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, ImageOps 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 import six import re from urllib.parse import urlparse, parse_qs, unquote from urllib.request import urlopen, Request import tempfile from tqdm import tqdm def Deps(force_reinstall): if not force_reinstall and os.path.exists('/usr/local/lib/python3.9/dist-packages/safetensors'): ntbk() call('pip install --root-user-action=ignore --disable-pip-version-check -qq ./diffusers', shell=True, stdout=open('/dev/null', 'w')) os.environ['TORCH_HOME'] = '/notebooks/cache/torch' os.environ['PYTHONWARNINGS'] = 'ignore' print('Modules and notebooks updated, dependencies already installed') else: 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-1.12.1+cu116.dist-info torchaudio* torchvision* PIL Pillow* transformers* numpy* gdown*", shell=True, stdout=open('/dev/null', 'w')) ntbk() 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')) depsinst("https://huggingface.co/TheLastBen/dependencies/resolve/main/ppsdeps.tar.zst", "/deps/ppsdeps.tar.zst") call('tar -C / --zstd -xf ppsdeps.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 main https://github.com/TheLastBen/diffusers /diffusers", shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) os.environ['TORCH_HOME'] = '/notebooks/cache/torch' os.environ['PYTHONWARNINGS'] = 'ignore' call("sed -i 's@text = _formatwarnmsg(msg)@text =\"\"@g' /usr/lib/python3.9/warnings.py", shell=True) call('pip install --root-user-action=ignore --disable-pip-version-check -qq gradio==3.41.2', 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 depsinst(url, dst): file_size = None req = Request(url, headers={"User-Agent": "torch.hub"}) u = urlopen(req) meta = u.info() if hasattr(meta, 'getheaders'): content_length = meta.getheaders("Content-Length") else: content_length = meta.get_all("Content-Length") if content_length is not None and len(content_length) > 0: file_size = int(content_length[0]) with tqdm(total=file_size, disable=False, mininterval=0.5, bar_format='Installing dependencies |{bar:20}| {percentage:3.0f}%') as pbar: with open(dst, "wb") as f: while True: buffer = u.read(8192) if len(buffer) == 0: break f.write(buffer) pbar.update(len(buffer)) f.close() def ntbk(): os.chdir('/notebooks') if not os.path.exists('Latest_Notebooks'): call('mkdir Latest_Notebooks', shell=True) else: call('rm -r Latest_Notebooks', shell=True) call('mkdir Latest_Notebooks', shell=True) os.chdir('/notebooks/Latest_Notebooks') call('wget -q -i https://huggingface.co/datasets/TheLastBen/PPS/raw/main/Notebooks.txt', shell=True) call('rm Notebooks.txt', shell=True) os.chdir('/notebooks') def downloadmodel_hf(Path_to_HuggingFace): import wget if os.path.exists('/models/stable-diffusion-custom'): call("rm -r /models/stable-diffusion-custom", shell=True) clear_output() if os.path.exists('/notebooks/Fast-Dreambooth/token.txt'): with open("/notebooks/Fast-Dreambooth/token.txt") as f: token = f.read() authe=f'https://USER:{token}@' else: authe="https://" clear_output() call("mkdir /models/stable-diffusion-custom", shell=True) os.chdir("/models/stable-diffusion-custom") call("git init", shell=True) call("git lfs install --system --skip-repo", shell=True) call('git remote add -f origin '+authe+'huggingface.co/'+Path_to_HuggingFace, shell=True) call("git config core.sparsecheckout true", shell=True) call('echo -e "\nscheduler\ntext_encoder\ntokenizer\nunet\nvae\nmodel_index.json\n!