| 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('[1;32mModules 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)
|
| 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('[1;31mCheck 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('[1;31mConversion error')
|
| time.sleep(5)
|
|
|
| else:
|
| while not os.path.exists(str(MODEL_PATH)):
|
| print('[1;31mWrong 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('[1;31mConversion error')
|
| time.sleep(5)
|
| else:
|
| while os.path.getsize(modelnm) < 1810671599:
|
| print('[1;31mWrong 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('[1;32mUsing the original V1.5 model')
|
|
|
| return MODEL_NAME
|
|
|
|
|
|
|
| def sess(Session_Name, Session_Link_optional, MODEL_NAME):
|
| import wget, gdown
|
| os.chdir('/notebooks')
|
| PT=""
|
|
|
| while Session_Name=="":
|
| print('[1;31mInput the Session Name:')
|
| Session_Name=input("")
|
| Session_Name=Session_Name.replace(" ","_")
|
|
|
| WORKSPACE='/notebooks/Fast-Dreambooth'
|
|
|
| if Session_Link_optional !="":
|
| print('[1;33mDownloading session...')
|
|
|
| if Session_Link_optional != "":
|
| if not os.path.exists(str(WORKSPACE+'/Sessions')):
|
| call("mkdir -p " +WORKSPACE+ "/Sessions", shell=True)
|
| time.sleep(1)
|
| os.chdir(WORKSPACE+'/Sessions')
|
| gdown.download_folder(url=Session_Link_optional, output=Session_Name, quiet=True, remaining_ok=True, use_cookies=False)
|
| os.chdir(Session_Name)
|
| call("rm -r " +instance_images, shell=True)
|
| call("unzip " +instance_images.zip, shell=True, stdout=open('/dev/null', 'w'))
|
| call("rm -r " +concept_images, shell=True)
|
| call("unzip " +concept_images.zip, shell=True, stdout=open('/dev/null', 'w'))
|
| call("rm -r " +captions, shell=True)
|
| call("unzip " +captions.zip, shell=True, stdout=open('/dev/null', 'w'))
|
| os.chdir('/notebooks')
|
| clear_output()
|
|
|
| INSTANCE_NAME=Session_Name
|
| OUTPUT_DIR="/models/"+Session_Name
|
| SESSION_DIR=WORKSPACE+"/Sessions/"+Session_Name
|
| CONCEPT_DIR=SESSION_DIR+"/concept_images"
|
| INSTANCE_DIR=SESSION_DIR+"/instance_images"
|
| CAPTIONS_DIR=SESSION_DIR+'/captions'
|
| MDLPTH=str(SESSION_DIR+"/"+Session_Name+'.ckpt')
|
| resume=False
|
|
|
| if os.path.exists(str(SESSION_DIR)):
|
| mdls=[ckpt for ckpt in listdir(SESSION_DIR) if ckpt.split(".")[-1]=="ckpt"]
|
| if not os.path.exists(MDLPTH) and '.ckpt' in str(mdls):
|
|
|
| def f(n):
|
| k=0
|
| for i in mdls:
|
| if k==n:
|
| call('mv '+SESSION_DIR+'/'+i+' '+MDLPTH, shell=True)
|
| k=k+1
|
|
|
| k=0
|
| print('[1;33mNo final checkpoint model found, select which intermediary checkpoint to use, enter only the number, (000 to skip):\n[1;34m')
|
|
|
| for i in mdls:
|
| print(str(k)+'- '+i)
|
| k=k+1
|
| n=input()
|
| while int(n)>k-1:
|
| n=input()
|
| if n!="000":
|
| f(int(n))
|
| print('[1;32mUsing the model '+ mdls[int(n)]+" ...")
|
| time.sleep(4)
|
| clear_output()
|
| else:
|
| print('[1;32mSkipping the intermediary checkpoints.')
|
|
|
|
|
| if os.path.exists(str(SESSION_DIR)) and not os.path.exists(MDLPTH):
|
| print('[1;32mLoading session with no previous model, using the original model or the custom downloaded model')
|
| if MODEL_NAME=="":
|
| print('[1;31mNo model found, use the "Model Download" cell to download a model.')
|
| else:
|
| print('[1;32mSession Loaded, proceed to uploading instance images')
|
|
|
| elif os.path.exists(MDLPTH):
|
| print('[1;32mSession found, loading the trained model ...')
|
| 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('[1;32mSession loaded.')
|
| else:
|
| print('[1;31mConversion error, if the error persists, remove the CKPT file from the current session folder')
|
|
|
| elif not os.path.exists(str(SESSION_DIR)):
|
| call('mkdir -p '+INSTANCE_DIR, shell=True)
|
| print('[1;32mCreating session...')
|
| if MODEL_NAME=="":
|
| print('[1;31mNo model found, use the "Model Download" cell to download a model.')
