModules and notebooks updated, dependencies already installed')
-    else:
-        print('[1;32mInstalling the dependencies...')
-        call("pip install --root-user-action=ignore --no-deps -q accelerate==0.12.0", shell=True, stdout=open('/dev/null', 'w'))
-        if not os.path.exists('/usr/local/lib/python3.9/dist-packages/safetensors'):
-            os.chdir('/usr/local/lib/python3.9/dist-packages')
-            call("rm -r torch torch-1.12.0+cu116.dist-info torchaudio* torchvision* PIL Pillow* transformers* numpy* gdown*", shell=True, stdout=open('/dev/null', 'w'))
-        os.chdir('/notebooks')
-        if not os.path.exists('Latest_Notebooks'):
-            call('mkdir Latest_Notebooks', shell=True)
-        else:
-            call('rm -r Latest_Notebooks', shell=True)
-            call('mkdir Latest_Notebooks', shell=True)
-        os.chdir('/notebooks/Latest_Notebooks')
-        call('wget -q -i https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks.txt', shell=True)
-        call('rm Notebooks.txt', shell=True)
-        os.chdir('/notebooks')
-        call('wget -q -i https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dependencies/aptdeps.txt', shell=True)
-        os.chdir('/notebooks')
-        if not os.path.exists('/models'):
-            call('mkdir /models', shell=True)
-        if not os.path.exists('/notebooks/models'):
-            call('ln -s /models /notebooks', shell=True)
-        if os.path.exists('/deps'):
-            call("rm -r /deps", shell=True)
-        call('mkdir /deps', shell=True)
-        if not os.path.exists('cache'):
-            call('mkdir cache', shell=True)
-        os.chdir('/deps')
-        call('wget -q -i https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dependencies/aptdeps.txt', shell=True)
-        call('dpkg -i *.deb', shell=True, stdout=open('/dev/null', 'w'))
-        call('wget -q https://huggingface.co/TheLastBen/dependencies/resolve/main/pps.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call('tar -C / --zstd -xf pps.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call("sed -i 's@~/.cache@/notebooks/cache@' /usr/local/lib/python3.9/dist-packages/transformers/utils/hub.py", shell=True)
-        os.chdir('/notebooks')
-        call("git clone --depth 1 -q --branch updt https://github.com/TheLastBen/diffusers /diffusers", shell=True, stdout=open('/dev/null', 'w'))
-        if not os.path.exists('/notebooks/diffusers'):
-            call('ln -s /diffusers /notebooks', shell=True)
-        call("rm -r /deps", shell=True)
-        os.chdir('/notebooks')
-        clear_output()
-        done()
-def downloadmodel_hf(Path_to_HuggingFace):
-  import wget
-  if os.path.exists('/models/stable-diffusion-custom'):
-    call("rm -r /models/stable-diffusion-custom", shell=True)
-  clear_output()
-  if os.path.exists('/notebooks/Fast-Dreambooth/token.txt'):
-    with open("/notebooks/Fast-Dreambooth/token.txt") as f:
-       token = f.read()
-    authe=f'https://USER:{token}@'
-  else:
-    authe="https://"
-  clear_output()
-  call("mkdir /models/stable-diffusion-custom", shell=True)
-  os.chdir("/models/stable-diffusion-custom")
-  call("git init", shell=True)
-  call("git lfs install --system --skip-repo", shell=True)
-  call('git remote add -f origin '+authe+'huggingface.co/'+Path_to_HuggingFace, shell=True)
-  call("git config core.sparsecheckout true", shell=True)
-  call('echo -e "\nscheduler\ntext_encoder\ntokenizer\nunet\nvae\nmodel_index.json\n!*.safetensors" > .git/info/sparse-checkout', shell=True)
-  call("git pull origin main", shell=True)
-  if os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-    call("rm -r /models/stable-diffusion-custom/.git", shell=True)
-    call("rm -r /models/stable-diffusion-custom/model_index.json", shell=True)
-    wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/model_index.json')
-    os.chdir('/notebooks')
-    clear_output()
-    done()
-  while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-        print('[1;31mCheck the link you provided')
-        os.chdir('/notebooks')
-        time.sleep(5)
-def downloadmodel_pth(CKPT_Path):
-  import wget
-  os.chdir('/notebooks')
-  clear_output()
-  if os.path.exists(str(CKPT_Path)):
-    wget.download('https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/refmdlz')
-    call('unzip -o -q refmdlz', shell=True)
-    call('rm -f refmdlz', shell=True)
-    wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv1.py')
-    clear_output()
-    call('python /notebooks/convertodiffv1.py '+CKPT_Path+' /models/stable-diffusion-custom --v1', shell=True)
-    call('rm /notebooks/convertodiffv1.py', shell=True)
-    call('rm -r /notebooks/refmdl', shell=True)
-    if os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-      clear_output()
-      done()
-    while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-      print('[1;31mConversion error')
-      time.sleep(5)
-  else:
-    while not os.path.exists(str(CKPT_Path)):
-       print('[1;31mWrong path, use the colab file explorer to copy the path')
-       time.sleep(5)
-def downloadmodel_lnk(CKPT_Link):
-    import wget
-    os.chdir('/notebooks')
-    call("gdown --fuzzy " +CKPT_Link+ " -O /models/model.ckpt", shell=True)
-    if os.path.exists('/models/model.ckpt'):
-      if os.path.getsize("/models/model.ckpt") > 1810671599:
-        wget.download('https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/refmdlz')
-        call('unzip -o -q refmdlz', shell=True)
-        call('rm -f refmdlz', shell=True)
-        wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv1.py')
-        clear_output()
-        call('python /notebooks/convertodiffv1.py /models/model.ckpt /models/stable-diffusion-custom --v1', shell=True)
-        call('rm /notebooks/convertodiffv1.py', shell=True)
-        call('rm -r /notebooks/refmdl', shell=True)
-        if os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-          call('rm -r /models/model.ckpt', shell=True)
-          clear_output()
-          done()
-        else:
-          while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-            print('[1;31mConversion error')
-            time.sleep(5)
-      else:
-        while os.path.getsize('/models/model.ckpt') < 1810671599:
-           print('[1;31mWrong link, check that the link is valid')
-           time.sleep(5)
-def dl(Path_to_HuggingFace, CKPT_Path, CKPT_Link):
-    if Path_to_HuggingFace != "":
-      downloadmodel_hf(Path_to_HuggingFace)
-      MODEL_NAME="/models/stable-diffusion-custom"
-    elif CKPT_Path !="":
-      downloadmodel_pth(CKPT_Path)
-      MODEL_NAME="/models/stable-diffusion-custom"
-    elif CKPT_Link !="":
-      downloadmodel_lnk(CKPT_Link)
-      MODEL_NAME="/models/stable-diffusion-custom"
-    else:
-      MODEL_NAME="/datasets/stable-diffusion-diffusers/stable-diffusion-v1-5"
-      print('[1;32mUsing the original V1.5 model')
-    return MODEL_NAME
-def sess(Session_Name, Session_Link_optional, MODEL_NAME):
-    import wget, gdown
-    os.chdir('/notebooks')
-    PT=""
-    while Session_Name=="":
-      print('[1;31mInput the Session Name:')
-      Session_Name=input("")
-    Session_Name=Session_Name.replace(" ","_")
-    WORKSPACE='/notebooks/Fast-Dreambooth'
-    if Session_Link_optional !="":
-      print('[1;32mDownloading session...')
-      if Session_Link_optional != "":
-        if not os.path.exists(str(WORKSPACE+'/Sessions')):
-          call("mkdir -p " +WORKSPACE+ "/Sessions", shell=True)
-          time.sleep(1)
-        os.chdir(WORKSPACE+'/Sessions')
-        gdown.download_folder(url=Session_Link_optional, output=Session_Name, quiet=True, remaining_ok=True, use_cookies=False)
-        os.chdir(Session_Name)
-        call("rm -r " +instance_images, shell=True)
-        call("unzip " +instance_images.zip, shell=True, stdout=open('/dev/null', 'w'))
-        call("rm -r " +concept_images, shell=True)
-        call("unzip " +concept_images.zip, shell=True, stdout=open('/dev/null', 'w'))
-        call("rm -r " +captions, shell=True)
-        call("unzip " +captions.zip, shell=True, stdout=open('/dev/null', 'w'))
-        os.chdir('/notebooks')
-        clear_output()
-    INSTANCE_NAME=Session_Name
-    OUTPUT_DIR="/models/"+Session_Name
-    SESSION_DIR=WORKSPACE+"/Sessions/"+Session_Name
-    CONCEPT_DIR=SESSION_DIR+"/concept_images"
-    INSTANCE_DIR=SESSION_DIR+"/instance_images"
-    CAPTIONS_DIR=SESSION_DIR+'/captions'
-    MDLPTH=str(SESSION_DIR+"/"+Session_Name+'.ckpt')
-    resume=False
-    if os.path.exists(str(SESSION_DIR)):
-      mdls=[ckpt for ckpt in listdir(SESSION_DIR) if ckpt.split(".")[-1]=="ckpt"]
-      if not os.path.exists(MDLPTH) and '.ckpt' in str(mdls):
-        def f(n):
-          k=0
-          for i in mdls:
-            if k==n:
-              call('mv '+SESSION_DIR+'/'+i+' '+MDLPTH, shell=True)
-            k=k+1
-        k=0
-        print('[1;33mNo final checkpoint model found, select which intermediary checkpoint to use, enter only the number, (000 to skip):\n[1;34m')
-        for i in mdls:
-          print(str(k)+'- '+i)
-          k=k+1
-        n=input()
-        while int(n)>k-1:
-          n=input()
-        if n!="000":
-          f(int(n))
-          print('[1;32mUsing the model '+ mdls[int(n)]+" ...")
-          time.sleep(8)
-          clear_output()
-        else:
-          print('[1;32mSkipping the intermediary checkpoints.')
-    if os.path.exists(str(SESSION_DIR)) and not os.path.exists(MDLPTH):
-      print('[1;32mLoading session with no previous model, using the original model or the custom downloaded model')
-      if MODEL_NAME=="":
-        print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-      else:
-        print('[1;32mSession Loaded, proceed to uploading instance images')
-    elif os.path.exists(MDLPTH):
-      print('[1;32mSession found, loading the trained model ...')
-      wget.download('https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/refmdlz')
-      call('unzip -o -q refmdlz', shell=True, stdout=open('/dev/null', 'w'))
-      call('rm -f refmdlz', shell=True, stdout=open('/dev/null', 'w'))
-      wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv1.py')
-      call('python /notebooks/convertodiffv1.py '+MDLPTH+' '+OUTPUT_DIR+' --v1', shell=True)
-      call('rm /notebooks/convertodiffv1.py', shell=True)
-      call('rm -r /notebooks/refmdl', shell=True)
-      if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-        resume=True
-        clear_output()
-        print('[1;32mSession loaded.')