*.safetensors" > .git/info/sparse-checkout', shell=True) call("git pull origin main", shell=True) if os.path.exists('unet/diffusion_pytorch_model.bin'): call("rm -r .git", shell=True) call("rm model_index.json", shell=True) wget.download('https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/model_index.json') os.chdir('/notebooks') clear_output() done() while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'): print('Check the link you provided') os.chdir('/notebooks') time.sleep(5) def downloadmodel_path(MODEL_PATH): modelname=os.path.basename(MODEL_PATH) sftnsr="" if modelname.split('.')[-1]=='safetensors': sftnsr="--from_safetensors" import wget os.chdir('/notebooks') clear_output() if os.path.exists(str(MODEL_PATH)): call('wget -q -O config.yaml https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml', shell=True) call('python /diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path '+MODEL_PATH+' --dump_path /models/stable-diffusion-custom --original_config_file config.yaml '+sftnsr, shell=True) clear_output() call('rm config.yaml', shell=True) if os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'): clear_output() done() while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'): print('Conversion error') time.sleep(5) else: while not os.path.exists(str(MODEL_PATH)): print('Wrong path, use the file explorer to copy the path') time.sleep(5) def downloadmodel_link(MODEL_LINK): import wget import gdown from gdown.download import get_url_from_gdrive_confirmation def getsrc(url): parsed_url = urlparse(url) if parsed_url.netloc == 'civitai.com': src='civitai' elif parsed_url.netloc == 'drive.google.com': src='gdrive' elif parsed_url.netloc == 'huggingface.co': src='huggingface' else: src='others' return src src=getsrc(MODEL_LINK) def get_name(url, gdrive): if not gdrive: response = requests.get(url, allow_redirects=False) if "Location" in response.headers: redirected_url = response.headers["Location"] quer = parse_qs(urlparse(redirected_url).query) if "response-content-disposition" in quer: disp_val = quer["response-content-disposition"][0].split(";") for vals in disp_val: if vals.strip().startswith("filename="): filenm=unquote(vals.split("=", 1)[1].strip()) return filenm.replace("\"","") else: headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36"} lnk="https://drive.google.com/uc?id={id}&export=download".format(id=url[url.find("/d/")+3:url.find("/view")]) res = requests.session().get(lnk, headers=headers, stream=True, verify=True) res = requests.session().get(get_url_from_gdrive_confirmation(res.text), headers=headers, stream=True, verify=True) content_disposition = six.moves.urllib_parse.unquote(res.headers["Content-Disposition"]) filenm = re.search(r"filename\*=UTF-8''(.*)", content_disposition).groups()[0].replace(os.path.sep, "_") return filenm if src=='civitai': modelname=get_name(MODEL_LINK, False) elif src=='gdrive': modelname=get_name(MODEL_LINK, True) else: modelname=os.path.basename(MODEL_LINK) sftnsr="" if modelname.split('.')[-1]!='safetensors': modelnm="model.ckpt" else: modelnm="model.safetensors" sftnsr="--from_safetensors" os.chdir('/notebooks') call("gdown --fuzzy " +MODEL_LINK+ " -O "+modelnm, shell=True) if os.path.exists(modelnm): if os.path.getsize(modelnm) > 1810671599: call('wget -q -O config.yaml https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml', shell=True) call('python /diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path '+modelnm+' --dump_path /models/stable-diffusion-custom --original_config_file config.yaml '+sftnsr, shell=True) clear_output() call('rm config.yaml', shell=True) if os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'): call('rm '+modelnm, shell=True) clear_output() done() else: while not os.path.exists('/models/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'): print('Conversion error') time.sleep(5) else: while os.path.getsize(modelnm) < 1810671599: print('Wrong link, check that the link is valid') time.sleep(5) def dls(Path_to_HuggingFace, Model_Path, Model_Link): if Path_to_HuggingFace != "": downloadmodel_hf(Path_to_HuggingFace) MODEL_NAME="/models/stable-diffusion-custom" elif Model_Path !