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Feat: Support both Google Colab and Paperspace Gradient notebook architectures #1010

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Feat: Support both Google Colab and Paperspace Gradient notebook architectures #1010

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dte
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@dte dte commented Dec 13, 2022

Goal

Paperspace offers a public notebook with free GPUs that for some users prefer to use instead of Google's Colab product. This PR adds notebook compatibility for both Google Colab and Gradient. Gradient has a few differences including persistent storage that does not require Google Drive and a slightly different storage model that supports multiple files and subdirectories.

Major Changes:

  • refactor hardcoded Google Drive and /content directories as configurable {persistent_storage_dir}, {working_notebook_dir}, and {packages_dir}
  • check for if IN_PAPERSPACE as opposed to in Colab to set up

Test Plan:

  • confirm works in Google Colab
  • confirm works in Paperspace Gradient
  • test all configurations

@dte
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dte commented Dec 13, 2022 •
edited

@TheLastBen Thanks for all your work in this repo! I was trying to get this to work as a notebook in Paperspace and figured it might be more useful upstreamed. Let me know if this approach of making the directories more modular is an abstraction that you would support.

@artificialguybr
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It's great to see Paperspace engaged in bringing great Colabs from the community to Paperspace. Great work!

@TheLastBen
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TheLastBen commented Dec 14, 2022 •
edited

@dte Hi, contact me on discord : TheLastBen#0087

@nawnie
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nawnie commented Dec 22, 2022

PLEASE YES!!!!

@askiiart
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Here's some repos that might help:

@gugleigos
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webui
community saturnenterprise io

import os, time
from IPython.display import display, clear_output
from subprocess import getoutput


import ipywidgets as widgets
from ipywidgets import Layout
from ipywidgets import GridspecLayout


passdesks = True
passlexss = True

lvs = int(widgets.__version__[:1])
if lvs==8:
    passdesks = False

commands_regex = getoutput('pip list | grep regex')# if "regex" in v_diffusers:
if "regex" in commands_regex:
    passlexss = False
    
if passdesks and passlexss:
    
    !pip install regex
    !pip install --upgrade ipywidgets
    clear_output(wait=False)
    print ('　　   \033[0;36m\033[1mipywidgets established\033[0m')
    time.sleep(0.5)
    os._exit(00)
    
else:
    
    import regex
    
    enable = {'button_color': '#3d85c6', 'text_color': '#033932'}
    emdown = {'button_color': '#008081', 'text_color': '#053c3c'}

    #ifoCOMifoCOMifoCOMifoCOMifoCOM

    opens = '<p style="text-align:center;"><span style="color:#25323b;font-family:"Courier New", Courier, monospace;"><strong>'
    cloug = '</strong></span></p>'
    migse = {'background': '#a3b8c8'}

    nunlfs = widgets.HTML(value=opens+'git-lfs'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nunxformers = widgets.HTML(value=opens+'xformers'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nundiffusers = widgets.HTML(value=opens+'diffusers'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nunfastapi = widgets.HTML(value=opens+'fastapi 0.90.1'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nunautomatic = widgets.HTML(value=opens+'automatic'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nuncontrolnet = widgets.HTML(value=opens+'controlnet'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))

    onoffcolos = ["solid arrow-down","solid rotate-right","solid check"]
    onoffcolosS = [enable, emdown]
    onoffcolosD = [False, True]

    nunlfsU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nunxformersU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nundiffusersU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nunfastapiU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nunautomaticU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nuncontrolnetU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])

    ifoCOM = GridspecLayout(2,6,width='500px',height='auto')

    ifoCOM[0, 0] = nunlfs
    ifoCOM[0, 1] = nunlfsU

    ifoCOM[1, 0] = nunxformers
    ifoCOM[1, 1] = nunxformersU

    ifoCOM[0, 2] = nundiffusers
    ifoCOM[0, 3] = nundiffusersU

    ifoCOM[1, 2] = nunfastapi
    ifoCOM[1, 3] = nunfastapiU

    ifoCOM[0, 4] = nunautomatic
    ifoCOM[0, 5] = nunautomaticU

    ifoCOM[1, 4] = nuncontrolnet
    ifoCOM[1, 5] = nuncontrolnetU

    def upload_ifoCOM ():

        upload_ifoCOM_lfs = False
        upload_ifoCOM_xfo = False
        upload_ifoCOM_diffusers = False
        upload_ifoCOM_ivs = False
        upload_ifoCOM_wui = False
        upload_ifoCOM_col = False

        commands_lfs = getoutput('git lfs install')
        if "Git LFS initialized" in commands_lfs:
             upload_ifoCOM_lfs = True

        commands_xfo = getoutput('python -m xformers.info')
        if not "Error while finding module specification" in commands_xfo:
            upload_ifoCOM_xfo = True

        commands_diffusers = getoutput('pip list | grep diffusers')
        if "diffusers" in commands_diffusers:
            upload_ifoCOM_diffusers = True

        try:
            import fastapi
            ivs = str(fastapi.__version__)
        except:
            ivs = str("except")
        if ivs == '0.90.1':
            upload_ifoCOM_ivs = True

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui'):
            upload_ifoCOM_wui = True

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/extensions/sd-webui-controlnet'):
            upload_ifoCOM_col = True

        if not upload_ifoCOM_lfs:
            nunlfsU.disabled=onoffcolosD[0]; nunlfsU.icon=onoffcolos[0]; nunlfsU.style=onoffcolosS[0]
        else:
            nunlfsU.disabled=onoffcolosD[1]; nunlfsU.icon=onoffcolos[2]; nunlfsU.style=onoffcolosS[1]
        if not upload_ifoCOM_xfo:
            nunxformersU.disabled=onoffcolosD[0]; nunxformersU.icon=onoffcolos[0]; nunxformersU.style=onoffcolosS[0]
        else:
            nunxformersU.disabled=onoffcolosD[1]; nunxformersU.icon=onoffcolos[2]; nunxformersU.style=onoffcolosS[1]
        if not upload_ifoCOM_diffusers:
            nundiffusersU.disabled=onoffcolosD[0]; nundiffusersU.icon=onoffcolos[0]; nundiffusersU.style=onoffcolosS[0]
        else:
            nundiffusersU.disabled=onoffcolosD[1]; nundiffusersU.icon=onoffcolos[2]; nundiffusersU.style=onoffcolosS[1]
        if not upload_ifoCOM_ivs:
            nunfastapiU.disabled=onoffcolosD[0]; nunfastapiU.icon=onoffcolos[0]; nunfastapiU.style=onoffcolosS[0]
        else:
            nunfastapiU.disabled=onoffcolosD[1]; nunfastapiU.icon=onoffcolos[2]; nunfastapiU.style=onoffcolosS[1]
        if not upload_ifoCOM_wui:
            nunautomaticU.disabled=onoffcolosD[0]; nunautomaticU.icon=onoffcolos[0]; nunautomaticU.style=onoffcolosS[0]
        else:
            nunautomaticU.disabled=onoffcolosD[0]; nunautomaticU.icon=onoffcolos[1]; nunautomaticU.style=onoffcolosS[0]
        if not upload_ifoCOM_col:
            if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui'):
                nuncontrolnetU.disabled=onoffcolosD[0]
            else:
                nuncontrolnetU.disabled=onoffcolosD[1]
            nuncontrolnetU.icon=onoffcolos[0]; nuncontrolnetU.style=onoffcolosS[0]
        else:
            nuncontrolnetU.disabled=onoffcolosD[1]; nuncontrolnetU.icon=onoffcolos[2]; nuncontrolnetU.style=onoffcolosS[1]

    upload_ifoCOM ()

    #ifoCOMifoCOMifoCOMifoCOMifoCOM
    #widCOMwidCOMwidCOMwidCOMwidCOM

    ckptsize =[]

    widV15 = widgets.Button(description='V15',disabled=False,button_style='',tooltip='',layout=Layout(width="50px",height="28px"),style = enable)
    widV21 = widgets.Button(description='V21',disabled=False,button_style='',tooltip='',layout=Layout(width="50px",height="28px"),style = enable)
    widDSS = widgets.Button(description='DSS',disabled=True,button_style='',tooltip='',layout=Layout(width="50px",height="28px"),style = emdown)

    widSELES = widgets.Select(options=ckptsize,disabled=False,layout=Layout(width="358px",height="95px"))

    emgNUN = widgets.Button(description='WUI',disabled=False,button_style='',tooltip='',layout=Layout(width="80px",height="auto"),style = enable)

    widCOM = GridspecLayout(3,3,width='500px',height='100px')

    widCOM[0, 0] = widV15
    widCOM[1, 0] = widV21
    widCOM[2, 0] = widDSS
    widCOM[:, 1] = widSELES
    widCOM[:, 2] = emgNUN

    def upload_widCOM ():

        global ckptsize

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui'):
            emgNUN.disabled=False
        else:
            emgNUN.disabled=True