-      else:
-        if not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-          print('[1;31mConversion error, if the error persists, remove the CKPT file from the current session folder')
-    elif not os.path.exists(str(SESSION_DIR)):
-        call('mkdir -p '+INSTANCE_DIR, shell=True)
-        print('[1;32mCreating session...')
-        if MODEL_NAME=="":
-          print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-        else:
-          print('[1;32mSession created, proceed to uploading instance images')
-    return PT, WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, CONCEPT_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAME, resume
-def done():
-    done = widgets.Button(
-        description='Done!',
-        disabled=True,
-        button_style='success',
-        tooltip='',
-        icon='check'
-    )
-    display(done)
-def uplder(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, ren):
-    uploader = widgets.FileUpload(description="Choose images",accept='image/*', multiple=True)
-    Upload = widgets.Button(
-        description='Upload',
-        disabled=False,
-        button_style='info',
-        tooltip='Click to upload the chosen instance images',
-        icon=''
-    )
-    def up(Upload):
-        with out:
-            uploader.close()
-            Upload.close()
-            upld(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader, ren)
-            done()
-    out=widgets.Output()
-    if IMAGES_FOLDER_OPTIONAL=="":
-      Upload.on_click(up)
-      display(uploader, Upload, out)
-    else:
-       upld(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader, ren)
-       done()
-def upld(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader, ren):
-    if os.path.exists(CAPTIONS_DIR+"off"):
-      call('mv '+CAPTIONS_DIR+"off"+' '+CAPTIONS_DIR, shell=True)
-      time.sleep(2)
-    if Remove_existing_instance_images:
-        if os.path.exists(str(INSTANCE_DIR)):
-            call("rm -r " +INSTANCE_DIR, shell=True)
-        if os.path.exists(str(CAPTIONS_DIR)):
-            call("rm -r " +CAPTIONS_DIR, shell=True)
-    if not os.path.exists(str(INSTANCE_DIR)):
-        call("mkdir -p " +INSTANCE_DIR, shell=True)
-    if not os.path.exists(str(CAPTIONS_DIR)):
-        call("mkdir -p " +CAPTIONS_DIR, shell=True)
-    if IMAGES_FOLDER_OPTIONAL !="":
-        if any(file.endswith('.{}'.format('txt')) for file in os.listdir(IMAGES_FOLDER_OPTIONAL)):
-            call('mv '+IMAGES_FOLDER_OPTIONAL+'/*.txt '+CAPTIONS_DIR, shell=True)
-        if Crop_images:
-            os.chdir(str(IMAGES_FOLDER_OPTIONAL))
-            call('find . -name "* *" -type f | rename ' "'s/ /-/g'", shell=True)
-            os.chdir('/notebooks')
-            for filename in tqdm(os.listdir(IMAGES_FOLDER_OPTIONAL), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]
-                new_path_with_file = os.path.join(INSTANCE_DIR, filename)
-                file = Image.open(IMAGES_FOLDER_OPTIONAL+"/"+filename)
-                width, height = file.size
-                image = file
-                if file.size !=(Crop_size, Crop_size):
-                    image=crop_image(file, Crop_size)
-                    if (extension.upper() == "JPG" or "jpg"):
-                        image[0].save(new_path_with_file, format="JPEG", quality = 100)
-                    else:
-                        image[0].save(new_path_with_file, format=extension.upper())
-                else:
-                   call("cp \'"+IMAGES_FOLDER_OPTIONAL+"/"+filename+"\' "+INSTANCE_DIR, shell=True)
-        else:
-            for filename in tqdm(os.listdir(IMAGES_FOLDER_OPTIONAL), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                call("cp -r " +IMAGES_FOLDER_OPTIONAL+"/. " +INSTANCE_DIR, shell=True)
-    elif IMAGES_FOLDER_OPTIONAL =="":
-        up=""
-        for filename, file in uploader.value.items():
-          if filename.split(".")[-1]=="txt":
-            with open(CAPTIONS_DIR+'/'+filename, 'w') as f:
-                f.write(file['content'].decode())
-          up=[(filename, file) for filename, file in uploader.value.items() if filename.split(".")[-1]!="txt"]
-        if Crop_images:
-            for filename, file_info in tqdm(up, bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                img = Image.open(io.BytesIO(file_info['content']))
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]
-                if (extension.upper() == "JPG" or "jpg"):
-                    img.save(INSTANCE_DIR+"/"+filename, format="JPEG", quality = 100)
-                else:
-                    img.save(INSTANCE_DIR+"/"+filename, format=extension.upper())
-                new_path_with_file = os.path.join(INSTANCE_DIR, filename)
-                file = Image.open(new_path_with_file)
-                width, height = file.size
-                image = img
-                if file.size !=(Crop_size, Crop_size):
-                    image=crop_image(file, Crop_size)
-                    if (extension.upper() == "JPG" or "jpg"):
-                        image[0].save(new_path_with_file, format="JPEG", quality = 100)
-                    else:
-                        image[0].save(new_path_with_file, format=extension.upper())
-        else:
-            for filename, file_info in tqdm(uploader.value.items(), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                img = Image.open(io.BytesIO(file_info['content']))
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]
-                if (extension.upper() == "JPG" or "jpg"):
-                    img.save(INSTANCE_DIR+"/"+filename, format="JPEG", quality = 100)
-                else:
-                    img.save(INSTANCE_DIR+"/"+filename, format=extension.upper())
-    if ren:
-        i=0
-        for filename in tqdm(os.listdir(INSTANCE_DIR), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Renamed'):
-          extension = filename.split(".")[-1]
-          identifier=filename.split(".")[0]
-          new_path_with_file = os.path.join(INSTANCE_DIR, "conceptimagedb"+str(i)+"."+extension)
-          call('mv "'+os.path.join(INSTANCE_DIR,filename)+'" "'+new_path_with_file+'"', shell=True)
-          i=i+1
-    os.chdir(INSTANCE_DIR)
-    call('find . -name "* *" -type f | rename ' "'s/ /-/g'", shell=True)
-    os.chdir(CAPTIONS_DIR)
-    call('find . -name "* *" -type f | rename ' "'s/ /-/g'", shell=True)
-    os.chdir('/notebooks')
-def caption(CAPTIONS_DIR, INSTANCE_DIR):
-  if os.path.exists(CAPTIONS_DIR+"off"):
-    call('mv '+CAPTIONS_DIR+"off"+' '+CAPTIONS_DIR, shell=True)
-    time.sleep(2)
-  paths=""
-  out=""
-  widgets_l=""
-  clear_output()
-  def Caption(path):
-      if path!="Select an instance image to caption":
-        name = os.path.splitext(os.path.basename(path))[0]
-        ext=os.path.splitext(os.path.basename(path))[-1][1:]
-        if ext=="jpg" or "JPG":
-          ext="JPEG"
-        if os.path.exists(CAPTIONS_DIR+"/"+name + '.txt'):
-          with open(CAPTIONS_DIR+"/"+name + '.txt', 'r') as f:
-              text = f.read()
-        else:
-          with open(CAPTIONS_DIR+"/"+name + '.txt', 'w') as f:
-              f.write("")
-              with open(CAPTIONS_DIR+"/"+name + '.txt', 'r') as f:
-                  text = f.read()
-        img=Image.open(os.path.join(INSTANCE_DIR,path))
-        img=img.resize((420, 420))
-        image_bytes = BytesIO()
-        img.save(image_bytes, format=ext, qualiy=10)
-        image_bytes.seek(0)
-        image_data = image_bytes.read()
-        img= image_data
-        image = widgets.Image(
-            value=img,
-            width=420,
-            height=420
-        )
-        text_area = widgets.Textarea(value=text, description='', disabled=False, layout={'width': '300px', 'height': '120px'})
-        def update_text(text):
-            with open(CAPTIONS_DIR+"/"+name + '.txt', 'w') as f:
-                f.write(text)
-        button = widgets.Button(description='Save', button_style='success')
-        button.on_click(lambda b: update_text(text_area.value))
-        return widgets.VBox([widgets.HBox([image, text_area, button])])
-  paths = os.listdir(INSTANCE_DIR)
-  widgets_l = widgets.Select(options=["Select an instance image to caption"]+paths, rows=25)
-  out = widgets.Output()
-  def click(change):
-      with out:
-          out.clear_output()
-          display(Caption(change.new))
-  widgets_l.observe(click, names='value')
-  display(widgets.HBox([widgets_l, out]))
-def dbtrain(Resume_Training, UNet_Training_Steps, UNet_Learning_Rate, Text_Encoder_Training_Steps, Text_Encoder_Concept_Training_Steps, Text_Encoder_Learning_Rate, Style_Training, Resolution, MODEL_NAME, SESSION_DIR, INSTANCE_DIR, CONCEPT_DIR, CAPTIONS_DIR, External_Captions,  INSTANCE_NAME, Session_Name, OUTPUT_DIR, PT, resume, Save_Checkpoint_Every_n_Steps, Start_saving_from_the_step, Save_Checkpoint_Every):
-    if resume and not Resume_Training:
-      print('[1;31mOverwrite your previously trained model ?, answering "yes" will train a new model, answering "no" will resume the training of the previous model?  yes or no ?[0m')
-      while True:
-        ansres=input('')
-        if ansres=='no':
-          Resume_Training = True
-          break
-        elif ansres=='yes':
-          Resume_Training = False
-          resume= False
-          break
-    while not Resume_Training and not os.path.exists(MODEL_NAME+'/unet/diffusion_pytorch_model.bin'):
-        print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-        time.sleep(5)
-    if os.path.exists(CAPTIONS_DIR+"off"):
-       call('mv '+CAPTIONS_DIR+"off"+' '+CAPTIONS_DIR, shell=True)
-       time.sleep(2)
-    MODELT_NAME=MODEL_NAME
-    Seed=random.randint(1, 999999)
-    Style=""
-    if Style_Training:
-      Style="--Style"
-    extrnlcptn=""
-    if External_Captions:
-      extrnlcptn="--external_captions"
-    precision="fp16"
-    GCUNET="--gradient_checkpointing"
-    if Resolution<=640:
-      GCUNET=""
-    resuming=""
-    if Resume_Training and os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-      MODELT_NAME=OUTPUT_DIR
-      print('[1;32mResuming Training...[0m')
-      resuming="Yes"
-    elif Resume_Training and not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-      print('[1;31mPrevious model not found, training a new model...[0m')
-      MODELT_NAME=MODEL_NAME
-      while MODEL_NAME=="":
-        print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-        time.sleep(5)
-    trnonltxt=""
-    if UNet_Training_Steps==0:
-       trnonltxt="--train_only_text_encoder"
-    Enable_text_encoder_training= True
-    Enable_Text_Encoder_Concept_Training= True
-    if Text_Encoder_Training_Steps==0 or External_Captions:
-       Enable_text_encoder_training= False
-    else:
-      stptxt=Text_Encoder_Training_Steps
-    if Text_Encoder_Concept_Training_Steps==0:
-       Enable_Text_Encoder_Concept_Training= False
-    else:
-      stptxtc=Text_Encoder_Concept_Training_Steps
-    if Save_Checkpoint_Every==None:
-      Save_Checkpoint_Every=1
-    stp=0
-    if Start_saving_from_the_step==None:
-      Start_saving_from_the_step=0
-    if (Start_saving_from_the_step < 200):
