Delete mainpaperspacev2.py

mainpaperspacev2.py

@@ -1,1255 +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'):
-        print('Modules updated, dependencies already installed')
-    else:
-        print('Installing 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('/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('Check 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('Conversion error')
-      os.chdir('/notebooks')
-      time.sleep(5)
-      
-  else:
-    while not os.path.exists(str(CKPT_Path)):
-       print('Wrong 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('Conversion error')
-              os.chdir('/notebooks')
-              time.sleep(5)
-    else:
-        while os.path.getsize('/models/model.ckpt') < 1810671599:
-           print('Wrong 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('Using the original V2-512 model')
-      elif Model_Version=="768":
-        MODEL_NAMEv2="/datasets/stable-diffusion-v2-1/stable-diffusion-2-1"
-        print('Using the original V2-768 model')
-      else:
-        MODEL_NAMEv2=""
-        print('Wrong 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('Input the Session Name:') 
-      Session_Name=input("")
-    Session_Name=Session_Name.replace(" ","_")
-
-    WORKSPACE='/notebooks/Fast-Dreambooth'
-
-    if Session_Link_optional !="":
-      print('Downloading 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('No final checkpoint model found, select which intermediary checkpoint to use, enter only the number, (000 to skip):\n')
-
-        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('Using the model '+ mdls[int(n)]+" ...")
-          time.sleep(8)
-        else:
-          print('Skipping the intermediary checkpoints.')
-  
-
-    if os.path.exists(str(SESSION_DIR)) and not os.path.exists(MDLPTH):
-      print('Loading session with no previous model, using the original model or the custom downloaded model')
-      if MODEL_NAMEv2=="":
-        print('No model found, use the "Model Download" cell to download a model.')
-      else:
-        print('Session Loaded, proceed to uploading instance images')
-
-    elif os.path.exists(MDLPTH):
-      print('Session 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('Session 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('Session 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('Session loaded.')
-      else:     
-        if not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-          print('Conversion 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('Creating session...')
-        if MODEL_NAMEv2=="":
-          print('No model found, use the "Model Download" cell to download a model.')
-        else:
-          print('Session 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('Overwrite your previously trained model ?, answering "yes" will train a new model, answering "no" will resumev2 the training of the previous model?  yes or no ?')
-      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('No 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('Resuming Training...')
-      resuming="Yes"
-    elif Resume_Training and not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
-      print('Previous model not found, training a new model...') 
-      MODELT_NAME=MODEL_NAMEv2
-      while MODEL_NAMEv2=="":
-        print('No 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('Resuming Training...')    
-        print('Training the UNet...')
-        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('Training the text encoder...')
-      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('Resuming Training...')    
-          print('Training the text encoder on the concept...')
-          dump_only_textenc(trnonltxt, MODELT_NAME, CONCEPT_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxtc)
-        else:
-          clear_output()
-          if resuming=="Yes":
-            print('Resuming Training...')      
-          print('No concept images found, skipping concept training...')
-          Text_Encoder_Concept_Training_Steps=0
-          time.sleep(8)
-      else:
-          clear_output()
-          if resuming=="Yes":
-            print('Resuming Training...')
-          print('No 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('Nothing 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("DONE, the CKPT model is in the session's folder")
-          else:
-            print("Something went wrong")    
-            
-        else:
-          print("Something went wrong")
-
-    return resumev2
-      
-      
-def test(Custom_Path, Previous_Session_Name, Session_Name, User, Password, Use_localtunnel):
-    
-    
-    if Previous_Session_Name!="":
-      print("Loading 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("There is no trained model in the previous session")
-         time.sleep(5)
-          
-    elif Custom_Path!="":
-      print("Loading model from a custom path")
-      path_to_trained_model=Custom_Path
-
-       
-      while not os.path.exists(path_to_trained_model):
-         print("Wrong Path")
-         time.sleep(5)
-           
-    else:
-        print("Loading 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("There 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('')
-    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("THE SESSION "+s.value+" HAS 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("NOTHING TO REMOVE")
-
-    d.on_click(rem)
-    if s.value is not None:
-        display(s,d,out)
-    else:
-        print("NOTHING 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('Your 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="Uploading to HuggingFace : " '|'+'█' * prg + ' ' * (25-prg)+'| ' +str(prg*4)+ "%"
-        return br
-
-    print("Loading...")
-
-    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("Your 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