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V15.ckpt'):
            widV15.disabled=True;widV15.style=emdown;
        else:
            widV15.disabled=False;widV15.style=enable;

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V21.ckpt'):
            widV21.disabled=True;widV21.style=emdown;
        else:
            widV21.disabled=False;widV21.style=enable;

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/DSS.ckpt'):
            widDSS.disabled=True;widDSS.style=emdown;
        else:
            widDSS.disabled=False;widDSS.style=enable;

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/'):
            ckptsize = []
            for e in os.listdir("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/"):
                name, ext = os.path.splitext(e)
                if ext == '.ckpt':
                    ckptsize.append(name)
            widSELES.options=ckptsize

    upload_widCOM ()

    #widCOMwidCOMwidCOMwidCOMwidCOM
    #sizCOMsizCOMsizCOMsizCOMsizCOM

    SZ = widgets.IntProgress(min=0,max=102400,
                             bar_style='',
                             style={'bar_color': '#3d85c6', 'description_width': 'initial'},
                             layout=Layout(width="315px",height="28px"),
                             orientation='horizontal'
                            )
    sizesU = widgets.Button(disabled=False,button_style='',tooltip='',icon="solid rotate-right",layout=Layout(width="80px",height="28px"),style = enable)
    sizesD = widgets.Button(disabled=False,button_style='',tooltip='',icon="sharp solid trash",layout=Layout(width="80px",height="28px"),style = enable)

    sizCOM = GridspecLayout(1,3,width='500px',height='auto')

    sizCOM[0, 0] = sizesD
    sizCOM[0, 1] = sizesU
    sizCOM[0, 2] = SZ

    def upload_sizCOM ():

        getpt = str(os.path.abspath(os.getcwd()))
        os.chdir('/')
        comsiz = str(getoutput('du -s')).split("\n")
        outsiz = int(int(regex.findall("\d+", comsiz[len(comsiz)-1])[0])/1024)
        os.chdir(getpt)
        SZ.value=outsiz

    upload_sizCOM ()

    #sizCOMsizCOMsizCOMsizCOMsizCOM

    allsizezCOMPIL = GridspecLayout(3,1,width='auto',height='233px')

    allsizezCOMPIL[0, 0] = ifoCOM
    allsizezCOMPIL[1, 0] = widCOM
    allsizezCOMPIL[2, 0] = sizCOM

    #bittenfigitten


    #downline

    def lisau():
        getpt = str(os.path.abspath(os.getcwd()))
        os.chdir('/home/jovyan') 
        !find -name '.ipynb_checkpoints' -exec rm -rf {} \;
        os.chdir(getpt)
        if os.path.exists("/home/jovyan/.local/share/Trash/files"):
            !rm -rf /home/jovyan/.local/share/Trash/files

    def sizesD_function(aug):
        lisau()
        upload_sizCOM()

    def sizesU_function(aug):
        upload_sizCOM()

    sizesU.on_click(sizesD_function)
    sizesD.on_click(sizesD_function)

    #downline

    #hensline

    intes = [nunlfsU,nunxformersU,nundiffusersU,nunfastapiU,nunautomaticU,nuncontrolnetU,widV15,widV21,widDSS,emgNUN,sizesD,sizesU]
    poses = [False,False,False,False,False,False,False,False,False,False,False,False]

    def setup_loads():
        global poses
        for i in range(len(poses)):
            poses[i]=intes[i].disabled
        for i in range(len(poses)):
            intes[i].disabled=True

    def exele_loads():
        for i in range(len(poses)):
            intes[i].disabled=poses[i]

    def nunlfsU_function(aug):

        setup_loads()

        with light_output:
            clear_output()
            !curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
            !sudo apt-get install git-lfs

        upload_sizCOM ()

        with light_output:
            clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nunxformersU_function(aug):

        setup_loads()

        with light_output:
            # clear_output()
            # !mamba install -q -c nvidia cuda-nvcc=11.7 python=3.9.13 --yes
            # clear_output()
            # !mamba install -q -c nvidia -c pytorch pytorch-cuda=11.7 python=3.9.13 --yes
            # clear_output()
            # !mamba install -q -c fastai python=3.9.13 fastai=2.7.11 --yes
            # clear_output()
            # !mamba install -q -c conda-forge python=3.9.13 huggingface_hub=0.12.1 --yes
            # clear_output()
            # !mamba install -q -c nvidia -c pytorch -c 'xformers/label/dev' python=3.9.13 pytorch=1.13.1 xformers --yes
            # clear_output()
            # !pip install --upgrade triton==2.0.0a2
            
            clear_output()
            !mamba install -q -c nvidia cuda-nvcc=11.7 python=3.9.13 --yes
            clear_output()
            !mamba install -q -c conda-forge python=3.9.13 transformers --yes
            clear_output()
            !mamba install -q -c nvidia -c pytorch -c 'xformers/label/dev' python=3.9.13 xformers --yes
            clear_output()
            !pip install --upgrade triton==2.0.0a2
            clear_output()
            !pip install --upgrade transformers==4.25.1
            

        upload_sizCOM ()

        with light_output:

            clear_output()

            qwe = getoutput('nvidia-smi')

            nvc = getoutput('nvcc --version')
            if not 'nvcc: not found' in nvc:
                asd = nvc
            else:
                asd = ''

            xfo = getoutput('python -m xformers.info')
            if not 'Error while finding module specification' in xfo:
                zxc = xfo
            else:
                zxc = ''

            print('\033[0;36m\033[1m',"\n",qwe,"\n\n",asd,"\n\n",zxc,'\033[0m')

        exele_loads ()

        upload_ifoCOM ()

    def nundiffusersU_function(aug):

        setup_loads()

        with light_output:
            clear_output()

            !pip install accelerate
            clear_output()

            !pip install git+https://github.com/huggingface/diffusers
            clear_output()

        upload_sizCOM ()

        # with light_output:
        #     !pip install transformers==4.25.1
        #     clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nunfastapiU_function(aug):

        setup_loads()

        with light_output:
            clear_output()

        upload_sizCOM ()

        with light_output:
            !pip install --upgrade fastapi==0.90.1
            clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nunautomaticU_function(aug):

        setup_loads()

        with light_output:

            clear_output()

            if not os.path.exists("/home/jovyan/workspace/stable-diffusion-webui"):
                os.chdir('/home/jovyan/workspace/')
                !git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui stable-diffusion-webui
            else:
                os.chdir('/home/jovyan/workspace/stable-diffusion-webui')
                !git pull

        upload_sizCOM ()

        with light_output:
            clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nuncontrolnetU_function(aug):

        setup_loads()

        with light_output:
            clear_output()

            os.chdir('/home/jovyan/workspace/stable-diffusion-webui/extensions')
            !git clone https://github.com/Mikubill/sd-webui-controlnet.git

            from torch.hub import download_url_to_file
            from urllib.parse import urlparse

            mdldir="/home/jovyan/workspace/stable-diffusion-webui/extensions/sd-webui-controlnet/models"

            os.chdir('/home/jovyan/workspace/')
            !wget -q -O CN_models.txt https://github.com/TheLastBen/fast-stable-diffusion/raw/main/AUTOMATIC1111_files/CN_models.txt

            with open("CN_models.txt", 'r') as f:
                mdllnk = f.read().splitlines()

            for lnk in mdllnk:
                clear_output()
                download_url_to_file(lnk, os.path.abspath(os.path.join(mdldir, os.path.basename(urlparse(lnk).path))), hash_prefix=None, progress=True)

            !rm CN_models.txt

        upload_sizCOM ()

        with light_output:
            clear_output()

        exele_loads ()

        upload_ifoCOM ()



    nunlfsU.on_click(nunlfsU_function)
    nunxformersU.on_click(nunxformersU_function)
    nundiffusersU.on_click(nundiffusersU_function)
    nunfastapiU.on_click(nunfastapiU_function)
    nunautomaticU.on_click(nunautomaticU_function)
    nuncontrolnetU.on_click(nuncontrolnetU_function)