-      Start_saving_from_the_step=Save_Checkpoint_Every
-    stpsv=Start_saving_from_the_step
-    if Save_Checkpoint_Every_n_Steps:
-      stp=Save_Checkpoint_Every
-    def dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps):
-        call('accelerate launch /notebooks/diffusers/examples/dreambooth/train_dreambooth_pps.py \
-        '+trnonltxt+' \
-        --train_text_encoder \
-        --image_captions_filename \
-        --dump_only_text_encoder \
-        --pretrained_model_name_or_path='+MODELT_NAME+' \
-        --instance_data_dir='+INSTANCE_DIR+' \
-        --output_dir='+OUTPUT_DIR+' \
-        --instance_prompt='+PT+' \
-        --seed='+str(Seed)+' \
-        --resolution=512 \
-        --mixed_precision='+str(precision)+' \
-        --train_batch_size=1 \
-        --gradient_accumulation_steps=1 --gradient_checkpointing \
-        --use_8bit_adam \
-        --learning_rate='+str(Text_Encoder_Learning_Rate)+' \
-        --lr_scheduler="polynomial" \
-        --lr_warmup_steps=0 \
-        --max_train_steps='+str(Training_Steps), shell=True)
-    def train_only_unet(stp, stpsv, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, Text_Encoder_Training_Steps, PT, Seed, Resolution, Style, extrnlcptn, precision, Training_Steps):
-        clear_output()
-        if resuming=="Yes":
-          print('[1;32mResuming Training...[0m')
-        print('[1;33mTraining the UNet...[0m')
-        call('accelerate launch /notebooks/diffusers/examples/dreambooth/train_dreambooth_pps.py \
-        '+Style+' \
-        '+extrnlcptn+' \
-        --stop_text_encoder_training='+str(Text_Encoder_Training_Steps)+' \
-        --image_captions_filename \
-        --train_only_unet \
-        --Session_dir='+SESSION_DIR+' \
-        --save_starting_step='+str(stpsv)+' \
-        --save_n_steps='+str(stp)+' \
-        --pretrained_model_name_or_path='+MODELT_NAME+' \
-        --instance_data_dir='+INSTANCE_DIR+' \
-        --output_dir='+OUTPUT_DIR+' \
-        --instance_prompt='+PT+' \
-        --seed='+str(Seed)+' \
-        --resolution='+str(Resolution)+' \
-        --mixed_precision='+str(precision)+' \
-        --train_batch_size=1 \
-        --gradient_accumulation_steps=1 '+GCUNET+' \
-        --use_8bit_adam \
-        --learning_rate='+str(UNet_Learning_Rate)+' \
-        --lr_scheduler="polynomial" \
-        --lr_warmup_steps=0 \
-        --max_train_steps='+str(Training_Steps), shell=True)
-    if Enable_text_encoder_training :
-      print('[1;33mTraining the text encoder...[0m')
-      if os.path.exists(OUTPUT_DIR+'/'+'text_encoder_trained'):
-        call('rm -r '+OUTPUT_DIR+'/text_encoder_trained', shell=True)
-      dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxt)
-    if Enable_Text_Encoder_Concept_Training:
-      if os.path.exists(CONCEPT_DIR):
-        if os.listdir(CONCEPT_DIR)!=[]:
-          clear_output()
-          if resuming=="Yes":
-            print('[1;32mResuming Training...[0m')
-          print('[1;33mTraining the text encoder on the concept...[0m')
-          dump_only_textenc(trnonltxt, MODELT_NAME, CONCEPT_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxtc)
-        else:
-          clear_output()
-          if resuming=="Yes":
-            print('[1;32mResuming Training...[0m')
-          print('[1;31mNo concept images found, skipping concept training...')
-          Text_Encoder_Concept_Training_Steps=0
-          time.sleep(8)
-      else:
-          clear_output()
-          if resuming=="Yes":
-            print('[1;32mResuming Training...[0m')
-          print('[1;31mNo concept images found, skipping concept training...')
-          Text_Encoder_Concept_Training_Steps=0
-          time.sleep(8)
-    if UNet_Training_Steps!=0:
-      train_only_unet(stp, stpsv, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, Text_Encoder_Training_Steps, PT, Seed, Resolution, Style, extrnlcptn, precision, Training_Steps=UNet_Training_Steps)
-    if UNet_Training_Steps==0 and Text_Encoder_Concept_Training_Steps==0 and External_Captions :
-      print('[1;32mNothing to do')
-    else:
-        if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-          call('python /notebooks/diffusers/scripts/convertosdv2.py --fp16 '+OUTPUT_DIR+' '+SESSION_DIR+'/'+Session_Name+'.ckpt', shell=True)
-          clear_output()
-          if os.path.exists(SESSION_DIR+"/"+INSTANCE_NAME+'.ckpt'):
-            clear_output()
-            print("[1;32mDONE, the CKPT model is in the session's folder")
-          else:
-            print("[1;31mSomething went wrong")
-        else:
-          print("[1;31mSomething went wrong")
-    return resume
-def test(Custom_Path, Previous_Session_Name, Session_Name, User, Password, Use_localtunnel):
-    if Previous_Session_Name!="":
-      print("[1;32mLoading a previous session model")
-      mdldir='/notebooks/Fast-Dreambooth/Sessions/'+Previous_Session_Name
-      path_to_trained_model=mdldir+"/"+Previous_Session_Name+'.ckpt'
-      while not os.path.exists(path_to_trained_model):
-         print("[1;31mThere is no trained model in the previous session")
-         time.sleep(5)
-    elif Custom_Path!="":
-      print("[1;32mLoading model from a custom path")
-      path_to_trained_model=Custom_Path
-      while not os.path.exists(path_to_trained_model):
-         print("[1;31mWrong Path")
-         time.sleep(5)
-    else:
-        print("[1;32mLoading the trained model")
-        mdldir='/notebooks/Fast-Dreambooth/Sessions/'+Session_Name
-        path_to_trained_model=mdldir+"/"+Session_Name+'.ckpt'
-        while not os.path.exists(path_to_trained_model):
-           print("[1;31mThere is no trained model in this session")
-           time.sleep(5)
-    auth=f"--gradio-auth {User}:{Password}"
-    if User =="" or Password=="":
-      auth=""
-    os.chdir('/notebooks')
-    if not os.path.exists('/notebooks/sd/stablediffusion'):
-       call('wget -q -O sd_rep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_rep.tar.zst', shell=True)
-       call('tar --zstd -xf sd_rep.tar.zst', shell=True)
-       call('rm sd_rep.tar.zst', shell=True)
-    os.chdir('/notebooks/sd')
-    if not os.path.exists('stable-diffusion-webui'):
-        call('git clone -q --depth 1 --branch master https://github.com/AUTOMATIC1111/stable-diffusion-webui', shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui/')
-    call('git reset --hard', shell=True, stdout=open('/dev/null', 'w'))
-    print('[1;32m')
-    call('git pull', shell=True, stdout=open('/dev/null', 'w'))
-    os.chdir('/notebooks')
-    clear_output()
-    if not os.path.exists('/usr/lib/node_modules/localtunnel'):
-       call('npm install -g localtunnel  --silent', shell=True, stdout=open('/dev/null', 'w'))
-    share=''
-    call('wget -q -O /usr/local/lib/python3.9/dist-packages/gradio/blocks.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/blocks.py', shell=True)
-    if not Use_localtunnel:
-      share='--share'
-    else:
-      share=''
-      os.chdir('/notebooks')
-      call('nohup lt --port 7860 > srv.txt 2>&1 &', shell=True)
-      time.sleep(2)
-      call("grep -o 'https[^ ]*' /notebooks/srv.txt >srvr.txt", shell=True)
-      time.sleep(2)
-      srv= getoutput('cat /notebooks/srvr.txt')
-      for line in fileinput.input('/usr/local/lib/python3.9/dist-packages/gradio/blocks.py', inplace=True):
-        if line.strip().startswith('self.server_name ='):
-            line = f'            self.server_name = "{srv[8:]}"\n'
-        if line.strip().startswith('self.server_port ='):
-            line = '            self.server_port = 443\n'
-        if line.strip().startswith('self.protocol = "https"'):
-            line = '            self.protocol = "https"\n'
-        if line.strip().startswith('if self.local_url.startswith("https") or self.is_colab'):
-            line = ''
-        if line.strip().startswith('else "http"'):
-            line = ''
-        sys.stdout.write(line)
-      call('rm /notebooks/srv.txt', shell=True)
-      call('rm /notebooks/srvr.txt', shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui/modules')
-    call('wget -q -O paths.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/paths.py', shell=True)
-    call("sed -i 's@/content/gdrive/MyDrive/sd/stablediffusion@/notebooks/sd/stablediffusion@' /notebooks/sd/stable-diffusion-webui/modules/paths.py", shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui')
-    clear_output()
-    configf="--disable-console-progressbars --no-half-vae --disable-safe-unpickle --api --xformers --medvram --skip-version-check --ckpt "+path_to_trained_model+" "+auth+" "+share
-    return configf
-def clean():
-    Sessions=os.listdir("/notebooks/Fast-Dreambooth/Sessions")
-    s = widgets.Select(
-        options=Sessions,
-        rows=5,
-        description='',
-        disabled=False
-    )
-    out=widgets.Output()
-    d = widgets.Button(
-        description='Remove',
-        disabled=False,
-        button_style='warning',
-        tooltip='Removet the selected session',
-        icon='warning'
-    )
-    def rem(d):
-        with out:
-            if s.value is not None:
-                clear_output()
-                print("[1;33mTHE SESSION [1;31m"+s.value+" [1;33mHAS BEEN REMOVED FROM THE STORAGE")
-                call('rm -r /notebooks/Fast-Dreambooth/Sessions/'+s.value, shell=True)
-                if os.path.exists('/notebooks/models/'+s.value):
-                  call('rm -r /notebooks/models/'+s.value, shell=True)
-                s.options=os.listdir("/notebooks/Fast-Dreambooth/Sessions")
-            else:
-                d.close()
-                s.close()
-                clear_output()
-                print("[1;32mNOTHING TO REMOVE")
-    d.on_click(rem)
-    if s.value is not None:
-        display(s,d,out)
-    else:
-        print("[1;32mNOTHING TO REMOVE")
-def hf(Name_of_your_concept, Save_concept_to, hf_token_write, INSTANCE_NAME, OUTPUT_DIR, Session_Name, MDLPTH):
-    from slugify import slugify
-    from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
-    from huggingface_hub import create_repo
-    from IPython.display import display_markdown
-    if(Name_of_your_concept == ""):
-      Name_of_your_concept = Session_Name
-    Name_of_your_concept=Name_of_your_concept.replace(" ","-")
-    if hf_token_write =="":
-      print('[1;32mYour Hugging Face write access token : ')
-      hf_token_write=input()
-    hf_token = hf_token_write
-    api = HfApi()
-    your_username = api.whoami(token=hf_token)["name"]
-    if(Save_concept_to == "Public_Library"):
-      repo_id = f"sd-dreambooth-library/{slugify(Name_of_your_concept)}"
-      #Join the Concepts Library organization if you aren't part of it already
-      call("curl -X POST -H 'Authorization: Bearer '"+hf_token+" -H 'Content-Type: application/json' https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", shell=True)
-    else:
-      repo_id = f"{your_username}/{slugify(Name_of_your_concept)}"
-    output_dir = f'/notebooks/models/'+INSTANCE_NAME
-    def bar(prg):
-        br="[1;33mUploading to HuggingFace : " '[0m|'+'█' * prg + ' ' * (25-prg)+'| ' +str(prg*4)+ "%"
-        return br
-    print("[1;32mLoading...")
