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A Hex Editor for Reverse Engineers, Programmers and people who value their retinas when working at 3 AM.


A Hex Editor for Reverse Engineers, Programmers and people who value their retinas when working at 3 AM.
/mhks/

'Build' workflow StatusDiscord ServerTotal DownloadsCode QualityTranslationPlugins

Download the latest version of ImHex!Download the latest nightly pre-release version of ImHexUse the Web version of ImHex right in your browser!Read the documentation of ImHex!

Supporting

If you like my work, please consider supporting me on GitHub Sponsors, Patreon or PayPal. Thanks a lot!

GitHub donate buttonPatreon donate buttonPayPal donate button

Screenshots

Hex editor, patterns and data informationBookmarks, disassembler and data processor

More Screenshots

Data Processor decrypting some data and displaying it as an imageSTL Parser written in the Pattern Language visualizing a 3D modelData Information view displaying various stats about the file

Features

Featureful hex view
  • Byte patching
  • Patch management
  • Infinite Undo/Redo
  • "Copy bytes as..."
    • Bytes
    • Hex string
    • C, C++, C#, Rust, Python, Java & JavaScript array
    • ASCII-Art hex view
    • HTML self-contained div
  • Simple string and hex search
  • Goto from start, end and current cursor position
  • Colorful highlighting
    • Configurable foreground highlighting rules
    • Background highlighting using patterns, find results and bookmarks
  • Displaying data as a list of many different types
    • Hexadecimal integers (8, 16, 32, 64 bit)
    • Signed and unsigned decimal integers (8, 16, 32, 64 bit)
    • Floats (16, 32, 64 bit)
    • RGBA8 Colors
    • HexII
    • Binary
  • Decoding data as ASCII and custom encodings
    • Built-in support for UTF-8, UTF-16, ShiftJIS, most Windows encodings and many more
  • Paged data view
Custom C++-like pattern language for parsing highlighting a file's content
  • Automatic loading based on MIME types and magic values
  • Arrays, pointers, structs, unions, enums, bitfields, namespaces, little and big endian support, conditionals and much more!
  • Useful error messages, syntax highlighting and error marking
  • Support for visualizing many different types of data
    • Images
    • Audio
    • 3D Models
    • Coordinates
    • Time stamps
Theming support
  • Doesn't burn out your retinas when used in late-night sessions
    • Dark mode by default, but a light mode is available as well
  • Customizable colors and styles for all UI elements through shareable theme files
  • Support for custom fonts
Importing and Exporting data
  • Base64 files
  • IPS and IPS32 patches
  • Markdown reports
Data Inspector
  • Interpreting data as many different types with endianness, decimal, hexadecimal and octal support and bit inversion
    • Unsigned and signed integers (8, 16, 24, 32, 48, 64 bit)
    • Floats (16, 32, 64 bit)
    • Signed and Unsigned LEB128
    • ASCII, Wide and UTF-8 characters and strings
    • time32_t, time64_t, DOS date and time
    • GUIDs
    • RGBA8 and RGB65 Colors
  • Copying and modifying bytes through the inspector
  • Adding new data types through the pattern language
  • Support for hiding rows that aren't used
Node-based data pre-processor
  • Modify, decrypt and decode data before it's being displayed in the hex editor
  • Modify data without touching the underlying source
  • Support for adding custom nodes
Loading data from many different data sources
  • Local Files
    • Support for huge files with fast and efficient loading
  • Raw Disks
    • Loading data from raw disks and partitions
  • GDB Server
    • Access the RAM of a running process or embedded devices through GDB
  • Intel Hex and Motorola SREC data
  • Process Memory
    • Inspect the entire address space of a running process
Data searching
  • Support for searching the entire file or only a selection
  • String extraction
    • Option to specify minimum length and character set (lower case, upper case, digits, symbols)
    • Option to specify encoding (ASCII, UTF-8, UTF-16 big and little endian)
  • Sequence search
    • Search for a sequence of bytes or characters
