automatic

**Image Diffusion implementation with advanced features**   [](https://discord.gg/VjvR2tabEX) [](https://github.com/sponsors/vladmandic) [Docs](https://vladmandic.github.io/sdnext-docs/) | [Wiki](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.gg/VjvR2tabEX) | [Changelog](/automatic/CHANGELOG.html)

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Table of contents

• Documentation
• SD.Next Features
• Model support and Specifications
• Platform support
• Getting started

SD.Next Features

All individual features are not listed here, instead check ChangeLog for full list of changes

• Multiple UIs!
  ▹ Standard | Modern
• Multiple diffusion models!
• Built-in Control for Text, Image, Batch and video processing!
• Multiplatform!
  ▹ Windows | Linux | MacOS | nVidia | AMD | IntelArc/IPEX | DirectML | OpenVINO | ONNX+Olive | ZLUDA
• Platform specific autodetection and tuning performed on install
• Optimized processing with latest torch developments with built-in support for model compile, quantize and compress
  Compile backends: Triton | StableFast | DeepCache | OneDiff
  Quantization and compression methods: BitsAndBytes | TorchAO | Optimum-Quanto | NNCF
• Built-in queue management
• Built in installer with automatic updates and dependency management
• Mobile compatible

Main interface using StandardUI:

Main interface using ModernUI:

For screenshots and informations on other available themes, see Themes

Model support

SD.Next supports broad range of models: supported models and model specs

Platform support

• nVidia GPUs using CUDA libraries on both Windows and Linux
• AMD GPUs using ROCm libraries on Linux
  Support will be extended to Windows once AMD releases ROCm for Windows
• Intel Arc GPUs using OneAPI with IPEX XPU libraries on both Windows and Linux
• Any GPU compatible with DirectX on Windows using DirectML libraries
  This includes support for AMD GPUs that are not supported by native ROCm libraries
• Any GPU or device compatible with OpenVINO libraries on both Windows and Linux
• Apple M1/M2 on OSX using built-in support in Torch with MPS optimizations
• ONNX/Olive
• AMD GPUs on Windows using ZLUDA libraries

Getting started

• Get started with SD.Next by following the installation instructions
• For more details, check out advanced installation guide
• List and explanation of command line arguments
• Install walkthrough video

[!TIP] And for platform specific information, check out
WSL | Intel Arc | DirectML | OpenVINO | ONNX & Olive | ZLUDA | AMD ROCm | MacOS | nVidia | Docker

[!WARNING] If you run into issues, check out troubleshooting and debugging guides

Credits

• Main credit goes to Automatic1111 WebUI for the original codebase
• Additional credits are listed in Credits
• Licenses for modules are listed in Licenses

Evolution

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• OSS Stats

Docs

If you’re unsure how to use a feature, best place to start is Docs and if its not there,
check ChangeLog for when feature was first introduced as it will always have a short note on how to use it

Sponsors

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