-    os.chdir(OUTPUT_DIR)
-    call('rm -r safety_checker feature_extractor .git', shell=True)
-    call('rm model_index.json', shell=True)
-    call('git init', shell=True)
-    call('git lfs install --system --skip-repo', shell=True)
-    call('git remote add -f origin  "https://USER:'+hf_token+'@huggingface.co/runwayml/stable-diffusion-v1-5"', shell=True)
-    call('git config core.sparsecheckout true', shell=True)
-    call('echo -e "\nfeature_extractor\nsafety_checker\nmodel_index.json" > .git/info/sparse-checkout', shell=True)
-    call('git pull origin main', shell=True)
-    call('rm -r .git', shell=True)
-    os.chdir('/notebooks')
-    print(bar(1))
-    readme_text = f'''---
-    license: creativeml-openrail-m
-    tags:
-    - text-to-image
-    - stable-diffusion
-    ---
-    ### {Name_of_your_concept} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
-    Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
-    Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
-    '''
-    #Save the readme to a file
-    readme_file = open("README.md", "w")
-    readme_file.write(readme_text)
-    readme_file.close()
-    operations = [
-      CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"),
-      CommitOperationAdd(path_in_repo=f"{Session_Name}.ckpt",path_or_fileobj=MDLPTH)
-    ]
-    create_repo(repo_id,private=True, token=hf_token)
-    api.create_commit(
-      repo_id=repo_id,
-      operations=operations,
-      commit_message=f"Upload the concept {Name_of_your_concept} embeds and token",
-      token=hf_token
-    )
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/feature_extractor",
-      path_in_repo="feature_extractor",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(4))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/safety_checker",
-      path_in_repo="safety_checker",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(8))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/scheduler",
-      path_in_repo="scheduler",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(9))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/text_encoder",
-      path_in_repo="text_encoder",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(12))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/tokenizer",
-      path_in_repo="tokenizer",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(13))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/unet",
-      path_in_repo="unet",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(21))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/vae",
-      path_in_repo="vae",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(23))
-    api.upload_file(
-      path_or_fileobj=OUTPUT_DIR+"/model_index.json",
-      path_in_repo="model_index.json",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(25))
-    print("[1;32mYour concept was saved successfully at https://huggingface.co/"+repo_id)
-    done()
-def crop_image(im, size):
-  GREEN = "#0F0"
-  BLUE = "#00F"
-  RED = "#F00"
-  def focal_point(im, settings):
-      corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
-      entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
-      face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
-      pois = []
-      weight_pref_total = 0
-      if len(corner_points) > 0:
-        weight_pref_total += settings.corner_points_weight
-      if len(entropy_points) > 0:
-        weight_pref_total += settings.entropy_points_weight
-      if len(face_points) > 0:
-        weight_pref_total += settings.face_points_weight
-      corner_centroid = None
-      if len(corner_points) > 0:
-        corner_centroid = centroid(corner_points)
-        corner_centroid.weight = settings.corner_points_weight / weight_pref_total
-        pois.append(corner_centroid)
-      entropy_centroid = None
-      if len(entropy_points) > 0:
-        entropy_centroid = centroid(entropy_points)
-        entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
-        pois.append(entropy_centroid)
-      face_centroid = None
-      if len(face_points) > 0:
-        face_centroid = centroid(face_points)
-        face_centroid.weight = settings.face_points_weight / weight_pref_total
-        pois.append(face_centroid)
-      average_point = poi_average(pois, settings)
-      return average_point
-  def image_face_points(im, settings):
-      np_im = np.array(im)
-      gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-      tries = [
-        [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-      ]
-      for t in tries:
-        classifier = cv2.CascadeClassifier(t[0])
-        minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-        try:
-          faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-            minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-        except:
-          continue
-        if len(faces) > 0:
-          rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-          return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
-      return []
-  def image_corner_points(im, settings):
-      grayscale = im.convert("L")
-      # naive attempt at preventing focal points from collecting at watermarks near the bottom
-      gd = ImageDraw.Draw(grayscale)
-      gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-      np_im = np.array(grayscale)
-      points = cv2.goodFeaturesToTrack(
-          np_im,
-          maxCorners=100,
-          qualityLevel=0.04,
-          minDistance=min(grayscale.width, grayscale.height)*0.06,
-          useHarrisDetector=False,
-      )
-      if points is None:
-          return []
-      focal_points = []
-      for point in points:
-        x, y = point.ravel()
-        focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
-      return focal_points
-  def image_entropy_points(im, settings):
-      landscape = im.height < im.width
-      portrait = im.height > im.width
-      if landscape:
-        move_idx = [0, 2]
-        move_max = im.size[0]
-      elif portrait:
-        move_idx = [1, 3]
-        move_max = im.size[1]
-      else:
-        return []
-      e_max = 0
-      crop_current = [0, 0, settings.crop_width, settings.crop_height]
-      crop_best = crop_current
-      while crop_current[move_idx[1]] < move_max:
-          crop = im.crop(tuple(crop_current))
-          e = image_entropy(crop)
-          if (e > e_max):
-            e_max = e
-            crop_best = list(crop_current)
-          crop_current[move_idx[0]] += 4
-          crop_current[move_idx[1]] += 4
-      x_mid = int(crop_best[0] + settings.crop_width/2)
-      y_mid = int(crop_best[1] + settings.crop_height/2)
-      return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
-  def image_entropy(im):
-      # greyscale image entropy
-      # band = np.asarray(im.convert("L"))
-      band = np.asarray(im.convert("1"), dtype=np.uint8)
-      hist, _ = np.histogram(band, bins=range(0, 256))
-      hist = hist[hist > 0]
-      return -np.log2(hist / hist.sum()).sum()
-  def centroid(pois):
-    x = [poi.x for poi in pois]
-    y = [poi.y for poi in pois]
-    return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
-  def poi_average(pois, settings):
-      weight = 0.0
-      x = 0.0
-      y = 0.0
-      for poi in pois:
-          weight += poi.weight
-          x += poi.x * poi.weight
-          y += poi.y * poi.weight
-      avg_x = round(weight and x / weight)
-      avg_y = round(weight and y / weight)
-      return PointOfInterest(avg_x, avg_y)
-  def is_landscape(w, h):
-    return w > h
-  def is_portrait(w, h):
-    return h > w
-  def is_square(w, h):
-    return w == h
-  class PointOfInterest:
-    def __init__(self, x, y, weight=1.0, size=10):
-      self.x = x
-      self.y = y
-      self.weight = weight
-      self.size = size
-    def bounding(self, size):
-      return [
-        self.x - size//2,
-        self.y - size//2,
-        self.x + size//2,
-        self.y + size//2
-      ]
-  class Settings:
-    def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5):
-      self.crop_width = crop_width
-      self.crop_height = crop_height
-      self.corner_points_weight = corner_points_weight
-      self.entropy_points_weight = entropy_points_weight
-      self.face_points_weight = face_points_weight
-  settings = Settings(
-      crop_width = size,
-      crop_height = size,
-      face_points_weight = 0.9,
-      entropy_points_weight = 0.15,
-      corner_points_weight = 0.5,
-  )
-  scale_by = 1
-  if is_landscape(im.width, im.height):
-    scale_by = settings.crop_height / im.height
-  elif is_portrait(im.width, im.height):
-    scale_by = settings.crop_width / im.width
-  elif is_square(im.width, im.height):
-    if is_square(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_width / im.width
-    elif is_landscape(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_width / im.width
-    elif is_portrait(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_height / im.height
-  im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
-  im_debug = im.copy()
-  focus = focal_point(im_debug, settings)
-  # take the focal point and turn it into crop coordinates that try to center over the focal
-  # point but then get adjusted back into the frame
-  y_half = int(settings.crop_height / 2)
-  x_half = int(settings.crop_width / 2)
-  x1 = focus.x - x_half
-  if x1 < 0:
-      x1 = 0
-  elif x1 + settings.crop_width > im.width:
-      x1 = im.width - settings.crop_width
-  y1 = focus.y - y_half
-  if y1 < 0:
-      y1 = 0
-  elif y1 + settings.crop_height > im.height:
-      y1 = im.height - settings.crop_height
-  x2 = x1 + settings.crop_width
-  y2 = y1 + settings.crop_height
-  crop = [x1, y1, x2, y2]
-  results = []
-  results.append(im.crop(tuple(crop)))
-  return results

Scripts/mainpaperspacev2.py DELETED Viewed

@@ -1,1279 +0,0 @@
-from IPython.display import clear_output
-from subprocess import call, getoutput
-from IPython.display import display
-import ipywidgets as widgets
-import io
-from PIL import Image, ImageDraw
-import fileinput
-import time
-import os
-from os import listdir
-from os.path import isfile
-from tqdm import tqdm
-import gdown
-import random
-import sys
-import cv2
-from io import BytesIO
-import requests
-from collections import defaultdict
-from math import log, sqrt
-import numpy as np
-def Deps(force_reinstall):
-    if not force_reinstall and os.path.exists('/usr/local/lib/python3.9/dist-packages/safetensors'):
-        os.chdir('/notebooks')
-        if not os.path.exists('Latest_Notebooks'):
-            call('mkdir Latest_Notebooks', shell=True)
-        else:
-            call('rm -r Latest_Notebooks', shell=True)
-            call('mkdir Latest_Notebooks', shell=True)
-        os.chdir('/notebooks/Latest_Notebooks')
-        call('wget -q -i https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks.txt', shell=True)
-        call('rm Notebooks.txt', shell=True)
-        os.chdir('/notebooks')
-        print('[1;32mModules and notebooks updated, dependencies already installed')
-    else:
-        print('[1;32mInstalling the dependencies...')
