REAL Video Enhancer is a complete evolution of the old Rife ESRGAN App for Linux, an update that gives easy access to AI-powered frame interpolation and scaling on Windows, Linux, and macOS. The proposal stands out for bringing together key functions in a single interface that previously required separate tools or complicated flows, and target users looking for modern results versus outdated alternatives like Flowframes or enhancr.
In practice, This application centralizes two highly demanded tasks: Increase the resolution of your videos (upscaling) and create intermediate frames for smoother animations (frame interpolation). Its multiplatform vocation makes it easy for you to work on your favorite system without reinventing your workflow, whether you compile from source or use recommended packages and build methods.
What is REAL Video Enhancer and why does it matter?
REAL Video Enhancer It was born as a redesigned and enriched version of the Rife ESRGAN application for Linux, incorporating previous experience and expanding it significantly. Its focus is on super-resolution AI and interpolation, relying on current models and libraries that allow for a leap in quality compared to software that has lagged behind in maintenance or compatibility.
Furthermore, The project offers a unified and more user-friendly solution than other traditional flows, avoiding dependence on multiple partial utilities. Compatibility with Windows, Linux and macOS making it especially attractive for creators, restorers, and enthusiasts who alternate between environments or collaborate in mixed teams.
If you are coming from tools like Flowframes or enhancr, you will find a more current approach which, according to the available notes, seeks to fill real gaps: from queue management to specific fixes with certain models (e.g., DRUNet) and performance improvements on macOS with MPS. This translates into less friction and better results. in everyday video enhancement scenarios.
Code download: Nightly branch and stable version
To work with the project from its repository, You can clone both the Nightly branch (continuous development) such as stable branch associated with a specific version. The idea is that if you need the latest (and assume possible frequent changes) use Nightly; if you prefer stability, opt for the labeled version.
The available cloning commands make it clear how to proceed with submodules and, in the stable case, with the specific branch. This way you ensure that the entire dependency tree is obtained to the first:
# Nightly
git clone --recurse-submodules https://github.com/TNTwise/REAL-Video-Enhancer
# Stable
git clone --recurse-submodules https://github.com/TNTwise/REAL-Video-Enhancer --branch 2.3.4When using –recurse-submodules ensures that the submodules that the project depends on are downloaded automatically, avoiding inconsistencies. If you want reproducibility and lower risk of breakages, the stable branch (for example, 2.3.4) is your best ally.
Build Methods: PyInstaller, cx_Freeze, and Nuitka
Once you have the repository, the construction of the executable can be done with different tools depending on the system and your preferences. The project recommends PyInstaller for Windows and macOS, cx_Freeze for Linux, and leaves Nuitka as an experimental option for those who want to explore.
It is an interesting flexibility: PyInstaller stands out for its ease to generate self-contained binaries in common desktop environments; cx_Freeze usually integrates well on Linux and their distributions; and Nuitka can offer benefits in performance and packaging, but with nuances, being more demanding in configuration and still considered experimental in this case.
The build process is simplified by a script that centralizes parameters. The so-called indicated base is this:
python3 build.py --build BUILD_OPTION --copy_backendIn this line, BUILD_OPTION would represent the chosen tool (for example, pyinstaller or cx_freeze), while –copy_backend suggests preparation of the runtime environment with the necessary components. This approach reduces manual errors and makes it easy to repeat the build on different machines.
Key new features in REAL Video Enhancer 2.3.7 (Pre-Release)
La preversion 2.3.7 comes with very specific changes. First of all, PyTorch 2.9 is added, a relevant update for modern AI models and workflows. This version jump allows for improvements which are directly reflected in macOS thanks to MPS.
In fact, PyTorch 2.9 is forced to be used with MPS on macOS, which “should allow support for uint16” according to the notes. This aims for greater compatibility and accuracy. in certain internal processes, especially on Apple hardware where MPS (Metal Performance Shaders) is the acceleration route.
In the corrections section, Fixed a bug with DRUNet on certain resolutions, a problem that affected the execution of the model in that specific range. Also fixed a crash when adding to queue on macOS and another that caused playback problems when there was multiple subtitle tracks in the same video.