="": downloadmodel_path(Model_Path) MODEL_NAME="/models/stable-diffusion-custom" elif Model_Link !="": downloadmodel_link(Model_Link) MODEL_NAME="/models/stable-diffusion-custom" else: MODEL_NAME="/datasets/stable-diffusion-diffusers/stable-diffusion-v1-5" print('Using the original V1.5 model') return MODEL_NAME def sess(Session_Name, Session_Link_optional, MODEL_NAME): import wget, gdown os.chdir('/notebooks') PT="" while Session_Name=="": print('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') resume=False if os.path.exists(str(SESSION_DIR)): mdls=[ckpt for ckpt in listdir(SESSION_DIR) if ckpt.split(".")[-1]=="ckpt"] if not os.path.exists(MDLPTH) and '.ckpt' in str(mdls): def f(n): k=0 for i in mdls: if k==n: call('mv '+SESSION_DIR+'/'+i+' '+MDLPTH, shell=True) k=k+1 k=0 print('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(4) clear_output() 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_NAME=="": 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 ...') call('wget -q -O config.yaml https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml', shell=True) call('python /diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path '+MDLPTH+' --dump_path '+OUTPUT_DIR+' --original_config_file config.yaml', shell=True) clear_output() call('rm config.yaml', shell=True) if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'): resume=True clear_output() print('Session loaded.') else: 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_NAME=="": 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_NAME, resume def done(): done = widgets.Button( description='Done!', disabled=True, button_style='success', tooltip='', icon='check' ) display(done) def uplder(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, ren): if os.path.exists(INSTANCE_DIR+"/.ipynb_checkpoints"): call('rm -r '+INSTANCE_DIR+'/.ipynb_checkpoints', shell=True) uploader = widgets.FileUpload(description="Choose images",accept='image/*, .txt', multiple=True) Upload = widgets.Button( description='Upload', disabled=False, button_style='info', tooltip='Click to upload the chosen instance images', icon='' ) def up(Upload): with out: uploader.close() Upload.close() upld(Remove_existing_instance_images, Crop_images, Crop_size, 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 Remove_existing_instance_images: if os.path.exists(str(INSTANCE_DIR)): call("rm -r " +INSTANCE_DIR, shell=True) if os.path.exists(str(CAPTIONS_DIR)): call("rm -r " +CAPTIONS_DIR, shell=True) if not os.path.exists(str(INSTANCE_DIR)): call("mkdir -p " +INSTANCE_DIR, shell=True) if not os.path.exists(str(CAPTIONS_DIR)): call("mkdir -p " +CAPTIONS_DIR, shell=True) if IMAGES_FOLDER_OPTIONAL !="": if os.path.exists(IMAGES_FOLDER_OPTIONAL+"/.ipynb_checkpoints"): call('rm -r '+IMAGES_FOLDER_OPTIONAL+'/.ipynb_checkpoints', shell=True) if any(file.endswith('.{}'.format('txt')) for file in os.listdir(IMAGES_FOLDER_OPTIONAL)): call('mv '+IMAGES_FOLDER_OPTIONAL+'/*.txt '+CAPTIONS_DIR, shell=True) if Crop_images: os.chdir(str(IMAGES_FOLDER_OPTIONAL)) call('find . -name "* *" -type f | rename ' "'s/ /-/g'", shell=True) os.chdir('/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) file=file.convert("RGB") file=ImageOps.exif_transpose(file) width, height = file.size if file.size !=(Crop_size, Crop_size): image=crop_image(file, Crop_size) if extension.upper()=="JPG" or extension.upper()=="jpg": image[0].save(new_path_with_file, format="JPEG", quality = 100) else: image[0].save(new_path_with_file, format=extension.upper()) else: call("cp \'"+IMAGES_FOLDER_OPTIONAL+"/"+filename+"\' "+INSTANCE_DIR, shell=True) else: for filename in tqdm(os.listdir(IMAGES_FOLDER_OPTIONAL), bar_format=' |{bar:15}| {n_fmt}/{total_fmt} Uploaded'): call("cp -r " +IMAGES_FOLDER_OPTIONAL+"/. " +INSTANCE_DIR, shell=True) elif IMAGES_FOLDER_OPTIONAL =="": up="" for file in uploader.value: filename = file['name'] if filename.split(".")