    • Option to ignore character case
  • Regex search
    • Search for strings using regular expressions
  • Binary Pattern
    • Search for sequences of bytes with optional wildcards
  • Numeric Value search
    • Search for signed/unsigned integers and floats
    • Search for ranges of values
    • Option to specify size and endianness
    • Option to ignore unaligned values
Data hashing support
  • Many different algorithms available
    • CRC8, CRC16 and CRC32 with custom initial values and polynomials
      • Many default polynomials available
    • MD5
    • SHA-1, SHA-224, SHA-256, SHA-384, SHA-512
    • Adler32
    • AP
    • BKDR
    • Bernstein, Bernstein1
    • DEK, DJB, ELF, FNV1, FNV1a, JS, PJW, RS, SDBM
    • OneAtTime, Rotating, ShiftAndXor, SuperFast
    • Murmur2_32, MurmurHash3_x86_32, MurmurHash3_x86_128, MurmurHash3_x64_128
    • SipHash64, SipHash128
    • XXHash32, XXHash64
    • Tiger, Tiger2
    • Blake2B, Blake2S
  • Hashing of specific regions of the loaded data
  • Hashing of arbitrary strings
Diffing support
  • Compare data of different data sources
  • Difference highlighting
  • Table view of differences
Integrated disassembler
  • Support for all architectures supported by Capstone
    • ARM32 (ARM, Thumb, Cortex-M, AArch32)
    • ARM64
    • MIPS (MIPS32, MIPS64, MIPS32R6, Micro)
    • x86 (16-bit, 32-bit, 64-bit)
    • PowerPC (32-bit, 64-bit)
    • SPARC
    • IBM SystemZ
    • xCORE
    • M68K
    • TMS320C64X
    • M680X
    • Ethereum
    • RISC-V
    • WebAssembly
    • MOS65XX
    • Berkeley Packet Filter
Bookmarks
  • Support for bookmarks with custom names and colors
  • Highlighting of bookmarked region in the hex editor
  • Jump to bookmarks
  • Open content of bookmark in a new tab
  • Add comments to bookmarks
Featureful data analyzer and visualizer
  • File magic-based file parser and MIME type database
  • Byte type distribution graph
  • Entropy graph
  • Highest and average entropy
  • Encrypted / Compressed file detection
  • Digram and Layered distribution graphs
YARA Rule support
  • Scan a file for vulnerabilities with official yara rules
  • Highlight matches in the hex editor
  • Jump to matches
  • Apply multiple rules at once
Helpful tools
  • Itanium, MSVC, Rust and D-Lang demangler based on LLVM
  • ASCII table
  • Regex replacer
  • Mathematical expression evaluator (Calculator)
  • Graphing calculator
  • Hexadecimal Color picker with support for many different formats
  • Base converter
  • Byte swapper
  • UNIX Permissions calculator
  • Wikipedia term definition finder
  • File utilities
    • File splitter
    • File combiner
    • File shredder
  • IEEE754 Float visualizer
  • Division by invariant multiplication calculator
  • TCP Client/Server
  • Euclidean algorithm calculator
Built-in Content updater
  • Download all files found in the database directly from within ImHex
    • Pattern files for decoding various file formats
    • Libraries for the pattern language
    • Magic files for file type detection
    • Custom data processor nodes
    • Custom encodings
    • Custom themes
    • Yara rules
Modern Interface
  • Support for multiple workspaces
  • Support for custom layouts
  • Detachable windows
Easy to get started
  • Support for many different languages
  • Simplified mode for beginners
  • Extensive documentation
  • Many example files available on the Database
  • Achievements guiding you through the features of ImHex
  • Interactive tutorials

Pattern Language

The Pattern Language is the completely custom programming language developed for ImHex. It allows you to define structures and data types in a C-like syntax and then use them to parse and highlight a file's content.

Database

For format patterns, libraries, magic and constant files, check out the ImHex-Patterns repository.

Feel free to PR your own files there as well!

Requirements

To use ImHex, the following minimal system requirements need to be met.

[!IMPORTANT] ImHex requires a GPU with OpenGL 3.0 support in general. There are releases available (with the -NoGPU suffix) that are software rendered and don't require a GPU, however these can be a lot slower than the GPU accelerated versions.