    #hensline

    #midasline

    def widV15_function(aug):
        with light_output:

            clear_output()
            os.chdir('/home/jovyan/workspace/') 
            !mkdir "/home/jovyan/workspace/tmp"
            os.chdir('/home/jovyan/workspace/tmp')
            !git init
            !git lfs install --system --skip-repo
            !git clone --depth 1 --branch fp16 "https://huggingface.co/runwayml/stable-diffusion-v1-5"
            !rm -rf /home/jovyan/workspace/tmp/.git

        lisau()
        with light_output:

            os.chdir('/home/jovyan/workspace/tmp')
            if os.path.exists('/home/jovyan/workspace/tmp/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin'):
                !wget -q -O stable-diffusion-v1-5/vae/diffusion_pytorch_model.bin https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.bin
                !wget -O convertosd.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertosd.py
                !sed -i '201s@.*@    model_path = "/home/jovyan/workspace/tmp/stable-diffusion-v1-5"@' /home/jovyan/workspace/tmp/convertosd.py
                !sed -i '202s@.*@    checkpoint_path= "/home/jovyan/workspace/V15.ckpt"@' /home/jovyan/workspace/tmp/convertosd.py      
                !python /home/jovyan/workspace/tmp/convertosd.py
            if os.path.exists("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion"):
                !mv /home/jovyan/workspace/V15.ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V15.ckpt
                widV15_function_out = "/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V15.ckpt"
            else:
                widV15_function_out = "/home/jovyan/workspace/V15.ckpt"

            os.chdir('/home/jovyan/workspace/')
            !rm -rf /home/jovyan/workspace/tmp

        lisau()
        with light_output:

            upload_sizCOM()

            upload_widCOM()

            clear_output()

            if os.path.exists(widV15_function_out):
                print ('\033[0;36m\033[1m'+widV15_function_out+'\033[0m')

    def widV21_function(aug):
        with light_output:

            clear_output()

            os.chdir('/home/jovyan/workspace/') 
            !mkdir "/home/jovyan/workspace/tmp"
            os.chdir('/home/jovyan/workspace/tmp')
            !git init
            !git lfs install --system --skip-repo
            !git remote add -f origin  "https://huggingface.co/stabilityai/stable-diffusion-2-1"
            !git config core.sparsecheckout true
            !echo -e "scheduler\ntext_encoder\ntokenizer\nunet\nvae\nfeature_extractor\nmodel_index.json\n!*.safetensors" > .git/info/sparse-checkout
            !git pull origin fp16
            !rm -rf /home/jovyan/workspace/tmp/.git


        lisau()
        with light_output:

            os.chdir('/home/jovyan/workspace/')
            !wget -O convertosdv2.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertosdv2.py
            !python /home/jovyan/workspace/convertosdv2.py --fp16 /home/jovyan/workspace/tmp /home/jovyan/workspace/V21.ckpt

            if os.path.exists("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion"):
                !mv /home/jovyan/workspace/V21.ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V21.ckpt
                widV21_function_out = "/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V21.ckpt"
            else:
                widV21_function_out = "/home/jovyan/workspace/V21.ckpt"

            !rm -rf /home/jovyan/workspace/tmp
            !rm -rf /home/jovyan/workspace/convertosdv2.py

        lisau()
        with light_output:

            upload_sizCOM()

            upload_widCOM()

            clear_output()

            if os.path.exists(widV21_function_out):
                print ('\033[0;36m\033[1m'+widV21_function_out+'\033[0m')

    def widDSS_function(aug):
        with light_output:

            clear_output()
            os.chdir('/home/jovyan/workspace/') 
            !mkdir "/home/jovyan/workspace/tmp"
            os.chdir('/home/jovyan/workspace/tmp')
            !git init
            !git lfs install --system --skip-repo
            !git clone "https://huggingface.co/dream-textures/texture-diffusion"
            !rm -rf /home/jovyan/workspace/tmp/.git

        lisau()
        with light_output:
            
            os.chdir('/home/jovyan/workspace/')
            !wget -O convertosdv2.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertosdv2.py
            !python /home/jovyan/workspace/convertosdv2.py --fp16 /home/jovyan/workspace/tmp/texture-diffusion /home/jovyan/workspace/DSS.ckpt

            if os.path.exists("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion"):
                !mv /home/jovyan/workspace/DSS.ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/DSS.ckpt
                widDSS_function_out = "/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/DSS.ckpt"
            else:
                widDSS_function_out = "/home/jovyan/workspace/DSS.ckpt"

            !rm -rf /home/jovyan/workspace/tmp
            !rm -rf /home/jovyan/workspace/convertosdv2.py
            

        lisau()
        with light_output:

            upload_sizCOM()

            upload_widCOM()

            clear_output()

            if os.path.exists(widDSS_function_out):
                print ('\033[0;36m\033[1m'+widDSS_function_out+'\033[0m')

    widV15.on_click(widV15_function)
    widV21.on_click(widV21_function)
    widDSS.on_click(widDSS_function)

    def emgNUN_function(aug):
        with light_output:

            clear_output()
            saind_widSELES = str(widSELES.value)

            alpha = str('cd /home/jovyan/workspace/stable-diffusion-webui\nCOMMANDLINE_ARGS="--share --enable-insecure-extension-access --xformers --lowram ')
            betas = str('" REQS_FILE="requirements.txt" python launch.py')

            if saind_widSELES == 'None':
                print ('\033[0;36m\033[1m'+alpha+betas+'\033[0m')
            else:
                modes = str('--ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/') + saind_widSELES + str('.ckpt')
                print ('\033[0;36m\033[1m'+alpha+modes+betas+'\033[0m')

    emgNUN.on_click(emgNUN_function)

    #midasline

    #bittenfigitten

    light_output = widgets.Output()

    display(allsizezCOMPIL,light_output)

@gugleigos
Copy link

dbooth

@gugleigos
Copy link 

import os, time
import regex
from IPython.display import display, clear_output
from subprocess import getoutput
import random
import ipywidgets as widgets
from ipywidgets import Layout
from ipywidgets import GridspecLayout

os.chdir('/home/jovyan/workspace/')

AGfloats = False

try:
    import accelerate
    avs = str(accelerate.__version__)
except:
    avs = str("except")
if avs == '0.12.0':
    AGfloats = True

DIfloats = False

try:
    import diffusers
    jvs = str(diffusers.__version__)
except:
    jvs = str("except")
if jvs == '0.9.0.dev0':
    DIfloats = True
    
DB = '/home/jovyan/workspace/DB/'
SDfloats = '/home/jovyan/workspace/DB/diffusers/'
sd15f = "/home/jovyan/workspace/DB/value-v1-5/"

if not os.path.exists(DB):
    os.makedirs(DB)
    
if not AGfloats:
    !pip install -q --no-deps accelerate==0.12.0
    
if not os.path.exists(SDfloats):
    os.chdir(DB)
    !git clone --depth 1 --branch updt https://github.com/TheLastBen/diffusers
else:
    os.chdir(SDfloats)
    !git pull
    
if not DIfloats:
    os.chdir('/home/jovyan/workspace/DB/diffusers/')
    !pip install -e ".[torch]"
    
try:
    import bitsandbytes
    
except:
    !pip install -q --no-deps bitsandbytes
    
if not os.path.exists(sd15f):
    os.makedirs(sd15f)
    os.chdir(sd15f)

    !git init
    !git lfs install --system --skip-repo
    !git remote add -f origin  "https://huggingface.co/runwayml/stable-diffusion-v1-5"
    !git config core.sparsecheckout true
    !echo -e "scheduler\ntext_encoder\ntokenizer\nunet\nvae\n!vae/diffusion_pytorch_model.bin\n!*.safetensors" > .git/info/sparse-checkout
    !git pull origin main

