Create mainpaperspacev2.py

mainpaperspacev2.py

@@ -0,0 +1,1255 @@
+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 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('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
+  os.chdir('/models')
+  if os.path.exists('stable-diffusion-custom'):
+    call("rm -r stable-diffusion-custom", shell=True)
+    
+  if os.path.exists('/content/gdrive/MyDrive/Fast-Dreambooth/token.txt'):
+    with open("/content/gdrive/MyDrive/Fast-Dreambooth/token.txt") as f:
+       token = f.read()
+    authe=f'https://USER:{token}@'
+  else:
+    authe="https://"  
+
+  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!*.safetensors" > .git/info/sparse-checkout', shell=True)
+  call("git pull origin main", shell=True)
+  if os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
+    call("rm -r stable-diffusion-custom/.git", shell=True)
+    clear_output()
+    done()
+  else:
+    while not os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
+          print('Check the link you provided')
+          time.sleep(5)
+  os.chdir('/notebooks')
+
+
+         
+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'):
+      clear_output()
+      done()
+    else:
+      call('rm -r stable-diffusion-custom', shell=True)
+      while not os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
+        print('Conversion error')
+        time.sleep(5)
+  else:
+    while not os.path.exists(str(CKPT_Path)):
+       print('Wrong path, use the colab file explorer to copy the path')
+       time.sleep(5)
+  os.chdir('/notebooks')
+      
+       
+       
+def downloadmodel_lnkv2(CKPT_Link, Custom_Model_Version): 
+    import wget
+    os.chdir('/models')
+    call("gdown --fuzzy " +CKPT_Link+ " -O model.ckpt", shell=True)
+    gdown.download(url=CKPT_Link, output="model.ckpt", quiet=False, fuzzy=True)
+    
+    if os.path.exists('model.ckpt'):
+      if os.path.getsize("model.ckpt") > 1810671599:
+        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)
+        call('rm model.ckpt', shell=True)
+        if os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
+          clear_output()
+          done()
+        else:
+          call('rm -r stable-diffusion-custom', shell=True)
+          while not os.path.exists('stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):
+            print('Conversion error')
+            time.sleep(5)
+      else:
+        while os.path.getsize('model.ckpt') < 1810671599:
+           print('Wrong link, check that the link is valid')
+           time.sleep(5)
+      os.chdir('/notebooks')
+           
+
+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="dataset"
+        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):
+
+    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 testv2(Custom_Path, Previous_Session_Name, Session_Name, Model_Version, 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('sd_db'):
+        call('mkdir sd_db', shell=True)
+    os.chdir('/notebooks/sd_db')
+    call('git clone --depth 1 --branch main https://github.com/Stability-AI/stablediffusion', shell=True)
+    call('git clone --depth 1 --branch Paperspacedb https://github.com/TheLastBen/stable-diffusion-webui', shell=True)
+    clear_output()
+    
+    if not os.path.exists('/notebooks/sd_db/stablediffusion/src/k-diffusion/k_diffusion'):
+      call('mkdir /notebooks/sd_db/stablediffusion/src', shell=True)
+      os.chdir('/notebooks/sd_db/stablediffusion/src')
+      call('git clone -q --depth 1 --no-tags https://github.com/TheLastBen/taming-transformers.git', shell=True)
+      call('git clone -q --depth 1 https://github.com/salesforce/BLIP', shell=True)
+      call('git clone -q --depth 1 https://github.com/sczhou/CodeFormer', shell=True)
+      call('git clone -q --depth 1 --branch master https://github.com/crowsonkb/k-diffusion', shell=True)
+      
+      
+    if not os.path.exists('/usr/lib/node_modules/localtunnel'):
+       call('npm install -g localtunnel  --silent', shell=True, stdout=open('/dev/null', 'w'))
+    
+    share=''
+    if not Use_localtunnel:
+      share='--share'
+      call('wget -q -O /usr/local/lib/python3.9/dist-packages/gradio/blocks.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/blocks.py', shell=True)
+
+    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_db/stable-diffusion-webui')
+    print('')
+    call('git pull', shell=True)  
+    clear_output()
+
+    if Model_Version == "768":
+      configf="--config /notebooks/sd_db/stablediffusion/configs/stable-diffusion/v2-inference-v.yaml --no-half"
+    elif Model_Version == "512":
+      configf="--config /notebooks/sd_db/stablediffusion/configs/stable-diffusion/v2-inference.yaml --no-half"
+    else:
+       configf="--config /notebooks/sd_db/stablediffusion/configs/stable-diffusion/v2-inference-v.yaml --no-half"
+
+    return path_to_trained_model, configf, auth
+      
+      
+
+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