6 Best Open Source Video Upscalers in 2026 (Tested)

An open source video upscaler becomes especially appealing when old videos meet modern 4K screens. If you have ever tried to watch an old DVD on a 4K monitor, you know the frustration immediately. The image looks blurry, blocky, and washed out, and every compression artifact feels magnified on a high-resolution display. A quick Google search for solutions usually leads to many AI video upscalers that look impressive, but most of them are commercial and come with high prices.

This is where open-source video upscalers stand out. Built by the community and designed to run locally, they let you enhance low-resolution footage such as 90s anime, classic DVDs, or personal home videos to 1080p or even 4K, without subscriptions or cloud uploads. To help you choose the right tool, I tested the most popular open-source video upscalers and shared the best options based on my hands-on results.

💡 How I Tested Open Source Video Upscalers

To make sure the results are comparable, I ran every open source video upscaler using the same hardware and the same set of videos.

Test environment: Intel Core i5-11400F, 16GB RAM, NVIDIA GeForce RTX 3060, Windows 11.

Test videos:

• Clip 1: 1920×1080, 25fps, 9.96s, woman applying lip gloss (download source: pexels)
• Clip 2: 1280×720, 24fps, 12.48s, winter landscape (download source: pexels)
• Clip 3: 640×360, 9s, added noise in Premiere (download source: pixabay)
• Clip 4: 640×368, 11.012s, cartoon footage (download source: archive.org)

Test criteria: I evaluated each tool based on how well it upscaled footage, reduced blur and noise, maintained temporal stability, and restored details. I also recorded processing speed, usability, and overall stability on my PC, along with a few additional practical factors.

What Is an Open Source Video Upscaler

An open source video upscaler is a software tool that increases the resolution of videos using AI models, and its source code is publicly available. Unlike closed or paid products, open source upscalers allow anyone to inspect, modify, and improve the code. Many open source projects also provide the actual model weights or make them easy to download.

Most open source video upscalers run locally on your computer, which means you don't need to upload your videos to a cloud service. This is especially important for privacy and for handling large files. Open source tools can range from simple command-line programs to full-featured GUIs, and they often support multiple AI models so users can choose between speed, quality, or compatibility with their hardware.

The open source nature also encourages community collaboration. Developers can contribute new models, optimize performance, fix bugs, and share settings or workflows. That's why many of the best video upscalers today come from open source projects—because they evolve quickly through community feedback and experimentation.

Notable Disadvantages of Open Source Video Upscalers

Open source video upscalers are powerful, but they also come with some common drawbacks. The biggest issues are usually related to usability, hardware requirements, and support.

⭕ Setup is complicated. Most open source video upscaler tools require manual installation and configuration in interfaces like ComfyUI or WebUI, which can be confusing and frustrating for beginners.

⭕ They need powerful hardware. High-quality upscaling usually requires a strong GPU with plenty of VRAM. Without it, processing will be very slow or heavily limited.

⭕ Results are inconsistent. The quality depends heavily on the video upscaling model you choose, and many models perform very differently. You often have to test several before finding one that works well for your footage.

⭕ There's no real customer support. If something goes wrong, help usually comes from GitHub issues or community forums, not from an official support team.

⭕ System updates can break everything. Many open-source upscalers rely on very specific versions of Python, CUDA, and GPU drivers. Updating your system (like upgrading CUDA or your NVIDIA driver) can suddenly cause the tool to crash or stop working altogether.

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Open Source Video Upscalers: GUIs and Models Explained

Open-source video upscalers are usually built from two distinct layers: the engine and the driver. Understanding the difference helps you choose the right tools and diagnose problems more easily.

Engines are the core AI models that perform the upscaling itself. Popular examples include Real-ESRGAN, Waifu2x, and Real-CUGAN. These engines define how frames are analyzed and enhanced, and they largely determine the final image quality. Most engines are distributed as command-line or research-focused Python projects, which can be powerful but difficult for beginners.

Drivers are graphical applications that make these engines usable in real workflows. Tools like Video2X act as wrappers that handle video decoding, frame extraction, model execution, and re-encoding. Drivers do not change how the AI works; they simply control how the engine is used. If the output quality looks wrong, the issue usually lies with the engine or model selection. If the software crashes or fails to process a video, the driver, system configuration, or hardware is more likely responsible.

Top 6 Open Source Video Upscalers

I expanded this list to give a deeper look at each tool's capabilities, strengths, and weaknesses. These selections are based on update frequency, community support, and stability. Some entries are models, while others are wrappers (GUI tools that run those models).