    !wget -q -O vae/diffusion_pytorch_model.bin https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.bin
    !rm -rf .git  
    !wget -q -O model_index.json https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/model_index.json
    
files = '/home/jovyan/workspace/DB/files/'

instancefiles = '/home/jovyan/workspace/DB/files/instancefiles/'
classfiles = '/home/jovyan/workspace/DB/files/classfiles/'
outputfiles = '/home/jovyan/workspace/DB/files/outputfiles/'
sesiontemp = '/home/jovyan/workspace/DB/files/sesiontemp/'
sesions = '/home/jovyan/workspace/DB/files/sesions/'

if not os.path.exists(files):
    os.makedirs(files)
if not os.path.exists(instancefiles):
    os.makedirs(instancefiles)
if not os.path.exists(classfiles):
    os.makedirs(classfiles)
if not os.path.exists(outputfiles):
    os.makedirs(outputfiles)
if not os.path.exists(sesiontemp):
    os.makedirs(sesiontemp)
if not os.path.exists(sesions):
    os.makedirs(sesions)

gendiffusion = GridspecLayout(1,6,width='1000px',height='auto')

opens = '<p style="text-align:center;"><span style="color:#25323b;font-family:"Courier New", Courier, monospace;"><strong>'
cloug = '</strong></span></p>'
migse = {'background': '#a3b8c8'}

enable = {'button_color': '#3d85c6', 'text_color': '#033932'}
emdown = {'button_color': '#008081', 'text_color': '#053c3c'}

onoffcolos = ["solid arrow-down","solid rotate-right","solid check"]
onoffcolosS = [enable, emdown]
onoffcolosD = [False, True]

labelssei = widgets.HTML(value=opens+'sesion'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
fieldsei = widgets.Text(value='',placeholder='Something',disabled=False,layout=Layout(width="330px",height="28px"))
gelsei = widgets.Button(disabled=False,button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])

labelstok = widgets.HTML(value=opens+'token'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
fieldtok = widgets.Text(value='',placeholder='Something',disabled=False,layout=Layout(width="330px",height="28px"))
geltok = widgets.Button(disabled=False,button_style='',tooltip='',icon=onoffcolos[1],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])

if fieldsei.value == '':
    gelsei.icon=onoffcolos[0]
    gelsei.style = onoffcolosS[0]
else:
    gelsei.icon=onoffcolos[1]
    gelsei.style = onoffcolosS[1]

if fieldsei.value == '':
    geltok.style = onoffcolosS[0]
else:
    geltok.style = onoffcolosS[1]
    
gendiffusion[0, 0] = labelssei
gendiffusion[0, 1] = fieldsei
gendiffusion[0, 2] = gelsei

gendiffusion[0, 3] = labelstok
gendiffusion[0, 4] = fieldtok
gendiffusion[0, 5] = geltok

inputSesion = '/home/jovyan/workspace/DB/value-v1-5/'
outputSesion = ''

inputInstanse = '/home/jovyan/workspace/DB/files/instancefiles/'
inputClassic = '/home/jovyan/workspace/DB/files/classfiles/'
outputInstanse = '/home/jovyan/workspace/DB/files/outputfiles/'

def gelsei_function(aug):

    if not fieldsei.value == '':
        
        global outputSesion
        outputSesion = '/home/jovyan/workspace/DB/files/sesions/' + fieldsei.value
        
        if not os.path.exists(outputSesion):
            os.makedirs(outputSesion)
        
        gelsei.icon=onoffcolos[1]
        gelsei.style = onoffcolosS[1]

def geltok_function(aug):
    
    filefields = str(open("/home/jovyan/workspace/SCD/simple.py", "r").read())
    split = filefields.split("###")[1]
    teg = split.split("@")
    compil = int(random.randint(0,len(teg)-1))
    fieldtok.value = str(teg[compil])
    geltok.style = onoffcolosS[1]
        
gelsei.on_click(gelsei_function)
geltok.on_click(geltok_function)

display(gendiffusion)

opens3 = '<p style="text-align:right;"><span style="color:#25323b;font-family:"Courier New", Courier, monospace;"><strong>'
cloug3 = '</strong></span></p>'

gen2diffusion = GridspecLayout(13,2,width='1000px',height='auto')

l_Resume_Training = widgets.HTML(value=opens3+'Resume_Training　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Resume_Training = widgets.Checkbox(value=False,disabled=False,indent=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_UNet_Training_Steps = widgets.HTML(value=opens3+'UNet_Training_Steps　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_UNet_Training_Steps = widgets.IntText(value=1500,disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_UNet_Learning_Rate = widgets.HTML(value=opens3+'UNet_Learning_Rate　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_UNet_Learning_Rate = widgets.Dropdown(options=['1e-5', '8e-6', '5e-6', '3e-6', '2e-6', '1e-6']\
                                        ,value='1e-6',disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Text_Encoder_Training_Steps = widgets.HTML(value=opens3+'l_Text_Encoder_Training_Steps　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Text_Encoder_Training_Steps = widgets.IntText(value=0,disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Text_Encoder_Concept_Training_Steps = widgets.HTML(value=opens3+'l_Text_Encoder_Concept_Training_Steps　'+cloug3,\
                                                     style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Text_Encoder_Concept_Training_Steps = widgets.IntText(value=0,disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Text_Encoder_Learning_Rate = widgets.HTML(value=opens3+'l_Text_Encoder_Learning_Rate　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Text_Encoder_Learning_Rate = widgets.Dropdown(options=['2e-6', '8e-8', '5e-8', '3e-8', '2e-8', '1e-8']\
                                                ,value='1e-8',disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Offset_Noise = widgets.HTML(value=opens3+'l_Offset_Noise　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Offset_Noise = widgets.Checkbox(value=False,disabled=False,indent=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_External_Captions = widgets.HTML(value=opens3+'l_External_Captions　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_External_Captions = widgets.Checkbox(value=False,disabled=False,indent=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Resolution = widgets.HTML(value=opens3+'l_Resolution　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Resolution = widgets.Dropdown(options=['1024', '512']\
                                        ,value='512',disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Save_Checkpoint_Every_n_Steps = widgets.HTML(value=opens3+'l_Save_Checkpoint_Every_n_Steps　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Save_Checkpoint_Every_n_Steps = widgets.Checkbox(value=False,disabled=False,indent=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Save_Checkpoint_Every = widgets.HTML(value=opens3+'l_Save_Checkpoint_Every　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Save_Checkpoint_Every = widgets.IntText(value=300,disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Start_saving_from_the_step = widgets.HTML(value=opens3+'l_Start_saving_from_the_step　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Start_saving_from_the_step = widgets.IntText(value=300,disabled=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))

l_Disconnect_after_training = widgets.HTML(value=opens3+'l_Disconnect_after_training　'+cloug3, style = migse ,layout=Layout(flex='1 1 0%', width="auto",height="28px"))
f_Disconnect_after_training = widgets.Checkbox(value=False,disabled=False,indent=False,layout=Layout(flex='1 1 0%', width='auto', height='28px'))


gen2diffusion[0, 0] = l_Resume_Training
gen2diffusion[0, 1] = f_Resume_Training
gen2diffusion[1, 0] = l_UNet_Training_Steps
gen2diffusion[1, 1] = f_UNet_Training_Steps
gen2diffusion[2, 0] = l_UNet_Learning_Rate
gen2diffusion[2, 1] = f_UNet_Learning_Rate
gen2diffusion[3, 0] = l_Text_Encoder_Training_Steps
gen2diffusion[3, 1] = f_Text_Encoder_Training_Steps
gen2diffusion[4, 0] = l_Text_Encoder_Concept_Training_Steps
gen2diffusion[4, 1] = f_Text_Encoder_Concept_Training_Steps
gen2diffusion[5, 0] = l_Text_Encoder_Learning_Rate
gen2diffusion[5, 1] = f_Text_Encoder_Learning_Rate
gen2diffusion[6, 0] = l_Offset_Noise
gen2diffusion[6, 1] = f_Offset_Noise
gen2diffusion[7, 0] = l_External_Captions
gen2diffusion[7, 1] = f_External_Captions
gen2diffusion[8, 0] = l_Resolution
gen2diffusion[8, 1] = f_Resolution
gen2diffusion[9, 0] = l_Save_Checkpoint_Every_n_Steps
gen2diffusion[9, 1] = f_Save_Checkpoint_Every_n_Steps
gen2diffusion[10, 0] = l_Save_Checkpoint_Every
gen2diffusion[10, 1] = f_Save_Checkpoint_Every
gen2diffusion[11, 0] = l_Start_saving_from_the_step
gen2diffusion[11, 1] = f_Start_saving_from_the_step
gen2diffusion[12, 0] = l_Disconnect_after_training
gen2diffusion[12, 1] = f_Disconnect_after_training

display(gen2diffusion)

INSTANCE_DIR = inputInstanse
CONCEPT_DIR = inputClassic
CAPTIONS_DIR = '/home/jovyan/workspace/DB/files/captionsfiles/'

if not os.path.exists(CAPTIONS_DIR):
    os.makedirs(CAPTIONS_DIR)

Resume_Training = f_Resume_Training.value

OUTPUT_DIR = outputSesion

resume=False
if os.path.exists(OUTPUT_DIR+'/unet/diffusion_pytorch_model.bin'):
    resume=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
      break
    elif ansres=='yes':
      Resume_Training = False
      resume= False
      break
    
MODEL_NAME = inputSesion

while not Resume_Training and MODEL_NAME=="":
  print('�[1;31mNo model found, use the "Model Download" cell to download a model.')
  time.sleep(5)
    
