Saravanan666/pypi-org huggingface dataset:
Package: pydra-fsl
pydra-fslPydra tasks for FSL.Pydrais a dataflow engine which provides
a set of lightweight abstractions for DAG
construction, manipulation, and distributed execution.FSLis a comprehensive library of analysis tools
for FMRI, MRI and DTI brain imaging data.Table of contentsAvailable tasksInstallationDevelopmentLicenseAvailable tasksModuleTasksbetBET, RobustFOVeddyEddy, ApplyTopup, TopupfastFASTflirtFLIRT, ApplyXFM, ConcatXFM, ConvertXFM, InvertXFM, FixScaleSkew, Img2ImgCoord, Img2StdCoord, Std2ImgCoordfnirtFNIRT, FNIRTFileUtils, ApplyWarp, ConvertWarp, InvWarpfugueFUGUE, PrepareFieldmap, Prelude, SigLossmaths(experimental) Maths, MulsusanSUSANutilsChFileType, FFT, Info, Interleave, Merge, Orient, Reorient2Std, ROI, SelectVols, Slice, SmoothFill, Split, SwapDimInstallationpip install pydra-fslA separate installation of FSL is required to use this package.
Please review the FSLinstallation instructionsandlicensing details.DevelopmentThis project is managed withHatch:pipx install hatchTo run the test suite:hatch run testTo fix linting issues:hatch run lint:fixLicenseThis project is distributed under the terms of theApache License, Version 2.0.
Package: pydrag
pydragpydragis a modern api wrapper for theLast.fmapi with a fluent syntax!Quick StartApply for a last.fmapi keyand write down yourkeyandsecret.Install$pipinstallpydragExample>>>frompydragimportUser,configure>>>configure(api_key='54062d8af7afdc_not_real_3459048a4')>>>rj=User.find("RJ")>>>rj.real_name'Richard Jones '>>>recent=rj.get_recent_tracks(limit=1,page=1)>>>first=recent.pop()>>>first.name'Fu-Gee-La'>>>similar=first.get_similar(limit=1)>>>similar[0].name'Family Business'>>>similar[0].artist.name'Fugees'>>>>>>forfriendinrj.get_friends(recent_tracks=True):...friend.name,friend.recent_track.name...('meichi','Pi')('demkod','(bottle back)')('STBKilla','Nowhere Fast')('keret221','Letter Home')('Lilfix','Namorar pra Quê?')('Yoji','Empire State of Mind (feat. Alicia Keys)')('Kastishka','Wipe Your Eyes')('comingsoon_','I Want It All')('Bagheera','Welcome Home')DevelopmentUse you favorite tool to create a python >= 3.6 virtual environment$gitclonegit@github.com:tefra/pydrag.git$pipinstall.[dev]$pre-commitinstall$pytest$toxpydrag usesvcrpylibrary to record and replay
last.fm responses for its unit tests andpython-dotenvto auto-configure itself.All sensitive information like keys and credentials are automatically censored.So when it’s necessary to record a new response it’s super useful to have a
.env file with your configuration!LASTFM_API_KEY=your_api_keyLASTFM_API_SECRET=your_api_secretLASTFM_USERNAME=YouLASTFM_PASSWORD=YouPassChangelog: 22.5 (2022-05-08)Replaced attrs with dataclassesAdded support for python 3.10
Package: pydragonfly
pyDragonflyRobust PythonSDKandCommand Line Clientfor interacting with Certego'sDragonflyservice's API. Built withdjango-rest-client.FeaturesEasy one-time configuration with self documented help and hints along the way.Supports all endpoints ofDragonfly's REST API.Analysis:Create new analysis by uploading sampleRevoke a running analysisView analysis reportList latest with filtering, ordering, pagination capabilitiesDownload sample of an existing analysisProfile and Rule:Create new Profile or Rule objectsUpdate existing Profile and Rule objectsList latest with filtering, ordering, pagination capabilitiesView and manage your dragonfly organization and invitationsView and manage active browser sessionsView account access and subscription infoDemoInstallationRequires python version >=3.6.$pip3installpydragonflyFor development/testing,pip3 install pydragonfly[dev]DocumentationDocumentation:https://certego-pydragonfly.readthedocs-hosted.com/Changelog:CHANGELOG.mdFAQGenerate API keyYou need a valid API key to interact with the Dragonfly server. For this,
head on over tohttps://dragonfly.certego.net/me/sessionsand click on theGenerate +button.