If possible at all, make ImHex use the dedicated GPU on your system instead of the integrated one. ImHex will usually run fine with integrated GPUs as well but certain Intel HD GPU drivers on Windows are known to cause graphical artifacts.

  • OS:
    • Windows: Windows 7 or higher (Windows 10/11 recommended)
    • macOS: macOS 12.1 (Monterey) or higher,
      • Lower versions are supported, but you'll need to compile ImHex yourself
    • Linux: "Modern" Linux. The following distributions have official releases available. Other distros are supported through the AppImage and Flatpak releases.
      • Ubuntu and Debian
      • Fedora
      • RHEL/AlmaLinux
      • Arch Linux
      • Basically any other distro will work as well when compiling ImHex from sources.
  • CPU: x86_64 (64 Bit)
  • GPU: OpenGL 3.0 or higher
    • Integrated Intel HD iGPUs are supported, however certain drivers are known to cause various graphical artifacts, especially on Windows. Use at your own risk.
    • In case you don't have a GPU available, there are software rendered releases available for Windows and macOS
  • RAM: 256MB, more may be required for more complicated analysis
  • Storage: 150MB

Installing

Information on how to install ImHex can be found in the Install guide

Compiling

To compile ImHex on any platform, GCC (or Clang) is required with a version that supports C++23 or higher. On macOS, Clang is also required to compile some ObjC code. All releases are being built using latest available GCC.

[!NOTE] Many dependencies are bundled into the repository using submodules so make sure to clone it using the --recurse-submodules option. All dependencies that aren't bundled, can be installed using the dependency installer scripts found in the /dist folder.

For more information, check out the Compiling guide.

Contributing

See Contributing

Plugin development

To develop plugins for ImHex, use the following template project to get started. You then have access to the entirety of libimhex as well as the ImHex API and the Content Registry to interact with ImHex or to add new content.

Credits

Contributors

  • iTrooz for getting ImHex onto the Web as well as hundreds of contributions in every part of the project
  • jumanji144 for huge contributions to the Pattern Language and ImHex's infrastructure
  • Mary for her immense help porting ImHex to MacOS and help during development
  • Roblabla for adding MSI Installer support to ImHex
  • Mailaender for getting ImHex onto Flathub
  • Everybody else who has reported issues on Discord or GitHub that I had great conversations with :)

Dependencies

  • Thanks a lot to ocornut for their amazing Dear ImGui which is used for building the entire interface
    • Thanks to epezent for ImPlot used to plot data in various places
    • Thanks to Nelarius for ImNodes used as base for the data processor
    • Thanks to BalazsJako for ImGuiColorTextEdit used for the pattern language syntax highlighting
  • Thanks to nlohmann for their json library used for configuration files
  • Thanks to vitaut for their libfmt library which makes formatting and logging so much better
  • Thanks to btzy for nativefiledialog-extended and their great support, used for handling file dialogs on all platforms
  • Thanks to danyspin97 for xdgpp used to handle folder paths on Linux
  • Thanks to aquynh for capstone which is the base of the disassembly window
  • Thanks to rxi for microtar used for extracting downloaded store assets
  • Thanks to VirusTotal for Yara used by the Yara plugin
  • Thanks to Martinsos for edlib used for sequence searching in the diffing view
  • Thanks to ron4fun for HashLibPlus which implements every hashing algorithm under the sun
  • Thanks to mackron for miniaudio used to play audio files
  • Thanks to all other groups and organizations whose libraries are used in ImHex

License

The biggest part of ImHex is under the GPLv2-only license. Notable exceptions to this are the following parts which are under the LGPLv2.1 license:

  • /lib/libimhex: The library that allows Plugins to interact with ImHex.
  • /plugins/ui: The UI plugin library that contains some common UI elements that can be used by other plugins

The reason for this is to allow for proprietary plugins to be developed for ImHex.

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice &



English | |

Documentation StatusDocumentation StatusReleaseLicenseTwitter

Welcome to the PaddlePaddle GitHub.