    
MODELT_NAME = MODEL_NAME
    
UNet_Training_Steps = int(f_UNet_Training_Steps.value)
UNet_Learning_Rate = float(f_UNet_Learning_Rate.value)
untlr=UNet_Learning_Rate

Text_Encoder_Training_Steps = int(f_Text_Encoder_Training_Steps.value)
Text_Encoder_Concept_Training_Steps = int(f_Text_Encoder_Concept_Training_Steps.value)

Text_Encoder_Learning_Rate = float(f_Text_Encoder_Learning_Rate.value)
txlr=Text_Encoder_Learning_Rate

trnonltxt=""
if UNet_Training_Steps==0:
   trnonltxt="--train_only_text_encoder"


ofstnse=""
Offset_Noise = f_Offset_Noise.value

if Offset_Noise:
  ofstnse="--offset_noise"

External_Captions = f_External_Captions.value

extrnlcptn=""
if External_Captions:
  extrnlcptn="--external_captions"

Resolution = f_Resolution.value
Res=int(Resolution)


Seed=''
if Seed =='' or Seed=='0':
  Seed=random.randint(1, 999999)
else:
  Seed=int(Seed)

GC="--gradient_checkpointing"

fp16 = True
if fp16:
  prec="fp16"
else:
  prec="no"
precision=prec

s = getoutput('nvidia-smi')
if 'A100' in s:
  GC=""
GCUNET=GC
if Res<=640:
  GCUNET=""


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)
    
    
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


Save_Checkpoint_Every_n_Steps = f_Save_Checkpoint_Every_n_Steps.value
Save_Checkpoint_Every = int(f_Save_Checkpoint_Every.value)

if Save_Checkpoint_Every == None:
  Save_Checkpoint_Every = 1

stp = 0
Start_saving_from_the_step = int(f_Start_saving_from_the_step.value)
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
    
    
Disconnect_after_training = f_Disconnect_after_training.value
    
   
PT=""

INSTANCE_NAME = fieldsei.value

Session_Name = INSTANCE_NAME
SESSION_DIR = OUTPUT_DIR
MDLPTH = str(OUTPUT_DIR+"/"+Session_Name+'.ckpt')

def dump_only_textenc(trnonltxt, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, precision, Training_Steps):
    
    !accelerate launch /home/jovyan/workspace/DB/diffusers/examples/dreambooth/train_dreambooth.py \
    $trnonltxt \
    $extrnlcptn \
    --image_captions_filename \
    --train_text_encoder \
    --dump_only_text_encoder \
    --pretrained_model_name_or_path="$MODELT_NAME" \
    --instance_data_dir="$INSTANCE_DIR" \
    --output_dir="$OUTPUT_DIR" \
    --instance_prompt="$PT" \
    --seed=$Seed \
    --resolution=512 \
    --mixed_precision=$precision \
    --train_batch_size=1 \
    --gradient_accumulation_steps=1 $GC \
    --use_8bit_adam \
    --learning_rate=$txlr \
    --lr_scheduler="linear" \
    --lr_warmup_steps=0 \
    --max_train_steps=$Training_Steps

def train_only_unet(stpsv, stp, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, Res, precision, Training_Steps):
    clear_output()
    if resuming=="Yes":
      print('�[1;32mResuming Training...�[0m')
    print('�[1;33mTraining the UNet...�[0m')
    !accelerate launch /home/jovyan/workspace/DB/diffusers/examples/dreambooth/train_dreambooth.py \
    $extrnlcptn \
    $ofstnse \
    --image_captions_filename \
    --train_only_unet \
    --save_starting_step=$stpsv \
    --save_n_steps=$stp \
    --Session_dir=$SESSION_DIR \
    --pretrained_model_name_or_path="$MODELT_NAME" \
    --instance_data_dir="$INSTANCE_DIR" \
    --output_dir="$OUTPUT_DIR" \
    --captions_dir="$CAPTIONS_DIR" \
    --instance_prompt="$PT" \
    --seed=$Seed \
    --resolution=$Res \
    --mixed_precision=$precision \
    --train_batch_size=1 \
    --gradient_accumulation_steps=1 $GCUNET \
    --use_8bit_adam \
    --learning_rate=$untlr \
    --lr_scheduler="linear" \
    --lr_warmup_steps=0 \
    --max_train_steps=$Training_Steps
    
    
if os.path.exists(INSTANCE_DIR+"/.ipynb_checkpoints"):
  %rm -r $INSTANCE_DIR"/.ipynb_checkpoints"

if os.path.exists(CONCEPT_DIR+"/.ipynb_checkpoints"):
  %rm -r $CONCEPT_DIR"/.ipynb_checkpoints"

if os.path.exists(CAPTIONS_DIR+"/.ipynb_checkpoints"):
  %rm -r $CAPTIONS_DIR"/.ipynb_checkpoints"

os.chdir('/home/jovyan/workspace/DB/')

if Enable_text_encoder_training :
  print('�[1;33mTraining the text encoder...�[0m')
  if os.path.exists(OUTPUT_DIR+'/'+'text_encoder_trained'):
    %rm -r $OUTPUT_DIR"/text_encoder_trained"
  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(stpsv, stp, SESSION_DIR, MODELT_NAME, INSTANCE_DIR, OUTPUT_DIR, PT, Seed, Res, 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'):
    prc="--fp16" if precision=="fp16" else ""
    !python /home/jovyan/workspace/DB/diffusers/scripts/convertosdv2.py $prc $OUTPUT_DIR $SESSION_DIR/$Session_Name".ckpt"
    clear_output()
    if os.path.exists(SESSION_DIR+"/"+INSTANCE_NAME+'.ckpt'):
      clear_output()
      print("�[1;32mDONE, the CKPT model is in your Gdrive in the sessions folder")
      if Disconnect_after_training :
        time.sleep(20)
        runtime.unassign()
    else:
      print("�[1;31mSomething went wrong")
  else:
    print("�[1;31mSomething went wrong")

from subprocess import getoutput

qwe = getoutput('nvidia-smi')

nvc = getoutput('nvcc --version')
if not 'nvcc: not found' in nvc:
    asd = nvc
else:
    asd = ''

xfo = getoutput('python -m xformers.info')
if not 'Error while finding module specification' in xfo:
    zxc = xfo
else:
    zxc = ''

print('\033[0;36m\033[1m',"\n",qwe,"\n\n",asd,"\n\n",zxc,'\033[0m')

@axvellyledmy
Copy link

~~

@axvellyledmy
Copy link

5\u001b[1;31m


import os, time
from IPython.display import display, clear_output
from subprocess import getoutput


import ipywidgets as widgets
from ipywidgets import Layout
from ipywidgets import GridspecLayout


passdesks = True
passlexss = True

lvs = int(widgets.__version__[:1])
if lvs==8:
    passdesks = False

commands_regex = getoutput('pip list | grep regex')# if "regex" in v_diffusers:
if "regex" in commands_regex:
    passlexss = False
    
if passdesks and passlexss:
    
    !pip install regex
    !pip install --upgrade ipywidgets
    clear_output(wait=False)
    print ('　　   \033[0;36m\033[1mipywidgets established\033[0m')
    time.sleep(0.5)
    os._exit(00)
    
else:
    
    import regex
    
    enable = {'button_color': '#3d85c6', 'text_color': '#033932'}
    emdown = {'button_color': '#008081', 'text_color': '#053c3c'}