-        call("pip install --root-user-action=ignore --no-deps -q accelerate==0.12.0", shell=True, stdout=open('/dev/null', 'w'))
-        if not os.path.exists('/usr/local/lib/python3.9/dist-packages/safetensors'):
-            os.chdir('/usr/local/lib/python3.9/dist-packages')
-            call("rm -r torch torch-1.12.0+cu116.dist-info torchaudio* torchvision* PIL Pillow* transformers* numpy* gdown*", shell=True, stdout=open('/dev/null', 'w'))
-        os.chdir('/notebooks')
-        if not os.path.exists('Latest_Notebooks'):
-            call('mkdir Latest_Notebooks', shell=True)
-        else:
-            call('rm -r Latest_Notebooks', shell=True)
-            call('mkdir Latest_Notebooks', shell=True)
-        os.chdir('/notebooks/Latest_Notebooks')
-        call('wget -q -i https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks.txt', shell=True)
-        call('rm Notebooks.txt', shell=True)
-        os.chdir('/notebooks')
-        call('wget -q -i https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dependencies/aptdeps.txt', shell=True)
-        os.chdir('/notebooks')
-        if not os.path.exists('/models'):
-            call('mkdir /models', shell=True)
-        if not os.path.exists('/notebooks/models'):
-            call('ln -s /models /notebooks', shell=True)
-        if os.path.exists('/deps'):
-            call("rm -r /deps", shell=True)
-        call('mkdir /deps', shell=True)
-        if not os.path.exists('cache'):
-            call('mkdir cache', shell=True)
-        os.chdir('/deps')
-        call('wget -q -i https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dependencies/aptdeps.txt', shell=True)
-        call('dpkg -i *.deb', shell=True, stdout=open('/dev/null', 'w'))
-        call('wget -q https://huggingface.co/TheLastBen/dependencies/resolve/main/pps.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call('tar -C / --zstd -xf pps.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call("sed -i 's@~/.cache@/notebooks/cache@' /usr/local/lib/python3.9/dist-packages/transformers/utils/hub.py", shell=True)
-        os.chdir('/notebooks')
-        call("git clone --depth 1 -q --branch updt https://github.com/TheLastBen/diffusers /diffusers", shell=True, stdout=open('/dev/null', 'w'))
-        if not os.path.exists('/notebooks/diffusers'):
-            call('ln -s /diffusers /notebooks', shell=True)
-        call("rm -r /deps", shell=True)
-        os.chdir('/notebooks')
-        clear_output()
-        done()
-def downloadmodel_hfv2(Path_to_HuggingFace):
-  import wget
-  if os.path.exists('/models/stable-diffusion-custom'):
-    call("rm -r /models/stable-diffusion-custom", shell=True)
-  clear_output()
-  if os.path.exists('/notebooks/Fast-Dreambooth/token.txt'):
-    with open("/notebooks/Fast-Dreambooth/token.txt") as f:
-       token = f.read()
-    authe=f'https://USER:{token}@'
-  else:
-    authe="https://"
-  clear_output()
-  call("mkdir /models/stable-diffusion-custom", shell=True)
-  os.chdir("/models/stable-diffusion-custom")
-  call("git init", shell=True)
-  call("git lfs install --system --skip-repo", shell=True)
-  call('git remote add -f origin '+authe+'huggingface.co/'+Path_to_HuggingFace, shell=True)
-  call("git config core.sparsecheckout true", shell=True)
-  call('echo -e "\nscheduler\ntext_encoder\ntokenizer\nunet\nvae\nfeature_extractor\nmodel_index.json\n!*.safetensors" > .git/info/sparse-checkout', shell=True)
-  call("git pull origin main", shell=True)
-  if os.path.exists('unet/diffusion_pytorch_model.bin'):
-    call("rm -r .git", shell=True)
-    os.chdir('/notebooks')
-    clear_output()
-    done()
-  while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-        print('[1;31mCheck the link you provided')
-        os.chdir('/notebooks')
-        time.sleep(5)
-def downloadmodel_pthv2(CKPT_Path, Custom_Model_Version):
-  import wget
-  os.chdir('/models')
-  clear_output()
-  if os.path.exists(str(CKPT_Path)):
-    if Custom_Model_Version=='512':
-      wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py')
-      clear_output()
-      call('python convertodiff.py '+CKPT_Path+' stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1-base', shell=True)
-    elif Custom_Model_Version=='768':
-      wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py')
-      clear_output()
-      call('python convertodiff.py '+CKPT_Path+' stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1', shell=True)
-    call('rm convertodiff.py', shell=True)
-    if os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-      os.chdir('/notebooks')
-      clear_output()
-      done()
-    while not os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-      print('[1;31mConversion error')
-      os.chdir('/notebooks')
-      time.sleep(5)
-  else:
-    while not os.path.exists(str(CKPT_Path)):
-       print('[1;31mWrong path, use the colab file explorer to copy the path')
-       os.chdir('/notebooks')
-       time.sleep(5)
-def downloadmodel_lnkv2(CKPT_Link, Custom_Model_Version):
-    import wget
-    os.chdir('/models')
-    call("gdown --fuzzy " +CKPT_Link+ " -O model.ckpt", shell=True)
-    if os.path.exists('model.ckpt'):
-      if os.path.getsize("model.ckpt") > 1810671599:
-        wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py')
-        if Custom_Model_Version=='512':
-          call('python convertodiffv2.py model.ckpt stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1-base', shell=True)
-        elif Custom_Model_Version=='768':
-          call('python convertodiffv2.py model.ckpt stable-diffusion-custom --v2 --reference_model stabilityai/stable-diffusion-2-1', shell=True)
-        call('rm convertodiffv2.py', shell=True)
-        if os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-          call('rm model.ckpt', shell=True)
-          os.chdir('/notebooks')
-          clear_output()
-          done()
-        else:
-            while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-              print('[1;31mConversion error')
-              os.chdir('/notebooks')
-              time.sleep(5)
-    else:
-        while os.path.getsize('/models/model.ckpt') < 1810671599:
-           print('[1;31mWrong link, check that the link is valid')
-           os.chdir('/notebooks')
-           time.sleep(5)
-def dlv2(Path_to_HuggingFace, CKPT_Path, CKPT_Link, Model_Version, Custom_Model_Version):
-    if Path_to_HuggingFace != "":
-      downloadmodel_hfv2(Path_to_HuggingFace)
-      MODEL_NAMEv2="/models/stable-diffusion-custom"
-    elif CKPT_Path !="":
-      downloadmodel_pthv2(CKPT_Path, Custom_Model_Version)
-      MODEL_NAMEv2="/models/stable-diffusion-custom"
-    elif CKPT_Link !="":
-      downloadmodel_lnkv2(CKPT_Link, Custom_Model_Version)
-      MODEL_NAMEv2="/models/stable-diffusion-custom"
-    else:
-      if Model_Version=="512":
-        MODEL_NAMEv2="dataset"
-        print('[1;32mUsing the original V2-512 model')
-      elif Model_Version=="768":
-        MODEL_NAMEv2="/datasets/stable-diffusion-v2-1/stable-diffusion-2-1"
-        print('[1;32mUsing the original V2-768 model')
-      else:
-        MODEL_NAMEv2=""
-        print('[1;31mWrong model version')
-    return MODEL_NAMEv2
-def sessv2(Session_Name, Session_Link_optional, Model_Version, MODEL_NAMEv2):
-    import gdown
-    os.chdir('/notebooks')
-    PT=""
-    while Session_Name=="":
-      print('[1;31mInput the Session Name:')
-      Session_Name=input("")
-    Session_Name=Session_Name.replace(" ","_")
-    WORKSPACE='/notebooks/Fast-Dreambooth'
-    if Session_Link_optional !="":
-      print('[1;32mDownloading session...')
-      if Session_Link_optional != "":
-        if not os.path.exists(str(WORKSPACE+'/Sessions')):
-          call("mkdir -p " +WORKSPACE+ "/Sessions", shell=True)
-          time.sleep(1)
-        os.chdir(WORKSPACE+'/Sessions')
-        gdown.download_folder(url=Session_Link_optional, output=Session_Name, quiet=True, remaining_ok=True, use_cookies=False)
-        os.chdir(Session_Name)
-        call("rm -r " +instance_images, shell=True)
-        call("unzip " +instance_images.zip, shell=True, stdout=open('/dev/null', 'w'))
-        call("rm -r " +concept_images, shell=True)
-        call("unzip " +concept_images.zip, shell=True, stdout=open('/dev/null', 'w'))
-        call("rm -r " +captions, shell=True)
-        call("unzip " +captions.zip, shell=True, stdout=open('/dev/null', 'w'))
-        os.chdir('/notebooks')
-        clear_output()
-    INSTANCE_NAME=Session_Name
-    OUTPUT_DIR="/models/"+Session_Name
-    SESSION_DIR=WORKSPACE+"/Sessions/"+Session_Name
-    CONCEPT_DIR=SESSION_DIR+"/concept_images"
-    INSTANCE_DIR=SESSION_DIR+"/instance_images"
-    CAPTIONS_DIR=SESSION_DIR+'/captions'
-    MDLPTH=str(SESSION_DIR+"/"+Session_Name+'.ckpt')
-    resumev2=False
-    if os.path.exists(str(SESSION_DIR)):
-      mdls=[ckpt for ckpt in listdir(SESSION_DIR) if ckpt.split(".")[-1]=="ckpt"]
-      if not os.path.exists(MDLPTH) and '.ckpt' in str(mdls):
-        def f(n):
-          k=0
-          for i in mdls:
-            if k==n:
-              call('mv '+SESSION_DIR+'/'+i+' '+MDLPTH, shell=True)
-            k=k+1
-        k=0
-        print('[1;33mNo final checkpoint model found, select which intermediary checkpoint to use, enter only the number, (000 to skip):\n[1;34m')
-        for i in mdls:
-          print(str(k)+'- '+i)
-          k=k+1
-        n=input()
-        while int(n)>k-1:
-          n=input()
-        if n!="000":
-          f(int(n))
-          print('[1;32mUsing the model '+ mdls[int(n)]+" ...")
-          time.sleep(8)
-        else:
-          print('[1;32mSkipping the intermediary checkpoints.')
-    if os.path.exists(str(SESSION_DIR)) and not os.path.exists(MDLPTH):
-      print('[1;32mLoading session with no previous model, using the original model or the custom downloaded model')
-      if MODEL_NAMEv2=="":
-        print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-      else:
-        print('[1;32mSession Loaded, proceed to uploading instance images')
-    elif os.path.exists(MDLPTH):
-      print('[1;32mSession found, loading the trained model ...')
