Many filmmakers, editors, or archivists have faced the same problems: low-resolution footage that looks blocky on modern displays, noisy shots captured in poor light, or old tapes and compressed files that feel unusable in today’s production standards. Manually fixing these flaws takes hours – time that could be spent on editing and storytelling instead of patching technical problems.
This is where Aiarty Video Enhancer fits in. Built for professionals who need reliable results, Aiarty uses carefully trained AI models to restore detail, upscale resolution, denoise footage, and smooth motion, all while keeping natural textures intact.

Whether you’re trying to unify mixed resolutions, revive digitized VHS or MiniDV archives, prepare client deliverables for 4K playback, or give YouTube content a professional polish, Aiarty helps you achieve more consistent quality with less effort.

What Is Aiarty Video Enhancer

Aiarty Video Enhancer is a desktop-based AI video enhancer and upscaler tool, designed to run safely offline with full support for Nvidia, AMD, and Apple Silicon acceleration. Unlike many cloud-based tools, it processes locally, ensuring privacy for client work and sensitive archives.

At its core is a hybrid Diffusion-GAN architecture, trained on a large dataset of both images and videos. This combination produces natural results without the “painterly” look or over-sharpen effects often seen in other AI enhancers. The software directly addresses today’s production challenges:

• Mixed sources, inconsistent quality: Footage from old phones, DSLRs, drones, digitized VHS, and screen captures often arrive at different resolutions and compression levels.
• High viewer expectations: For video zoom-in effects or on 4K screens, compression artifacts, noise, and blur are more visible than ever.
• Time pressure: Tight deadlines make manual cleanup workflows impractical. Batch queuing in Aiarty streamlines workflows.
• Privacy concerns: Because Aiarty runs offline, you don’t need to upload sensitive footage to a cloud server.

How Aiarty Video Enhancer Fits Into Your Workflow

Aiarty is designed to slide into your workflow without forcing you to rethink your entire setup. The interface is straightforward: import, select a model and target resolution, preview, and export. Under the hood, though, the software is making deep-learning based decisions about video noise reduction, texture recovery, and frame interpolation that would take hours to attempt manually.

Step 1. Import your footage.

Aiarty supports common formats, including MP4, MOV, MKV, AVI, MXF, TS, M2TS and more. You can drop in anything from HEVC to ProRes codecs. Batch processing is available if you’re working on multiple shots.

Step 2. Choose an AI model.

The strength of Aiarty lies in its specialized models, each tuned for a different type of footage or scenario:

• moDetail-HQ v2 recovers fine detail in skin, fabric, hair, or foliage, and applies decent denosing and deblurring when necessary.
• Smooth-HQ v2 prioritizes faithful restoration with minimal artifacts, ideal for interviews, weddings, or polished commercial work.
• superVideo vHQ tackles low-light and grain-heavy footage, with more dedicated noise reduction algorithms for challenging dark scenes.

More details about these AI models are further explained in the technology section below.

Step 3. Select your output resolution.

You can upscale x2, x4, or target a specific resolution such as 4K UHD. For quality restoration without changing the dimension, you can also select x1.

Step 4. Preview before export.

Aiarty’s real-time preview lets you pause and test different models on a frame before running a full export. This saves enormous time and makes it easy to find the best settings.

Step 5. Export or apply quick editing.

For delivery, Aiarty supports ProRes for editing workflows, high-bitrate H.264, H.265 (HEVC), and AV1 for efficient web publishing.

Beyond video upscaling, Aiarty also includes frame interpolation (for 90fps or 120fps slow-motion playback), AI audio denoise (removing background hiss or hum), and a basic color panel to adjust white balance, saturation, contrast, etc. These aren’t meant to replace a grading suite, but they’re invaluable for prep work and fast corrections.

With this feature set, Aiarty Video Enhancer slots seamlessly into any production pipeline.

• Creators: Clean up noisy vlogs, restore old recordings, or upscale legacy content for social platforms.
• Studios: Batch-process hundreds of hours of raw footage with consistent, high-quality results.
• Marketers and agencies: Deliver sharp, artifact-free videos that meet brand standards without blowing budgets or timelines.

