Update parquet files

Notebooks.txt

@@ -1,2 +0,0 @@
-https://huggingface.co/datasets/apollo812/RNPD_SD/raw/main/Notebooks/RNPD-SD.ipynb
-https://huggingface.co/datasets/apollo812/RNPD_SD/resolve/main/Notebooks/SDXL-LoRA-RNPD.ipynb

Notebooks/RNPD-SD.ipynb

@@ -1,162 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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",
-    "with open('/dev/null', 'w') as devnull:import requests, os, time, importlib;open('/workspace/runpod_server.py', 'wb').write(requests.get('https://huggingface.co/datasets/TheLastBen/RNPD/raw/main/Scripts/mainrunpodA1111.py').content);os.chdir('/workspace');time.sleep(2);import mainrunpodA1111;importlib.reload(mainrunpodA1111);from mainrunpodA1111 import *;Deps(force_reinstall)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Install/Update AUTOMATIC1111 repo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Huggingface_token_optional=\"\"\n",
-    "\n",
-    "# Restore your backed-up SD folder by entering your huggingface token, leave it empty to start fresh or continue with the existing sd folder (if any).\n",
-    "\n",
-    "#--------------------\n",
-    "repo(Huggingface_token_optional)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model Download/Load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Original_Model_Version = \"SDXL\"\n",
-    "\n",
-    "# Choices are \"SDXL\", \"v1.5\", \"v2-512\", \"v2-768\"\n",
-    "\n",
-    "#-------------- Or\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",
-    "\n",
-    "#--------------------\n",
-    "model=mdl(Original_Model_Version, Path_to_MODEL, MODEL_LINK)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# LoRA Download"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Download/update ControlNet extension and its models.\n",
-    "\n",
-    "ControlNet_v1_Model = \"all\"\n",
-    "\n",
-    "# Choices are : none; all; 1: Canny; 2: Depth; 3: Lineart; 4: MLSD; 5: Normal; 6: OpenPose; 7: Scribble; 8: Seg; 9: ip2p; 10:Shuffle; 11: Inpaint; 12: Softedge; 13: Lineart_Anime; 14: Tile; 15: T2iadapter_Models\n",
-    "\n",
-    "ControlNet_XL_Model = \"all\"\n",
-    "\n",
-    "# Choices are : none; all; 1: Canny; 2: Depth; 3: Sketch; 4: OpenPose; 5: Recolor\n",
-    "\n",
-    "#--------------------\n",
-    "CNet(ControlNet_v1_Model, ControlNet_XL_Model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Start Stable-Diffusion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "User = \"\"\n",
-    "\n",
-    "Password= \"\"\n",
-    "\n",
-    "# Add credentials to your Gradio interface (optional).\n",
-    "\n",
-    "#-----------------\n",
-    "configf=sd(User, Password, model) if 'model' in locals() else sd(User, Password, \"\");import gradio;gradio.close_all()\n",
-    "!python /workspace/sd/stable-diffusion-webui/webui.py $configf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backup SD folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This will backup your sd folder -without the models- to your huggingface account, so you can restore it whenever you start an instance.\n",
-    "\n",
-    "Huggingface_Write_token=\"\"\n",
-    "\n",
-    "# Must be a WRITE token, get yours here : https://huggingface.co/settings/tokens\n",
-    "\n",
-    "#--------------------\n",
-    "save(Huggingface_Write_token)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

Notebooks/SDXL-LoRA-RNPD.ipynb

@@ -1,281 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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",
-    "with open('/dev/null', 'w') as devnull:import requests, os, time, importlib;open('/workspace/sdxllorarunpod.py', 'wb').write(requests.get('https://huggingface.co/datasets/TheLastBen/RNPD/raw/main/Scripts/sdxllorarunpod.py').content);os.chdir('/workspace');import sdxllorarunpod;importlib.reload(sdxllorarunpod);from sdxllorarunpod import *;restored=False;restoreda=False;Deps(force_reinstall)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Download the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run the cell to download the model\n",
-    "\n",
-    "#-------------\n",
-    "MODEL_NAMExl=dls_xlf(\"\", \"\", \"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Create/Load a Session"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Session_Name = \"Example-Session\"\n",
-    "\n",
-    "# Enter the session name, it if it exists, it will load it, otherwise it'll create an new session.\n",
-    "\n",
-    "#-----------------\n",
-    "[WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAMExl]=sess_xl(Session_Name, MODEL_NAMExl if 'MODEL_NAMExl' in locals() else \"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Instance Images\n",
-    "The most important step is to rename the instance pictures to one unique unknown identifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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",
-    "Crop_size = 1024\n",
-    "\n",
-    "# 1024 is the native resolution\n",
-    "\n",
-    "\n",
-    "#--------------------------------------------\n",
-    "\n",
-    "# Disabled when \"Smart_crop_images\" is set to \"True\"\n",
-    "\n",
-    "Resize_to_1024_and_keep_aspect_ratio = False\n",
-    "\n",
-    "# Will resize the smallest dimension to 1024 without cropping while keeping the aspect ratio (make sure you have enough VRAM)\n",
-    "\n",
-    "\n",
-    "# Check out this example for naming : https://i.imgur.com/d2lD3rz.jpeg\n",
-    "\n",
-    "#-----------------\n",
-    "uplder(Remove_existing_instance_images, Smart_crop_images, Crop_size, Resize_to_1024_and_keep_aspect_ratio, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Manual Captioning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Open a tool to manually caption the instance images.\n",
-    "\n",
-    "#-----------------\n",
-    "caption(CAPTIONS_DIR, INSTANCE_DIR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Train LoRA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Training Settings\n",
-    "\n",
-    "# Epoch = Number of steps/images\n",
-    "\n",
-    "\n",
-    "UNet_Training_Epochs= 120\n",
-    "\n",
-    "UNet_Learning_Rate= \"1e-6\"\n",
-    "\n",
-    "# Keep the learning rate between 1e-6 and 3e-6\n",
-    "\n",
-    "\n",
-    "Text_Encoder_Training_Epochs= 40\n",
-    "\n",
-    "# The training is highly affected by this value, a total of 300 steps (not epochs) is enough, set to 0 if enhancing existing concepts\n",
-    "\n",
-    "Text_Encoder_Learning_Rate= \"1e-6\"\n",
-    "\n",
-    "# Keep the learning rate at 1e-6 or lower\n",
-    "\n",
-    "\n",
-    "External_Captions= False\n",
-    "\n",
-    "# Load the captions from a text file for each instance image\n",
-    "\n",
-    "\n",
-    "LoRA_Dim = 64\n",
-    "\n",
-    "# Dimension of the LoRa model, between 64 and 128 is good enough\n",
-    "\n",