[-1]=="txt": with open(CAPTIONS_DIR+'/'+filename, 'w') as f: f.write(bytes(file['content']).decode()) up=[file for file in uploader.value if not file['name'].endswith('.txt')] if Crop_images: for file in tqdm(up, bar_format=' |{bar:15}| {n_fmt}/{total_fmt} Uploaded'): filename = file['name'] img = Image.open(io.BytesIO(file['content'])) extension = filename.split(".")[-1] identifier=filename.split(".")[0] img=img.convert("RGB") img=ImageOps.exif_transpose(img) if extension.upper()=="JPG" or extension.upper()=="jpg": img.save(INSTANCE_DIR+"/"+filename, format="JPEG", quality = 100) else: img.save(INSTANCE_DIR+"/"+filename, format=extension.upper()) new_path_with_file = os.path.join(INSTANCE_DIR, filename) file = Image.open(new_path_with_file) width, height = file.size if file.size !=(Crop_size, Crop_size): image=crop_image(file, Crop_size) if extension.upper()=="JPG" or extension.upper()=="jpg": image[0].save(new_path_with_file, format="JPEG", quality = 100) else: image[0].save(new_path_with_file, format=extension.upper()) else: for file in tqdm(uploader.value, bar_format=' |{bar:15}| {n_fmt}/{total_fmt} Uploaded'): filename = file['name'] img = Image.open(io.BytesIO(file['content'])) img=img.convert("RGB") extension = filename.split(".")[-1] identifier=filename.split(".")[0] if extension.upper()=="JPG" or extension.upper()=="jpg": img.save(INSTANCE_DIR+"/"+filename, format="JPEG", quality = 100) else: img.save(INSTANCE_DIR+"/"+filename, format=extension.upper()) 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): paths="" out="" widgets_l="" clear_output() def Caption(path): if path!="Select an instance image to caption": name = os.path.splitext(os.path.basename(path))[0] ext=os.path.splitext(os.path.basename(path))[-1][1:] if ext=="jpg" or "JPG": ext="JPEG" if os.path.exists(CAPTIONS_DIR+"/"+name + '.txt'): with open(CAPTIONS_DIR+"/"+name + '.txt', 'r') as f: text = f.read() else: with open(CAPTIONS_DIR+"/"+name + '.txt', 'w') as f: f.write("") with open(CAPTIONS_DIR+"/"+name + '.txt', 'r') as f: text = f.read() img=Image.open(os.path.join(INSTANCE_DIR,path)) img=img.convert("RGB") img=img.resize((420, 420)) image_bytes = BytesIO() img.save(image_bytes, format=ext, qualiy=10) image_bytes.seek(0) image_data = image_bytes.read() img= image_data image = widgets.Image( value=img, width=420, height=420 ) text_area = widgets.Textarea(value=text, description='', disabled=False, layout={'width': '300px', 'height': '120px'}) def update_text(text): with open(CAPTIONS_DIR+"/"+name + '.txt', 'w') as f: f.write(text) button = widgets.Button(description='Save', button_style='success') button.on_click(lambda b: update_text(text_area.value)) return widgets.VBox([widgets.HBox([image, text_area, button])]) paths = os.listdir(INSTANCE_DIR) widgets_l = widgets.Select(options=["Select an instance image to caption"]+paths, rows=25) out = widgets.Output() def click(change): with out: out.clear_output() display(Caption(change.new)) widgets_l.observe(click, names='value') display(widgets.HBox([widgets_l, out])) def dbtrain(Resume_Training, UNet_Training_Steps, UNet_Learning_Rate, Text_Encoder_Training_Steps, Text_Encoder_Concept_Training_Steps, Text_Encoder_Learning_Rate, Offset_Noise, Resolution, MODEL_NAME, SESSION_DIR, INSTANCE_DIR, CONCEPT_DIR, CAPTIONS_DIR, External_Captions, INSTANCE_NAME, Session_Name, OUTPUT_DIR, PT, resume, Save_Checkpoint_Every_n_Steps, Start_saving_from_the_step, Save_Checkpoint_Every): if os.path.exists(INSTANCE_DIR+"/.ipynb_checkpoints"): call('rm -r '+INSTANCE_DIR+'/.ipynb_checkpoints', shell=True) if os.path.exists(CONCEPT_DIR+"/.ipynb_checkpoints"): call('rm -r '+CONCEPT_DIR+'/.ipynb_checkpoints', shell=True) if os.path.exists(CAPTIONS_DIR+"/.ipynb_checkpoints"): call('rm -r '+CAPTIONS_DIR+'/.ipynb_checkpoints', shell=True) if resume and not Resume_Training: print('Overwrite your previously trained model ?, answering "yes" will train a new model, answering "no" will resume the training of the previous model?  