|
| else:
|
| print('[1;32mSession created, proceed to uploading instance images')
|
|
|
| return PT, WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, CONCEPT_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAME, resume
|
|
|
|
|
|
|
| def done():
|
| done = widgets.Button(
|
| description='Done!',
|
| disabled=True,
|
| button_style='success',
|
| tooltip='',
|
| icon='check'
|
| )
|
| display(done)
|
|
|
|
|
|
|
| def uplder(Remove_existing_instance_images, Crop_images, Crop_size, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR, ren):
|
|
|
| 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('[1;31mOverwrite your previously trained model ?, answering "yes" will train a new model, answering "no" will resume the training of the previous model? yes or no ?[0m')
|
| while True:
|
| ansres=input('')
|
| if ansres=='no':
|
| Resume_Training = True
|
| 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('[1;31mNo 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('[1;32mResuming Training...[0m')
|
| resuming="Yes"
|
| elif Resume_Training and not os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
|
| print('[1;31mPrevious model not found, training a new model...[0m')
|
| MODELT_NAME=MODEL_NAME
|
| while MODEL_NAME=="":
|
| print('[1;31mNo model found, use the "Model Download" cell to download a model.')
|
| time.sleep(5)
|
|
|
|
|
| trnonltxt=""
|
| if UNet_Training_Steps==0:
|
| trnonltxt="--train_only_text_encoder"
|
|
|
| Enable_text_encoder_training= True
|
| Enable_Text_Encoder_Concept_Training= True
|
|
|
|
|
| if Text_Encoder_Training_Steps==0:
|
| 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('[1;32mResuming Training...[0m')
|
| print('[1;33mTraining the UNet...[0m')
|
| 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('[1;33mTraining the text encoder...[0m')
|
| if os.path.exists(OUTPUT_DIR+'/'+'text_encoder_trained'):
|
| call('rm -r '+OUTPUT_DIR+'/text_encoder_trained', shell=True)
|
| dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxt)
|
|
|
| if Enable_Text_Encoder_Concept_Training:
|
| if os.path.exists(CONCEPT_DIR):
|
| if os.listdir(CONCEPT_DIR)!=[]:
|
| clear_output()
|
| if resuming=="Yes":
|
| print('[1;32mResuming Training...[0m')
|
| print('[1;33mTraining the text encoder on the concept...[0m')
|
| dump_only_textenc(trnonltxt, MODELT_NAME, CONCEPT_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps=stptxtc)
|
| else:
|
| clear_output()
|
| if resuming=="Yes":
|
| print('[1;32mResuming Training...[0m')
|
| print('[1;31mNo concept images found, skipping concept training...')
|
| Text_Encoder_Concept_Training_Steps=0
|
| time.sleep(8)
|
| else:
|
| clear_output()
|
| if resuming=="Yes":
|
| print('[1;32mResuming Training...[0m')
|
| print('[1;31mNo concept images found, skipping concept training...')
|
| Text_Encoder_Concept_Training_Steps=0
|
| time.sleep(8)
|
|
|
| if UNet_Training_Steps!=0:
|
| train_only_unet(stp, stpsv, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, Text_Encoder_Training_Steps, PT, Seed, Resolution, 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('[1;32mNothing to do')
|
| else:
|
| if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
|
|
|
| call('python /notebooks/diffusers/scripts/convertosdv2.py --fp16 '+OUTPUT_DIR+' '+SESSION_DIR+'/'+Session_Name+'.ckpt', shell=True)
|
| clear_output()
|
| if os.path.exists(SESSION_DIR+"/"+INSTANCE_NAME+'.ckpt'):
|
| clear_output()
|
| print("[1;32mDONE, the CKPT model is in the session's folder")
|
| else:
|
| print("[1;31mSomething went wrong")
|
|
|
| else:
|
| print("[1;31mSomething went wrong")
|
|
|
| return resume
|
|
|
|
|
|
|
| def testui(Custom_Path, Previous_Session_Name, Session_Name, User, Password):
|
|
|
|
|
| if Previous_Session_Name!="":
|
| print("[1;32mLoading a previous session model")
|
| mdldir='/notebooks/Fast-Dreambooth/Sessions/'+Previous_Session_Name
|
| path_to_trained_model=mdldir+"/"+Previous_Session_Name+'.ckpt'
|
|
|
|
|
| while not os.path.exists(path_to_trained_model):
|
| print("[1;31mThere is no trained model in the previous session")
|
| time.sleep(5)
|
|
|
| elif Custom_Path!="":
|
| print("[1;32mLoading model from a custom path")
|
| path_to_trained_model=Custom_Path
|
|
|
|
|
| while not os.path.exists(path_to_trained_model):
|
| print("[1;31mWrong Path")
|
| time.sleep(5)
|
|
|
| else:
|
| print("[1;32mLoading the trained model")
|
| mdldir='/notebooks/Fast-Dreambooth/Sessions/'+Session_Name
|
| path_to_trained_model=mdldir+"/"+Session_Name+'.ckpt'
|
|
|
|
|
| while not os.path.exists(path_to_trained_model):
|
| print("[1;31mThere is no trained model in this session")
|
| time.sleep(5)
|
|
|
| auth=f"--gradio-auth {User}:{Password}"
|
| if User =="" or Password=="":
|
| auth=""
|
|
|
| os.chdir('/notebooks')
|