-    "\n",
-    "Save_VRAM = False\n",
-    "\n",
-    "# Use as low as 10GB VRAM with Dim = 64\n",
-    "\n",
-    "\n",
-    "Intermediary_Save_Epoch = \"[30,60]\"\n",
-    "\n",
-    "# [30,60] means it will save intermediary models at epoch 30 and epoch 60, you can add as many as you want like [30,60,80,100]\n",
-    "\n",
-    "\n",
-    "#-----------------\n",
-    "dbtrainxl(UNet_Training_Epochs, Text_Encoder_Training_Epochs, UNet_Learning_Rate, Text_Encoder_Learning_Rate, LoRA_Dim, False, 1024, MODEL_NAMExl, SESSION_DIR, INSTANCE_DIR, CAPTIONS_DIR, External_Captions, INSTANCE_NAME, Session_Name, OUTPUT_DIR, 0, Save_VRAM, Intermediary_Save_Epoch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test the Trained Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ComfyUI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Args=\"--listen --port 3000 --preview-method auto\"\n",
-    "\n",
-    "\n",
-    "Huggingface_token_optional= \"\"\n",
-    "\n",
-    "# Restore your backed-up Comfy folder by entering your huggingface token, leave it empty to start fresh or continue with the existing sd folder (if any).\n",
-    "\n",
-    "#--------------------\n",
-    "restored=sdcmff(Huggingface_token_optional, MDLPTH, restored)\n",
-    "!python /workspace/ComfyUI/main.py $Args"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# A1111"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "User = \"\"\n",
-    "\n",
-    "Password= \"\"\n",
-    "\n",
-    "# Add credentials to your Gradio interface (optional).\n",
-    "\n",
-    "\n",
-    "Huggingface_token_optional= \"\"\n",
-    "\n",
-    "# Restore your backed-up SD folder by entering your huggingface token, leave it empty to start fresh or continue with the existing sd folder (if any).\n",
-    "\n",
-    "#-----------------\n",
-    "configf, restoreda=test(MDLPTH, User, Password, Huggingface_token_optional, restoreda)\n",
-    "!python /workspace/sd/stable-diffusion-webui/webui.py $configf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Free up space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

README.md

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

Scripts/runpodLoRA.py

@@ -1,1160 +0,0 @@
-from IPython.display import clear_output
-from subprocess import call, getoutput, Popen
-from IPython.display import display
-import ipywidgets as widgets
-import io
-from PIL import Image, ImageDraw, ImageOps
-import fileinput
-import time
-import os
-from os import listdir
-from os.path import isfile
-import random
-import sys
-from io import BytesIO
-import requests
-from collections import defaultdict
-from math import log, sqrt
-import numpy as np
-import sys
-import fileinput
-from subprocess import check_output
-import six
-import base64
-import re
-
-from urllib.parse import urlparse, parse_qs, unquote
-import urllib.request
-from urllib.request import urlopen, Request
-
-import tempfile
-from tqdm import tqdm
-
-
-
-
-def Deps(force_reinstall):
-
-    if not force_reinstall and os.path.exists('/usr/local/lib/python3.10/dist-packages/safetensors'):
-        ntbks()
-        call('pip install --root-user-action=ignore --disable-pip-version-check -qq diffusers==0.18.1', shell=True, stdout=open('/dev/null', 'w'))
-        print('Modules and notebooks updated, dependencies already installed')        
-        os.environ['TORCH_HOME'] = '/workspace/cache/torch'
-        os.environ['PYTHONWARNINGS'] = 'ignore'
-    else:
-        call('pip install --root-user-action=ignore --disable-pip-version-check --no-deps -qq gdown PyWavelets numpy==1.23.5 accelerate==0.12.0 --force-reinstall', shell=True, stdout=open('/dev/null', 'w'))
-        ntbks()
-        if os.path.exists('deps'):
-            call("rm -r deps", shell=True)
-        if os.path.exists('diffusers'):
-            call("rm -r diffusers", shell=True)
-        call('mkdir deps', shell=True)
-        if not os.path.exists('cache'):
-            call('mkdir cache', shell=True)
-        os.chdir('deps')
-        dwn("https://huggingface.co/TheLastBen/dependencies/resolve/main/rnpddeps-t2.tar.zst", "/workspace/deps/rnpddeps-t2.tar.zst", "Installing dependencies")
-        call('tar -C / --zstd -xf rnpddeps-t2.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call("sed -i 's@~/.cache@/workspace/cache@' /usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", shell=True)
-        os.chdir('/workspace')
-        call('pip install --root-user-action=ignore --disable-pip-version-check -qq diffusers==0.18.1', shell=True, stdout=open('/dev/null', 'w'))
-        call("git clone --depth 1 -q --branch main https://github.com/TheLastBen/diffusers", shell=True, stdout=open('/dev/null', 'w'))
-        call('pip install --root-user-action=ignore --disable-pip-version-check -qq gradio==3.41.2', shell=True, stdout=open('/dev/null', 'w'))
-        call("rm -r deps", shell=True)
-        os.chdir('/workspace')
-        os.environ['TORCH_HOME'] = '/workspace/cache/torch'
-        os.environ['PYTHONWARNINGS'] = 'ignore'
-        call("sed -i 's@text = _formatwarnmsg(msg)@text =\"\"@g' /usr/lib/python3.10/warnings.py", shell=True)
-        clear_output()
-
-        done()
-
-
-def dwn(url, dst, msg):
-    file_size = None
-    req = Request(url, headers={"User-Agent": "torch.hub"})
-    u = urlopen(req)
-    meta = u.info()
-    if hasattr(meta, 'getheaders'):
-        content_length = meta.getheaders("Content-Length")
-    else:
-        content_length = meta.get_all("Content-Length")
-    if content_length is not None and len(content_length) > 0:
-        file_size = int(content_length[0])
-
-    with tqdm(total=file_size, disable=False, mininterval=0.5,
-              bar_format=msg+' |{bar:20}| {percentage:3.0f}%') as pbar:
-        with open(dst, "wb") as f:
-            while True:
-                buffer = u.read(8192)
-                if len(buffer) == 0:
-                    break
-                f.write(buffer)
-                pbar.update(len(buffer))
-            f.close()
-
-
-def ntbks():
-
-    os.chdir('/workspace')
-    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('/workspace/Latest_Notebooks')
-    call('wget -q -i https://huggingface.co/datasets/TheLastBen/RNPD/raw/main/Notebooks.txt', shell=True)
-    call('rm Notebooks.txt', shell=True)
-    os.chdir('/workspace')
-
-def done():
-    done = widgets.Button(
-        description='Done!',
-        disabled=True,
-        button_style='success',
-        tooltip='',
-        icon='check'
-    )
-    display(done)
-
-
-
-def mdlvxl():
-
-  os.chdir('/workspace')
-
-  if os.path.exists('stable-diffusion-XL') and not os.path.exists('/workspace/stable-diffusion-XL/unet/diffusion_pytorch_model.safetensors'):
-     call('rm -r stable-diffusion-XL', shell=True)
-  if not os.path.exists('stable-diffusion-XL'):
-      print('Downloading SDXL model...')