    #ifoCOMifoCOMifoCOMifoCOMifoCOM

    opens = '<p style="text-align:center;"><span style="color:#25323b;font-family:"Courier New", Courier, monospace;"><strong>'
    cloug = '</strong></span></p>'
    migse = {'background': '#a3b8c8'}

    nunlfs = widgets.HTML(value=opens+'git-lfs'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nunxformers = widgets.HTML(value=opens+'xformers'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nundiffusers = widgets.HTML(value=opens+'diffusers'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nunfastapi = widgets.HTML(value=opens+'fastapi 0.90.1'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nunautomatic = widgets.HTML(value=opens+'automatic'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))
    nuncontrolnet = widgets.HTML(value=opens+'controlnet'+cloug, style = migse ,layout=Layout(width="120px",height="28px"))

    onoffcolos = ["solid arrow-down","solid rotate-right","solid check"]
    onoffcolosS = [enable, emdown]
    onoffcolosD = [False, True]

    nunlfsU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nunxformersU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nundiffusersU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nunfastapiU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nunautomaticU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])
    nuncontrolnetU = widgets.Button(disabled=onoffcolosD[0],button_style='',tooltip='',icon=onoffcolos[0],layout=Layout(width="38px",height="28px"),style = onoffcolosS[0])

    ifoCOM = GridspecLayout(2,6,width='500px',height='auto')

    ifoCOM[0, 0] = nunlfs
    ifoCOM[0, 1] = nunlfsU

    ifoCOM[1, 0] = nunxformers
    ifoCOM[1, 1] = nunxformersU

    ifoCOM[0, 2] = nundiffusers
    ifoCOM[0, 3] = nundiffusersU

    ifoCOM[1, 2] = nunfastapi
    ifoCOM[1, 3] = nunfastapiU

    ifoCOM[0, 4] = nunautomatic
    ifoCOM[0, 5] = nunautomaticU

    ifoCOM[1, 4] = nuncontrolnet
    ifoCOM[1, 5] = nuncontrolnetU

    def upload_ifoCOM ():

        upload_ifoCOM_lfs = False
        upload_ifoCOM_xfo = False
        upload_ifoCOM_diffusers = False
        upload_ifoCOM_ivs = False
        upload_ifoCOM_wui = False
        upload_ifoCOM_col = False

        commands_lfs = getoutput('git lfs install')
        if "Git LFS initialized" in commands_lfs:
             upload_ifoCOM_lfs = True

        commands_xfo = getoutput('python -m xformers.info')
        if not "Error while finding module specification" in commands_xfo:
            upload_ifoCOM_xfo = True

        commands_diffusers = getoutput('pip list | grep diffusers')
        if "diffusers" in commands_diffusers:
            upload_ifoCOM_diffusers = True

        AGfloats = False#DIFFUSE

        try:
            import accelerate
            avs = str(accelerate.__version__)
        except:
            avs = str("except")
        if avs == '0.12.0' or avs == 'except':
            AGfloats = True

        DIfloats = False

        try:
            import diffusers
            jvs = str(diffusers.__version__)
        except:
            jvs = str("except")
        if jvs == '0.9.0.dev0' or jvs == 'except':
            DIfloats = True
            
        if AGfloats or DIfloats:
            upload_ifoCOM_diffusers = False#DIFFUSE
            
        try:
            import fastapi
            ivs = str(fastapi.__version__)
        except:
            ivs = str("except")
        if ivs == '0.90.1':
            upload_ifoCOM_ivs = True

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui'):
            upload_ifoCOM_wui = True

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/extensions/sd-webui-controlnet'):
            upload_ifoCOM_col = True

        if not upload_ifoCOM_lfs:
            nunlfsU.disabled=onoffcolosD[0]; nunlfsU.icon=onoffcolos[0]; nunlfsU.style=onoffcolosS[0]
        else:
            nunlfsU.disabled=onoffcolosD[1]; nunlfsU.icon=onoffcolos[2]; nunlfsU.style=onoffcolosS[1]
        if not upload_ifoCOM_xfo:
            nunxformersU.disabled=onoffcolosD[0]; nunxformersU.icon=onoffcolos[0]; nunxformersU.style=onoffcolosS[0]
        else:
            nunxformersU.disabled=onoffcolosD[1]; nunxformersU.icon=onoffcolos[2]; nunxformersU.style=onoffcolosS[1]
        if not upload_ifoCOM_diffusers:
            nundiffusersU.disabled=onoffcolosD[0]; nundiffusersU.icon=onoffcolos[0]; nundiffusersU.style=onoffcolosS[0]
        else:
            nundiffusersU.disabled=onoffcolosD[1]; nundiffusersU.icon=onoffcolos[2]; nundiffusersU.style=onoffcolosS[1]
        if not upload_ifoCOM_ivs:
            nunfastapiU.disabled=onoffcolosD[0]; nunfastapiU.icon=onoffcolos[0]; nunfastapiU.style=onoffcolosS[0]
        else:
            nunfastapiU.disabled=onoffcolosD[1]; nunfastapiU.icon=onoffcolos[2]; nunfastapiU.style=onoffcolosS[1]
        if not upload_ifoCOM_wui:
            nunautomaticU.disabled=onoffcolosD[0]; nunautomaticU.icon=onoffcolos[0]; nunautomaticU.style=onoffcolosS[0]
        else:
            nunautomaticU.disabled=onoffcolosD[0]; nunautomaticU.icon=onoffcolos[1]; nunautomaticU.style=onoffcolosS[0]
        if not upload_ifoCOM_col:
            if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui'):
                nuncontrolnetU.disabled=onoffcolosD[0]
            else:
                nuncontrolnetU.disabled=onoffcolosD[1]
            nuncontrolnetU.icon=onoffcolos[0]; nuncontrolnetU.style=onoffcolosS[0]
        else:
            nuncontrolnetU.disabled=onoffcolosD[1]; nuncontrolnetU.icon=onoffcolos[2]; nuncontrolnetU.style=onoffcolosS[1]

    upload_ifoCOM ()

    #ifoCOMifoCOMifoCOMifoCOMifoCOM
    #widCOMwidCOMwidCOMwidCOMwidCOM

    ckptsize =[]

    widV15 = widgets.Button(description='V15',disabled=False,button_style='',tooltip='',layout=Layout(width="50px",height="28px"),style = enable)
    widV21 = widgets.Button(description='V21',disabled=False,button_style='',tooltip='',layout=Layout(width="50px",height="28px"),style = enable)
    widDSS = widgets.Button(description='DSS',disabled=True,button_style='',tooltip='',layout=Layout(width="50px",height="28px"),style = emdown)

    widSELES = widgets.Select(options=ckptsize,disabled=False,layout=Layout(width="358px",height="95px"))

    emgNUN = widgets.Button(description='WUI',disabled=False,button_style='',tooltip='',layout=Layout(width="80px",height="auto"),style = enable)

    widCOM = GridspecLayout(3,3,width='500px',height='100px')

    widCOM[0, 0] = widV15
    widCOM[1, 0] = widV21
    widCOM[2, 0] = widDSS
    widCOM[:, 1] = widSELES
    widCOM[:, 2] = emgNUN

    def upload_widCOM ():

        global ckptsize

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui'):
            emgNUN.disabled=False
        else:
            emgNUN.disabled=True

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V15.ckpt'):
            widV15.disabled=True;widV15.style=emdown;
        else:
            widV15.disabled=False;widV15.style=enable;

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V21.ckpt'):
            widV21.disabled=True;widV21.style=emdown;
        else:
            widV21.disabled=False;widV21.style=enable;

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/DSS.ckpt'):
            widDSS.disabled=True;widDSS.style=emdown;
        else:
            widDSS.disabled=False;widDSS.style=enable;

        if os.path.exists('/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/'):
            ckptsize = []
            for e in os.listdir("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/"):
                name, ext = os.path.splitext(e)
                if ext == '.ckpt':
                    ckptsize.append(name)
            widSELES.options=ckptsize

    upload_widCOM ()

    #widCOMwidCOMwidCOMwidCOMwidCOM
    #sizCOMsizCOMsizCOMsizCOMsizCOM

    SZ = widgets.IntProgress(min=0,max=102400,
                             bar_style='',
                             style={'bar_color': '#3d85c6', 'description_width': 'initial'},
                             layout=Layout(width="315px",height="28px"),
                             orientation='horizontal'
                            )
    sizesU = widgets.Button(disabled=False,button_style='',tooltip='',icon="solid rotate-right",layout=Layout(width="80px",height="28px"),style = enable)
    sizesD = widgets.Button(disabled=False,button_style='',tooltip='',icon="sharp solid trash",layout=Layout(width="80px",height="28px"),style = enable)

    sizCOM = GridspecLayout(1,3,width='500px',height='auto')

    sizCOM[0, 0] = sizesD
    sizCOM[0, 1] = sizesU
    sizCOM[0, 2] = SZ

    def upload_sizCOM ():

        getpt = str(os.path.abspath(os.getcwd()))
        os.chdir('/')
        comsiz = str(getoutput('du -s')).split("\n")
        outsiz = int(int(regex.findall("\d+", comsiz[len(comsiz)-1])[0])/1024)
        os.chdir(getpt)
        SZ.value=outsiz

    upload_sizCOM ()

    #sizCOMsizCOMsizCOMsizCOMsizCOM

    allsizezCOMPIL = GridspecLayout(3,1,width='auto',height='233px')

    allsizezCOMPIL[0, 0] = ifoCOM
    allsizezCOMPIL[1, 0] = widCOM
    allsizezCOMPIL[2, 0] = sizCOM