-      if Model_Version=='512':
-        call("wget -q -O convertodiff.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py", shell=True)
-        clear_output()
-        print('[1;32mSession found, loading the trained model ...')
-        call('python /notebooks/convertodiff.py '+MDLPTH+' '+OUTPUT_DIR+' --v2 --reference_model stabilityai/stable-diffusion-2-1-base', shell=True)
-      elif Model_Version=='768':
-        call('wget -q -O convertodiff.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py', shell=True)
-        clear_output()
-        print('[1;32mSession found, loading the trained model ...')
-        call('python /notebooks/convertodiff.py '+MDLPTH+' '+OUTPUT_DIR+' --v2 --reference_model stabilityai/stable-diffusion-2-1', shell=True)
-      call('rm /notebooks/convertodiff.py', shell=True)
-      if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-        resumev2=True
-        clear_output()
-        print('[1;32mSession loaded.')
-      else:
-        if not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-          print('[1;31mConversion error, if the error persists, remove the CKPT file from the current session folder')
-    elif not os.path.exists(str(SESSION_DIR)):
-        call('mkdir -p '+INSTANCE_DIR, shell=True)
-        print('[1;32mCreating session...')
-        if MODEL_NAMEv2=="":
-          print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-        else:
-          print('[1;32mSession created, proceed to uploading instance images')
-    return PT, WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, CONCEPT_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAMEv2, resumev2
-def done():
-    done = widgets.Button(
-        description='Done!',
-        disabled=True,
-        button_style='success',
-        tooltip='',
-        icon='check'
-    )
-    display(done)
-def uplder(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, ren):
-    uploader = widgets.FileUpload(description="Choose images",accept='image/*', multiple=True)
-    Upload = widgets.Button(
-        description='Upload',
-        disabled=False,
-        button_style='info',
-        tooltip='Click to upload the chosen instance images',
-        icon=''
-    )
-    def up(Upload):
-        with out:
-            uploader.close()
-            Upload.close()
-            upld(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader, ren)
-            done()
-    out=widgets.Output()
-    if IMAGES_FOLDER_OPTIONAL=="":
-      Upload.on_click(up)
-      display(uploader, Upload, out)
-    else:
-       upld(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader, ren)
-       done()
-def upld(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader, ren):
-    if os.path.exists(CAPTIONS_DIR+"off"):
-      call('mv '+CAPTIONS_DIR+"off"+' '+CAPTIONS_DIR, shell=True)
-      time.sleep(2)
-    if Remove_existing_instance_images:
-        if os.path.exists(str(INSTANCE_DIR)):
-            call("rm -r " +INSTANCE_DIR, shell=True)
-        if os.path.exists(str(CAPTIONS_DIR)):
-            call("rm -r " +CAPTIONS_DIR, shell=True)
-    if not os.path.exists(str(INSTANCE_DIR)):
-        call("mkdir -p " +INSTANCE_DIR, shell=True)
-    if not os.path.exists(str(CAPTIONS_DIR)):
-        call("mkdir -p " +CAPTIONS_DIR, shell=True)
-    if IMAGES_FOLDER_OPTIONAL !="":
-        if any(file.endswith('.{}'.format('txt')) for file in os.listdir(IMAGES_FOLDER_OPTIONAL)):
-            call('mv '+IMAGES_FOLDER_OPTIONAL+'/*.txt '+CAPTIONS_DIR, shell=True)
-        if Crop_images:
-            os.chdir(str(IMAGES_FOLDER_OPTIONAL))
-            call('find . -name "* *" -type f | rename ' "'s/ /-/g'", shell=True)
-            os.chdir('/notebooks')
-            for filename in tqdm(os.listdir(IMAGES_FOLDER_OPTIONAL), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]
-                new_path_with_file = os.path.join(INSTANCE_DIR, filename)
-                file = Image.open(IMAGES_FOLDER_OPTIONAL+"/"+filename)
-                width, height = file.size
-                image = file
-                if file.size !=(Crop_size, Crop_size):
-                    image=crop_image(file, Crop_size)
-                    if (extension.upper() == "JPG" or "jpg"):
-                        image[0].save(new_path_with_file, format="JPEG", quality = 100)
-                    else:
-                        image[0].save(new_path_with_file, format=extension.upper())
-                else:
-                   call("cp \'"+IMAGES_FOLDER_OPTIONAL+"/"+filename+"\' "+INSTANCE_DIR, shell=True)
-        else:
-            for filename in tqdm(os.listdir(IMAGES_FOLDER_OPTIONAL), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                call("cp -r " +IMAGES_FOLDER_OPTIONAL+"/. " +INSTANCE_DIR, shell=True)
-    elif IMAGES_FOLDER_OPTIONAL =="":
-        up=""
-        for filename, file in uploader.value.items():
-          if filename.split(".")[-1]=="txt":
-            with open(CAPTIONS_DIR+'/'+filename, 'w') as f:
-                f.write(file['content'].decode())
-          up=[(filename, file) for filename, file in uploader.value.items() if filename.split(".")[-1]!="txt"]
-        if Crop_images:
-            for filename, file_info in tqdm(up, bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                img = Image.open(io.BytesIO(file_info['content']))
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]
-                if (extension.upper() == "JPG" or "jpg"):
-                    img.save(INSTANCE_DIR+"/"+filename, format="JPEG", quality = 100)
-                else:
-                    img.save(INSTANCE_DIR+"/"+filename, format=extension.upper())
-                new_path_with_file = os.path.join(INSTANCE_DIR, filename)
-                file = Image.open(new_path_with_file)
-                width, height = file.size
-                image = img
-                if file.size !=(Crop_size, Crop_size):
-                    image=crop_image(file, Crop_size)
-                    if (extension.upper() == "JPG" or "jpg"):
-                        image[0].save(new_path_with_file, format="JPEG", quality = 100)
-                    else:
-                        image[0].save(new_path_with_file, format=extension.upper())
-        else:
-            for filename, file_info in tqdm(uploader.value.items(), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                img = Image.open(io.BytesIO(file_info['content']))
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]
-                if (extension.upper() == "JPG" or "jpg"):
-                    img.save(INSTANCE_DIR+"/"+filename, format="JPEG", quality = 100)
-                else:
-                    img.save(INSTANCE_DIR+"/"+filename, format=extension.upper())
-    if ren:
-        i=0
-        for filename in tqdm(os.listdir(INSTANCE_DIR), bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Renamed'):
-          extension = filename.split(".")[-1]
-          identifier=filename.split(".")[0]
-          new_path_with_file = os.path.join(INSTANCE_DIR, "conceptimagedb"+str(i)+"."+extension)
-          call('mv "'+os.path.join(INSTANCE_DIR,filename)+'" "'+new_path_with_file+'"', shell=True)
-          i=i+1
-    os.chdir(INSTANCE_DIR)
-    call('find . -name "* *" -type f | rename ' "'s/ /-/g'", shell=True)
-    os.chdir(CAPTIONS_DIR)
-    call('find . -name "* *" -type f | rename ' "'s/ /-/g'", shell=True)
-    os.chdir('/notebooks')
-def caption(CAPTIONS_DIR, INSTANCE_DIR):
-  if os.path.exists(CAPTIONS_DIR+"off"):
-    call('mv '+CAPTIONS_DIR+"off"+' '+CAPTIONS_DIR, shell=True)
-    time.sleep(2)
-  paths=""
-  out=""
-  widgets_l=""
-  clear_output()
-  def Caption(path):
-      if path!="Select an instance image to caption":
-        name = os.path.splitext(os.path.basename(path))[0]
-        ext=os.path.splitext(os.path.basename(path))[-1][1:]
-        if ext=="jpg" or "JPG":
-          ext="JPEG"
-        if os.path.exists(CAPTIONS_DIR+"/"+name + '.txt'):
-          with open(CAPTIONS_DIR+"/"+name + '.txt', 'r') as f:
-              text = f.read()
-        else:
-          with open(CAPTIONS_DIR+"/"+name + '.txt', 'w') as f:
-              f.write("")
-              with open(CAPTIONS_DIR+"/"+name + '.txt', 'r') as f:
-                  text = f.read()
-        img=Image.open(os.path.join(INSTANCE_DIR,path))
-        img=img.resize((420, 420))
-        image_bytes = BytesIO()
-        img.save(image_bytes, format=ext, qualiy=10)
-        image_bytes.seek(0)
-        image_data = image_bytes.read()
-        img= image_data
-        image = widgets.Image(
-            value=img,
-            width=420,
-            height=420
-        )
-        text_area = widgets.Textarea(value=text, description='', disabled=False, layout={'width': '300px', 'height': '120px'})
-        def update_text(text):
-            with open(CAPTIONS_DIR+"/"+name + '.txt', 'w') as f:
-                f.write(text)
-        button = widgets.Button(description='Save', button_style='success')
-        button.on_click(lambda b: update_text(text_area.value))
-        return widgets.VBox([widgets.HBox([image, text_area, button])])
-  paths = os.listdir(INSTANCE_DIR)
-  widgets_l = widgets.Select(options=["Select an instance image to caption"]+paths, rows=25)
-  out = widgets.Output()
-  def click(change):
-      with out:
-          out.clear_output()
-          display(Caption(change.new))
-  widgets_l.observe(click, names='value')
-  display(widgets.HBox([widgets_l, out]))
-def dbtrainv2(Resume_Training, UNet_Training_Steps, UNet_Learning_Rate, Text_Encoder_Training_Steps, Text_Encoder_Concept_Training_Steps, Text_Encoder_Learning_Rate, Style_Training, Resolution, MODEL_NAMEv2, SESSION_DIR, INSTANCE_DIR, CONCEPT_DIR, CAPTIONS_DIR, External_Captions,  INSTANCE_NAME, Session_Name, OUTPUT_DIR, PT, resumev2, Save_Checkpoint_Every_n_Steps, Start_saving_from_the_step, Save_Checkpoint_Every):
-    if resumev2 and not Resume_Training:
-      print('[1;31mOverwrite your previously trained model ?, answering "yes" will train a new model, answering "no" will resumev2 the training of the previous model?  yes or no ?[0m')
-      while True:
-        ansres=input('')
-        if ansres=='no':
-          Resume_Training = True
-          break
-        elif ansres=='yes':
-          Resume_Training = False
-          resumev2= False
-          break
-    while not Resume_Training and not os.path.exists(MODEL_NAMEv2+'/unet/diffusion_pytorch_model.bin'):
-        print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-        time.sleep(5)
-    if os.path.exists(CAPTIONS_DIR+"off"):
-       call('mv '+CAPTIONS_DIR+"off"+' '+CAPTIONS_DIR, shell=True)
-       time.sleep(2)
-    MODELT_NAME=MODEL_NAMEv2
-    Seed=random.randint(1, 999999)
-    Style=""
-    if Style_Training:
-      Style="--Style"
-    extrnlcptn=""
-    if External_Captions:
-      extrnlcptn="--external_captions"
-    precision="fp16"
-    GCUNET="--gradient_checkpointing"
-    if Resolution<=640:
-      GCUNET=""
-    resuming=""
-    if Resume_Training and os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-      MODELT_NAME=OUTPUT_DIR
-      print('[1;32mResuming Training...[0m')
-      resuming="Yes"
-    elif Resume_Training and not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-      print('[1;31mPrevious model not found, training a new model...[0m')
-      MODELT_NAME=MODEL_NAMEv2
-      while MODEL_NAMEv2=="":
-        print('[1;31mNo model found, use the "Model Download" cell to download a model.')
