FlashVSR vs Real-CUGAN
FlashVSR
github.comReal-CUGAN
github.comSide by side
| Figure | FlashVSR | Real-CUGAN |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Android | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AI Video Upscalers | 7.7 (#21) | 8.1 (#12) |
| Purpose | FlashVSR is a one-step diffusion framework for streaming video super-resolution. | Real-CUGAN is an AI super-resolution model trained on a million-scale anime dataset and designed for anime images. |
| Support | The maintainers welcome feedback and issues, and list Junhao Zhuang as a contact. | The maker invites users to leave suggestions and requests in the repository issues. |
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.