    #bittenfigitten


    #downline

    def lisau():
        getpt = str(os.path.abspath(os.getcwd()))
        os.chdir('/home/jovyan') 
        !find -name '.ipynb_checkpoints' -exec rm -rf {} \;
        os.chdir(getpt)
        if os.path.exists("/home/jovyan/.local/share/Trash/files"):
            !rm -rf /home/jovyan/.local/share/Trash/files

    def sizesD_function(aug):
        lisau()
        upload_sizCOM()

    def sizesU_function(aug):
        upload_sizCOM()

    sizesU.on_click(sizesD_function)
    sizesD.on_click(sizesD_function)

    #downline

    #hensline

    intes = [nunlfsU,nunxformersU,nundiffusersU,nunfastapiU,nunautomaticU,nuncontrolnetU,widV15,widV21,widDSS,emgNUN,sizesD,sizesU]
    poses = [False,False,False,False,False,False,False,False,False,False,False,False]

    def setup_loads():
        global poses
        for i in range(len(poses)):
            poses[i]=intes[i].disabled
        for i in range(len(poses)):
            intes[i].disabled=True

    def exele_loads():
        for i in range(len(poses)):
            intes[i].disabled=poses[i]

    def nunlfsU_function(aug):

        setup_loads()

        with light_output:
            clear_output()
            !curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
            !sudo apt-get install git-lfs

        upload_sizCOM ()

        with light_output:
            clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nunxformersU_function(aug):

        setup_loads()

        with light_output:
            # clear_output()
            # !mamba install -q -c nvidia cuda-nvcc=11.7 python=3.9.13 --yes
            # clear_output()
            # !mamba install -q -c nvidia -c pytorch pytorch-cuda=11.7 python=3.9.13 --yes
            # clear_output()
            # !mamba install -q -c fastai python=3.9.13 fastai=2.7.11 --yes
            # clear_output()
            # !mamba install -q -c conda-forge python=3.9.13 huggingface_hub=0.12.1 --yes
            # clear_output()
            # !mamba install -q -c nvidia -c pytorch -c 'xformers/label/dev' python=3.9.13 pytorch=1.13.1 xformers --yes
            # clear_output()
            # !pip install --upgrade triton==2.0.0a2
            
            clear_output()
            !mamba install -q -c nvidia cuda-nvcc=11.7 python=3.9.13 --yes
            clear_output()
            !mamba install -q -c conda-forge python=3.9.13 transformers --yes
            clear_output()
            !mamba install -q -c nvidia -c pytorch -c 'xformers/label/dev' python=3.9.13 xformers --yes
            clear_output()
            !pip install --upgrade triton==2.0.0a2
            clear_output()
            !pip install --upgrade transformers==4.25.1
            

        upload_sizCOM ()

        with light_output:

            clear_output()

            qwe = getoutput('nvidia-smi')

            nvc = getoutput('nvcc --version')
            if not 'nvcc: not found' in nvc:
                asd = nvc
            else:
                asd = ''

            xfo = getoutput('python -m xformers.info')
            if not 'Error while finding module specification' in xfo:
                zxc = xfo
            else:
                zxc = ''

            print('\033[0;36m\033[1m',"\n",qwe,"\n\n",asd,"\n\n",zxc,'\033[0m')

        exele_loads ()

        upload_ifoCOM ()

    def nundiffusersU_function(aug):

        setup_loads()

        with light_output:
            clear_output()

            !pip install --upgrade --no-deps accelerate
            clear_output()

            !pip install --upgrade --no-deps git+https://github.com/huggingface/diffusers
            clear_output()

        upload_sizCOM ()

        # with light_output:
        #     !pip install transformers==4.25.1
        #     clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nunfastapiU_function(aug):

        setup_loads()

        with light_output:
            clear_output()

        upload_sizCOM ()

        with light_output:
            !pip install --upgrade fastapi==0.90.1
            clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nunautomaticU_function(aug):

        setup_loads()

        with light_output:

            clear_output()

            if not os.path.exists("/home/jovyan/workspace/stable-diffusion-webui"):
                os.chdir('/home/jovyan/workspace/')
                !git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui stable-diffusion-webui
            else:
                os.chdir('/home/jovyan/workspace/stable-diffusion-webui')
                !git pull

        upload_sizCOM ()

        with light_output:
            clear_output()

        exele_loads ()

        upload_ifoCOM ()

    def nuncontrolnetU_function(aug):

        setup_loads()

        with light_output:
            clear_output()

            os.chdir('/home/jovyan/workspace/stable-diffusion-webui/extensions')
            !git clone https://github.com/Mikubill/sd-webui-controlnet.git

            from torch.hub import download_url_to_file
            from urllib.parse import urlparse

            mdldir="/home/jovyan/workspace/stable-diffusion-webui/extensions/sd-webui-controlnet/models"

            os.chdir('/home/jovyan/workspace/')
            !wget -q -O CN_models.txt https://github.com/TheLastBen/fast-stable-diffusion/raw/main/AUTOMATIC1111_files/CN_models.txt

            with open("CN_models.txt", 'r') as f:
                mdllnk = f.read().splitlines()

            for lnk in mdllnk:
                clear_output()
                download_url_to_file(lnk, os.path.abspath(os.path.join(mdldir, os.path.basename(urlparse(lnk).path))), hash_prefix=None, progress=True)

            !rm CN_models.txt

        upload_sizCOM ()

        with light_output:
            clear_output()

        exele_loads ()

        upload_ifoCOM ()



    nunlfsU.on_click(nunlfsU_function)
    nunxformersU.on_click(nunxformersU_function)
    nundiffusersU.on_click(nundiffusersU_function)
    nunfastapiU.on_click(nunfastapiU_function)
    nunautomaticU.on_click(nunautomaticU_function)
    nuncontrolnetU.on_click(nuncontrolnetU_function)


    #hensline

    #midasline

    def widV15_function(aug):
        with light_output:

            clear_output()
            os.chdir('/home/jovyan/workspace/') 
            !mkdir "/home/jovyan/workspace/tmp"
            os.chdir('/home/jovyan/workspace/tmp')
            !git init
            !git lfs install --system --skip-repo
            !git clone --depth 1 --branch fp16 "https://huggingface.co/runwayml/stable-diffusion-v1-5"
            !rm -rf /home/jovyan/workspace/tmp/.git

        lisau()
        with light_output:

            os.chdir('/home/jovyan/workspace/tmp')
            if os.path.exists('/home/jovyan/workspace/tmp/stable-diffusion-v1-5/unet/diffusion_pytorch_model.bin'):
                !wget -q -O stable-diffusion-v1-5/vae/diffusion_pytorch_model.bin https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.bin
                !wget -O convertosd.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertosd.py
                !sed -i '201s@.*@    model_path = "/home/jovyan/workspace/tmp/stable-diffusion-v1-5"@' /home/jovyan/workspace/tmp/convertosd.py
                !sed -i '202s@.*@    checkpoint_path= "/home/jovyan/workspace/V15.ckpt"@' /home/jovyan/workspace/tmp/convertosd.py      
                !python /home/jovyan/workspace/tmp/convertosd.py
            if os.path.exists("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion"):
                !mv /home/jovyan/workspace/V15.ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V15.ckpt
                widV15_function_out = "/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V15.ckpt"
            else:
                widV15_function_out = "/home/jovyan/workspace/V15.ckpt"

            os.chdir('/home/jovyan/workspace/')
            !rm -rf /home/jovyan/workspace/tmp

        lisau()
        with light_output:

            upload_sizCOM()

            upload_widCOM()

            clear_output()

            if os.path.exists(widV15_function_out):
                print ('\033[0;36m\033[1m'+widV15_function_out+'\033[0m')

    def widV21_function(aug):
        with light_output:

            clear_output()

            os.chdir('/home/jovyan/workspace/') 
            !mkdir "/home/jovyan/workspace/tmp"
            os.chdir('/home/jovyan/workspace/tmp')
            !git init
            !git lfs install --system --skip-repo
            !git remote add -f origin  "https://huggingface.co/stabilityai/stable-diffusion-2-1"
            !git config core.sparsecheckout true
            !echo -e "scheduler\ntext_encoder\ntokenizer\nunet\nvae\nfeature_extractor\nmodel_index.json\n!*.safetensors" > .git/info/sparse-checkout
            !git pull origin fp16
            !rm -rf /home/jovyan/workspace/tmp/.git


        lisau()
        with light_output:

            os.chdir('/home/jovyan/workspace/')
            !wget -O convertosdv2.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertosdv2.py
            !python /home/jovyan/workspace/convertosdv2.py --fp16 /home/jovyan/workspace/tmp /home/jovyan/workspace/V21.ckpt

            if os.path.exists("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion"):
                !mv /home/jovyan/workspace/V21.ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V21.ckpt
                widV21_function_out = "/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/V21.ckpt"
            else:
                widV21_function_out = "/home/jovyan/workspace/V21.ckpt"