1. Video2X

• Best for: users who want open-source upscaling models and don't mind a technical interface, but still want a structured workflow instead of building command-line pipelines.
• Upscale ratio: 2x, 3x, 4x, and higher (depends on the model)

Video2X is an open source video upscaler that primarily acts as a wrapper around popular AI upscaling models. Instead of forcing users to manually chain video decoding, frame extraction, model execution, and re-encoding, it provides a unified interface that automates the entire workflow. While Video2X reduces the need for direct command-line scripting, it is not a polished, beginner-friendly GUI. Users still need to understand concepts like model selection, scaling factors, and output formats, and the interface relies heavily on technical terminology with no real-time preview.

Behind the scenes, Video2X coordinates tools like FFmpeg and AI models such as Real-ESRGAN to extract frames, upscale them, and reassemble the video while preserving audio. Its real strength lies in workflow management rather than user experience. When processing large frames or targeting 4K output, Video2X can automatically apply tiling to split frames into smaller segments, reducing GPU memory pressure and allowing high-resolution upscaling on mid-range hardware without manual VRAM tuning.

Pros

• Handles audio extraction, frame splitting, upscaling, and stitching automatically.
• Comes pre-packaged with Waifu2x (for anime), Anime4K, SRMD, and RealSR, allowing you to switch algorithms without installing new software.
• Supports exporting to high-quality containers like MKV with lossless codecs.

Cons

• Requires a full installation of Python and FFmpeg, which can be tricky to configure.
• It can sometimes be slower than running a compiled binary directly.

System Requirement

• CPU (AVX2 required): Intel Haswell (Q2 2013) or newer | AMD Excavator (Q2 2015) or newer
• GPU (Vulkan required): NVIDIA Kepler GTX 600 series (Q2 2012) or newer | AMD GCN 1.0 Radeon HD 7000 series (Q1 2012) or newer | Intel HD Graphics 4000 (Q2 2012) or newer

How Video2X Feels in Real Use

Since Video2X is a wrapper rather than an upscaling model, its real-world performance depends on the backend model you choose. In tests across different video types, output quality was dictated by the model, while Video2X remained stable and consistent in workflow. The GUI is functional but outdated, with no preview and many technical terms, so it still feels hard for beginners. It handles high resolutions well through tiling, but speed is mainly limited by your GPU and chosen model.

2. Real-ESRGAN

• Best for: Archivists restoring old family vacation videos, wedding footage, or live-action movies.
• Upscale ratio: 2x, 3x, 4x, and higher (depends on the model variant).
• The variants: realESRGAN-x4plus, realESRGAN-x4plus,realESRGAN-x4plus-anime, realesr-general-x4v3 , and realESRGAN AnimeVideo-v3.
• Official comparisons with other models: realESRGAN AnimeVideo-v3 vs Real-CUGAN, realESRGAN-anime vs Waifu2.

Real-ESRGAN is an open-source AI video upscaling engine built for real-world footage. It is especially popular for live-action videos like old movies, home recordings, documentaries, and camera footage. Compared to anime-focused models, it is better at handling natural textures and real noise.

The reason Real-ESRGAN works so well is its training method. Instead of using clean images that are artificially downscaled, it is trained on synthetic data designed to imitate real degradation. This includes camera noise, motion blur, sensor artifacts, and JPEG compression. In short, the model learns to restore footage that actually looks like the kind of low-quality material people have in their archives. The “GAN” part stands for Generative Adversarial Network. During training, two neural networks compete: one generates an enhanced image, while the other tries to tell whether it looks real. This competition forces this video upscaling model to produce more natural textures and sharper details. When it succeeds, surfaces like skin, grass, brick, and fabric look more realistic instead of turning into smooth, plastic-like areas.

Pros

• Good at recovering organic textures like skin, brick, or fabric that other upscalers smooth over.
• It recognizes noise patterns automatically, so you can denoise footage easily.
• The "ncnn" version (Vulkan) allows it to run on AMD and Intel GPUs, not just NVIDIA cards.

Cons

• Sometimes the AI "invents" wrong details , such as adding weird textures to a clear blue sky.
• It requires significant VRAM to process 1080p or 4K streams efficiently.

System Requirement

There is no official documentation, but user reports suggest:

• PyTorch ≥ 0.4.1 is required for ESRGAN
• CUDA version depends on your PyTorch build (check the PyTorch previous versions page)
• NVIDIA GPU architecture or card model is not critical, since CUDA handles compatibility
• CPU can be used, but processing speed is very slow

Real-ESRGAN: How It Performs in Practice

In my test of upscaling a 10-second, 1080p, 24fps clip with to 4K with realesr-general-x4v3, it delivered noticeable sharpening and cleaner edges on real footage, but it didn't fully match the "true-to-life" portrait enhancement that many Reddit users praise. Asides, its too slow, taking almost 3 hours to get the job done.

