Aiarty Video Enhancer vs Real-CUGAN

Aiarty Video Enhancer is desktop software for upscaling, denoising, deblurring and restoring video, including output up to 4K and a 4× upscale factor. Three AI models—moDetail-HQ v3, Smooth-HQ v3 and superVideo vHQ—address different detail, fidelity and low-light enhancement needs. Frame interpolation can add frames for playback at 90 fps, 120 fps or higher. The software also includes AI audio noise removal and controls for color correction, HDR, white balance, exposure, contrast and saturation. It supports Windows 10 and macOS 13.0 or higher, and lists MP4, MOV and MKV output with H.264, H.265, ProRes and AV1 codecs. Processing takes place locally, though the software may connect to download updates and AI models. The free version processes videos under 120 seconds each, adds a watermark to exports and does not support batch export. The Standard License is $79 a year; the Lifetime License is $149 as a one-time purchase. Paid licenses include personal and commercial use.

Real-CUGAN is a free AI super-resolution model designed for anime images. It enlarges images by 2×, 3×, or 4× and offers denoising strengths, a conservative mode, and adjustable alpha. The 1× model is still in training. The English README describes five model weights for 2× and three each for 3× and 4×, covering different enhancement strengths. The Windows configuration processes both images and video, and the repository includes a video inference script. A linked NCNN implementation provides Windows, Linux, and macOS executables for Intel, AMD, Nvidia, and Apple-Silicon GPUs using Vulkan; its portable package includes binaries and models and states that it needs no CUDA or PyTorch runtime. Browser options include a demo and a Web-CPU version, though the README says the CPU demo is slow and recommends smaller images. Windows cache and tile settings can trade speed for lower GPU-memory use, including modes intended for very large images. The project also lists PyTorch and VapourSynth use paths and describes its architecture as compatible with Waifu2x-CUNet. The maker invites suggestions and requests through repository issues.