-      call('mkdir stable-diffusion-XL', shell=True)
-      os.chdir('stable-diffusion-XL')
-      call('git init', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      call('git lfs install --system --skip-repo', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      call('git remote add -f origin  https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      call('git config core.sparsecheckout true', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      call('echo -e "\nscheduler\ntext_encoder\ntext_encoder_2\ntokenizer\ntokenizer_2\nunet\nvae\nfeature_extractor\nmodel_index.json\n!*.safetensors\n!*.bin\n!*.onnx*\n!*.xml" > .git/info/sparse-checkout', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      call('git pull origin main', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      dwn('https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/text_encoder/model.safetensors', 'text_encoder/model.safetensors', '1/4')
-      dwn('https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/text_encoder_2/model.safetensors', 'text_encoder_2/model.safetensors', '2/4')
-      dwn('https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/vae/diffusion_pytorch_model.safetensors', 'vae/diffusion_pytorch_model.safetensors', '3/4')
-      dwn('https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/unet/diffusion_pytorch_model.safetensors', 'unet/diffusion_pytorch_model.safetensors', '4/4')  
-      call('rm -r .git', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      os.chdir('/workspace')
-      clear_output()
-      while not os.path.exists('/workspace/stable-diffusion-XL/unet/diffusion_pytorch_model.safetensors'):
-            print('Invalid HF token, make sure you have access to the model')
-            time.sleep(8)
-      if os.path.exists('/workspace/stable-diffusion-XL/unet/diffusion_pytorch_model.safetensors'):
-          print('Using SDXL model')
-  else:
-    print('Using SDXL model')
-    
-  call("sed -i 's@\"force_upcast.*@@' /workspace/stable-diffusion-XL/vae/config.json", shell=True)
-
-  
-
-def downloadmodel_hfxl(Path_to_HuggingFace):
-
-  os.chdir('/workspace')
-  if os.path.exists('stable-diffusion-custom'):
-    call("rm -r stable-diffusion-custom", shell=True)
-  clear_output()
-  
-  if os.path.exists('Fast-Dreambooth/token.txt'):
-    with open("Fast-Dreambooth/token.txt") as f:
-       token = f.read()
-    authe=f'https://USER:{token}@'
-  else:
-    authe="https://"
-
-  clear_output()
-  call("mkdir stable-diffusion-custom", shell=True)
-  os.chdir("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!*.fp16.safetensors" > .git/info/sparse-checkout', shell=True)
-  call("git pull origin main", shell=True)
-  if os.path.exists('unet/diffusion_pytorch_model.safetensors'):
-    call("rm -r .git", shell=True)
-    os.chdir('/workspace')
-    clear_output()
-    done()
-  while not os.path.exists('/workspace/stable-diffusion-custom/unet/diffusion_pytorch_model.safetensors'):
-        print('Check the link you provided')
-        os.chdir('/workspace')
-        time.sleep(5)
-  
-        
-
-def downloadmodel_link_xl(MODEL_LINK): 
-
-    import wget
-    import gdown
-    from gdown.download import get_url_from_gdrive_confirmation    
-    
-    def getsrc(url):
-        parsed_url = urlparse(url)
-        if parsed_url.netloc == 'civitai.com':
-            src='civitai'
-        elif parsed_url.netloc == 'drive.google.com':
-            src='gdrive'
-        elif parsed_url.netloc == 'huggingface.co':
-            src='huggingface'
-        else:
-            src='others'
-        return src
-        
-    src=getsrc(MODEL_LINK)
-
-    def get_name(url, gdrive):
-        if not gdrive:
-            response = requests.get(url, allow_redirects=False)
-            if "Location" in response.headers:
-                redirected_url = response.headers["Location"]
-                quer = parse_qs(urlparse(redirected_url).query)
-                if "response-content-disposition" in quer:
-                    disp_val = quer["response-content-disposition"][0].split(";")
-                    for vals in disp_val:
-                        if vals.strip().startswith("filename="):
-                            filenm=unquote(vals.split("=", 1)[1].strip())
-                            return filenm.replace("\"","")
-        else:
-            headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36"}
-            lnk="https://drive.google.com/uc?id={id}&export=download".format(id=url[url.find("/d/")+3:url.find("/view")])
-            res = requests.session().get(lnk, headers=headers, stream=True, verify=True)
-            res = requests.session().get(get_url_from_gdrive_confirmation(res.text), headers=headers, stream=True, verify=True)
-            content_disposition = six.moves.urllib_parse.unquote(res.headers["Content-Disposition"])
-            filenm = re.search(r"filename\*=UTF-8''(.*)", content_disposition).groups()[0].replace(os.path.sep, "_")
-            return filenm   
-
-    if src=='civitai':
-       modelname=get_name(MODEL_LINK, False)
-    elif src=='gdrive':
-       modelname=get_name(MODEL_LINK, True)
-    else:
-       modelname=os.path.basename(MODEL_LINK)
-
-
-    os.chdir('/workspace')
-    if src=='huggingface':
-        dwn(MODEL_LINK, modelname,'Downloading the Model')
-    else:
-        call("gdown --fuzzy " +MODEL_LINK+ " -O "+modelname, shell=True)
-    
-    if os.path.exists(modelname):
-      if os.path.getsize(modelname) > 1810671599:
-          
-        print('Converting to diffusers...')    
-        call('python /workspace/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path '+modelname+' --dump_path stable-diffusion-custom --from_safetensors', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-       
-        if os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-          os.chdir('/workspace')
-          clear_output()
-          done()
-        else:
-            while not os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-              print('Conversion error')
-              os.chdir('/workspace')
-              time.sleep(5)
-    else:
-        while os.path.getsize(modelname) < 1810671599:
-           print('Wrong link, check that the link is valid')
-           os.chdir('/workspace')
-           time.sleep(5)
-
-
-
-def downloadmodel_path_xl(MODEL_PATH):
-
-  import wget
-  os.chdir('/workspace')
-  clear_output() 
-  if os.path.exists(str(MODEL_PATH)):
-  
-    print('Converting to diffusers...')
-    call('python /workspace/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path '+MODEL_PATH+' --dump_path stable-diffusion-custom --from_safetensors', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-
-    if os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-      clear_output()
-      done()
-    while not os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
-      print('Conversion error')
-      os.chdir('/workspace')
-      time.sleep(5)
-  else:
-    while not os.path.exists(str(MODEL_PATH)):
-       print('Wrong path, use the file explorer to copy the path')
-       os.chdir('/workspace')
-       time.sleep(5)
-
-
-
-
-def dls_xlf(Path_to_HuggingFace, MODEL_PATH, MODEL_LINK):
-
-    os.chdir('/workspace')
-    
-    if Path_to_HuggingFace != "":
-      downloadmodel_hfxl(Path_to_HuggingFace)
-      MODEL_NAMExl="/workspace/stable-diffusion-custom"
-
-    elif MODEL_PATH !="":
-        
-      downloadmodel_path_xl(MODEL_PATH)
-      MODEL_NAMExl="/workspace/stable-diffusion-custom"
-
-    elif MODEL_LINK !="":
-
-      downloadmodel_link_xl(MODEL_LINK)
-      MODEL_NAMExl="/workspace/stable-diffusion-custom"
-
-    else:
-        mdlvxl()
-        MODEL_NAMExl="/workspace/stable-diffusion-XL"
-
-    return MODEL_NAMExl    
-    
-
-
-def sess_xl(Session_Name, MODEL_NAMExl):
-    import gdown
-    import wget
-    os.chdir('/workspace')
-    PT=""
-
-    while Session_Name=="":
-      print('Input the Session Name:') 
-      Session_Name=input("")
-    Session_Name=Session_Name.replace(" ","_")
-
-    WORKSPACE='/workspace/Fast-Dreambooth'
-
-    INSTANCE_NAME=Session_Name
-    OUTPUT_DIR="/workspace/models/"+Session_Name
-    SESSION_DIR=WORKSPACE+"/Sessions/"+Session_Name
-    INSTANCE_DIR=SESSION_DIR+"/instance_images"
-    CAPTIONS_DIR=SESSION_DIR+'/captions'
-    MDLPTH=str(SESSION_DIR+"/"+Session_Name+'.safetensors')
-    
-
-    if os.path.exists(str(SESSION_DIR)) and not os.path.exists(MDLPTH):
-        print('Loading session with no previous LoRa model')
-        if MODEL_NAMExl=="":
-            print('No model found, use the "Model Download" cell to download a model.')
-        else:
-            print('Session Loaded, proceed')
-
-    elif not os.path.exists(str(SESSION_DIR)):
-        call('mkdir -p '+INSTANCE_DIR, shell=True)
-        print('Creating session...')
-        if MODEL_NAMExl=="":
-          print('No model found, use the "Model Download" cell to download a model.')