Both realESRGAN AnimeVideo-v3 and realesrgan-x4plus-anime model perform very well on cartoons and anime clips, though it occasionally produces strange artifacts in detailed areas. realESRGAN AnimeVideo-v3 has even betetr ability in bringing back details from low-resolution material.

3. Real-CUGAN

• Best for: Users with large anime libraries who need fast processing and consistent results, especially when dealing with many episodes in 480p or 720p.
• Upscaling ratio: 2x,3x,and 4x.
• Official comparisons with other models: Real-CUCAN vs Real-ESRGAN

Real-CUGAN is an anime-focused AI upscaling engine designed for speed and efficiency. It was built specifically for 2D animation, so it handles sharp lines, flat colors, and stylized shading much better than general-purpose models. Compared to Waifu2x, Real-CUGAN has become the preferred choice for many users because it can process large libraries of anime episodes far faster while still preserving detail. The main advantage of Real-CUGAN is its lightweight architecture. It uses cascade U-Nets, which are simpler and faster than the heavier models used by engines like Real-ESRGAN. This makes Real-CUGAN especially suitable for batch processing, where you need to upscale dozens or hundreds of episodes without waiting weeks for the job to finish.

Real-CUGAN also offers multiple model weights and adjustable enhancement strength. This allows users to control how much sharpening and denoising is applied, which is useful when dealing with different source quality levels. In practice, it is the best option for users who want a fast, consistent anime upscaling workflow.

Pros

• Benchmarks often show 2x to 3x speed improvements compared to Real-ESRGAN on the same hardware.
• Keeps lines sharp and colors clean without over-smoothing.
• Multiple model weights allow you to balance sharpening and noise reduction.

Cons

• Real faces and natural textures can look flat or cartoonish.
• While Vulkan versions exist, the engine is optimized for NVIDIA CUDA, so AMD users may see slower performance.

Minimum System requirements

• CPU: SSE4 + AVX (required)
• GPU: NVIDIA CUDA 10.0+ (light version) / CUDA 11.1+ (heavy version) | Vulkan NCNN supported for AMD and Intel GPUs
• VRAM: ≥ 1.5GB
• CPU-only: supported but very slow

Real-CUGAN: How It Performs in Practice

Real-CUGAN was designed to address some of the early weaknesses of Real-ESRGAN, and in my tests it generally meets that goal. It consistently delivered solid results on low-resolution 2D cartoon footage, with cleaner edges and more reliable handling of line art and flat-color regions. Compared to older Real-ESRGAN model variants, Real-CUGAN feels more stable and less prone to artifacts when upscaling animated content. However, when compared directly with Real-ESRGAN Anime Video v3, my personal experience is that Real-ESRGAN v3 performs better overall, preserving fine details more accurately while avoiding the slightly over-sharpened look that Real-CUGAN can sometimes introduce.

4. Real Video Enhancer

• Best for: Prosumer users who want high-quality results without managing Python environments or command-line tools.
• Upscale ratio: 2x, 3x, 4x, and higher (depends on the model).

Real Video Enhancer is a GUI designed for users who want the power of open-source upscaling engines without dealing with command-line tools or Python setup. Compared to Video2X, its interface is cleaner and more modern, and it supports 10+ different upscaling models, plus options like decompress, denoise, and upscale in one workflow. It also lets you customize output format, codec, and bitrate, which is rare for open-source wrappers. The biggest barrier for beginners is usually dependency management (Python, PyTorch, CUDA), and Real Video Enhancer removes that by packaging everything into a ready-to-run application. In practice, you simply drag and drop videos, choose a model and settings, and queue multiple files for batch processing. Behind the scenes it handles frame extraction, AI processing, and re-encoding automatically.

Pros

• Clean, modern interface with drag-and-drop support.
• Includes Test Time Augmentation, which can improve quality by averaging multiple AI passes.
• Great for upscaling large libraries without manual intervention.

Cons

• You may not always get the newest Real-ESRGAN improvements.
• As a community project, it can be unstable on certain Windows builds or driver configurations.

Minimum System Requirement

• CPU: Dual Core x64 bit
• GPU: Vulkan 1.3 capable device
• VRAM: 4 GB (NCNN)
• RAM: 16 GB
• Storage: 1 GB free (NCNN)

How Real Video Enhancer Feels in Real Use

In my tests, Real Video Enhancer feels like the most "complete" open-source GUI so far. Its interface is much cleaner than Video2X, and it supports 10+ built-in upscaling models (including 4x-SPANkendata, 4x-Nomos8k-SPAN series, and the Real-ESRGAN series). It also offers practical options like decompress, denoise, and upscale, plus flexible output formats, which makes it powerful for batch processing. If it added a preview feature, I think it could seriously compete with many paid video upscalers.

