WinDiffusion

Made with RealVisXL Lightning, 8 steps Euler A and upscaled with 4xUltraSharp

What is this?

WinDiffusion is a Stable Diffusion frontend written in C++/Qt, without a single line of Python involved, using the ONNX runtime and DirectML to execute models

Showcase of combined drawing-img2img Canvas tab

So, what's the deal?

1. Natively compatible with all GPU vendors. The DirectML backend supports any DirectX 12-capable GPU
2. Lightweight. Everything needed to run the models is ~200MB, compared to the around 10GB of pip or conda-installed libraries.
3. Easy to install. Installation is a breeze—simply unzip and launch the executable. It's so simple, even your grandma could do it.
4. Self-contained, reliable. Without having to lug around lots of libraries, it remains unaffected by unforeseen changes in dependencies.

Support

Marked with ❌ means not currently available, but is on high priority.

Supported models (tested)

• ✔️ Stable Diffusion 1.5
• ✔️ Stable Diffusion XL
• ✔️ Stable Diffusion XL Turbo
• ✔️ Stable Diffusion XL Lightning

Samplers

• ✔️ DPM 2M++ Karras
• ✔️ Euler Ancestral
• ❌ DPM++ SDE Karras (for models that demand it, use Euler Ancestral instead for now)

Features

• ✔️ Text-to-image
• ✔️ Image-to-image
• ✔️ Inpainting
• ✔️ Upscaling with ESRGAN
• ✔️ (Prompt:1.5) ((weighting))
• ❌ Long prompts (longer than CLIP limit)
• ❌ Face fix

Compilation

TODO: fill out this section

Externals (and thanks)

• Axodox-machinelearning: C++ implementation of Stable Diffusion
• QGoodWindow: Fancy windows for Qt
• JSON
• Qt Color Widgets