-        else:
-          print('Session created, proceed to uploading instance images')
-          if MODEL_NAMExl=="":
-             print('No model found, use the "Model Download" cell to download a model.')        
-
-    else:
-        print('Session Loaded, proceed')
-        
-        
-    return WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAMExl
-
-
-
-def uplder(Remove_existing_instance_images, Crop_images, Crop_size, Resize_to_1024_and_keep_aspect_ratio, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR):
-
-    if os.path.exists(INSTANCE_DIR+"/.ipynb_checkpoints"):
-      call('rm -r '+INSTANCE_DIR+'/.ipynb_checkpoints', shell=True)
-
-    uploader = widgets.FileUpload(description="Choose images",accept='image/*, .txt', 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, Resize_to_1024_and_keep_aspect_ratio, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader)
-            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, Resize_to_1024_and_keep_aspect_ratio, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader)
-       done()
-    
-    
-
-def upld(Remove_existing_instance_images, Crop_images, Crop_size, Resize_to_1024_and_keep_aspect_ratio, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, uploader):
-
-    from tqdm import tqdm
-    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 os.path.exists(IMAGES_FOLDER_OPTIONAL+"/.ipynb_checkpoints"):
-          call('rm -r '+IMAGES_FOLDER_OPTIONAL+'/.ipynb_checkpoints', shell=True)    
-    
-        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('/workspace')    
-            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)
-                file=file.convert("RGB")
-                file=ImageOps.exif_transpose(file)
-                width, height = file.size
-                if file.size !=(Crop_size, Crop_size):
-                    image=crop_image(file, Crop_size)
-                    if extension.upper()=="JPG" or extension.upper()=="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 file in uploader.value:
-          filename = file['name']
-          if filename.split(".")[-1]=="txt":
-            with open(CAPTIONS_DIR+'/'+filename, 'w') as f:
-                f.write(bytes(file['content']).decode())            
-          up=[file for file in uploader.value if not file['name'].endswith('.txt')]
-        if Crop_images:
-            for file in tqdm(up, bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                filename = file['name']
-                img = Image.open(io.BytesIO(file['content']))
-                img=img.convert("RGB")
-                img=ImageOps.exif_transpose(img)
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]
-
-                if extension.upper()=="JPG" or extension.upper()=="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    
-                if file.size !=(Crop_size, Crop_size):
-                    image=crop_image(file, Crop_size)
-                    if extension.upper()=="JPG" or extension.upper()=="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 file in tqdm(uploader.value, bar_format='  |{bar:15}| {n_fmt}/{total_fmt} Uploaded'):
-                filename = file['name']
-                img = Image.open(io.BytesIO(file['content']))
-                img=img.convert("RGB")
-                extension = filename.split(".")[-1]
-                identifier=filename.split(".")[0]   
-                
-                if extension.upper()=="JPG" or extension.upper()=="jpg":
-                    img.save(INSTANCE_DIR+"/"+filename, format="JPEG", quality = 100) 
-                else:
-                    img.save(INSTANCE_DIR+"/"+filename, format=extension.upper())                  
-   
-
-    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('/workspace')
-    
-    if Resize_to_1024_and_keep_aspect_ratio and not Crop_images:
-        resize_keep_aspect(INSTANCE_DIR)
-
-
-
-
-def caption(CAPTIONS_DIR, INSTANCE_DIR):
-   
-  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.convert("RGB")
-        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 dbtrainxl(Unet_Training_Epochs, Text_Encoder_Training_Epochs, Unet_Learning_Rate, Text_Encoder_Learning_Rate, dim, Offset_Noise, Resolution, MODEL_NAME, SESSION_DIR, INSTANCE_DIR, CAPTIONS_DIR, External_Captions,  INSTANCE_NAME, Session_Name, OUTPUT_DIR, ofstnselvl, Save_VRAM, Intermediary_Save_Epoch):
-
-
-    if os.path.exists(INSTANCE_DIR+"/.ipynb_checkpoints"):
-      call('rm -r '+INSTANCE_DIR+'/.ipynb_checkpoints', shell=True)
-    if os.path.exists(CAPTIONS_DIR+"/.ipynb_checkpoints"):
-      call('rm -r '+CAPTIONS_DIR+'/.ipynb_checkpoints', shell=True)
-
-
-    Seed=random.randint(1, 999999)
-    
-    ofstnse=""
-    if Offset_Noise:
-      ofstnse="--offset_noise"
-      
-    GC=''
-    if Save_VRAM:
-        GC='--gradient_checkpointing'
-        
-    extrnlcptn=""
-    if External_Captions:
-      extrnlcptn="--external_captions"      
-
-    precision="fp16"
-
-   
-   
-    def train_only_text(SESSION_DIR, MODEL_NAME, INSTANCE_DIR, OUTPUT_DIR, Seed, Resolution, ofstnse, extrnlcptn, precision, Training_Epochs):
-        print('Training the Text Encoder...')
-        call('accelerate launch /workspace/diffusers/examples/dreambooth/train_dreambooth_sdxl_TI.py \
-        '+ofstnse+' \
-        '+extrnlcptn+' \
-        --dim='+str(dim)+' \
-        --ofstnselvl='+str(ofstnselvl)+' \
-        --image_captions_filename \
-        --Session_dir='+SESSION_DIR+' \
-        --pretrained_model_name_or_path='+MODEL_NAME+' \
-        --instance_data_dir='+INSTANCE_DIR+' \
-        --output_dir='+OUTPUT_DIR+' \
-        --captions_dir='+CAPTIONS_DIR+' \
-        --seed='+str(Seed)+' \
-        --resolution='+str(Resolution)+' \
-        --mixed_precision='+str(precision)+' \
-        --train_batch_size=1 \
-        --gradient_accumulation_steps=1 '+GC+ ' \
-        --use_8bit_adam \
-        --learning_rate='+str(Text_Encoder_Learning_Rate)+' \
-        --lr_scheduler="cosine" \
-        --lr_warmup_steps=0 \
-        --num_train_epochs='+str(Training_Epochs), shell=True)
-   
-   
-
-    def train_only_unet(SESSION_DIR, MODEL_NAME, INSTANCE_DIR, OUTPUT_DIR, Seed, Resolution, ofstnse, extrnlcptn, precision, Training_Epochs):
-        print('Training the UNet...')