PaddlePaddle, as the first independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms. PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 10.7 million developers, 235,000 companies and generating 860,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.

Installation

Latest PaddlePaddle Release: v2.6

Our vision is to enable deep learning for everyone via PaddlePaddle. Please refer to our release announcement to track the latest features of PaddlePaddle.

Install Latest Stable Release

# CPUpip install paddlepaddle# GPUpip install paddlepaddle-gpu

For more information about installation, please view Quick Install

Now our developers can acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you will obtain 8 hours to train models online per day. Click here to start.

FOUR LEADING TECHNOLOGIES

  • Agile Framework for Industrial Development of Deep Neural Networks

    The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden, through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved. The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts.

  • Support Ultra-Large-Scale Training of Deep Neural Networks

    PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open-source training platform that supports the training of deep networks with 100 billion features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved real-time model updating with more than 1 trillion parameters. Click here to learn more

  • High-Performance Inference Engines for Comprehensive Deployment Environments

    PaddlePaddle is not only compatible with models trained in 3rd party open-source frameworks , but also offers complete inference products for various production scenarios. Our inference product line includes Paddle Inference: Native inference library for high-performance server and cloud inference; FastDeploy: Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge without-of-the-box and unified experience; Paddle Lite: Ultra-Lightweight inference engine for mobile and IoT environments; Paddle.js: A frontend inference engine for browser and mini-apps. Furthermore, by great amounts of optimization with leading hardware in each scenario, Paddle inference engines outperform most of the other mainstream frameworks.

  • Industry-Oriented Models and Libraries with Open Source Repositories

    PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications. Click here to learn more

Documentation

We provide English and Chinese documentation.

  • Guides

    You might want to start from how to implement deep learning basics with PaddlePaddle.

  • Practice

    So far you have already been familiar with Fluid. And the next step should be building a more efficient model or inventing your original Operator.

  • API Reference

    Our new API enables much shorter programs.

  • How to Contribute

    We appreciate your contributions!

Open Source Community

Courses

  • Server Deployments: Courses introducing high performance server deployments via local and remote services.
  • Edge Deployments: Courses introducing edge deployments from mobile, IoT to web and applets.

Copyright and License

PaddlePaddle is provided under the Apache-2.0 license.

Dear ImGui: Bloat-free Graphical User interface for C++ with minimal dependencies


Dear ImGui

"Give someone state and they'll have a bug one day, but teach them how to represent state in two separate locations that have to be kept in sync and they'll have bugs for a lifetime."
-ryg

Build StatusStatic Analysis StatusTests Status

(This library is available under a free and permissive license, but needs financial support to sustain its continued improvements. In addition to maintenance and stability there are many desirable features yet to be added. If your company is using Dear ImGui, please consider reaching out.)

Businesses: support continued development and maintenance via invoiced sponsoring/support contracts:
  E-mail: contact @ dearimgui dot com
Individuals: support continued development and maintenance here. Also see Funding page.

The Pitch - Usage - How it works - Releases & Changelogs - Demo - Integration
Gallery - Support, FAQ - How to help - Funding & Sponsors - Credits - License
Wiki - Extensions - Languages bindings & frameworks backends - Software using Dear ImGui - User quotes

The Pitch

Dear ImGui is a bloat-free graphical user interface library for C++. It outputs optimized vertex buffers that you can render anytime in your 3D-pipeline-enabled application. It is fast, portable, renderer agnostic, and self-contained (no external dependencies).

Dear ImGui is designed to enable fast iterations and to empower programmers to create content creation tools and visualization / debug tools (as opposed to UI for the average end-user). It favors simplicity and productivity toward this goal and lacks certain features commonly found in more high-level libraries.

Dear ImGui is particularly suited to integration in game engines (for tooling), real-time 3D applications, fullscreen applications, embedded applications, or any applications on console platforms where operating system features are non-standard.

