Real-CUGAN vs SVFI
Real-CUGAN
github.comSVFI
doc.svfi.groupSide by side
| Figure | Real-CUGAN | SVFI |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | — |
| Linux | Yes | — |
| Android | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 1 | 1 |
| Spec score · AI Video Upscalers | 8.1 (#12) | 7.2 (#29) |
| Purpose | Real-CUGAN is an AI super-resolution model trained on a million-scale anime dataset and designed for anime images. | SVFI uses AI to turn low-frame-rate videos or image sequences into higher-frame-rate footage. |
| Support | The maker invites users to leave suggestions and requests in the repository issues. | The documentation identifies the Steam forum as the only after-sales channel. |
Plans
In their own words
Real-CUGAN
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.