yes or no ?') while True: ansres=input('') if ansres=='no': Resume_Training = True resume= False break elif ansres=='yes': Resume_Training = False resume= False break while not Resume_Training and not os.path.exists(MODEL_NAME+'/unet/diffusion_pytorch_model.bin'): print('No model found, use the "Model Download" cell to download a model.') time.sleep(5) MODELT_NAME=MODEL_NAME Seed=random.randint(1, 999999) ofstnse="" if Offset_Noise: ofstnse="--offset_noise" extrnlcptn="" if External_Captions: extrnlcptn="--external_captions" precision="fp16" 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_NAME while MODEL_NAME=="": 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: 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+' \ '+extrnlcptn+' \ '+ofstnse+' \ --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+' \ --captions_dir='+CAPTIONS_DIR+' \ --instance_prompt='+PT+' \ --seed='+str(Seed)+' \ --resolution='+str(Resolution)+' \ --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="linear" \ --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, 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 \ '+extrnlcptn+' \ '+ofstnse+' \ --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+' \ --captions_dir='+CAPTIONS_DIR+' \ --instance_prompt='+PT+' \ --seed='+str(Seed)+' \ --resolution='+str(Resolution)+' \ --mixed_precision='+str(precision)+' \ --train_batch_size=1 \ --gradient_accumulation_steps=1 --gradient_checkpointing \ --use_8bit_adam \ --learning_rate='+str(UNet_Learning_Rate)+' \ --lr_scheduler="linear" \ --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, extrnlcptn, precision, Training_Steps=UNet_Training_Steps) if UNet_Training_Steps==0 and Text_Encoder_Concept_Training_Steps==0 and Text_Encoder_Training_Steps==0 : 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 resume def testui(Custom_Path, Previous_Session_Name, Session_Name, User, Password): 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/stablediffusiond'): #reset later call('wget -q -O sd_mrep.tar.zst https://huggingface.co/TheLastBen/dependencies/resolve/main/sd_mrep.tar.zst', shell=True) call('tar --zstd -xf sd_mrep.tar.zst', shell=True) call('rm sd_mrep.tar.zst', shell=True) os.chdir('/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 checkout master', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) call('git pull', shell=True, stdout=open('/dev/null', 'w')) os.makedirs('/notebooks/sd/stable-diffusion-webui/repositories', exist_ok=True) call('git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets /notebooks/sd/stable-diffusion-webui/repositories/stable-diffusion-webui-assets', shell=True) os.chdir('/notebooks') clear_output() 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) localurl="tensorboard-"+os.environ.get('PAPERSPACE_FQDN') 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 = "{localurl}"\n' if line.strip().startswith('self.protocol = "https"'): line = ' self.protocol = "https"\n' if line.strip().startswith('if self.local_url.startswith("https") or self.is_colab'): line = '' if line.strip().startswith('else "http"'): line = '' sys.stdout.write(line) os.chdir('/notebooks/sd/stable-diffusion-webui/modules') call("sed -i 's@possible_sd_paths =.