  • Minimize state synchronization.
  • Minimize UI-related state storage on user side.
  • Minimize setup and maintenance.
  • Easy to use to create dynamic UI which are the reflection of a dynamic data set.
  • Easy to use to create code-driven and data-driven tools.
  • Easy to use to create ad hoc short-lived tools and long-lived, more elaborate tools.
  • Easy to hack and improve.
  • Portable, minimize dependencies, run on target (consoles, phones, etc.).
  • Efficient runtime and memory consumption.
  • Battle-tested, used by many major actors in the game industry.

Usage

The core of Dear ImGui is self-contained within a few platform-agnostic files which you can easily compile in your application/engine. They are all the files in the root folder of the repository (imgui*.cpp, imgui*.h). No specific build process is required. You can add the .cpp files into your existing project.

Backends for a variety of graphics API and rendering platforms are provided in the backends/ folder, along with example applications in the examples/ folder. You may also create your own backend. Anywhere where you can render textured triangles, you can render Dear ImGui.

See the Getting Started guide and Integration section of this document for more details.

After Dear ImGui is set up in your application, you can use it from _anywhere_ in your program loop:

ImGui::Text("Hello, world %d", 123);if (ImGui::Button("Save")) MySaveFunction();ImGui::InputText("string", buf, IM_ARRAYSIZE(buf));ImGui::SliderFloat("float", &f, 0.0f, 1.0f);

sample code output (dark, segoeui font, freetype)sample code output (light, segoeui font, freetype)

// Create a window called "My First Tool", with a menu bar.ImGui::Begin("My First Tool", &my_tool_active, ImGuiWindowFlags_MenuBar);if (ImGui::BeginMenuBar()){ if (ImGui::BeginMenu("File")) { if (ImGui::MenuItem("Open..", "Ctrl+O")) { /* Do stuff */ } if (ImGui::MenuItem("Save", "Ctrl+S")) { /* Do stuff */ } if (ImGui::MenuItem("Close", "Ctrl+W")) { my_tool_active = false; } ImGui::EndMenu(); } ImGui::EndMenuBar();}// Edit a color stored as 4 floatsImGui::ColorEdit4("Color", my_color);// Generate samples and plot themfloat samples[100];for (int n = 0; n < 100; n++) samples[n] = sinf(n * 0.2f + ImGui::GetTime() * 1.5f);ImGui::PlotLines("Samples", samples, 100);// Display contents in a scrolling regionImGui::TextColored(ImVec4(1,1,0,1), "Important Stuff");ImGui::BeginChild("Scrolling");for (int n = 0; n < 50; n++) ImGui::Text("%04d: Some text", n);ImGui::EndChild();ImGui::End();

my_first_tool_v188

Dear ImGui allows you to create elaborate tools as well as very short-lived ones. On the extreme side of short-livedness: using the Edit&Continue (hot code reload) feature of modern compilers you can add a few widgets to tweak variables while your application is running, and remove the code a minute later! Dear ImGui is not just for tweaking values. You can use it to trace a running algorithm by just emitting text commands. You can use it along with your own reflection data to browse your dataset live. You can use it to expose the internals of a subsystem in your engine, to create a logger, an inspection tool, a profiler, a debugger, an entire game-making editor/framework, etc.

How it works

The IMGUI paradigm through its API tries to minimize superfluous state duplication, state synchronization, and state retention from the user's point of view. It is less error-prone (less code and fewer bugs) than traditional retained-mode interfaces, and lends itself to creating dynamic user interfaces. Check out the Wiki's About the IMGUI paradigm section for more details.

Dear ImGui outputs vertex buffers and command lists that you can easily render in your application. The number of draw calls and state changes required to render them is fairly small. Because Dear ImGui doesn't know or touch graphics state directly, you can call its functions anywhere in your code (e.g. in the middle of a running algorithm, or in the middle of your own rendering process). Refer to the sample applications in the examples/ folder for instructions on how to integrate Dear ImGui with your existing codebase.