            !rm -rf /home/jovyan/workspace/tmp
            !rm -rf /home/jovyan/workspace/convertosdv2.py

        lisau()
        with light_output:

            upload_sizCOM()

            upload_widCOM()

            clear_output()

            if os.path.exists(widV21_function_out):
                print ('\033[0;36m\033[1m'+widV21_function_out+'\033[0m')

    def widDSS_function(aug):
        with light_output:

            clear_output()
            os.chdir('/home/jovyan/workspace/') 
            !mkdir "/home/jovyan/workspace/tmp"
            os.chdir('/home/jovyan/workspace/tmp')
            !git init
            !git lfs install --system --skip-repo
            !git clone "https://huggingface.co/dream-textures/texture-diffusion"
            !rm -rf /home/jovyan/workspace/tmp/.git

        lisau()
        with light_output:
            
            os.chdir('/home/jovyan/workspace/')
            !wget -O convertosdv2.py https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/convertosdv2.py
            !python /home/jovyan/workspace/convertosdv2.py --fp16 /home/jovyan/workspace/tmp/texture-diffusion /home/jovyan/workspace/DSS.ckpt

            if os.path.exists("/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion"):
                !mv /home/jovyan/workspace/DSS.ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/DSS.ckpt
                widDSS_function_out = "/home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/DSS.ckpt"
            else:
                widDSS_function_out = "/home/jovyan/workspace/DSS.ckpt"

            !rm -rf /home/jovyan/workspace/tmp
            !rm -rf /home/jovyan/workspace/convertosdv2.py
            

        lisau()
        with light_output:

            upload_sizCOM()

            upload_widCOM()

            clear_output()

            if os.path.exists(widDSS_function_out):
                print ('\033[0;36m\033[1m'+widDSS_function_out+'\033[0m')

    widV15.on_click(widV15_function)
    widV21.on_click(widV21_function)
    widDSS.on_click(widDSS_function)

    def emgNUN_function(aug):
        with light_output:

            clear_output()
            saind_widSELES = str(widSELES.value)

            alpha = str('cd /home/jovyan/workspace/stable-diffusion-webui\nCOMMANDLINE_ARGS="--share --enable-insecure-extension-access --xformers --lowram ')
            betas = str('" REQS_FILE="requirements.txt" python launch.py')

            if saind_widSELES == 'None':
                print ('\033[0;36m\033[1m'+alpha+betas+'\033[0m')
            else:
                modes = str('--ckpt /home/jovyan/workspace/stable-diffusion-webui/models/Stable-diffusion/') + saind_widSELES + str('.ckpt')
                print ('\033[0;36m\033[1m'+alpha+modes+betas+'\033[0m')

    emgNUN.on_click(emgNUN_function)

    #midasline

    #bittenfigitten

    light_output = widgets.Output()

    display(allsizezCOMPIL,light_output)



import subprocess

empyelouds = '/home/jovyan/workspace/DB/value-v1-5/empye.png'

def empye():
    
    subprocess.call("ylsed=123 apt='low_dark_space' \
    nenamefile='empye' nunamodel='/home/jovyan/workspace/DB/value-v1-5' \
    nunafile='/home/jovyan/workspace/DB/value-v1-5' python /home/jovyan/workspace/SCD/simple.py", shell=True)
    
if not os.path.exists(empyelouds):
    empye()

gen2value = GridspecLayout(3,6,width='1020px',height="258px")

empyefile = open("/home/jovyan/workspace/DB/value-v1-5/empye.png", "rb").read()
empyewidget = widgets.Image( format='png', width=190, layout=Layout(visibility = 'visible')) # visible hidden

S_Fielde = []
M_Fielde = []
Y_Fielde = []

SizeFielse = 5

for i in range(SizeFielse):
    
    S_A = widgets.BoundedIntText(value=1,min=1,max=999999,step=1,disabled=False,layout=Layout(flex='1 1 0%', width="190px",height="100%"))
    M_A = widgets.Text(value='',placeholder='CPT',layout=Layout(flex='1 1 0%', width="190px",height="100%"))
    Y_A = widgets.Image(value=empyefile, format='png', width=190, layout=Layout(visibility = 'hidden')) # visible hidden
    S_Fielde.append(S_A)
    M_Fielde.append(M_A)
    Y_Fielde.append(Y_A)

for i in range(SizeFielse):
    
    gen2value[0, i+1] = S_Fielde[i]
    gen2value[1, i+1] = M_Fielde[i]
    gen2value[2, i+1] = Y_Fielde[i]
    
gen2dousy = widgets.Button(disabled=False,button_style='',tooltip='',icon="thin atom",layout=Layout(width="auto",height="auto"),style = onoffcolosS[0])

gen2value[:, 0] = gen2dousy

gen2likewools = widgets.Output()

def likewools(aug):
    
    with gen2likewools:
        
        nunamodel = outputSesion
        nunafile = outputSesion
        
        model_ylsed = ''
        model_apt = ''
        
        for i in range(SizeFielse):
        
            sed = gen2value[0,i+1].value
            cpt = gen2value[1,i+1].value
            
            ylsed = str(sed)
            apt = str(cpt).replace(" ","_")
            
            if apt == '':
                apt = str('_')
                
            if i == 0:
                model_ylsed = ylsed
                model_apt = apt
            else:
                model_ylsed += str('MASSIV') + ylsed
                model_apt += str('MASSIV') + apt
            
        nenamefile = str("modelAMASSIVmodelBMASSIVmodelCMASSIVmodelDMASSIVmodelE")
        gen2table = ["modelA","modelB","modelC","modelD","modelE"]
        
        subprocess.call("ylsed="+model_ylsed+" apt="+model_apt+" \
        nenamefile="+nenamefile+" nunamodel="+nunamodel+" \
        nunafile="+nunafile+" python /home/jovyan/workspace/SCD/simple.py", shell=True)
        
        time.sleep(1.05)
        clear_output()
        
        for i in range(SizeFielse):
            
            gen2valuefile = open(nunafile+"/"+gen2table[i]+".png", "rb").read()
            gen2value[2, i+1].value = gen2valuefile
            gen2value[2, i+1].layout.visibility = 'visible'
        
gen2dousy.on_click(likewools)

display(gen2value)

/home/jovyan/workspace/SCD/simple.py


import os
import time
import torch


from diffusers import StableDiffusionPipeline
    
aspipe = StableDiffusionPipeline.from_pretrained(os.environ['nunamodel'], torch_dtype=torch.float16).to("cuda")
        
checkMassive = False

if 'MASSIV' in os.environ['apt']:
    checkMassive = True
    
if checkMassive:
    
    sedMassive = []
    cptMassive = []
    filMassive = []
    
    SizeFielse = 5
    
    slides_sedMassive = os.environ['ylsed'].split("MASSIV")
    slides_cptMassive = os.environ['apt'].split("MASSIV")
    slides_filMassive = os.environ['nenamefile'].split("MASSIV")
    
    for i in range(SizeFielse):
        sedMassive.append(int(slides_sedMassive[i]))
        cptMassive.append(str(slides_cptMassive[i]).replace("_"," "))
        filMassive.append(str(slides_filMassive[i]))
        
    for i in range(SizeFielse):
        sgenerator = torch.Generator("cuda").manual_seed(sedMassive[i])
        filemodel = aspipe(cptMassive[i], num_inference_steps=50, guidance_scale=7.5, generator=sgenerator).images[0]
        filemodel.save(os.environ['nunafile']+"/"+filMassive[i]+".png")
        
else:
    if not os.environ['ylsed']=='':
        longnewsed=int(os.environ['ylsed'])
        sgenerator = torch.Generator("cuda").manual_seed(longnewsed)
        aspipe = aspipe(os.environ['apt'].replace("_"," "), num_inference_steps=50, guidance_scale=7.5, generator=sgenerator).images[0]
    else:
        aspipe = aspipe(os.environ['apt'].replace("_"," "), num_inference_steps=50, guidance_scale=7.5).images[0]

    aspipe.save(os.environ['nunafile']+"/"+os.environ['nenamefile']+".png")

time.sleep(1.05)

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@dte @artificialguybr @TheLastBen @nawnie @askiiart @gugleigos @axvellyledmy