In addition to the above, the following are collected: Important additional notes for the community and the facility: there is a Discord active (link: https://discord.gg/S5UxqWgEBC), the Installation on macOS requires following a guide from YouTube (link: https://www.youtube.com/watch?v=o4J_GCuiIZc) and, in some cases, It may be necessary to temporarily disable Gatekeeper If the video/guide doesn't work for you as is. It is also made clear that Support for macOS is limited because the tests are performed on a virtual machine; if something fails, the recommendation is open issues so that he can be treated.
Finally, the version in Flatpak can now install any backend, although you may encounter two restrictions: space-time problems and the need for memory to complete the installation. Reportedly, at least 16 GB of RAM would be required (with 8 GB of tmpfs) for the process to go smoothly, and CUDA errors may appear depending on the configuration and GPU. There's no guarantee that everything will work on all systems.
Other published versions
In addition to the 2.3.7 pre-release, recent history includes several iterations that help to understand the pace of development: 2.3.5, 2.3.4, 2.3.3, 2.3.2, 2.3.1, 2.3.0, 2.2.5 and 2.2.1. Although the changes are not broken down here one by one, that they appear listed reinforces the idea of continuous evolution, with frequent corrections and improvements.
For those who value stability, the existence of stable branches/tags (as 2.3.4) is a guarantee that a working environment can be established. For its part, Pre-releases help test progress and report issues before they are promoted to stable.
Compatibility, requirements and specificities by platform
REAL Video Enhancer works on Windows, Linux and macOS, which covers the most common environments on both personal desktops and workstations. On macOS, using MPS With PyTorch 2.9 fixed, it suggests a clear path to take advantage of integrated GPU acceleration, with the advantages and limitations of the Apple platform.
If you opt for Flatpak, you must take into account the memory requirement: at least are recommended 16 GB of RAM totals with 8 GB of tmpfs during the installation of certain backends. The warning about possible CUDA errors indicates that on systems with NVIDIA GPUs and specific drivers, there may be fine-tuning to be done.
On Linux, cx_Freeze is the suggested build tool, which usually integrates well with the usual dependencies of the ecosystem. On Windows and macOS, PyInstaller is recommended. as a fast track to packaging, facilitating distribution and deployment without breaking routes or libraries.
One point to keep in mind is that macOS support is limited at this stage, since the tests are performed on a virtual machine. It is vital to report errors and strange behaviors so that they can be evaluated and resolved in future builds.
Interpolation and scaling: the essentials of flow
The beauty of REAL Video Enhancer is that allows you to perform two key operations in AI video restoration and enhancement: frame interpolation and upscalingThe first one is used for increase fluency generating intermediate frames, while the second increases resolution trying to preserve details and textures.
These tasks are usually supported by deep learning models (e.g., based on ESRGAN for super-resolution), and on libraries such as PyTorch to run computation on CPU or GPU. Update the project to PyTorch 2.9 This is a good sign, as it introduces compatibility improvements and optimizations that users perceive in terms of time and stability.
Regarding interpolation, The typical goal is to reduce judder or duplication in clips with low framerate or in fast-moving content. In scaling, the challenge is to avoid artifacts (hard edges, overfocus, noise) and maintain a natural texture. The balance between quality and performance It will be determined by the model chosen, your GPU and the configuration.
A community comment collected on the project pages precisely points out the need to control the output when upscaling throws “too big” resolutionsThis reflects a real concern: to be able to set more measured destination resolutions or adjust the scaling factor to balance file size, compatibility and final quality.
Controlling output size and resolution
If you are worried that the result will “shoot up” in size, The first tip is to check if the interface or flow allows choose a scale factor plus content (for example, 1.5x or 1.8x) or directly an objective resolution intermediate instead of going to 2x or 4x. Reduce the target impacts the file size and makes subsequent editing easier.
In the absence of an explicit selector, A practical strategy is to chain the process: first perform a moderate scaling with the chosen model and then, perform a fine rescaling with a classic tool that allows you to leave the clip right where you need it. This is how you maintain the detail improvement and you control the final product.