A common misunderstanding is to mistake immediate mode GUI for immediate mode rendering, which usually implies hammering your driver/GPU with a bunch of inefficient draw calls and state changes as the GUI functions are called. This is NOT what Dear ImGui does. Dear ImGui outputs vertex buffers and a small list of draw calls batches. It never touches your GPU directly. The draw call batches are decently optimal and you can render them later, in your app or even remotely.

Releases & Changelogs

See Releases page for decorated Changelogs. Reading the changelogs is a good way to keep up to date with the things Dear ImGui has to offer, and maybe will give you ideas of some features that you've been ignoring until now!

Demo

Calling the ImGui::ShowDemoWindow() function will create a demo window showcasing a variety of features and examples. The code is always available for reference in imgui_demo.cpp. Here's how the demo looks.

You should be able to build the examples from sources. If you don't, let us know! If you want to have a quick look at some Dear ImGui features, you can download Windows binaries of the demo app here:

The demo applications are not DPI aware so expect some blurriness on a 4K screen. For DPI awareness in your application, you can load/reload your font at a different scale and scale your style with style.ScaleAllSizes() (see FAQ).

Integration

See the Getting Started guide for details.

On most platforms and when using C++, you should be able to use a combination of the imgui_impl_xxxx backends without modification (e.g. imgui_impl_win32.cpp + imgui_impl_dx11.cpp). If your engine supports multiple platforms, consider using more imgui_impl_xxxx files instead of rewriting them: this will be less work for you, and you can get Dear ImGui running immediately. You can later decide to rewrite a custom backend using your custom engine functions if you wish so.

Integrating Dear ImGui within your custom engine is a matter of 1) wiring mouse/keyboard/gamepad inputs 2) uploading a texture to your GPU/render engine 3) providing a render function that can bind textures and render textured triangles, which is essentially what Backends are doing. The examples/ folder is populated with applications doing just that: setting up a window and using backends. If you follow the Getting Started guide it should in theory takes you less than an hour to integrate Dear ImGui. Make sure to spend time reading the FAQ, comments, and the examples applications!

Officially maintained backends/bindings (in repository):

  • Renderers: DirectX9, DirectX10, DirectX11, DirectX12, Metal, OpenGL/ES/ES2, SDL_Renderer, Vulkan, WebGPU.
  • Platforms: GLFW, SDL2/SDL3, Win32, Glut, OSX, Android.
  • Frameworks: Allegro5, Emscripten.

Third-party backends/bindings wiki page:

  • Languages: C, C# and: Beef, ChaiScript, CovScript, Crystal, D, Go, Haskell, Haxe/hxcpp, Java, JavaScript, Julia, Kotlin, Lobster, Lua, Nim, Odin, Pascal, PureBasic, Python, ReaScript, Ruby, Rust, Swift, Zig...
  • Frameworks: AGS/Adventure Game Studio, Amethyst, Blender, bsf, Cinder, Cocos2d-x, Defold, Diligent Engine, Ebiten, Flexium, GML/Game Maker Studio, GLEQ, Godot, GTK3, Irrlicht Engine, JUCE, LÖVE+LUA, Mach Engine, Magnum, Marmalade, Monogame, NanoRT, nCine, Nim Game Lib, Nintendo 3DS/Switch/WiiU (homebrew), Ogre, openFrameworks, OSG/OpenSceneGraph, Orx, Photoshop, px_render, Qt/QtDirect3D, raylib, SFML, Sokol, Unity, Unreal Engine 4/5, UWP, vtk, VulkanHpp, VulkanSceneGraph, Win32 GDI, WxWidgets.
  • Many bindings are auto-generated (by good old cimgui or newer/experimental dear_bindings), you can use their metadata output to generate bindings for other languages.

Useful Extensions/Widgets wiki page:

  • Automation/testing, Text editors, node editors, timeline editors, plotting, software renderers, remote network access, memory editors, gizmos, etc. Notable and well supported extensions include ImPlot and Dear ImGui Test Engine.

Also see Wiki for more links and ideas.

Gallery

Examples projects using Dear ImGui: Tracy (profiler), ImHex (hex editor/data analysis), RemedyBG (debugger) and hundreds of others.

For more user-submitted screenshots of projects using Dear ImGui, check out the Gallery Threads!