Package: pydrama
PyDramaWelcome to PyDrama, the ultimate simulation tool that spices up your terminal with a dash of developer drama! Whether you're coding in solitude or in a bustling open office, PyDrama adds that extra zing to your development life.InstallationGet ready to add some theatrics to your terminal with a simple pip command:pipinstallpydramaFor a full list of commands and modules, and to get behind-the-scenes access, check out our Official Documentation.LicensePyDrama is released under the MIT License, so feel free to use it, modify it, and share it with your fellow thespians!
Package: pydra-nipype1
Nipype 1 Tasks for PydraPydrais a redesign of the Nipype dataflow
engine, and the core of the emergingNipype 2 ecosystem.This is a task package, a set of Pydra Tasks that inhabits thepydra.tasks.*namespace, to ease wrapping Nipype 1 Interfaces.Additional tools for migrating from Nipype 1 to Pydra may find their home here.Installationpip install pydra-nipype1Usagefrom pydra.tasks.nipype1.utils import Nipype1Task
from nipype.interfaces import fsl

thresh = Nipype1Task(fsl.Threshold())
res = thresh(in_file=fname, thresh=0.5)For developersInstall repo in developer mode from the source directory. It is also useful to
install pre-commit to take care of styling via black:pip install -e .[dev]
pre-commit install
Package: pydra-petpvc
pydra-petpvcPydra tasks for PETPVC.Pydrais a dataflow engine which provides
a set of lightweight abstractions for DAG
construction, manipulation, and distributed execution.PETPVCis a toolbox for partial volume correction (PVC)
in positron emission tomography (PET).Installationpip install pydra-petpvcA separate installation of PETPVC is required to use this package.
Please review the project'shomepagefor installation instructions and licensing information.An official conda package is available throughconda-forge:conda install -c conda-forge petpvcDevelopmentThis project is managed withHatch:pipx install hatchTo run the test suite:hatch run testTo fix linting issues:hatch run lint:fixLicensingThis project is distributed under the terms of theApache License, Version 2.0.
Package: pydra-templateflow
pydra-templateflowPydra tasks for TemplateFlow.Pydrais a dataflow engine which provides
a set of lightweight abstractions for DAG
construction, manipulation, and distributed execution.TemplateFlowprovides a framework
for publicly hosting and distributing neuroimaging templates
for human and nonhuman brains.Table of contentsAvailable tasksInstallationDevelopmentLicenseAvailable tasksThis package provides aget_templatetask, which takes a mandatorytemplate_idargument
and an optionaloutput_queriesmapping from output names to their respective query entities.The following example showcases usingget_templateto download the template image,
brain and head masks for the Linear ICBM Average Brain (ICBM152).frompydra.tasksimporttemplateflowtask=templateflow.get_template(template_id="MNI152Lin",output_queries={"brain_mask":{"resolution":"1","suffix":"mask","desc":"head"},"head_mask":{"resolution":"1","suffix":"mask","desc":"brain"},"t1w_image":{"resolution":"1","suffix":"T1w"},},)result=task()# result.output.brain_mask# result.output.head_mask# result.output.t1w_imagePlease check the list of available templateshere.Installationpip install pydra-templateflowDevelopmentThis project is managed withHatch:pipx install hatchTo run the test suite:hatch run testTo fix linting issues:hatch run lint:fixLicenseThis project is distributed under the terms of theApache License, Version 2.0.
