SD-Trainer. LoRA & Dreambooth training scripts & GUI use kohya-ss's trainer, for diffusion model.

A UI made in Pyside6 to make training LoRA/LoCon and other LoRA type models in sd-scripts easy

AI-powered multi-voice audiobook generator — LLM script annotation, voice cloning, voice design, LoRA training, per-line style control, and export to MP3, chaptered M4B, or Audacity multi-track. Built on Qwen3-TTS.

Open in app Get started ITNEXT Published in ITNEXT You have 2 free member-only stories left this month. Sign up for Medium and get an extra one Kush Kush Follow Apr 15, 2021 · 7 min read · Listen Save How you can Control your Android Device with Python Photo by Caspar Camille Rubin on Unsplash Photo by Caspar Camille Rubin on Unsplash Introduction A while back I was thinking of ways in which I could annoy my friends by spamming them with messages for a few minutes, and while doing some research I came across the Android Debug Bridge. In this quick guide I will show you how you can interface with it using Python and how to create 2 quick scripts. The ADB (Android Debug Bridge) is a command line tool (CLI) which can be used to control and communicate with an Android device. You can do many things such as install apps, debug apps, find hidden features and use a shell to interface with the device directly. To enable the ADB, your device must firstly have Developer Options unlocked and USB debugging enabled. To unlock developer options, you can go to your devices settings and scroll down to the about section and find the build number of the current software which is on the device. Click the build number 7 times and Developer Options will be enabled. Then you can go to the Developer Options panel in the settings and enable USB debugging from there. Now the only other thing you need is a USB cable to connect your device to your computer. Here is what todays journey will look like: Installing the requirements Getting started The basics of writing scripts Creating a selfie timer Creating a definition searcher Installing the requirements The first of the 2 things we need to install, is the ADB tool on our computer. This comes automatically bundled with Android Studio, so if you already have that then do not worry. Otherwise, you can head over to the official docs and at the top of the page there should be instructions on how to install it. Once you have installed the ADB tool, you need to get the python library which we will use to interface with the ADB and our device. You can install the pure-python-adb library using pip install pure-python-adb. Optional: To make things easier for us while developing our scripts, we can install an open-source program called scrcpy which allows us to display and control our android device with our computer using a mouse and keyboard. To install it, you can head over to the Github repo and download the correct version for your operating system (Windows, macOS or Linux). If you are on Windows, then extract the zip file into a directory and add this directory to your path. This is so we can access the program from anywhere on our system just by typing in scrcpy into our terminal window. Getting started Now that all the dependencies are installed, we can start up our ADB and connect our device. Firstly, connect your device to your PC with the USB cable, if USB debugging is enabled then a message should pop up asking if it is okay for your PC to control the device, simply answer yes. Then on your PC, open up a terminal window and start the ADB server by typing in adb start-server. This should print out the following messages: * daemon not running; starting now at tcp:5037 * daemon started successfully If you also installed scrcpy, then you can start that by just typing scrcpy into the terminal. However, this will only work if you added it to your path, otherwise you can open the executable by changing your terminal directory to the directory of where you installed scrcpy and typing scrcpy.exe. Hopefully if everything works out, you should be able to see your device on your PC and be able to control it using your mouse and keyboard. Now we can create a new python file and check if we can find our connected device using the library: Here we import the AdbClient class and create a client object using it. Then we can get a list of devices connected. Lastly, we get the first device out of our list (it is generally the only one there if there is only one device connected). The basics of writing scripts The main way we are going to interface with our device is using the shell, through this we can send commands to simulate a touch at a specific location or to swipe from A to B. To simulate screen touches (taps) we first need to work out how the screen coordinates work. To help with these we can activate the pointer location setting in the developer options. Once activated, wherever you touch on the screen, you can see that the coordinates for that point appear at the top. The coordinate system works like this: A diagram to show how the coordinate system works A diagram to show how the coordinate system works The top left corner of the display has the x and y coordinates (0, 0) respectively, and the bottom right corners’ coordinates are the largest possible values of x and y. Now that we know how the coordinate system works, we need to check out the different commands we can run. I have made a list of commands and how to use them below for quick reference: Input tap x y Input text “hello world!” Input keyevent eventID Here is a list of some common eventID’s: 3: home button 4: back button 5: call 6: end call 24: volume up 25: volume down 26: turn device on or off 27: open camera 64: open browser 66: enter 67: backspace 207: contacts 220: brightness down 221: brightness up 277: cut 278: copy 279: paste If you wanted to find more, here is a long list of them here. Creating a selfie timer Now we know what we can do, let’s start doing it. In this first example I will show you how to create a quick selfie timer. To get started we need to import our libraries and create a connect function to connect to our device: You can see that the connect function is identical to the previous example of how to connect to your device, except here we return the device and client objects for later use. In our main code, we can call the connect function to retrieve the device and client objects. From there we can open up the camera app, wait 5 seconds and take a photo. It’s really that simple! As I said before, this is simply replicating what you would usually do, so thinking about how to do things is best if you do them yourself manually first and write down the steps. Creating a definition searcher We can do something a bit more complex now, and that is to ask the browser to find the definition of a particular word and take a screenshot to save it on our computer. The basic flow of this program will be as such: 1. Open the browser 2. Click the search bar 3. Enter the search query 4. Wait a few seconds 5. Take a screenshot and save it But, before we get started, you need to find the coordinates of your search bar in your default browser, you can use the method I suggested earlier to find them easily. For me they were (440, 200). To start, we will have to import the same libraries as before, and we will also have our same connect method. In our main function we can call the connect function, as well as assign a variable to the x and y coordinates of our search bar. Notice how this is a string and not a list or tuple, this is so we can easily incorporate the coordinates into our shell command. We can also take an input from the user to see what word they want to get the definition for: We will add that query to a full sentence which will then be searched, this is so that we can always get the definition. After that we can open the browser and input our search query into the search bar as such: Here we use the eventID 66 to simulate the press of the enter key to execute our search. If you wanted to, you could change the wait timings per your needs. Lastly, we will take a screenshot using the screencap method on our device object, and we can save that as a .png file: Here we must open the file in the write bytes mode because the screencap method returns bytes representing the image. If all went according to plan, you should have a quick script which searches for a specific word. Here it is working on my phone: A GIF to show how the definition searcher example works on my phone A GIF to show how the definition searcher example works on my phone Final thoughts Hopefully you have learned something new today, personally I never even knew this was a thing before I did some research into it. The cool thing is, that you can do anything you normal would be able to do, and more since it just simulates your own touches and actions! I hope you enjoyed the article and thank you for reading! 💖 468 9 468 9 More from ITNEXT Follow ITNEXT is a platform for IT developers & software engineers to share knowledge, connect, collaborate, learn and experience next-gen technologies. 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Scripts of LLM pre-training and fine-tuning (w/wo LoRA, DeepSpeed)

