Neural-Pixel

July 11, 2026 ยท View on GitHub

Neural-Pixel

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A simple GUI wrapper for stable-diffusion.cpp written using C and GTK 4. Screenshot1

Neural Pixel is a fast, Vulkan-powered image generation tool that runs on almost any GPU from 2014+ (requires at least 2GB VRAM for SD 1.5 or 3GB for SDXL). Skip the CUDA/ROCm headache and Python dependency hell, Neural Pixel is simple, portable, and high-performing!

Compatibility

  • Neural Pixel supports leading image generation models such as SDXL and FLUX, plus a broad range of community models, extensions, and runtimes.
  • By default, the release ZIP packages include support for Vulkan and CPU inference only.
  • No video support at the moment.
  • See docs for the full list of supported models, features, formats, platforms, and backends.

Linux Setup & Running

1. Requirements

  • OS: Linux kernel >= 5.14 (Tested on RHEL 9, Fedora 42, and Arch).
  • Dependencies: GTK >= 4.12, libpng, zlib.
  • Vulkan backend (Optional): Vulkan driver/loader/tools & >= 2GB of VRAM.

2. How to Run

  • Download the Linux bundle
  • Extract the archive and execute the run_neural_pixel file.
  • Tip: For debugging, launch from a terminal and enable Terminal Verbose under Extra Options.

Windows Setup & Running

1. Requirements

  • Microsoft Visual C++ Redistributable latest: vc_redist.
  • A GPU or iGPU with at least 2GB of VRAM for Vulkan.

2. How to Run

  • Download the Windows bundle
  • Extract the archive and execute the neural_pixel.bat file.
  • Due to hardware limitations, I used q5_1 quantization and the DMD2 speed LoRA (0.7 weight) to generate these examples. The Sam semi-realistic model is the only exception, as it has the LoRA built-in. If you use the base model, your results may differ slightly.

Warning

The links to get the models may display NSFW, mature, or suggestive preview images. Viewer discretion is advised.

Mistoon NAI Hoseki V2 Dixar 4: DG Sam semi. V1
Model A Output Model B Output Model C Output Model D Output
Model A Output Model B Output Model C Output Model D Output
Model A Output Model B Output Model C Output Model D Output

Settings used

  • Sampler: Euler A (more detail) or LCM (smoother/less detail)
  • Scheduler: ays, exponential, or smoothstep
  • Steps: 8-12

Build (Linux)

You'll need GTK 4 and the libpng development libraries installed. Then, clone this repository using:

git clone https://github.com/Luiz-Alcantara/Neural-Pixel.git

Next, navigate into the cloned directory and run:

mkdir build && cd build && cmake .. && make

Build (Widows)

To build on Windows follow Windows Build.

Build Stable-Diffusion.cpp

To build sd.cpp follow the instructions on its github page: Stable-diffusion.cpp.

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