Keep in mind that the chosen bitrate and codec during export also have a decisive influence on the file size. Although scaling increases the resolution, adjusting the codec profile and bitrate will allow you to moderate the weight without destroying the quality. The key is to try and find the sweet spot for your case.
Queue management, subtitles and stability
According to the 2.3.7 pre-release notes, Fixed an issue when adding tasks to the queue on macOS, something important if you usually automate batches. A solid queue prevents bottlenecks and reduces downtime in front of the screen, especially during long renders.
It has also been addressed a bug related to videos that include multiple subtitle tracks, which could cause reproduction problems. If you work with multilingual material or embedded subtitles, This correction will save you from surprises halfway through the process.
With DRUNet, An error has been fixed in certain resolutions. This type of correction shows that the project handles real cases of use and, with the passage of versions, is polishing compatibility and robustness of the integrated models.
macOS Installation Tips and Gatekeeper Note
For macOS, It is recommended to follow an installation guide available on YouTube (https://www.youtube.com/watch?v=o4J_GCuiIZc). If you find that the video or method doesn't work for you as is, you may need temporarily disable Gatekeeper to allow execution. Remember to reactivate it later and assess common security measures.
The author himself warns that macOS support is limited for testing on a virtual machine. This implies that certain real hardware specific errors They can escape until the community reports them. The more detail you provide on your issues (Mac model, GPU, macOS version), the easier it will be to reproduce and fix.
Flatpak: Backends, Memory, and Potential CUDA Bugs
The Flatpak version you can install any backend, which gives quite a bit of flexibility, but is not without conditions. The note of requiring at least 16 GB of RAM (with 8GB of tmpfs) during installation of heavy backends It is critical to avoid errors due to lack of space or intermediate failures that require the process to be repeated.
Moreover, You may encounter CUDA errors depending on your combination of GPU, drivers and runtime. There are no guarantees that everything will work on every system., so it is advisable to review the logs and, if necessary, open an issue with details of the hardware, versions and steps that led to the failure.
Community, feedback, and support
The project invites the community to join the Discord (https://discord.gg/S5UxqWgEBC), a useful meeting point for quick questions, profile sharing, and progress tracking. Report problems through issues remains essential to maintaining quality and prioritizing fixes.
Among the visible comments, There are users who rate it with a “5” their experience and highlight that, in their free time, enjoy enhancing footage with this programThey also ask for more control over output resolution when upscaling, a clear signal of the direction the tool can follow to satisfy those seeking fine-tuning.
Good practices for a stable flow
If you are going to compile, Use the stable branch when looking for reliability and leaves Nightly for testing. Document your environment (OS version, drivers, GPU, PyTorch version) and save the exact commands you run; This will help you reproduce the environment and get support if something goes wrong.
When working with AI, controls VRAM consumption and RAM: close heavy parallel applications and limit background processes can prevent crashes. If you notice instability, reduces batch load, lower the intermediate resolution or change to a less demanding model.
Quick comparison with “old” alternatives
The reference pages emphasize that REAL Video Enhancer It is positioned as an alternative to Flowframes or enhancr, pointing out that these options “have become outdated”. Beyond the nuance, what is noteworthy is that This project is moving with current libraries and models (e.g. PyTorch 2.9 and improvements with MPS), and publishes specific corrections in the face of real incidents such as DRUNet or multiple subtitles.
If you come from those environments, The logical thing is to test this flow in a controlled project and compare times, quality and stability. The presence of guides, Discord channel and detailed release notes make it easier to adopt the tool with confidence.
REAL Video Enhancer consolidates into a single solution AI-powered frame interpolation and upscaling for Windows, Linux, and macOS, with key recent changes such as the jump to PyTorch 2.9, the forced use of MPS on macOS to enable uint16, and useful corrections in macOS queues, DRUNet, and handling multiple subtitles. You have branch cloning (Nightly/stable), recommended build methods (PyInstaller, cx_Freeze, Nuitka in experimental) and a Flatpak route which requires sufficient memory and can encounter CUDA errors. The community is active (Discord), there are positive reviews And there's a real interest in fine-tuning in-demand features like fine-tuning output resolution. If you want an updated and consistent workflow, Here's a solid foundation for your video enhancement projects..