| if not os.path.exists('/notebooks/sd/stablediffusiond'):
|
| 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('[1;32m')
|
| 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, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
|
| os.chdir('/notebooks')
|
| clear_output()
|
|
|
| call('wget -q -O /notebooks/sd/stable-diffusion-webui/modules/styles.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/AUTOMATIC1111_files/styles.py', shell=True)
|
| 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@|@or@' /notebooks/sd/stable-diffusion-webui/extensions/adetailer/aaaaaa/helper.py", shell=True, stdout=open('/dev/null', 'w'), stderr=open('/dev/null', 'w'))
|
|
|
| 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("[1;33mTHE SESSION [1;31m"+s.value+" [1;33mHAS BEEN REMOVED FROM THE STORAGE")
|
| call('rm -r /notebooks/Fast-Dreambooth/Sessions/'+s.value, shell=True)
|
| if os.path.exists('/notebooks/models/'+s.value):
|
| call('rm -r /notebooks/models/'+s.value, shell=True)
|
| s.options=os.listdir("/notebooks/Fast-Dreambooth/Sessions")
|
|
|
|
|
| else:
|
| d.close()
|
| s.close()
|
| clear_output()
|
| print("[1;32mNOTHING TO REMOVE")
|
|
|
| d.on_click(rem)
|
| if s.value is not None:
|
| display(s,d,out)
|
| else:
|
| print("[1;32mNOTHING TO REMOVE")
|
|
|
|
|
|
|
| def hf(Name_of_your_concept, hf_token_write, INSTANCE_NAME, OUTPUT_DIR, Session_Name, MDLPTH):
|
|
|
| from slugify import slugify
|
| from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
|
| from huggingface_hub import create_repo
|
| from IPython.display import display_markdown
|
|
|
|
|
| if(Name_of_your_concept == ""):
|
| Name_of_your_concept = Session_Name
|
| Name_of_your_concept=Name_of_your_concept.replace(" ","-")
|
|
|
|
|
|
|
| if hf_token_write =="":
|
| print('[1;32mYour Hugging Face write access token : ')
|
| hf_token_write=input()
|
|
|
| hf_token = hf_token_write
|
|
|
| api = HfApi()
|
| your_username = api.whoami(token=hf_token)["name"]
|
|
|
| repo_id = f"{your_username}/{slugify(Name_of_your_concept)}"
|
| output_dir = f'/notebooks/models/'+INSTANCE_NAME
|
|
|
| def bar(prg):
|
| clear_output()
|
| br="[1;33mUploading to HuggingFace : " '[0m|'+'█' * prg + ' ' * (25-prg)+'| ' +str(prg*4)+ "%"
|
| return br
|
|
|
| print("[1;33mLoading...")
|
|
|
| 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
|
|
|
| '''
|
|
|
| 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("[1;32mYour concept was saved successfully at https://huggingface.co/"+repo_id)
|
| done()
|
|
|
|
|
|
|
| def crop_image(im, size):
|
|
|
| GREEN = "#0F0"
|
| BLUE = "#00F"
|
| RED = "#F00"
|
|
|
| def focal_point(im, settings):
|
| corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
|
| entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
|
| face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
|
|
|
| pois = []
|
|
|
| weight_pref_total = 0
|
| if len(corner_points) > 0:
|
| weight_pref_total += settings.corner_points_weight
|
| if len(entropy_points) > 0:
|
| weight_pref_total += settings.entropy_points_weight
|
| if len(face_points) > 0:
|
| weight_pref_total += settings.face_points_weight
|
|
|
| corner_centroid = None
|
| if len(corner_points) > 0:
|
| corner_centroid = centroid(corner_points)
|
| corner_centroid.weight = settings.corner_points_weight / weight_pref_total
|
| pois.append(corner_centroid)
|
|
|
| entropy_centroid = None
|
| if len(entropy_points) > 0:
|
| entropy_centroid = centroid(entropy_points)
|
| entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
|
| pois.append(entropy_centroid)
|
|
|
| face_centroid = None
|
| if len(face_points) > 0:
|
| face_centroid = centroid(face_points)
|
| face_centroid.weight = settings.face_points_weight / weight_pref_total
|
| pois.append(face_centroid)
|
|
|
| average_point = poi_average(pois, settings)
|
|
|
| return average_point
|
|
|
|
|
| def image_face_points(im, settings):
|
|
|
| np_im = np.array(im)
|
| gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
|
|
|
| tries = [
|
| [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
|
| [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
|
| [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
|
| [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
|
| [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
|
| [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
|
| [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
|
| [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
|
| ]
|
| for t in tries:
|
| classifier = cv2.CascadeClassifier(t[0])
|
| minsize = int(min(im.width, im.height) * t[1])
|
| 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):
|
|
|
|
|
| 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)
|
|
|
|
|
|
|
| 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 |