Package: pydraughts
pydraughtspydraughts is a draughts (checkers) library for Python with move generation, PDN reading and writing, engine communication and balloted openings. It is based onImparaAI/checkers.InstallingDownload and install the latest release:pip install pydraughtsFeaturesVariants:Standard (International)Frisianfrysk!AntidraughtsBreakthroughRussianBrazilianEnglish/AmericanItalianTurkishEngine protocols:HubDXPCheckerBoardPDN Reading and WritingImport pydraughtsfromdraughtsimportBoard,Move,WHITE,BLACKCreate a gameboard=Board(variant="standard",fen="startpos")Make a movemove=Move(board,steps_move=[34,30])board.push(move)# Multi-captureboard2=Board(fen="W:WK40:B19,29")board2.push(Move(board2,pdn_move='40x14'))Get a visual representation of the boardprint(board)"""| b |   | b |   | b |   | b |   | b---------------------------------------b |   | b |   | b |   | b |   | b |---------------------------------------| b |   | b |   | b |   | b |   | b---------------------------------------b |   | b |   | b |   | b |   | b |---------------------------------------|   |   |   |   |   |   |   |   |---------------------------------------|   |   |   |   |   |   |   | w |---------------------------------------| w |   | w |   | w |   |   |   | w---------------------------------------w |   | w |   | w |   | w |   | w |---------------------------------------| w |   | w |   | w |   | w |   | w---------------------------------------w |   | w |   | w |   | w |   | w |"""Get legal movesmoves=board.legal_moves()Detect wins and drawshas_white_won=board.winner()==WHITEis_draw=board.winner()==0winnner=board.winner()is_game_over=board.is_over()Convert move to other typesmove=Move(board,board_move=moves[0].board_move).pdn_moveGet fenfen=game.fenCommunicate with enginesfromdraughts.engineimportHubEngine,Limitengine=HubEngine(["scan.exe","hub"])engine.init()limit=Limit(time=10)engine_move=engine.play(board,limit,ponder=False)Read PDN gamesfromdraughts.PDNimportPDNReadergames=PDNReader(filename=filepath)game=games.games[0]moves=game.movesWrite PDN gamesfromdraughts.PDNimportPDNWritergames=PDNWriter(filename=filepath,board=board)Get a ballotfromdraughts.ballotsimportBallotsballots=Ballots('english')ballot1=ballots.get_ballot()ballot2=ballots.get_ballot()Run tournamentsfromdraughts.tournamentimportRoundRobinplayer1=(["scan.exe","hub"],"hub",{},None)player2=("kr_hub.exe","hub",{},None)players=[player1,player2]tournament=RoundRobin("tournament.pdn",players,start_time=20,increment=0.2,games_per_pair=4)scores=tournament.play()print(scores)tournament.print_standings()AcknowledgementsThanks tofishnetwhich was modified to add support for Hub engines. Thanks toakalverboerfor theirDXC100_draughts_clientwhich was modified to add support for DXP engines.Licensepydraughts is licensed under The MIT License. Check outLICENSEfor the full text.
The licenses of the other projects that pydraughts uses are in theother_licensesfolder.
Package: py-draughts
py-draughtsEfficient modern and flexible implementation of the draughts game with a beautiful web interface.
Supports multiple variants of the game and allows playing against AI.Installationpipinstallpy-draughtsDocumentationKey featuresDisplays simple ascii board for different variants of the game.>>>fromdraughtsimportget_board>>>board=get_board('standard',"W:W4,11,28,31,K33,K34,38,40,K41,43,K44,45,K46,47:BK3,21,27,32")>>>board.....B.w.............w...................b..........b.w......w.b.W.W......w...w..W...w.W.wW.w.......