-        call('accelerate launch /workspace/diffusers/examples/dreambooth/train_dreambooth_sdxl_lora.py \
-        '+ofstnse+' \
-        '+extrnlcptn+' \
-        --saves='+Intermediary_Save_Epoch+' \
-        --dim='+str(dim)+' \
-        --ofstnselvl='+str(ofstnselvl)+' \
-        --image_captions_filename \
-        --Session_dir='+SESSION_DIR+' \
-        --pretrained_model_name_or_path='+MODEL_NAME+' \
-        --instance_data_dir='+INSTANCE_DIR+' \
-        --output_dir='+OUTPUT_DIR+' \
-        --captions_dir='+CAPTIONS_DIR+' \
-        --seed='+str(Seed)+' \
-        --resolution='+str(Resolution)+' \
-        --mixed_precision='+str(precision)+' \
-        --train_batch_size=1 \
-        --gradient_accumulation_steps=1 '+GC+ ' \
-        --use_8bit_adam \
-        --learning_rate='+str(Unet_Learning_Rate)+' \
-        --lr_scheduler="cosine" \
-        --lr_warmup_steps=0 \
-        --num_train_epochs='+str(Training_Epochs), shell=True)
-
-      
-
-    if Unet_Training_Epochs!=0:
-        if Text_Encoder_Training_Epochs!=0:
-            train_only_text(SESSION_DIR, MODEL_NAME, INSTANCE_DIR, OUTPUT_DIR, Seed, Resolution, ofstnse, extrnlcptn, precision, Training_Epochs=Text_Encoder_Training_Epochs)
-            clear_output()
-        train_only_unet(SESSION_DIR, MODEL_NAME, INSTANCE_DIR, OUTPUT_DIR, Seed, Resolution, ofstnse, extrnlcptn, precision, Training_Epochs=Unet_Training_Epochs)
-    else  :
-      print('Nothing to do')
-
-
-    if os.path.exists(SESSION_DIR+'/'+Session_Name+'.safetensors'):
-        clear_output()
-        print("DONE, the LoRa model is in the session's folder")
-    else:
-        print("Something went wrong")
-
-
-
-
-def sdcmff(Huggingface_token_optional, MDLPTH, restored):
-
-    from slugify import slugify
-    from huggingface_hub import HfApi, CommitOperationAdd, create_repo
-    
-    os.chdir('/workspace')
-
-    if restored:
-        Huggingface_token_optional=""
-
-    if Huggingface_token_optional!="":
-       username = HfApi().whoami(Huggingface_token_optional)["name"]
-       backup=f"https://huggingface.co/datasets/{username}/fast-stable-diffusion/resolve/main/sdcomfy_backup_rnpd.tar.zst"
-       headers = {"Authorization": f"Bearer {Huggingface_token_optional}"}
-       response = requests.head(backup, headers=headers)
-       if response.status_code == 302:
-          restored=True
-          print('Restoring ComfyUI...')
-          open('/workspace/sdcomfy_backup_rnpd.tar.zst', 'wb').write(requests.get(backup, headers=headers).content)
-          call('tar --zstd -xf sdcomfy_backup_rnpd.tar.zst', shell=True)
-          call('rm sdcomfy_backup_rnpd.tar.zst', shell=True)
-       else:
-          print('Backup not found, using a fresh/existing repo...')
-          time.sleep(2)
-          if not os.path.exists('ComfyUI'):
-              call('git clone -q --depth 1 https://github.com/comfyanonymous/ComfyUI', shell=True)
-    else:
-        print('Installing/Updating the repo...')
-        if not os.path.exists('ComfyUI'):
-            call('git clone -q --depth 1 https://github.com/comfyanonymous/ComfyUI', shell=True)
-
-    os.chdir('ComfyUI')
-    call('git reset --hard', shell=True)
-    print('')
-    call('git pull', shell=True)
-
-    if os.path.exists(MDLPTH):
-        call('ln -s '+os.path.dirname(MDLPTH)+' models/loras', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-        
-    clean_symlinks('models/loras')        
-
-    if not os.path.exists('models/checkpoints/sd_xl_base_1.0.safetensors'):
-        call('ln -s /workspace/auto-models/* models/checkpoints', shell=True)
-
-
-    podid=os.environ.get('RUNPOD_POD_ID')
-    localurl=f"https://{podid}-3001.proxy.runpod.net"
-    call("sed -i 's@print(\"To see the GUI go to: http://{}:{}\".format(address, port))@print(\"\u2714 Connected\")\\n            print(\""+localurl+"\")@' /workspace/ComfyUI/server.py", shell=True)
-    os.chdir('/workspace')
-    
-    return restored 
-
-
-
-
-def test(MDLPTH, User, Password, Huggingface_token_optional, restoreda):
-
-    from slugify import slugify
-    from huggingface_hub import HfApi, CommitOperationAdd, create_repo
-    import gradio
-    
-    gradio.close_all()
-               
-
-    auth=f"--gradio-auth {User}:{Password}"
-    if User =="" or Password=="":
-      auth=""
-
-
-    if restoreda:
-        Huggingface_token_optional=""
-
-    if Huggingface_token_optional!="":
-       username = HfApi().whoami(Huggingface_token_optional)["name"]
-       backup=f"https://huggingface.co/datasets/{username}/fast-stable-diffusion/resolve/main/sd_backup_rnpd.tar.zst"
-       headers = {"Authorization": f"Bearer {Huggingface_token_optional}"}
-       response = requests.head(backup, headers=headers)
-       if response.status_code == 302:
-          restoreda=True
-          print('Restoring the SD folder...')
-          open('/workspace/sd_backup_rnpd.tar.zst', 'wb').write(requests.get(backup, headers=headers).content)
-          call('tar --zstd -xf sd_backup_rnpd.tar.zst', shell=True)
-          call('rm sd_backup_rnpd.tar.zst', shell=True)
-       else:
-          print('Backup not found, using a fresh/existing repo...')
-          time.sleep(2)
-          if not os.path.exists('/workspace/sd/stablediffusiond'): #reset later
-             call('wget -q -O sd_mrep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_mrep.tar.zst', shell=True)
-             call('tar --zstd -xf sd_mrep.tar.zst', shell=True)
-             call('rm sd_mrep.tar.zst', shell=True)        
-          os.chdir('/workspace/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)            
-        
-    else:
-        print('Installing/Updating the repo...')
-        os.chdir('/workspace')
-        if not os.path.exists('/workspace/sd/stablediffusiond'): #reset later
-           call('wget -q -O sd_mrep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_mrep.tar.zst', shell=True)
-           call('tar --zstd -xf sd_mrep.tar.zst', shell=True)
-           call('rm sd_mrep.tar.zst', shell=True)        
-
-        os.chdir('/workspace/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('/workspace/sd/stable-diffusion-webui/')
-    call('git reset --hard', shell=True)
-    print('')
-    call('git pull', shell=True)
-    
-    
-    if os.path.exists(MDLPTH):
-        call('mkdir models/Lora', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-        call('ln -s '+os.path.dirname(MDLPTH)+' models/Lora', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-    
-    if not os.path.exists('models/Stable-diffusion/sd_xl_base_1.0.safetensors'):
-        call('ln -s /workspace/auto-models/* models/Stable-diffusion', shell=True)
-
-    clean_symlinks('models/Lora')
-   
-    os.chdir('/workspace')
-
-
-    call('wget -q -O /usr/local/lib/python3.10/dist-packages/gradio/blocks.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/blocks.py', shell=True)
-   
-    os.chdir('/workspace/sd/stable-diffusion-webui/modules')
-    
-    call("sed -i 's@possible_sd_paths =.*@possible_sd_paths = [\"/workspace/sd/stablediffusion\"]@' /workspace/sd/stable-diffusion-webui/modules/paths.py", shell=True)
-    call("sed -i 's@\.\.\/@src/@g' /workspace/sd/stable-diffusion-webui/modules/paths.py", shell=True)
-    call("sed -i 's@src\/generative-models@generative-models@g' /workspace/sd/stable-diffusion-webui/modules/paths.py", shell=True)
-    