Extra Tip: Enhancing Stills with Aiarty Image Enhancer

For creators who also work with low-resolution images, old photos, or AI-generated art, Aiarty Image Enhancer fits into your workflow. While the video enhancer cleans up footage, the image enhancer can denoise, sharpen, and upscale still images to 8K, 16K, and higher. Ideal for printing, e-commerce assets, 4K wallpapers, or slideshows. This way, both your videos and images maintain a consistent level of polish.

Technical Analysis – Behind the Scenes

The promise of any AI video enhancer rests on how well its models are trained and how intelligently they process footage. Aiarty isn’t just applying filters on top of video or stretching pixels with mathematical bicubic interpolation; it’s built around machine learning models trained on a proprietary dataset of diverse imagery, from low-light live action to high-detail clips featuring organic textures like skin and fabric. This variety gives the system a strong foundation to handle different production scenarios.

At the core, Aiarty relies on Diffusion + GAN-based architectures. Diffusion models excel at generating fine details for a more realistic look, while GANs (Generative Adversarial Networks) enforce realism by comparing outputs against “ground truth” references, without hallucinating harsh artifacts. The combination produces sharp textures that feel natural rather than artificially sharpened.

Each of the three main models reflects a specific training emphasis:

moDetail-HQ v2 (Diffusion+GAN) is trained to restore details and enhance edge definition. It’s especially effective with faces, clothing, outdoor plants, and objects with organic textures.

Smooth-HQ v2 (Diffusion) uses a gentler training objective to maintain fidelity and color realism, focusing on smooth denoise and deblur with texture consistency. Editors who prioritize naturalism often find this the safest option.

superVideo vHQ (Diffusion+GAN) leans on a hybrid approach with additional noise-heavy datasets, making it suited for restoring flawed dark scenes, high-ISO noise footage, or grainy archives.

Performance is another area where the engineering shows. Aiarty makes full use of GPU acceleration, supporting Nvidia, AMD, Intel, and Apple Silicon natively. In practice, this means real-time or near real-time 4K upscaling on modern systems, with GPU utilization reaching above 95%.

While no AI enhancer is perfect, Aiarty’s technology gives confidence. The system doesn’t “hallucinate” unnatural details or push footage into an “oily” artificial look; instead, it rebuilds clarity in a way that fits naturally into professional post workflows.

Practical Tips: Getting the Most Out of Aiarty

Even with a strong AI backbone, the results you get depend on how you approach the tool. Here are some practices that users and testers have found most effective:

1. Match the model to the footage

Not all footage benefits from the same algorithm. For fine detail like hair or textiles, the moDetail-HQ v2 model is often best. If you do client weddings or interviews where natural skin tones matter more than hyper-sharpness, Smooth-HQ v2 usually gives cleaner, more faithful results. For grainy VHS or low-light archives, superVideo vHQ is worth the extra render time.

2. Scale step-by-step if needed

Jumping directly from SD to 4K doesn’t always yield the cleanest result. In some cases, scaling in two passes (x2, then x2 again) produces smoother edges and better texture consistency.

3. Zoom in for previews

The preview tool isn’t just for quick looks, it’s your best way to compare different models side by side. Testing parts of the clip before committing to a full export can save hours, especially on long projects.

4. Think about export formats early

If you’re planning to color grade or edit in NLEs, exporting to ProRes (MOV) gives you room to push the footage further. For lighter web-ready files, H.264, H.265 (HEVC), or AV1 balances quality and size well.

5. Take advantage of interpolation

Several users mentioned that frame interpolation was the feature that changed their creative process. As graphic designer Nathalie Boucher explained on Product Hunt, being able to generate natural-looking slow-motion with Aiarty opens up “creative flexibility for music videos and experimental projects“.

6. Keep hardware in mind

While Aiarty is optimized for GPUs, older cards may struggle with large batch jobs. Users working with heavy archive restorations have found it helpful to trim down sequences for testing before scaling up to the full workflow.

For professionals, the biggest takeaway is that Aiarty is more about having a reliable AI companion to enhance video quality. As videographer Pascal Gauthier put it, “The ability to upscale to 4K while also cleaning up blurry or noisy clips is a massive time saver. Having everything handled in one tool rather than using multiple plugins makes the workflow so much easier“.