>>>board=get_board("american")>>>board.b.b.b.bb.b.b.b..b.b.b.b................w.w.w.w..w.w.w.ww.w.w.w.Make and undo moves>>>board.push_uci("31-27")>>>board.pop()# undo last moveMove:31->27>>>board.turnColor.WHITEdetects draws and wins[!Important]Those methods are variant specific. Each variant has different set of them.>>>board.game_overFalse>>>board.is_threefold_repetitionFalse>>>board.is_5_moves_ruleFalse>>>board.is_16_moves_ruleFalse>>>board.is_25_moves_ruleFalse>>>board.is_drawFalseValidate and generate moves>>>board.push_uci("31-22")ValueError:31-22iscorrect,butnotlegalingivenposition.Legalmovesare:['31-27','31-26','32-28','32-27','33-29','33-28','34-30','34-29','35-30']>>>list(board.legal_moves)['31-27','31-26','32-28','32-27','33-29','33-28','34-30','34-29','35-30']Reads and writes fen stringsWrites PDN strings>>>board.push_uci("31-27")>>>board.push_uci("32-28")>>>board.push_uci("27-23")>>>board.push_uci("28-24")>>>board.pdn'[GameType "20"][Variant"Standard (international) checkers"][Result"-"]1.31-2732-282.27-2328-24'>>>board=get_board('standard',"W:W4,11,28,31,K33,K34,38,40,K41,43,K44,45,K46,47:BK3,21,27,32")>>>board.fen'[FEN "W:W4,11,28,31,K33,K34,38,40,K41,43,K44,45,K46,47:BK3,21,27,32"]'Has simple engine>>>fromdraughts.engineimportAlphaBetaEngine>>>engine=AlphaBetaEngine(depth=5)>>>engine.get_best_move(board,with_evaluation=True)Move:28->37,3.0UIAllows to play against AI.Allows to play vs another player. (on the same computer)Allows to test and find bugs in your engine.python-mdraughts.server.serverUse for testing your engine.Example with simplest possible engine.>>>fromdraughtsimportServer>>>importnumpyasnp>>>get_best_mv=lambdaboard:np.random.choice(list(board.legal_moves))>>>server=Server(get_best_move_method=get_best_mv)>>>server.run()INFO:Startedserverprocess[1617]INFO:Waitingforapplicationstartup.INFO:Applicationstartupcomplete.INFO:Uvicornrunningonhttp://127.0.0.1:8000(PressCTRL+Ctoquit)It is as simple as that![!Warning]Server will not start when usinggoogle colabContributingContributions to this project are welcome. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request on the project repository.BibliographyNotationRules and variantsList of PDNsDraughts onlineAdditional 1 (Checkers online)Additional 2 (Chinook)
Package: pydrawing
Documents:https://pydrawing.readthedocs.io/PydrawingBeautify your image or video.
You can star this repository to keep track of the project if it's helpful for you, thank you for your support.Support ListBeautifierIntroductionRelated PaperCodein ChineseglitchclickN/Aclick信号故障特效douyineffectclickN/Aclick抖音特效characterizeclickN/Aclick视频转字符画cartooniseclickN/Aclick图像卡通化photomosaicclickN/Aclick拼马赛克图片beziercurveclickN/Aclick贝塞尔曲线画画geneticfittingcircleclickN/Aclick遗传算法拟合图像-圆形geneticfittingpolygonclickN/Aclick遗传算法拟合图像-多边形nostalgicstyleclickN/Aclick照片怀旧风格photocorrectionclickN/Aclick简单的照片矫正pencildrawingclickclickclick铅笔素描画cartoonganclickclickclick卡通GANfastneuralstyletransferclickclickclick快速风格迁移cartoonizefaceclickclickclick人脸卡通化noteprocessorclickclickclick手写笔记处理oilpaintingclickclickclick照片油画化InstallPreparationffmpeg: You should set ffmpeg in environment variable.Pytorch: To apply some of the supported beautifiers (e.g., cartoongan), you need to install pytorch and corresponding dependencies followingtutorial.Pip installrun"pip install pydrawing"Source code install(1)Offline
Step1:gitclonehttps://github.com/CharlesPikachu/pydrawing.git
Step2:cdpydrawing->run"python setup.py install"(2)Online