-    call("sed -i 's@\[\"sd_model_checkpoint\"\]@\[\"sd_model_checkpoint\", \"sd_vae\", \"CLIP_stop_at_last_layers\", \"inpainting_mask_weight\", \"initial_noise_multiplier\"\]@g' /workspace/sd/stable-diffusion-webui/modules/shared.py", shell=True)
-    call("sed -i 's@print(\"No module.*@@' /workspace/sd/stablediffusion/ldm/modules/diffusionmodules/model.py", shell=True)
-    os.chdir('/workspace/sd/stable-diffusion-webui')
-    clear_output()
-
-    podid=os.environ.get('RUNPOD_POD_ID')
-    localurl=f"{podid}-3001.proxy.runpod.net"
-
-    for line in fileinput.input('/usr/local/lib/python3.10/dist-packages/gradio/blocks.py', inplace=True):
-      if line.strip().startswith('self.server_name ='):
-          line = f'            self.server_name = "{localurl}"\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)
-
-
-    configf="--disable-console-progressbars --upcast-sampling --no-half-vae --disable-safe-unpickle --api --opt-sdp-attention --enable-insecure-extension-access --no-download-sd-model  --skip-version-check  --listen --port 3000 --ckpt /workspace/sd/stable-diffusion-webui/models/Stable-diffusion/sd_xl_base_1.0.safetensors "+auth
-    
-    
-    return configf, restoreda
-
-
-
-
-def clean():
-    
-    Sessions=os.listdir("/workspace/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("THE SESSION "+s.value+" HAS BEEN REMOVED FROM THE STORAGE")
-                call('rm -r /workspace/Fast-Dreambooth/Sessions/'+s.value, shell=True)
-                if os.path.exists('/workspace/models/'+s.value):
-                  call('rm -r /workspace/models/'+s.value, shell=True)
-                s.options=os.listdir("/workspace/Fast-Dreambooth/Sessions")       
-
-
-            else:
-                d.close()
-                s.close()
-                clear_output()
-                print("NOTHING TO REMOVE")
-
-    d.on_click(rem)
-    if s.value is not None:
-        display(s,d,out)
-    else:
-        print("NOTHING TO REMOVE")
-
-
-
-def crop_image(im, size):
-
-  import cv2
-
-  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
-  
-
-
-def resize_keep_aspect(DIR):
-
-    import cv2
-
-    min_dimension=1024
-    
-    for filename in os.listdir(DIR):
-        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):    
-            image = cv2.imread(os.path.join(DIR, filename))
-
-            org_height, org_width = image.shape[0], image.shape[1]
-
-            if org_width < org_height:
-                new_width = min_dimension
-                new_height = int(org_height * (min_dimension / org_width))
-            else:
-                new_height = min_dimension
-                new_width = int(org_width * (min_dimension / org_height))
-
-            resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_LANCZOS4)
-
-            cv2.imwrite(os.path.join(DIR, filename), resized_image, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
-
-
-
-
-def clean_symlinks(path):
-    for item in os.listdir(path):
-        lnk = os.path.join(path, item)
-        if os.path.islink(lnk) and not os.path.exists(os.readlink(lnk)):
-            os.remove(lnk)

Scripts/runpodSD.py

@@ -1,499 +0,0 @@
-import os
-from IPython.display import clear_output
-from subprocess import call, getoutput, Popen, run
-import time
-import ipywidgets as widgets
-import requests
-import sys
-import fileinput
-from torch.hub import download_url_to_file
-from urllib.parse import urlparse, parse_qs, unquote
-import re
-import six
-
-from urllib.request import urlopen, Request
-import tempfile
-from tqdm import tqdm 
-
-
-
-
-def Deps(force_reinstall):
-
-    if not force_reinstall and os.path.exists('/usr/local/lib/python3.10/dist-packages/safetensors'):
-        ntbks()
-        print('Modules and notebooks updated, dependencies already installed')
-        os.environ['TORCH_HOME'] = '/workspace/cache/torch'
-        os.environ['PYTHONWARNINGS'] = 'ignore'
-    else:
-        call('pip install --root-user-action=ignore --disable-pip-version-check --no-deps -qq gdown PyWavelets numpy==1.23.5 accelerate==0.12.0 --force-reinstall', shell=True, stdout=open('/dev/null', 'w'))
-        ntbks()
-        if os.path.exists('deps'):
-            call("rm -r deps", shell=True)
-        if os.path.exists('diffusers'):
-            call("rm -r diffusers", shell=True)
-        call('mkdir deps', shell=True)
-        if not os.path.exists('cache'):
-            call('mkdir cache', shell=True)
-        os.chdir('deps')
-        dwn("https://huggingface.co/TheLastBen/dependencies/resolve/main/rnpddeps-t2.tar.zst", "/workspace/deps/rnpddeps-t2.tar.zst", "Installing dependencies")
-        call('tar -C / --zstd -xf rnpddeps-t2.tar.zst', shell=True, stdout=open('/dev/null', 'w'))
-        call("sed -i 's@~/.cache@/workspace/cache@' /usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", shell=True)
-        os.chdir('/workspace')
-        call("git clone --depth 1 -q --branch main https://github.com/TheLastBen/diffusers", shell=True, stdout=open('/dev/null', 'w'))
-        call('pip install --root-user-action=ignore --disable-pip-version-check -qq gradio==3.41.2', shell=True, stdout=open('/dev/null', 'w'))
-        call("rm -r deps", shell=True)
-        os.chdir('/workspace')
-        os.environ['TORCH_HOME'] = '/workspace/cache/torch'
-        os.environ['PYTHONWARNINGS'] = 'ignore'
-        call("sed -i 's@text = _formatwarnmsg(msg)@text =\"\"@g' /usr/lib/python3.10/warnings.py", shell=True)
-        clear_output()
-
-        done()
-
-
-def dwn(url, dst, msg):
-    file_size = None
-    req = Request(url, headers={"User-Agent": "torch.hub"})
-    u = urlopen(req)
-    meta = u.info()
-    if hasattr(meta, 'getheaders'):
-        content_length = meta.getheaders("Content-Length")
-    else:
-        content_length = meta.get_all("Content-Length")
-    if content_length is not None and len(content_length) > 0:
-        file_size = int(content_length[0])
-
-    with tqdm(total=file_size, disable=False, mininterval=0.5,
-              bar_format=msg+' |{bar:20}| {percentage:3.0f}%') as pbar:
-        with open(dst, "wb") as f:
-            while True:
-                buffer = u.read(8192)
-                if len(buffer) == 0:
-                    break
-                f.write(buffer)
-                pbar.update(len(buffer))
-            f.close()
-
-
-def ntbks():
-
-    os.chdir('/workspace')
-    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('/workspace/Latest_Notebooks')
-    call('wget -q -i https://huggingface.co/datasets/TheLastBen/RNPD/raw/main/Notebooks.txt', shell=True)
-    call('rm Notebooks.txt', shell=True)
-    os.chdir('/workspace')
-
-
-def repo(Huggingface_token_optional):
-
-    from slugify import slugify
-    from huggingface_hub import HfApi, CommitOperationAdd, create_repo
-    
-    os.chdir('/workspace')
-    if Huggingface_token_optional!="":
-       username = HfApi().whoami(Huggingface_token_optional)["name"]
-       backup=f"https://huggingface.co/datasets/{username}/fast-stable-diffusion/resolve/main/sd_backup_rnpd.tar.zst"
-       headers = {"Authorization": f"Bearer {Huggingface_token_optional}"}
-       response = requests.head(backup, headers=headers)
-       if response.status_code == 302:
-          print('Restoring the SD folder...')
-          open('/workspace/sd_backup_rnpd.tar.zst', 'wb').write(requests.get(backup, headers=headers).content)
-          call('tar --zstd -xf sd_backup_rnpd.tar.zst', shell=True)
-          call('rm sd_backup_rnpd.tar.zst', shell=True)
-       else:
-          print('Backup not found, using a fresh/existing repo...')