-        time.sleep(5)
-    trnonltxt=""
-    if UNet_Training_Steps==0:
-       trnonltxt="--train_only_text_encoder"
-    Enable_text_encoder_training= True
-    Enable_Text_Encoder_Concept_Training= True
-    if Text_Encoder_Training_Steps==0 or External_Captions:
-       Enable_text_encoder_training= False
-    else:
-      stptxt=Text_Encoder_Training_Steps
-    if Text_Encoder_Concept_Training_Steps==0:
-       Enable_Text_Encoder_Concept_Training= False
-    else:
-      stptxtc=Text_Encoder_Concept_Training_Steps
-    if Save_Checkpoint_Every==None:
-      Save_Checkpoint_Every=1
-    stp=0
-    if Start_saving_from_the_step==None:
-      Start_saving_from_the_step=0
-    if (Start_saving_from_the_step < 200):
-      Start_saving_from_the_step=Save_Checkpoint_Every
-    stpsv=Start_saving_from_the_step
-    if Save_Checkpoint_Every_n_Steps:
-      stp=Save_Checkpoint_Every
-    def dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps):
-        call('accelerate launch /notebooks/diffusers/examples/dreambooth/train_dreambooth_pps.py \
-        '+trnonltxt+' \
-        --train_text_encoder \
-        --image_captions_filename \
-        --dump_only_text_encoder \
-        --pretrained_model_name_or_path='+MODELT_NAME+' \
-        --instance_data_dir='+INSTANCE_DIR+' \
-        --output_dir='+OUTPUT_DIR+' \
-        --instance_prompt='+PT+' \
-        --seed='+str(Seed)+' \
-        --resolution=512 \
-        --mixed_precision='+str(precision)+' \
-        --train_batch_size=1 \
-        --gradient_accumulation_steps=1 --gradient_checkpointing \
-        --use_8bit_adam \
-        --learning_rate='+str(Text_Encoder_Learning_Rate)+' \
-        --lr_scheduler="polynomial" \
-        --lr_warmup_steps=0 \
-        --max_train_steps='+str(Training_Steps), shell=True)
-    def train_only_unet(stp, stpsv, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, Text_Encoder_Training_Steps, PT, Seed, Resolution, Style, extrnlcptn, precision, Training_Steps):
-        clear_output()
-        if resuming=="Yes":
-          print('[1;32mResuming Training...[0m')
-        print('[1;33mTraining the UNet...[0m')
-        call('accelerate launch /notebooks/diffusers/examples/dreambooth/train_dreambooth_pps.py \
-        '+Style+' \
-        '+extrnlcptn+' \
-        --stop_text_encoder_training='+str(Text_Encoder_Training_Steps)+' \
-        --image_captions_filename \
-        --train_only_unet \
-        --Session_dir='+SESSION_DIR+' \
-        --save_starting_step='+str(stpsv)+' \
-        --save_n_steps='+str(stp)+' \
-        --pretrained_model_name_or_path='+MODELT_NAME+' \
-        --instance_data_dir='+INSTANCE_DIR+' \
-        --output_dir='+OUTPUT_DIR+' \
-        --instance_prompt='+PT+' \
-        --seed='+str(Seed)+' \
-        --resolution='+str(Resolution)+' \
-        --mixed_precision='+str(precision)+' \
-        --train_batch_size=1 \
-        --gradient_accumulation_steps=1 '+GCUNET+' \
-        --use_8bit_adam \
-        --learning_rate='+str(UNet_Learning_Rate)+' \
-        --lr_scheduler="polynomial" \
-        --lr_warmup_steps=0 \
-        --max_train_steps='+str(Training_Steps), shell=True)
-    if Enable_text_encoder_training :
-      print('[1;33mTraining the text encoder...[0m')
-      if os.path.exists(OUTPUT_DIR+'/'+'text_encoder_trained'):
-        call('rm -r '+OUTPUT_DIR+'/text_encoder_trained', shell=True)
-      dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxt)
-    if Enable_Text_Encoder_Concept_Training:
-      if os.path.exists(CONCEPT_DIR):
-        if os.listdir(CONCEPT_DIR)!=[]:
-          clear_output()
-          if resuming=="Yes":
-            print('[1;32mResuming Training...[0m')
-          print('[1;33mTraining the text encoder on the concept...[0m')
-          dump_only_textenc(trnonltxt, MODELT_NAME, CONCEPT_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxtc)
-        else:
-          clear_output()
-          if resuming=="Yes":
-            print('[1;32mResuming Training...[0m')
-          print('[1;31mNo concept images found, skipping concept training...')
-          Text_Encoder_Concept_Training_Steps=0
-          time.sleep(8)
-      else:
-          clear_output()
-          if resuming=="Yes":
-            print('[1;32mResuming Training...[0m')
-          print('[1;31mNo concept images found, skipping concept training...')
-          Text_Encoder_Concept_Training_Steps=0
-          time.sleep(8)
-    if UNet_Training_Steps!=0:
-      train_only_unet(stp, stpsv, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, Text_Encoder_Training_Steps, PT, Seed, Resolution, Style, extrnlcptn, precision, Training_Steps=UNet_Training_Steps)
-    if UNet_Training_Steps==0 and Text_Encoder_Concept_Training_Steps==0 and External_Captions :
-      print('[1;32mNothing to do')
-    else:
-        if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-          call('python /notebooks/diffusers/scripts/convertosdv2.py --fp16 '+OUTPUT_DIR+' '+SESSION_DIR+'/'+Session_Name+'.ckpt', shell=True)
-          clear_output()
-          if os.path.exists(SESSION_DIR+"/"+INSTANCE_NAME+'.ckpt'):
-            clear_output()
-            print("[1;32mDONE, the CKPT model is in the session's folder")
-          else:
-            print("[1;31mSomething went wrong")
-        else:
-          print("[1;31mSomething went wrong")
-    return resumev2
-def test(Custom_Path, Previous_Session_Name, Session_Name, User, Password, Use_localtunnel):
-    if Previous_Session_Name!="":
-      print("[1;32mLoading a previous session model")
-      mdldir='/notebooks/Fast-Dreambooth/Sessions/'+Previous_Session_Name
-      path_to_trained_model=mdldir+"/"+Previous_Session_Name+'.ckpt'
-      while not os.path.exists(path_to_trained_model):
-         print("[1;31mThere is no trained model in the previous session")
-         time.sleep(5)
-    elif Custom_Path!="":
-      print("[1;32mLoading model from a custom path")
-      path_to_trained_model=Custom_Path
-      while not os.path.exists(path_to_trained_model):
-         print("[1;31mWrong Path")
-         time.sleep(5)
-    else:
-        print("[1;32mLoading the trained model")
-        mdldir='/notebooks/Fast-Dreambooth/Sessions/'+Session_Name
-        path_to_trained_model=mdldir+"/"+Session_Name+'.ckpt'
-        while not os.path.exists(path_to_trained_model):
-           print("[1;31mThere is no trained model in this session")
-           time.sleep(5)
-    auth=f"--gradio-auth {User}:{Password}"
-    if User =="" or Password=="":
-      auth=""
-    os.chdir('/notebooks')
-    if not os.path.exists('/notebooks/sd/stablediffusion'):
-       call('wget -q -O sd_rep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_rep.tar.zst', shell=True)
-       call('tar --zstd -xf sd_rep.tar.zst', shell=True)
-       call('rm sd_rep.tar.zst', shell=True)
-    os.chdir('/notebooks/sd')
-    if not os.path.exists('stable-diffusion-webui'):
-        call('git clone -q --depth 1 --branch master https://github.com/AUTOMATIC1111/stable-diffusion-webui', shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui/')
-    call('git reset --hard', shell=True, stdout=open('/dev/null', 'w'))
-    print('[1;32m')
-    call('git pull', shell=True, stdout=open('/dev/null', 'w'))
-    os.chdir('/notebooks')
-    clear_output()
-    if not os.path.exists('/usr/lib/node_modules/localtunnel'):
-       call('npm install -g localtunnel  --silent', shell=True, stdout=open('/dev/null', 'w'))
-    share=''
-    call('wget -q -O /usr/local/lib/python3.9/dist-packages/gradio/blocks.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/blocks.py', shell=True)
-    if not Use_localtunnel:
-      share='--share'
-    else:
-      share=''
-      os.chdir('/notebooks')
-      call('nohup lt --port 7860 > srv.txt 2>&1 &', shell=True)
-      time.sleep(2)
-      call("grep -o 'https[^ ]*' /notebooks/srv.txt >srvr.txt", shell=True)
-      time.sleep(2)
-      srv= getoutput('cat /notebooks/srvr.txt')
-      for line in fileinput.input('/usr/local/lib/python3.9/dist-packages/gradio/blocks.py', inplace=True):
-        if line.strip().startswith('self.server_name ='):
-            line = f'            self.server_name = "{srv[8:]}"\n'
-        if line.strip().startswith('self.server_port ='):
-            line = '            self.server_port = 443\n'
-        if line.strip().startswith('self.protocol = "https"'):
-            line = '            self.protocol = "https"\n'
-        if line.strip().startswith('if self.local_url.startswith("https") or self.is_colab'):
-            line = ''
-        if line.strip().startswith('else "http"'):
-            line = ''
-        sys.stdout.write(line)
-      call('rm /notebooks/srv.txt', shell=True)
-      call('rm /notebooks/srvr.txt', shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui/modules')
-    call('wget -q -O paths.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/paths.py', shell=True)
-    call("sed -i 's@/content/gdrive/MyDrive/sd/stablediffusion@/notebooks/sd/stablediffusion@' /notebooks/sd/stable-diffusion-webui/modules/paths.py", shell=True)
-    os.chdir('/notebooks/sd/stable-diffusion-webui')
-    clear_output()
-    configf="--disable-console-progressbars --no-half-vae --disable-safe-unpickle --api --xformers --medvram --skip-version-check --ckpt "+path_to_trained_model+" "+auth+" "+share
-    return configf
-def clean():
-    Sessions=os.listdir("/notebooks/Fast-Dreambooth/Sessions")
-    s = widgets.Select(
-        options=Sessions,
-        rows=5,
-        description='',
-        disabled=False
-    )
-    out=widgets.Output()
-    d = widgets.Button(
-        description='Remove',
-        disabled=False,
-        button_style='warning',
-        tooltip='Removet the selected session',
-        icon='warning'
-    )
-    def rem(d):
-        with out:
-            if s.value is not None:
-                clear_output()
-                print("[1;33mTHE SESSION [1;31m"+s.value+" [1;33mHAS BEEN REMOVED FROM THE STORAGE")
-                call('rm -r /notebooks/Fast-Dreambooth/Sessions/'+s.value, shell=True)
-                if os.path.exists('/notebooks/models/'+s.value):
-                  call('rm -r /notebooks/models/'+s.value, shell=True)
-                s.options=os.listdir("/notebooks/Fast-Dreambooth/Sessions")
-            else:
-                d.close()
-                s.close()
-                clear_output()
-                print("[1;32mNOTHING TO REMOVE")
-    d.on_click(rem)
-    if s.value is not None:
-        display(s,d,out)
-    else:
-        print("[1;32mNOTHING TO REMOVE")
-def hfv2(Name_of_your_concept, Save_concept_to, hf_token_write, INSTANCE_NAME, OUTPUT_DIR, Session_Name, MDLPTH):
-    from slugify import slugify
-    from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
-    from huggingface_hub import create_repo
-    from IPython.display import display_markdown
-    if(Name_of_your_concept == ""):
-      Name_of_your_concept = Session_Name
-    Name_of_your_concept=Name_of_your_concept.replace(" ","-")
-    if hf_token_write =="":
-      print('[1;32mYour Hugging Face write access token : ')
-      hf_token_write=input()
-    hf_token = hf_token_write
-    api = HfApi()
-    your_username = api.whoami(token=hf_token)["name"]
-    if(Save_concept_to == "Public_Library"):
-      repo_id = f"sd-dreambooth-library/{slugify(Name_of_your_concept)}"
-      #Join the Concepts Library organization if you aren't part of it already
-      call("curl -X POST -H 'Authorization: Bearer '"+hf_token+" -H 'Content-Type: application/json' https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", shell=True)
-    else:
-      repo_id = f"{your_username}/{slugify(Name_of_your_concept)}"
-    output_dir = f'/notebooks/models/'+INSTANCE_NAME
-    def bar(prg):
-        br="[1;33mUploading to HuggingFace : " '[0m|'+'█' * prg + ' ' * (25-prg)+'| ' +str(prg*4)+ "%"
-        return br
-    print("[1;32mLoading...")