run"pip install git+https://github.com/CharlesPikachu/pydrawing.git@master"Quick Startimportrandomfrompydrawingimportpydrawingfilepath='asserts/dog.jpg'config={"savedir":"outputs","savename":"output"}drawing_client=pydrawing.pydrawing()drawing_client.execute(filepath,random.choice(drawing_client.getallsupports()),config=config)ScreenshotProjects in Charles_pikachuGames: Create interesting games by pure python.DecryptLogin: APIs for loginning some websites by using requests.Musicdl: A lightweight music downloader written by pure python.Videodl: A lightweight video downloader written by pure python.Pytools: Some useful tools written by pure python.PikachuWeChat: Play WeChat with itchat-uos.Pydrawing: Beautify your image or video.ImageCompressor: Image compressors written by pure python.FreeProxy: Collecting free proxies from internet.Paperdl: Search and download paper from specific websites.Sciogovterminal: Browse "The State Council Information Office of the People's Republic of China" in the terminal.CodeFree: Make no code a reality.DeepLearningToys: Some deep learning toys implemented in pytorch.DataAnalysis: Some data analysis projects in charles_pikachu.Imagedl: Search and download images from specific websites.MoreWeChat Official AccountsCharles_pikachu
Package: pydrawio
PydrawioQuick Start~$ pip install pydrawioUsageMxGraphModel# get compressed string from Mxfile or Mxlibrary
        compressed = 'lJI9b8MgGIR/DWMlA1WaDl5i5WNIl2ZIOyLz1iDxJUJip7++ChCMoizdfA9nOO4F0U5PW8+c+LAcFCINn1pECMbNAhFy09ebXuJlkoOXPBpmeZC/EFGT2VlyOFWmYK0K0tWot8ZAHyrCvLdjbfmxqj7JsQEe5KFn6pEdJQ8iJiZvM92BHEQ6DS/eE9csGXPqk2DcjgXQNaKdtzakLz11oG71pNvPFkQ2ee2ZrwTzYMJ/foRud/naD+bTuOG4N99yp8lL3u3C1DleOwcP19SCt2fDgbcNoqtRyAAHx3poR88coisRtGoxoqvHSPddwQeYCsihtmA1BH9FpIlrZcjxVeDXrMZ76/hepyiNLzJhcbhD2bDuoRzzrBpENvMgkn1+sHT9BwAA//8BAAD//w=='
        mxgraphmodel = decompress(compressed)
        # processing mxgraphmodel
        mxgraphmodel.compress()Mxlibrary# create Mxlibrary instance
        mxlibrary = Mxlibrary() # or Mxlibrary(Mxlibrary_file_contents)
        # make mxlibobject
        mxlibobj1 = mxlibrary.make_mxlibobject(mxgraphmodel, 'test_lib_1',  25, 25)
        mxlibobj2 = mxlibrary.make_mxlibobject(mxgraphmodel, 'test_lib_2',  25, 25)
        # set Mxlibobject
        mxlibrary.set_mxlibobject([ mxlibobj1, mxlibobj2 ])
        # write mxlibrary file
        mxlibrary.write(filepath)
        
        # get Mxlibobject
        mxlibobj = json.loads(mxlibrary.value)
        for mxlibobj in mxlibobjs:
            mxlibobj = Mxlibobject(**mxlibobj)Mxfilemxfile = Mxfile() # or Mxfile(Mxfile_file_contents)
        # get MxGraphModel from Mxfile
        compressed = mxfile.diagram[0].value
        mxgraphmodel = decompress(compressed)

        # processing MxGraphModel

        # write mxfile file
        mxfile.write(filepath)
Package: pydrawise
PydrawisePydrawise is an asynchronous Python 3 library for interacting with Hydrawise sprinkler controllers.Note that this project has no official relationship with Hydrawise or Hunter. Use at your own risk.UsageimportasynciofrompydrawiseimportAuth,Hydrawiseasyncdefmain():# Create a Hydrawise object and authenticate with your credentials.h=Hydrawise(Auth("username","password"))# List the controllers attached to your account.controllers=awaith.get_controllers()# List the zones controlled by the first controller.zones=awaith.get_zones(controllers[0])# Start the first zone.awaith.start_zone(zones[0])if__name__=="__main__":asyncio.run(main())InstallationPipTo install pydrawse, run this command in your terminal:$pipinstallpydrawiseSource codePydrawise is actively developed on Github, where the code isalways available.You can either clone the public repository:$gitclonehttps://github.com/dknowles2/pydrawiseOr download the latesttarball:$curl-OLhttps://github.com/dknowles2/pydrawise/tarball/mainOnce you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily:$cdpydrawise
$python-mpipinstall.