Get Started with Aiarty

Aiarty Video Enhancer doesn’t replace the artistry of editing, but it does reduce the technical roadblocks. From client work to independent projects, creators are finding new efficiency. Freelancers can restore and deliver client archives faster. YouTubers can elevate production quality without expensive gear. Institutions can revive decades-old collections.

And if your workflow also involves stills (whether it’s for 300DPI posters and photo prints, 4K wallpapers, or website banners), Aiarty Image Enhancer extends the same AI-powered restoration to images. It uses AI inference to denoise, sharpen, and upscale images, while restoring details based on deep learning. Grab a Free License of Aiarty Video Enhancer to explore full features, with exclusive support for creators.

For avid users, enjoy the software bundle of Aiarty Image Enhancer and Aiarty Video Enhancer. Both tools come with a lifetime license that never expires, along with unlimited free updates to future versions. No extra upgrade costs are required, so users can always enjoy the latest features.

System Requirements of Aiarty Video Enhancer:

• Windows 10 or higher, Intel/AMD/Nvidia. 8GB RAM or more.
• macOS 13.0 or higher, Intel CPU/Apple Silicon Chips. 8GB RAM or more.
• Optimized for DirectML/TensorRT/CoreML acceleration on supported hardware.

System Requirements of Aiarty Image Enhancer:

• Windows 10 or higher, Intel/AMD/Nvidia. 8GB RAM or more.
• macOS Catalina 10.15 or higher (Intel CPU), macOS Big Sur 11 or higher (Apple Silicon), 8GB or more.
• CoreML hardware acceleration for macOS 12 or higher. DirectML/TensorRT acceleration on supported hardware.

About Aiarty

Aiarty is the AI-focused innovation brand from Digiarty Software, a trusted name in multimedia since 2006. Backed by a team of passionate engineers, Aiarty develops state-of-the-art AI tools that elevate digital experiences worldwide. Learn more about Aiarty at https://www.aiarty.com/

The post Need an AI Video Enhancer in Your Workflow? Aiarty Video Enhancer! first appeared on DIGITAL PRODUCTION and was written by Advertorial.

Just like DaVinci Resolve’s Speed Warp (DR for short) and Topaz’ Video AI (TVAI), Twixtor is now using neural networks aka machine learning to generate additional frames for slow motion (slo-mo) in post-production. We have already reviewed the early beta of its version 8 last year (the link includes examples of the results). But now the software is not only final, it is also available as a standalone version for Windows and MacOS. Any big changes?

Features of the standalone Version

The standalone version of Twixtor is easier to use for beginners than the plug-in, you don’t need any workarounds to extend the clips for slo-mo (see my other article). You can choose a faster or a more precise algorithm and the mode of control between a simple speed change or speed ramping. The latter offers quite intuitive controls you may wish to have in other software, but setting keyframes was not yet completely bug-free. Choices for the treatment of audio are self-explanatory, as is the speed slider. You can define the range of frames to be treated and choose the frame rate for the output independently of the input rate. Slightly confusing is the activation of the GPU (which is normally the right choice), since clicking on the field makes it dark. Now, is that on or off? BTW, the installer is looking very “Windows” on a Mac, but that works flawlessly.

For input, all flavours of AVC, HEVC and ProRes are accepted, but not DNxHR or Cineform, which are also high quality intermediate codecs. They are needed by users of DR under Windows, who can’t export ProRes. Neither is the MXF wrapper accepted, which is an important alternative to MOV. Unfortunately, you don’t even get an explanation when trying to use one of these, the picture just stays black. In the end, the plug-in might be the better choice for professional users, and for log, HDR, or RAW you should also resort to that. But then, RE:Vision FX gives you a license for the standalone together with the plug-in version.

The most serious limitation is in the output codecs you can choose from: there are only HEVC and H264, and both come in 8 bit with 4:2:0 chroma subsampling only. That happens without being mentioned anywhere, you’ll need a tool like MediaInfo (free) to find this out. Not a perfect choice if you want to treat the results further in any NLE with colour grading or filtering. At least you can up the bitrate for that purpose. The fact that they are wrapped as .m4v doesn’t make things better, some NLEs only accepted those after a re-wrap into .mov. And then, they contain no timecode, which can wreak havoc if you have to move files after editing or if you need to send them to collaborators. Definitely add TC while you are re-wrapping for such situations (with a tool like e.g. QTchange).