-    os.chdir(OUTPUT_DIR)
-    call('rm -r feature_extractor .git', shell=True)
-    clear_output()
-    call('git init', shell=True)
-    call('git lfs install --system --skip-repo', shell=True)
-    call('git remote add -f origin  "https://USER:'+hf_token+'@huggingface.co/stabilityai/stable-diffusion-2-1"', shell=True)
-    call('git config core.sparsecheckout true', shell=True)
-    call('echo -e "\nfeature_extractor" > .git/info/sparse-checkout', shell=True)
-    call('git pull origin main', shell=True)
-    call('rm -r .git', shell=True)
-    os.chdir('/notebooks')
-    clear_output()
-    print(bar(1))
-    readme_text = f'''---
-    license: creativeml-openrail-m
-    tags:
-    - text-to-image
-    - stable-diffusion
-    ---
-    ### {Name_of_your_concept} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
-    Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
-    '''
-    #Save the readme to a file
-    readme_file = open("README.md", "w")
-    readme_file.write(readme_text)
-    readme_file.close()
-    operations = [
-      CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"),
-      CommitOperationAdd(path_in_repo=f"{Session_Name}.ckpt",path_or_fileobj=MDLPTH)
-    ]
-    create_repo(repo_id,private=True, token=hf_token)
-    api.create_commit(
-      repo_id=repo_id,
-      operations=operations,
-      commit_message=f"Upload the concept {Name_of_your_concept} embeds and token",
-      token=hf_token
-    )
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/feature_extractor",
-      path_in_repo="feature_extractor",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(8))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/scheduler",
-      path_in_repo="scheduler",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(9))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/text_encoder",
-      path_in_repo="text_encoder",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(12))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/tokenizer",
-      path_in_repo="tokenizer",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(13))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/unet",
-      path_in_repo="unet",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(21))
-    api.upload_folder(
-      folder_path=OUTPUT_DIR+"/vae",
-      path_in_repo="vae",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(23))
-    api.upload_file(
-      path_or_fileobj=OUTPUT_DIR+"/model_index.json",
-      path_in_repo="model_index.json",
-      repo_id=repo_id,
-      token=hf_token
-    )
-    clear_output()
-    print(bar(25))
-    print("[1;32mYour concept was saved successfully at https://huggingface.co/"+repo_id)
-    done()
-def crop_image(im, size):
-  GREEN = "#0F0"
-  BLUE = "#00F"
-  RED = "#F00"
-  def focal_point(im, settings):
-      corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
-      entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
-      face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
-      pois = []
-      weight_pref_total = 0
-      if len(corner_points) > 0:
-        weight_pref_total += settings.corner_points_weight
-      if len(entropy_points) > 0:
-        weight_pref_total += settings.entropy_points_weight
-      if len(face_points) > 0:
-        weight_pref_total += settings.face_points_weight
-      corner_centroid = None
-      if len(corner_points) > 0:
-        corner_centroid = centroid(corner_points)
-        corner_centroid.weight = settings.corner_points_weight / weight_pref_total
-        pois.append(corner_centroid)
-      entropy_centroid = None
-      if len(entropy_points) > 0:
-        entropy_centroid = centroid(entropy_points)
-        entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
-        pois.append(entropy_centroid)
-      face_centroid = None
-      if len(face_points) > 0:
-        face_centroid = centroid(face_points)
-        face_centroid.weight = settings.face_points_weight / weight_pref_total
-        pois.append(face_centroid)
-      average_point = poi_average(pois, settings)
-      return average_point
-  def image_face_points(im, settings):
-      np_im = np.array(im)
-      gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-      tries = [
-        [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-      ]
-      for t in tries:
-        classifier = cv2.CascadeClassifier(t[0])
-        minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-        try:
-          faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-            minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-        except:
-          continue
-        if len(faces) > 0:
-          rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-          return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
-      return []
-  def image_corner_points(im, settings):
-      grayscale = im.convert("L")
-      gd = ImageDraw.Draw(grayscale)
-      gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-      np_im = np.array(grayscale)
-      points = cv2.goodFeaturesToTrack(
-          np_im,
-          maxCorners=100,
-          qualityLevel=0.04,
-          minDistance=min(grayscale.width, grayscale.height)*0.06,
-          useHarrisDetector=False,
-      )
-      if points is None:
-          return []
-      focal_points = []
-      for point in points:
-        x, y = point.ravel()
-        focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
-      return focal_points
-  def image_entropy_points(im, settings):
-      landscape = im.height < im.width
-      portrait = im.height > im.width
-      if landscape:
-        move_idx = [0, 2]
-        move_max = im.size[0]
-      elif portrait:
-        move_idx = [1, 3]
-        move_max = im.size[1]
-      else:
-        return []
-      e_max = 0
-      crop_current = [0, 0, settings.crop_width, settings.crop_height]
-      crop_best = crop_current
-      while crop_current[move_idx[1]] < move_max:
-          crop = im.crop(tuple(crop_current))
-          e = image_entropy(crop)
-          if (e > e_max):
-            e_max = e
-            crop_best = list(crop_current)
-          crop_current[move_idx[0]] += 4
-          crop_current[move_idx[1]] += 4
-      x_mid = int(crop_best[0] + settings.crop_width/2)
-      y_mid = int(crop_best[1] + settings.crop_height/2)
-      return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
-  def image_entropy(im):
-      # greyscale image entropy
-      # band = np.asarray(im.convert("L"))
-      band = np.asarray(im.convert("1"), dtype=np.uint8)
-      hist, _ = np.histogram(band, bins=range(0, 256))
-      hist = hist[hist > 0]
-      return -np.log2(hist / hist.sum()).sum()
-  def centroid(pois):
-    x = [poi.x for poi in pois]
-    y = [poi.y for poi in pois]
-    return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
-  def poi_average(pois, settings):
-      weight = 0.0
-      x = 0.0
-      y = 0.0
-      for poi in pois:
-          weight += poi.weight
-          x += poi.x * poi.weight
-          y += poi.y * poi.weight
-      avg_x = round(weight and x / weight)
-      avg_y = round(weight and y / weight)
-      return PointOfInterest(avg_x, avg_y)
-  def is_landscape(w, h):
-    return w > h
-  def is_portrait(w, h):
-    return h > w
-  def is_square(w, h):
-    return w == h
-  class PointOfInterest:
-    def __init__(self, x, y, weight=1.0, size=10):
-      self.x = x
-      self.y = y
-      self.weight = weight
-      self.size = size
-    def bounding(self, size):
-      return [
-        self.x - size//2,
-        self.y - size//2,
-        self.x + size//2,
-        self.y + size//2
-      ]
-  class Settings:
-    def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5):
-      self.crop_width = crop_width
-      self.crop_height = crop_height
-      self.corner_points_weight = corner_points_weight
-      self.entropy_points_weight = entropy_points_weight
-      self.face_points_weight = face_points_weight
-  settings = Settings(
-      crop_width = size,
-      crop_height = size,
-      face_points_weight = 0.9,
-      entropy_points_weight = 0.15,
-      corner_points_weight = 0.5,
-  )
-  scale_by = 1
-  if is_landscape(im.width, im.height):
-    scale_by = settings.crop_height / im.height
-  elif is_portrait(im.width, im.height):
-    scale_by = settings.crop_width / im.width
-  elif is_square(im.width, im.height):
-    if is_square(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_width / im.width
-    elif is_landscape(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_width / im.width
-    elif is_portrait(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_height / im.height
-  im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
-  im_debug = im.copy()
-  focus = focal_point(im_debug, settings)
-  # take the focal point and turn it into crop coordinates that try to center over the focal
-  # point but then get adjusted back into the frame
-  y_half = int(settings.crop_height / 2)
-  x_half = int(settings.crop_width / 2)
-  x1 = focus.x - x_half
-  if x1 < 0:
-      x1 = 0
-  elif x1 + settings.crop_width > im.width:
-      x1 = im.width - settings.crop_width
-  y1 = focus.y - y_half
-  if y1 < 0:
-      y1 = 0
-  elif y1 + settings.crop_height > im.height:
-      y1 = im.height - settings.crop_height
-  x2 = x1 + settings.crop_width
-  y2 = y1 + settings.crop_height
-  crop = [x1, y1, x2, y2]
-  results = []
-  results.append(im.crop(tuple(crop)))
-  return results

TheLastBen--PPS/text-train.parquet ADDED Viewed

	@@ -0,0 +1,3 @@

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