-          time.sleep(2)
-          if not os.path.exists('/workspace/sd/stablediffusiond'): #reset later
-             call('wget -q -O sd_mrep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_mrep.tar.zst', shell=True)
-             call('tar --zstd -xf sd_mrep.tar.zst', shell=True)
-             call('rm sd_mrep.tar.zst', shell=True)        
-          os.chdir('/workspace/sd')
-          if not os.path.exists('SD'):
-              call('git clone -q --depth 1 --branch main https://github.com/apollo812/SD', shell=True)
-        
-    else:
-        print('Installing/Updating the repo...')
-        os.chdir('/workspace')
-        if not os.path.exists('/workspace/sd/stablediffusiond'): #reset later
-           call('wget -q -O sd_mrep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_mrep.tar.zst', shell=True)
-           call('tar --zstd -xf sd_mrep.tar.zst', shell=True)
-           call('rm sd_mrep.tar.zst', shell=True)        
-
-        os.chdir('/workspace/sd')
-        if not os.path.exists('SD'):
-            call('git clone -q --depth 1 --branch main https://github.com/apollo812/SD', shell=True)
-
-
-    os.chdir('/workspace/sd/SD/')
-    call('git reset --hard', shell=True)
-    print('')
-    call('git pull', shell=True)
-    os.chdir('/workspace')
-    clear_output()
-    done()
-
-
-
-def mdl(Original_Model_Version, Path_to_MODEL, MODEL_LINK):
-
-    import gdown
-    
-    src=getsrc(MODEL_LINK)  
-
-    if not os.path.exists('/workspace/sd/SD/models/Stable-diffusion/SDv1-5.ckpt'):
-        call('ln -s /workspace/auto-models/* /workspace/sd/SD/models/Stable-diffusion', shell=True)
-
-    if Path_to_MODEL !='':
-      if os.path.exists(str(Path_to_MODEL)):
-        print('Using the custom model')
-        model=Path_to_MODEL
-      else:
-          print('Wrong path, check that the path to the model is correct')
-
-    elif MODEL_LINK !="":
-
-      if src=='civitai':
-         modelname=get_name(MODEL_LINK, False)
-         model=f'/workspace/sd/SD/models/Stable-diffusion/{modelname}'
-         if not os.path.exists(model):
-            dwn(MODEL_LINK, model, 'Downloading the custom model')
-            clear_output()
-         else:
-            print('Model already exists')
-      elif src=='gdrive':
-         modelname=get_name(MODEL_LINK, True)
-         model=f'/workspace/sd/SD/models/Stable-diffusion/{modelname}'
-         if not os.path.exists(model):
-            gdown.download(url=MODEL_LINK, output=model, quiet=False, fuzzy=True)
-            clear_output()
-         else:
-            print('Model already exists')
-      else:
-         modelname=os.path.basename(MODEL_LINK)
-         model=f'/workspace/sd/SD/models/Stable-diffusion/{modelname}'
-         if not os.path.exists(model):
-            gdown.download(url=MODEL_LINK, output=model, quiet=False, fuzzy=True)
-            clear_output()
-         else:
-            print('Model already exists')
-
-      if os.path.exists(model) and os.path.getsize(model) > 1810671599:
-        print('Model downloaded, using the custom model.')
-      else:
-        call('rm '+model, shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-        print('Wrong link, check that the link is valid')
-
-    else:
-        if Original_Model_Version == "v1.5":
-           model="/workspace/sd/SD/models/Stable-diffusion/SDv1-5.ckpt"
-           print('Using the original V1.5 model')
-        elif Original_Model_Version == "v2-512":
-            model='/workspace/sd/SD/models/Stable-diffusion/SDv2-512.ckpt'
-            if not os.path.exists('/workspace/sd/SD/models/Stable-diffusion/SDv2-512.ckpt'):
-               print('Downloading the V2-512 model...')
-               call('gdown -O '+model+' https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-nonema-pruned.ckpt', shell=True)
-               clear_output()
-            print('Using the original V2-512 model')
-        elif Original_Model_Version == "v2-768":
-           model="/workspace/sd/SD/models/Stable-diffusion/SDv2-768.ckpt"
-           print('Using the original V2-768 model')
-        elif Original_Model_Version == "SDXL":
-            model="/workspace/sd/SD/models/Stable-diffusion/sd_xl_base_1.0.safetensors"
-            print('Using the original SDXL model')
-
-        else:
-            model="/workspace/sd/SD/models/Stable-diffusion"
-            print('Wrong model version, try again')
-    try:
-        model
-    except:
-        model="/workspace/sd/SD/models/Stable-diffusion"
-
-    return model
-
-
-def loradwn(LoRA_LINK):
-
-    if LoRA_LINK=='':
-        print('Nothing to do')
-    else:
-        os.makedirs('/workspace/sd/SD/models/Lora', exist_ok=True)
-
-        src=getsrc(LoRA_LINK)
-
-        if src=='civitai':
-            modelname=get_name(LoRA_LINK, False)
-            loramodel=f'/workspace/sd/SD/models/Lora/{modelname}'
-            if not os.path.exists(loramodel):
-              dwn(LoRA_LINK, loramodel, 'Downloading the LoRA model')
-              clear_output()
-            else:
-              print('Model already exists')
-        elif src=='gdrive':
-            modelname=get_name(LoRA_LINK, True)
-            loramodel=f'/workspace/sd/SD/models/Lora/{modelname}'
-            if not os.path.exists(loramodel):
-              gdown.download(url=LoRA_LINK, output=loramodel, quiet=False, fuzzy=True)
-              clear_output()
-            else:
-              print('Model already exists')
-        else:
-            modelname=os.path.basename(LoRA_LINK)
-            loramodel=f'/workspace/sd/SD/models/Lora/{modelname}'
-            if not os.path.exists(loramodel):
-              gdown.download(url=LoRA_LINK, output=loramodel, quiet=False, fuzzy=True)
-              clear_output()
-            else:
-              print('Model already exists')
-
-        if os.path.exists(loramodel) :
-          print('LoRA downloaded')
-        else:
-          print('Wrong link, check that the link is valid')
-
-
-
-def CNet(ControlNet_Model, ControlNet_XL_Model):
-    
-    def download(url, model_dir):
-
-        filename = os.path.basename(urlparse(url).path)
-        pth = os.path.abspath(os.path.join(model_dir, filename))
-        if not os.path.exists(pth):
-            print('Downloading: '+os.path.basename(url))
-            download_url_to_file(url, pth, hash_prefix=None, progress=True)
-        else:
-          print(f"The model {filename} already exists")    
-
-    wrngv1=False
-    os.chdir('/workspace/sd/SD/extensions')
-    if not os.path.exists("sd-webui-controlnet"):
-      call('git clone https://github.com/Mikubill/sd-webui-controlnet.git', shell=True)
-      os.chdir('/workspace')
-    else:
-      os.chdir('sd-webui-controlnet')
-      call('git reset --hard', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      call('git pull', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
-      os.chdir('/workspace')
-
-    mdldir="/workspace/sd/SD/extensions/sd-webui-controlnet/models"
-    for filename in os.listdir(mdldir):
-      if "_sd14v1" in filename:
-        renamed = re.sub("_sd14v1", "-fp16", filename)
-        os.rename(os.path.join(mdldir, filename), os.path.join(mdldir, renamed))
-
