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REAL Video Enhancer
What it is
REAL Video Enhancer is a free application for Windows, Linux, and macOS that processes video through frame interpolation, upscaling, decompression, and denoising. Interpolation models include RIFE, GMFSS, and IFRNet, with a listed example of converting 24 FPS video to 48 FPS. Upscaling choices include SPAN, OpenProteus, AnimeJaNai, AniSD, and AnimeSR. DeH264 handles decompression, while DRUnet and DnCNN are listed for denoising. Scene-change detection is included to preserve sharp transitions, and the app previews the latest rendered frame. Batch processing is supported. Compute backends include TensorRT for NVIDIA RTX GPUs, PyTorch for CUDA 12.6 or ROCm 6.2 capable GPUs, and NCNN for Vulkan 1.3 capable GPUs. Minimum requirements list a dual-core x64 CPU, Vulkan 1.3-capable GPU, and 16 GB RAM; NCNN also lists 4 GB VRAM and 1 GB free storage. The project is AGPL-3.0 licensed. Steam offers an optional $2.99 one-time donation DLC with no content.
For and against
For
- Supports interpolation, upscaling, decompression, and denoising
- Offers multiple processing models
- Includes batch processing
- Provides scene-change detection
- Free base application
Against
- Requires Vulkan 1.3-capable graphics hardware
- Lists 16 GB RAM minimum
- TensorRT may fail with limited VRAM
- Windows binary may trigger a false-positive trojan alert
The verdict
REAL Video Enhancer combines several video-processing tasks with multiple model and hardware-backend choices. Hardware requirements, TensorRT limitations, and the Windows detection warning should be considered before installation.
Who it suits
This app suits users enhancing video on Windows, Linux, or macOS who have compatible graphics hardware. It is relevant for interpolation, enlargement, decompression, denoising, or batch work.
Plans and prices
REAL Video Enhancer
Free video enhancement application; optional $2.99 Steam donation DLC contains no additional content
Read 5 Oct 2026 · github.com
Steam donation DLC
Donation to support development; requires the base Steam application and contains no content
Read 5 Oct 2026 · store.steampowered.com
On the record
- Compute backends
- TensorRT supports NVIDIA RTX GPUs, PyTorch supports CUDA 12.6 and ROCm 6.2 capable GPUs, and NCNN supports Vulkan 1.3 capable GPUs.github.com · read 5 Oct 2026
- Frame interpolation
- The README describes interpolation such as converting video from 24 to 48 FPS and lists RIFE, GMFSS, and IFRNet models.github.com · read 5 Oct 2026
- Hardware requirements
- The README gives minimum requirements of a dual-core x64 CPU, Vulkan 1.3-capable GPU, 4 GB VRAM for NCNN, 16 GB RAM, and 1 GB free storage for NCNN.github.com · read 5 Oct 2026
- Integration
- Discord Rich Presence is supported for the Discord system package and Discord Flatpak.github.com · read 5 Oct 2026
- Known constraint
- The README says TensorRT optimization can fail with limited VRAM and suggests using CUDA or NCNN instead.github.com · read 5 Oct 2026
- License
- The repository identifies its license as AGPL-3.0.github.com · read 5 Oct 2026
- Other processing
- The project lists DeH264 decompression and DRUnet and DnCNN denoising models.github.com · read 5 Oct 2026
- Purpose
- REAL Video Enhancer provides video frame interpolation, upscaling, decompression, and denoising.github.com · read 5 Oct 2026
- Scene handling
- Scene change detection is included to preserve sharp transitions, and the app previews the latest rendered frame.github.com · read 5 Oct 2026
- Security notice
- The project warns that the Windows binary may be detected as a trojan and attributes this to a PyInstaller false positive.github.com · read 5 Oct 2026
- Steam purchase
- Steam lists a $2.99 donation DLC that contains no content and requires the base REAL Video Enhancer application.store.steampowered.com · read 5 Oct 2026
- Support
- The project links to a Discord community and offers support through GitHub issues.github.com · read 5 Oct 2026
- Upscaling
- The README lists multiple upscaling models, including SPAN, OpenProteus, AnimeJaNai, AniSD, and AnimeSR.github.com · read 5 Oct 2026