For a list of third-party widgets and extensions, check out the Useful Extensions/Widgets wiki page.

Custom engine erhe (docking branch)
erhe
Custom engine for Wonder Boy: The Dragon's Trap (2017)
the dragon's trap
Custom engine (untitled)
editor white
Tracy Profiler (github)
tracy profiler

Support, Frequently Asked Questions (FAQ)

See: Frequently Asked Questions (FAQ) where common questions are answered.

See: Getting Started and Wiki for many links, references, articles.

See: Articles about the IMGUI paradigm to read/learn about the Immediate Mode GUI paradigm.

See: Upcoming Changes.

See: Dear ImGui Test Engine + Test Suite for Automation & Testing.

For the purposes of getting search engines to crawl the wiki, here's a link to the Crawlable Wiki (not for humans, here's why).

Getting started? For first-time users having issues compiling/linking/running or issues loading fonts, please use GitHub Discussions. For ANY other questions, bug reports, requests, feedback, please post on GitHub Issues. Please read and fill the New Issue template carefully.

Private support is available for paying business customers (E-mail: contact @ dearimgui dot com).

Which version should I get?

We occasionally tag Releases (with nice releases notes) but it is generally safe and recommended to sync to latest master or docking branch. The library is fairly stable and regressions tend to be fixed fast when reported. Advanced users may want to use the docking branch with Multi-Viewport and Docking features. This branch is kept in sync with master regularly.

Who uses Dear ImGui?

See the Quotes, Funding & Sponsors, and Software using Dear ImGui Wiki pages for an idea of who is using Dear ImGui. Please add your game/software if you can! Also, see the Gallery Threads!

How to help

How can I help?

  • See GitHub Forum/Issues.
  • You may help with development and submit pull requests! Please understand that by submitting a PR you are also submitting a request for the maintainer to review your code and then take over its maintenance forever. PR should be crafted both in the interest of the end-users and also to ease the maintainer into understanding and accepting it.
  • See Help wanted on the Wiki for some more ideas.
  • Be a Funding Supporter! Have your company financially support this project via invoiced sponsors/maintenance or by buying a license for Dear ImGui Test Engine (please reach out: omar AT dearimgui DOT com).

Sponsors

Ongoing Dear ImGui development is and has been financially supported by users and private sponsors.
Please see the detailed list of current and past Dear ImGui funding supporters and sponsors for details.
From November 2014 to December 2019, ongoing development has also been financially supported by its users on Patreon and through individual donations.

THANK YOU to all past and present supporters for helping to keep this project alive and thriving!

Dear ImGui is using software and services provided free of charge for open source projects:

Credits

Developed by Omar Cornut and every direct or indirect contributors to the GitHub. The early version of this library was developed with the support of Media Molecule and first used internally on the game Tearaway (PS Vita).

Recurring contributors include Rokas Kupstys @rokups (2020-2022): a good portion of work on automation system and regression tests now available in Dear ImGui Test Engine.

Maintenance/support contracts, sponsoring invoices and other B2B transactions are hosted and handled by Disco Hello.

Omar: "I first discovered the IMGUI paradigm at Q-Games where Atman Binstock had dropped his own simple implementation in the codebase, which I spent quite some time improving and thinking about. It turned out that Atman was exposed to the concept directly by working with Casey. When I moved to Media Molecule I rewrote a new library trying to overcome the flaws and limitations of the first one I've worked with. It became this library and since then I have spent an unreasonable amount of time iterating and improving it."

Embeds ProggyClean.ttf font by Tristan Grimmer (MIT license).
Embeds stb_textedit.h, stb_truetype.h, stb_rect_pack.h by Sean Barrett (public domain).

Inspiration, feedback, and testing for early versions: Casey Muratori, Atman Binstock, Mikko Mononen, Emmanuel Briney, Stefan Kamoda, Anton Mikhailov, Matt Willis. Also thank you to everyone posting feedback, questions and patches on GitHub.

License

Dear ImGui is licensed under the MIT License, see LICENSE.txt for more information.