-    call('wget -q -O CN_models.txt https://github.com/TheLastBen/fast-stable-diffusion/raw/main/AUTOMATIC1111_files/CN_models.txt', shell=True)
-    call('wget -q -O CN_models_XL.txt https://github.com/TheLastBen/fast-stable-diffusion/raw/main/AUTOMATIC1111_files/CN_models_XL.txt', shell=True)
-      
-    with open("CN_models.txt", 'r') as f:
-        mdllnk = f.read().splitlines()
-    with open("CN_models_XL.txt", 'r') as d:
-        mdllnk_XL = d.read().splitlines()
-    call('rm CN_models.txt CN_models_XL.txt', shell=True)
-    
-    os.chdir('/workspace')
-
-    if ControlNet_Model == "All" or ControlNet_Model == "all" :     
-      for lnk in mdllnk:
-          download(lnk, mdldir)
-      clear_output()
-
-      
-    elif ControlNet_Model == "15":
-      mdllnk=list(filter(lambda x: 't2i' in x, mdllnk))
-      for lnk in mdllnk:
-          download(lnk, mdldir)
-      clear_output()        
-
-
-    elif ControlNet_Model.isdigit() and int(ControlNet_Model)-1<14 and int(ControlNet_Model)>0:
-      download(mdllnk[int(ControlNet_Model)-1], mdldir)
-      clear_output()
-      
-    elif ControlNet_Model == "none":
-       pass
-       clear_output()
-
-    else:
-      print('Wrong ControlNet V1 choice, try again')
-      wrngv1=True
-
-
-    if ControlNet_XL_Model == "All" or ControlNet_XL_Model == "all" :
-      for lnk_XL in mdllnk_XL:
-          download(lnk_XL, mdldir)
-      if not wrngv1:
-        clear_output()
-      done()
-
-    elif ControlNet_XL_Model.isdigit() and int(ControlNet_XL_Model)-1<5:
-      download(mdllnk_XL[int(ControlNet_XL_Model)-1], mdldir)
-      if not wrngv1:
-        clear_output()
-      done()
-    
-    elif ControlNet_XL_Model == "none":
-       pass
-       if not wrngv1:
-        clear_output()
-       done()       
-
-    else:
-      print('Wrong ControlNet XL choice, try again')
-
-
-
-def sd(User, Password, model):
-
-    import gradio
-    
-    gradio.close_all()
-    
-    auth=f"--gradio-auth {User}:{Password}"
-    if User =="" or Password=="":
-      auth=""
-
-    call('wget -q -O /usr/local/lib/python3.10/dist-packages/gradio/blocks.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/AUTOMATIC1111_files/blocks.py', shell=True)
-   
-    os.chdir('/workspace/sd/SD/modules')
-
-    call("sed -i 's@possible_sd_paths =.*@possible_sd_paths = [\"/workspace/sd/stablediffusion\"]@' /workspace/sd/SD/modules/paths.py", shell=True)
-    call("sed -i 's@\.\.\/@src/@g' /workspace/sd/SD/modules/paths.py", shell=True)
-    call("sed -i 's@src\/generative-models@generative-models@g' /workspace/sd/SD/modules/paths.py", shell=True)
-    
-    call("sed -i 's@\[\"sd_model_checkpoint\"\]@\[\"sd_model_checkpoint\", \"sd_vae\", \"CLIP_stop_at_last_layers\", \"inpainting_mask_weight\", \"initial_noise_multiplier\"\]@g' /workspace/sd/SD/modules/shared.py", shell=True)
-    
-    call("sed -i 's@print(\"No module.*@@' /workspace/sd/stablediffusion/ldm/modules/diffusionmodules/model.py", shell=True)
-    os.chdir('/workspace/sd/SD')
-    clear_output()
-
-    podid=os.environ.get('RUNPOD_POD_ID')
-    localurl=f"{podid}-3001.proxy.runpod.net"
-
-    for line in fileinput.input('/usr/local/lib/python3.10/dist-packages/gradio/blocks.py', inplace=True):
-      if line.strip().startswith('self.server_name ='):
-          line = f'            self.server_name = "{localurl}"\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)
-      
-    if model=="":
-        mdlpth=""
-    else:
-        if os.path.isfile(model):
-            mdlpth="--ckpt "+model
-        else:
-            mdlpth="--ckpt-dir "+model
-
-    configf="--disable-console-progressbars --no-half-vae --disable-safe-unpickle --api --no-download-sd-model --opt-sdp-attention --enable-insecure-extension-access  --skip-version-check --listen --port 3000 "+auth+" "+mdlpth
-
-    return configf
-
-
-
-def save(Huggingface_Write_token):
-    
-    from slugify import slugify
-    from huggingface_hub import HfApi, CommitOperationAdd, create_repo
-    
-    if Huggingface_Write_token=="":
-        print('A huggingface write token is required')
-        
-    else:
-        os.chdir('/workspace')
-        
-        if os.path.exists('sd'):
-        
-            call('tar --exclude="SD/models/*/*" --exclude="sd-webui-controlnet/models/*" --zstd -cf sd_backup_rnpd.tar.zst sd', shell=True)
-            api = HfApi()
-            username = api.whoami(token=Huggingface_Write_token)["name"]
-
-            repo_id = f"{username}/{slugify('fast-stable-diffusion')}"
-
-            print("Backing up...")
-
-            operations = [CommitOperationAdd(path_in_repo="sd_backup_rnpd.tar.zst", path_or_fileobj="/workspace/sd_backup_rnpd.tar.zst")]
-
-            create_repo(repo_id,private=True, token=Huggingface_Write_token, exist_ok=True, repo_type="dataset")
-
-            api.create_commit(
-              repo_id=repo_id,
-              repo_type="dataset",
-              operations=operations,
-              commit_message="SD folder Backup",
-              token=Huggingface_Write_token
-            )
-
-            call('rm sd_backup_rnpd.tar.zst', shell=True)
-            clear_output()
-
-            done()
-     
-        else:
-            print('Nothing to backup')
-        
-
-
-
-def getsrc(url):
-
-    parsed_url = urlparse(url)
-    
-    if parsed_url.netloc == 'civitai.com':
-        src='civitai'
-    elif parsed_url.netloc == 'drive.google.com':
-        src='gdrive'
-    elif parsed_url.netloc == 'huggingface.co':
-        src='huggingface'
-    else:
-        src='others'
-    return src
-
-
-
-def get_name(url, gdrive):
-
-    from gdown.download import get_url_from_gdrive_confirmation
-
-    if not gdrive:
-        response = requests.get(url, allow_redirects=False)
-        if "Location" in response.headers:
-            redirected_url = response.headers["Location"]
-            quer = parse_qs(urlparse(redirected_url).query)
-            if "response-content-disposition" in quer:
-                disp_val = quer["response-content-disposition"][0].split(";")
-                for vals in disp_val:
-                    if vals.strip().startswith("filename="):
-                        filenm=unquote(vals.split("=", 1)[1].strip())
-                        return filenm.replace("\"","")
-    else:
-        headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36"}
-        lnk="https://drive.google.com/uc?id={id}&export=download".format(id=url[url.find("/d/")+3:url.find("/view")])
-        res = requests.session().get(lnk, headers=headers, stream=True, verify=True)
-        res = requests.session().get(get_url_from_gdrive_confirmation(res.text), headers=headers, stream=True, verify=True)
-        content_disposition = six.moves.urllib_parse.unquote(res.headers["Content-Disposition"])
-        filenm = re.search(r"filename\*=UTF-8''(.*)", content_disposition).groups()[0].replace(os.path.sep, "_")
-        return filenm   
-
-
-
-
-def done():
-    done = widgets.Button(
-        description='Done!',
-        disabled=True,
-        button_style='success',
-        tooltip='',
-        icon='check'
-    )
-    display(done)

default/train/0000.parquet

@@ -0,0 +1,3 @@
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+oid sha256:7417b2b721df95568514a010f6c9e1327720c78470972915af79b57a3db44df1
+size 1366