Package: pydrda
This is a DRDA protocol (https://en.wikipedia.org/wiki/DRDA) database driver.Pure pythonCompliant with PEP-249 (https://www.python.org/dev/peps/pep-0249/)RequirementsPython 3.8+Installation$ pip install pydrdaIf you want to connect to Db2, you may need to install pyDes.$ pip install pyDesSupported DatabasesDb2https://www.ibm.com/analytics/db2ExampleNo SSLimport drda

conn = drda.connect(host='serverhost', database='dbname', user='user', password='password', port=xxxxx)
cur = conn.cursor()
cur.execute('select * from foo where name=?', ['alice'])
for r in cur.fetchall():
    print(r[0], r[1])With SSL connectionimport drda

conn = drda.connect(host='serverhost', database='dbname', use_ssl=True, user='user', password='password', port=xxxxx)
cur = conn.cursor()
cur.execute('select * from foo where name=?', ['alice'])
for r in cur.fetchall():
    print(r[0], r[1])With SSL and client certificateimport drda

conn = drda.connect(host='serverhost', database='dbname', use_ssl=True, ssl_ca_certs='/some/what/path/cert.crt', user='user', password='password', port=xxxxx)Apache Derbyhttps://db.apache.org/derby/You need to start derby as a network server.http://db.apache.org/derby/papers/DerbyTut/ns_intro.html#start_nsExampleimport drda

conn = drda.connect(host='serverhost', database='dbname', port=1527)
cur = conn.cursor()
cur.execute('select * from foo')
for r in cur.fetchall():
    print(r[0], r[1])This driver can’t execute with parameters against Apache Derby.Unit TestsI have tested the following steps.Db2Start Db2 server$ docker run -itd --name db2 --privileged=true -p 50000:50000 -e LICENSE=accept -e DB2INST1_PASSWORD=password -e DBNAME=testdb --platform=linux/amd64 icr.io/db2_community/db2Execute test$ python test_db2.pyApache DerbyInstall Apatch Derbyhttps://db.apache.org/derby/and start as a server$ curl -O https://downloads.apache.org//db/derby/db-derby-10.15.2.0/db-derby-10.15.2.0-bin.tar.gz
$ tar zxf db-derby-10.15.2.0-bin.tar.gz
$ echo 'grant {permission java.lang.RuntimePermission "getenv.SOURCE_DATE_EPOCH", "read";};' > ${HOME}/.java.policy
$ db-derby-10.15.2.0-bin/bin/startNetworkServer &Execute test$ python test_derby.py
Package: py-dreambooth
Py-DreamboothPy-Dreamboothis a Python package that makes it easy to create AI avatar images from photos of you, your family, friends, or pets!Tasks are pre-configured with the most efficient defaults, which greatly streamlines the workload. A number of helper functions are also provided.This is designed to be modular and extensible to many different models. Currently supported models are theStable Diffusion Dreambooth,Stable Diffusion Dreambooth LoRA, andStable Diffusion XL Dreambooth LoRA.This is designed to give you the flexibility to choose local or cloud resources to train your model and generate images.⚙️ How to Installpipinstallpy-dreambooth🚀 Quick StartPrepare about 10-20 high-quality solo selfie photos (jpg or png) and put them in a specific directory.Please run on a machine with a GPU of 16GB or more. (If you're fine-tuningSDXL, you'll need 24GB of VRAM.)frompy_dreambooth.datasetimportLocalDatasetfrompy_dreambooth.modelimportSdDreamboothModelfrompy_dreambooth.trainerimportLocalTrainerfrompy_dreambooth.utils.image_helpersimportdisplay_imagesfrompy_dreambooth.utils.prompt_helpersimportmake_promptDATA_DIR="data"# The directory where you put your prepared photosOUTPUT_DIR="models"dataset=LocalDataset(DATA_DIR)dataset=dataset.preprocess_images(detect_face=True)SUBJECT_NAME="<YOUR-NAME>"CLASS_NAME="person"model=SdDreamboothModel(subject_name=SUBJECT_NAME,class_name=CLASS_NAME)trainer=LocalTrainer(output_dir=OUTPUT_DIR)predictor=trainer.fit(model,dataset)# Use the prompt helper to create an awesome AI avatar!prompt=next(make_prompt(SUBJECT_NAME,CLASS_NAME))images=predictor.predict(prompt,height=768,width=512,num_images_per_prompt=2,)display_images(images,fig_size=10)🏃‍♀️ TutorialsTake a look at the01-local-tutorial.ipynbfile to learn how to get it running on your localJupyter Notebook.If you're interested in running it with AWS cloud resources, take a look at the02-aws-tutorial.ipynbfile.Or, get started right away with theGoogle Colab Notebookhere!📚 DocumentationFull documentation can be found here:https://py-dreambooth.readthedocs.io.ReferencesDreamBooth: Fine-Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (Paper)LoRA: Low-Rank Adaptation of Large Language Models (Paper)Fine-Tune Text-to-ImageStable DiffusionModels withAmazon SageMaker JumpStart(Blog)TrainingStable DiffusionwithDreamboothUsing 🧨Diffusers(Blog)Diffusers:DreamBoothTraining ExampleDiffusers:DreamBoothTraining Example forStable Diffusion XL(SDXL)
Package: pydreambrook
PyDreamBrookOfficial Python Toolbox for DreamBrook Apps, Platforms, and ServicesFree software: MIT licenseDocumentation:https://pydreambrook.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2023-10-16)First release on PyPI.