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Media.io AI Video Upscaler vs Real-CUGAN

Media.io AI Video Upscaler

media.io

Real-CUGAN

github.com

Side by side

FigureMedia.io AI Video UpscalerReal-CUGAN
Price from$13.99/moFree
Free planYesYes
Free trialYes—
Open source——
WebYesYes
WindowsYesYes
macOSYesYes
LinuxYesYes
iOSYes—
AndroidYesYes
APIYes—
Self-hosted—Yes
Plans published71
Spec score · Video Upscaling Software9.2 (#1)8.0 (#17)
Spec score · AI Video Upscalers9.2 (#2)8.1 (#12)
SupportMedia.io says advisors provide live-chat or email support within 24 hours.The maker invites users to leave suggestions and requests in the repository issues.

Plans

Media.io AI Video Upscaler

  • Free TrialFree
  • PremiumFree
  • Premium monthly introductory$14 a month
  • Premium annual$167.88 a year
  • Monthly Credit PlanPrice not published
  • Pay As You GoPrice not published
  • StandardPrice not published

Media.io AI Video Upscaler pricing →

Real-CUGAN

  • Real-CUGANFree

Real-CUGAN pricing →

In their own words

Media.io AI Video Upscaler

Media.io AI Video Upscaler has no write-up on Specifiction yet; its figures are above.

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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.

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