AI Image Upscaler by SnapEdit
Professional-grade 4K image resolution enhancement powered by Generative AI super-resolution models.

Professional-grade, local AI image upscaling powered by ESRGAN and ONNX for privacy-conscious workflows.
NMKD Superscale is a high-performance, desktop-based image upscaling solution designed for Windows environments. It leverages the ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) architecture and the ONNX runtime to provide state-of-the-art image enhancement without the need for cloud-based processing. Its architecture is specifically optimized for local hardware, supporting both NVIDIA (CUDA) and AMD/Intel (Vulkan/DirectML) GPUs. In the 2026 market, NMKD Superscale positions itself as the leading privacy-centric alternative to SaaS platforms like Gigapixel AI or Magnific. It provides users with full control over the inference pipeline, allowing for the injection of custom-trained models and fine-tuned parameters. Technically, it excels in maintaining structural integrity in low-resolution textures and removing heavy JPEG artifacts while preserving alpha channels—a critical requirement for game developers and digital restorers. By utilizing tiled rendering techniques, it bypasses the VRAM limitations typically associated with high-resolution neural network inference, enabling the upscaling of images to 8K and beyond on consumer-grade hardware.
Ability to distribute batch processing tasks across multiple installed GPUs to reduce total rendering time.
Professional-grade 4K image resolution enhancement powered by Generative AI super-resolution models.
Professional-grade 8K image upscaling and restoration using GAN-based neural networks.
Automated high-fidelity image enhancement and background removal powered by neural super-resolution.
Professional-grade logo reconstruction and brand asset enhancement using specialized neural edge-refinement.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Segments large images into smaller tiles for processing, then seamlessly stitches them back together.
Users can import .pth or .onnx models trained on specialized datasets (e.g., anime, medical, satellite).
Directly processes images with transparency, ensuring edges remain crisp without halo artifacts.
Utilizes Vulkan and DirectML alongside CUDA for cross-vendor GPU support.
Optional neural network layer that removes noise before the upscaling phase begins.
Automatically matches the color profile of the output to the original input to prevent shift.
Low-resolution 256x256 textures look blurry on modern 4K monitors.
Registry Updated:2/7/2026
Execute batch process.
Web-optimized logos (72dpi) need to be printed on large banners (300dpi).
Stable Diffusion or Midjourney images often lack fine detail or suffer from blur.