Run popular Open-Source AI tools on native Windows 10/11 (NVIDIA GPU only)

🌟 Easy to Use

🌟 Free

  • Your wallets are safe 🤑

🌟 Native Performance

  • No CPU or GPU performance loss, run natively on Windows 10/11

🌟 Privacy & Safety

  • Run entirely local so your data is completely safe
  • Is Open-source (GNU General Public License v3.0), you can freely inspect for malicious code


Component Info Usage GPU VRAM Usage
Stable-Diffusion Latent Text-to-Image Diffusion Generate unique photo, styled arts from text prompts High16GB for 512×512 Output Image
Real-ESRGANGFPGAN General Image/Video RestorationReal-world Face Restoration Enhance low-quality image or videoEnhance, restore face details High28GB for 2048×2048 Input Image
Practical-RIFE Video Frame Interpolation Increase video framerate, create slow-motion video Low6GB for 2048×2048 Video Input


Please refer to this article on how to use Stable-Diffusion, there are several options you need to understand : https://www.howtogeek.com/833169/how-to-write-an-awesome-stable-diffusion-prompt

Model Sample
[Default]Example Prompts:A high tech solarpunk utopia in the Amazon rainforestA pikachu fine dining with a view to the Eiffel TowerA mecha robot in a favela in expressionist stylean insect robot preparing a delicious meal image image
Arcane Style

Fine-tuned Stable Diffusion model trained on images from the TV Show Arcane

Use the tokens arcane style in your prompts for the effect

Spider-Verse Style

Fine-tuned Stable Diffusion model trained on movie stills from Sony’s Into the Spider-Verse

Use the tokens spiderverse style in your prompts for the effect.

Elden Ring Style

Fine-tuned Stable Diffusion model trained on the game art from Elden Ring

Use the tokens elden ring style in your prompts for the effect

Archer Style

Fine-tuned Stable Diffusion model trained on screenshots from the TV-show Archer

Use the tokens archer style in your prompts for the effect



Model Sample
RealESRGAN_x4plusRealESRGAN_x4plus_anime_6BRealESRGAN_x2plusrealesr-animevideov3realesr-general-x4v3 imageimage image


Model Sample
v4.6 – 2022.9.26 imageClick to view the full sample video

Get Started

System Requirements

Item Minimum Recommended
CPU Dual Core 2.0GHz Quad Core 3.0GHz
Memory >=8GB RAM >=32GB RAM
NVIDIA GPU (Mandatory) Driver Version >=452.39 (CUDA 11.x) Driver Version >=452.39 (CUDA 11.x)
Disk Space ~20GB for Working Directory ~20GB for Working Directory
Operating System Windows 10 64-bit Windows 11 64-bit

Structure View

graph TD;


Step Description Illustration
1 Install Python3.10 from Microsoft Store ____________________________________________________ image
2 Create a new empty folder as your Working Directory(C:/work, D:/mywork …) self-explanatory
3 Download installer.ps1 and put it into your Working Directory self-explanatory
4 Enable local Powershell Script Execution:Change execution policy to allow local PowerShell scripts to run without signing. Require signing for remote scripts image image
4 Right-click installer.ps1 and select Properties, then choose Unblock image
5 Right-click installer.ps1 script and choose Run with Powershell self-explanatory


Step Description Illustration
1 1. Right click on any file, or folder as input2. Pick the tool you want to run image
2 1. Input desired values2. Wait for Windows Explorer to open Results Location ____________________________________________________image


Low VRAM Solution

It is possible to run on GPU with low VRAM (4GB~6GB) without CUDA out of memory* error. However, you must scarify quality and speed.

Component Solution
Real-ESRGAN 1. Use a smaller resolution input image: resize, downscale the image to lower resolution 2. Try a smaller model size: ie, use RealESRGAN_x2plus model instead of RealESRGAN_x4plus 3. Use tile_size value different than 0. It will splits the image into multiple tiles, causing Face Restoration artifacts if the facial parts was between the split, and take more times to execute. There is a tile padding options to reduce this behavior in the code, but somehow it doesn’t work properly yet
Stable-Diffusion 1. Use a smaller output image value: H (256), W (256). Then use Real-ESRGAN to upscale that result. 2. Wait for the next update, there will be FP16 mode (Half-Precision) instead of current FP32 mode (Single-Precision), saving VRAM

Low GPU Ultilization Problems

  • Reason: These tools were originally built and optimized for a high VRAM but low CUDA Cores GPU (Google Colab Tesla T4 16GB Turing ~ 2560 CUDA Cores)
  • Solution: If you still have VRAM left, just run another instance of the tool to ultilize the rest of the GPU CUDA Cores

Run without GPU ?

  • Yes, you can run without GPU but it is really slow, and some codes will have to be rewritten because CPU doesnt support FP16

Intel and AMD GPU Support ?

  • AMD: No, currently PyTorch ROCm is only available for Linux, use WSL2 if you have an AMD GPU
  • Intel: No, Intel PyTorch Extension is only for Intel AVX-512 CPU

Support Development (Optional)

  • If you find this tool useful, please share with your friends 😃
  • If you find this tool is saving you a lot of time and headaches, feel free to donate to support development, adding more components, features and optimization 🥰 Patreon


View Github