Spirit Lora Trainer is a robust toolkit for training Flux1-LoRA models with a focus on simplicity and reliability and based on kohya-ss script. 智灵训练器.

Lora traing script for Lightricks LTX-video

在ComfyUI中进行lora微调的节点,依赖于kohya-ss/sd-scripts等训练工具(Nodes for fine-tuning lora in ComfyUI, dependent on training tools such as kohya-ss/sd-scripts)

Tools needed for training B-LoRA method with sd-scripts.

LoRA Pilot is an ultimate docker image for all Stable Diffusion LoRA trainers. Includes kohya_ss, diffusion pipes and TensorBoard for trainings and ComfyUI and InvokeAI for validation. Features shared models, modules, custom integrations and automatization scripts.

This script processes a grid image generated with the 4lph4bet family of LoRAs for Stable Diffusion 1.5 for font creation using Calligraphr.

A Script that help you generate a batch of Lora, using same prompt & settings. Helpful for compare Lora, Or just switching Lora easily without switch tab.

Command-line script for inferencing from models such as LLaMA, in a chat scenario, with LoRA adaptations

多平台 Stable Diffusion 部署，管理脚本

The serverside backend created for use with the LoRA Easy Training Scripts Frontend

Llama-style transformer in PyTorch with multi-node / multi-GPU training. Includes pretraining, fine-tuning, DPO, LoRA, and knowledge distillation. Scripts for dataset mixing and training from scratch.

Bash and Python scripts to get IP over LoRa working on Raspberry Pi using a variety of EBYTE chips and programs (tncattach, kissattach, slattach, pppd...).

The repository contains scripts and merge scripts that have been modified to adapt an Alpaca-Lora adapter for LoRA tuning when assuming the use of the "rinna/japanese-gpt-neox..." [gpt-neox] model converted to ggml.

Colab for training 1.5 and SDXL Loras based on Derrian Distro's Lora_Easy_Training_scripts_Backend

Training Script and Dataset for Wealth Alpaca-LoRa

A Python script to help you install meshtasticd and enable all of the related dtoverlays in order to get a lora pi hat working on raspberry pi.

This repo helps to transform text into a better form for lora training

A script for merging a LLM model and a LoRA

Scripts and files for LoRa gateway produced by Heltec automation(TM)

Analyze Lora Models is a Python script that scans a folder of LoRA models (.safetensors), analyzes their parameters, and generates a report grouping compatible models. It helps optimize disk space by reducing duplicates and simplifies the management of LoRA models in your workflow.

A simple script to install rnsd on pi and configure it for LoRa using a reticulum rnode device

Python script to automate the classification and organization of 'LORA' and 'LyCORIS' files based on '.civitai.info' information. Optimized for use with the Stable-Diffusion-Webui-Civitai-Helper extension. Perfect solution for keeping your files organized!

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Cleaned up parameterized script for running dashtoon's Hunyuan Keyframe Control lora for start/end frames I2V, with ffmpeg, batching, and other options/fixes (including cpu offloading and sageattention/flash).

Pipeline for uncensoring gpt-oss-20b with external uncensored teachers (LLaMA-2/3). Includes refusal/jailbreak prompt libraries, automated response generation, cleaning, dataset building, and LoRA/QLoRA fine-tuning. Produces streamlined uncensored checkpoints with evaluation scripts, configs, and adapter management for efficient local deployment.