Setup for the Test

Our earlier tests suggested that higher image quality, more fps and shorter exposure times yield better results with any of these softwares. We didn’t want to make it too easy for the candidates, so this time we have used some smartphone footage. It was shot in HD only, at 30 fps and under available light. Of course, we have chosen a scene with intense activity, in this case dancers and drummers with criss-crossing movements. Get the source file here for your own tests. This is the stuff where trying to get good slo-mo in post usually fails. But, after all, you can shoot good slo-mo in most modern cameras if you have thought of it beforehand.

You can help Twixtor Pro as a plug-in to get pretty perfect results with masks and tracking points, but it’s not really easy to use in DaVinci Resolve or other hosts, as explained in my older article. The standalone version is rather simplified and straightforward, which can be very helpful for beginners. So, we also used standard presets for Speed Warp Better and the model offered in TVAI automatically for slo-mo without scaling, which is now Apollo. Fine tuning can result in even better results from both of these. TVAI is offering only 4x, 6x or 8x of slo-mo, so we decided to try 6x, since 8x seems a bit too adventurous for such footage. We ran the tests on a M1 Pro MacBook with 32 GB under Sonoma 14.7.2.

Test Results

Our test results at the highest quality setting were close, you need to do watch carefully to see the differences. All three are struggling with small objects like the drumsticks, which flicker on and off, and all three can show some pushing and pulling of the background or of criss-cross movement, in particular where there is motion blur. For this less than optimal source footage, the results of all three are still impressive. But personally, I’d say TVAI gets the crown here, in particular for the least background distortion. The faster mode in Twixtor reverts to optical flow, which would also be an option in DR. Both are considerably faster than the AI and can suffice for less critical footage. TVAI also offers a faster alternative for the Apollo model.

Of course, speed also matters, and TVAI’s Apollo model needed only 2:19 if nothing else was activated, the time is predicted quite well (the result is here). So, it needs only 3.5 times the playback time of the final sample to render. This applies once the model is downloaded for the first time, which can need quite a while depending on your internet connection and their servers. Its only shortcoming: you can’t do speed ramping, it works with fixed ratios. But it can generate its output in high quality codecs. Blackmagic’s Speed Warp Better is considerably slower at 7:11, a factor of 11, but the visual results are close (see here). Being part of DR, it handles speed ramps, albeit a bit clunky, and a broad choice of codecs. Twixtor was the slowest at 12:15, which is a factor of 18.8 (the result). All of them are predicting the time needed pretty well, and all three got the GPU cores of our humble laptop fully loaded when rendering, so you can expect considerably better speeds with higher numbers of cores on a Mac or with any strong GPU on a PC.

We tested some very high quality footage in UHD at 50 fps too, you can see a frame of those Bougainvilleas in strong wind in the header above. But the results of the best algorithm in either candidate were so close that only speed would matter. Considering quality and speed, TVAI’s Apollo model was the best, their optimisation for speed in particular is truly impressive, when compared to early versions. But it’s also quite costly at 300 US$ initially, being subscription based. The deal is fair: You are only excluded from further updates if you don’t pay any more, but it’ll still work. And then, it can also do excellent de-interlacing and upscaling (see my article here). Speed Warp is a Studio feature in DR, which costs just the same at 300 US$, but also includes dozens of other valuable features, and until today there was never a charge for updates. Like in our earlier test, Twixtor is still the slowest, even after being out of beta. But at the current price just short of 100 US$ for a permanent license, it is also the cheapest, while you pay as much for TVAI every year.

Recommendation

Twixtor standalone is easy to operate and the best bid for synthetic slo-mo if you don’t own DaVinci Resolve Studio anyway. But it has its shortcomings and is the slowest of the bunch. If you own DR Studio, its internal Speed Warp will suffice for the occasional slo-mo, even if it’s not that much faster. But TVAI is winning the crown here, and not only for quality and speed. If you are planning for slo-mo, you can shoot higher speed in most cameras at a lower resolution, and then let TVAI do the additional slo-mo plus upscaling. Even if this approach takes more time than slo-mo alone, it’ll still be faster than the others and can yield excellent results. But it comes at a hefty price tag.

The post Out on its own: Twixtor Standalone first appeared on DIGITAL PRODUCTION and was written by Uli Plank.