*@possible_sd_paths = [\"/notebooks/sd/stablediffusion\"]@' /notebooks/sd/stable-diffusion-webui/modules/paths.py", shell=True) call("sed -i 's@\.\.\/@src/@g' /notebooks/sd/stable-diffusion-webui/modules/paths.py", shell=True) call("sed -i 's@src\/generative-models@generative-models@g' /notebooks/sd/stable-diffusion-webui/modules/paths.py", shell=True) call("sed -i 's@-> Network | None@@g' /notebooks/sd/stable-diffusion-webui/extensions-builtin/Lora/network.py", shell=True) call("sed -i 's@\"quicksettings\": OptionInfo(.*@\"quicksettings\": OptionInfo(\"sd_model_checkpoint, sd_vae, CLIP_stop_at_last_layers, inpainting_mask_weight, initial_noise_multiplier\", \"Quicksettings list\"),@' /notebooks/sd/stable-diffusion-webui/modules/shared.py", shell=True) os.chdir('/notebooks/sd/stable-diffusion-webui') clear_output() configf="--disable-console-progressbars --no-gradio-queue --no-hashing --no-half-vae --disable-safe-unpickle --api --no-download-sd-model --xformers --enable-insecure-extension-access --port 6006 --listen --skip-version-check --ckpt "+path_to_trained_model+" "+auth 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 hf(Name_of_your_concept, 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"] repo_id = f"{your_username}/{slugify(Name_of_your_concept)}" output_dir = f'/notebooks/models/'+INSTANCE_NAME def bar(prg): clear_output() br="Uploading to HuggingFace : " '|'+'█' * prg + ' ' * (25-prg)+'| ' +str(prg*4)+ "%" return br print("Loading...") os.chdir(OUTPUT_DIR) call('rm -r safety_checker feature_extractor .git', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) call('rm model_index.json', shell=True) call('git init', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) call('git lfs install --system --skip-repo', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) call('git remote add -f origin https://huggingface.co/runwayml/stable-diffusion-v1-5', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) call('git config core.sparsecheckout true', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) call('echo -e "\nfeature_extractor\nsafety_checker\nmodel_index.json\n!*.safetensors" > .git/info/sparse-checkout', shell=True) call('git pull origin main', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) call('rm -r .git', shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w')) os.chdir('/notebooks') print(bar(1)) readme_text = f'''--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### {Name_of_your_concept} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with TheLastBen's fast-DreamBooth notebook ''' #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 ) print(bar(4)) api.upload_folder( folder_path=OUTPUT_DIR+"/safety_checker", path_in_repo="safety_checker", repo_id=repo_id, token=hf_token ) print(bar(8)) api.upload_folder( folder_path=OUTPUT_DIR+"/scheduler", path_in_repo="scheduler", repo_id=repo_id, token=hf_token ) print(bar(9)) api.upload_folder( folder_path=OUTPUT_DIR+"/text_encoder", path_in_repo="text_encoder", repo_id=repo_id, token=hf_token ) print(bar(12)) api.upload_folder( folder_path=OUTPUT_DIR+"/tokenizer", path_in_repo="tokenizer", repo_id=repo_id, token=hf_token ) print(bar(13)) api.upload_folder( folder_path=OUTPUT_DIR+"/unet", path_in_repo="unet", repo_id=repo_id, token=hf_token ) print(bar(21)) api.upload_folder( folder_path=OUTPUT_DIR+"/vae", path_in_repo="vae", repo_id=repo_id, token=hf_token ) 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 ) 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") # naive attempt at preventing focal points from collecting at watermarks near the bottom gd = ImageDraw.Draw(grayscale) gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999") np_im = np.array(grayscale) points = cv2.goodFeaturesToTrack( np_im, maxCorners=100, qualityLevel=0.04, minDistance=min(grayscale.width, grayscale.height)*0.06, useHarrisDetector=False, ) if points is None: return [] focal_points = [] for point in points: x, y = point.ravel() focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points))) return focal_points def image_entropy_points(im, settings): landscape = im.height < im.width portrait = im.height > im.width if landscape: move_idx = [0, 2] move_max = im.size[0] elif portrait: move_idx = [1, 3] move_max = im.size[1] else: return [] e_max = 0 crop_current = [0, 0, settings.crop_width, settings.crop_height] crop_best = crop_current while crop_current[move_idx[1]] < move_max: crop = im.crop(tuple(crop_current)) e = image_entropy(crop) if (e > e_max): e_max = e crop_best = list(crop_current) crop_current[move_idx[0]] += 4 crop_current[move_idx[1]] += 4 x_mid = int(crop_best[0] + settings.crop_width/2) y_mid = int(crop_best[1] + settings.crop_height/2) return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)] def image_entropy(im): # greyscale image entropy # band = np.asarray(im.convert("L")) band = np.asarray(im.convert("1"), dtype=np.uint8) hist, _ = np.histogram(band, bins=range(0, 256)) hist = hist[hist > 0] return -np.log2(hist / hist.sum()).sum() def centroid(pois): x = [poi.x for poi in pois] y = [poi.y for poi in pois] return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois)) def poi_average(pois, settings): weight = 0.0 x = 0.0 y = 0.0 for poi in pois: weight += poi.weight x += poi.x * poi.weight y += poi.y * poi.weight avg_x = round(weight and x / weight) avg_y = round(weight and y / weight) return PointOfInterest(avg_x, avg_y) def is_landscape(w, h): return w > h def is_portrait(w, h): return h > w def is_square(w, h): return w == h class PointOfInterest: def __init__(self, x, y, weight=1.0, size=10): self.x = x self.y = y self.weight = weight self.size = size def bounding(self, size): return [ self.x - size//2, self.y - size//2, self.x + size//2, self.y + size//2 ] class Settings: def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5): self.crop_width = crop_width self.crop_height = crop_height self.corner_points_weight = corner_points_weight self.entropy_points_weight = entropy_points_weight self.face_points_weight = face_points_weight settings = Settings( crop_width = size, crop_height = size, face_points_weight = 0.9, entropy_points_weight = 0.15, corner_points_weight = 0.5, ) scale_by = 1 if is_landscape(im.width, im.height): scale_by = settings.crop_height / im.height elif is_portrait(im.width, im.height): scale_by = settings.crop_width / im.width elif is_square(im.width, im.height): if is_square(settings.crop_width, settings.crop_height): scale_by = settings.crop_width / im.width elif is_landscape(settings.crop_width, settings.crop_height): scale_by = settings.crop_width / im.width elif is_portrait(settings.crop_width, settings.crop_height): scale_by = settings.crop_height / im.height im = im.resize((int(im.width * scale_by), int(im.height * scale_by))) im_debug = im.copy() focus = focal_point(im_debug, settings) # take the focal point and turn it into crop coordinates that try to center over the focal # point but then get adjusted back into the frame y_half = int(settings.crop_height / 2) x_half = int(settings.crop_width / 2) x1 = focus.x - x_half if x1 < 0: x1 = 0 elif x1 + settings.crop_width > im.width: x1 = im.width - settings.crop_width y1 = focus.y - y_half if y1 < 0: y1 = 0 elif y1 + settings.crop_height > im.height: y1 = im.height - settings.crop_height x2 = x1 + settings.crop_width y2 = y1 + settings.crop_height crop = [x1, y1, x2, y2] results = [] results.append(im.crop(tuple(crop))) return results