The downside is that the installation can be unstable. During my setup it failed to recognize backends, which matches the official explanation about PIP/portable Python issues, so I had to reinstall to fix it.

5. FlashVSR

• Best for: Users who care most about smooth playback and consistent motion quality, especially for footage with a lot of movement — like animated sequences, sports clips, and dynamic camera shots.
• Upscale ratio: 2x, 3x, and 4k.

FlashVSR is an open-source video upscaler designed to address a common limitation of many AI upscaling engines: frame-to-frame inconsistency. Traditional upscalers like Real-ESRGAN treat a video as a stack of independent images, enhancing each frame separately. Because the AI's predictions vary slightly from frame to frame, this often leads to flickering or shimmering artifacts in motion areas.

FlashVSR solves this by using spatio-temporal modeling. Instead of processing each frame in isolation, it analyzes the current frame along with neighboring frames. This lets it track motion and preserve textures more consistently as objects move through time. The result is a noticeably smoother, more coherent video that looks less like separate enhanced images and more like a native high-resolution remaster.

Pros

• Greatly reduces flickering, texture popping, and other motion artifacts.
• Keeps details tied across frames so moving objects and backgrounds look continuous.
• Reuses data between frames instead of recalculating everything from scratch.

Cons

• Holding multiple frames in memory at once means 8 GB VRAM or more is recommended.
• Setup and use are less beginner-friendly compared with Video2X or other GUI wrappers.

FlashVSR: How It Performs in Practice

From my tests, FlashVSR can achieve results comparable to some advanced commercial models, especially in how it upscales video while maintaining strong temporal consistency. The restored details generally look natural and close to the original, with motion appearing smoother and more coherent than frame-by-frame upscalers. However, FlashVSR can still misinterpret certain fine details. In one of my test clips, the lipstick area was reconstructed inaccurately after upscaling and looked slightly unnatural, whereas Real-ESRGAN handled the same region in a more realistic way.

6. SeedVR2

• Best For: Developers and power users who want research-grade upscaling and are comfortable tuning settings. It's ideal for high-end restoration projects, professional archiving, or anyone who wants the most accurate result possible.
• Upscale ratio: 2x to 10x (up to 4k)

SeedVR2 is a diffusion-based upscaling framework designed for high-fidelity video enhancement, especially when you care more about accuracy and detail than speed. Unlike most consumer tools that treat upscaling as a simple "resize + sharpen" job, SeedVR2 uses a one-step diffusion model that understands and reconstructs image details in a more principled way.

The most notable feature is temporal consistency: it processes videos in batches and keeps the frames coherent across time, which reduces flickering and artifacts that commonly appear in frame-by-frame upscalers. This is why SeedVR2 is popular among users who want the cleanest possible output and are willing to accept longer processing times.

Pros

• Diffusion-based upscaling produces more realistic textures and fewer “AI artifacts” in complex scenes.
• The model keeps details stable across frames, so motion looks smoother and more natural.
• You can choose between 3B or 7B parameter models and switch precision models.

Cons

• For some low-resolution footage, it sometimes makes the output worse with artifacts.
• Best results usually require high VRAM or advanced optimizations.

Minimum System Requirement (Simplified)

• CPU: x64 (modern multi-core recommended)
• GPU: Vulkan-capable or CUDA-capable GPU
• VRAM: 8 GB+ (GGUF + BlockSwap can reduce this)
• Software: Python 3.12+, PyTorch 2.0+ (for torch.compile support)

SeedVR2: How It's Performance in Practice

SeedVR2 is widely praised on Reddit, and in my tests it restores detail effectively while keeping results mostly natural. Fine textures become clearer and sharpness improves without noticeable flickering.

The downside? It can over-interpret some details. In the before-and-after comparison below, the eye color shifted slightly, and facial textures show a subtle “AI-processed” look up close. Even so, the output is clearly sharper than the original, showing SeedVR2's strength in detail reconstruction—along with its tendency to occasionally add details that weren't really there.

Which Open Source Video Upscaler Should You Use

Choosing the right open source video upscaler is not just about picking "the best open source video upscaling model". In real-world use, performance depends on the type of footage, the model's strengths, and how much you're willing to tune settings.

In my hands-on tests, each open source video upscaler behaved differently across anime, real-life footage, and noisy/grainy clips. Even within the same category, subtle differences like lighting, fabric texture, or animation style can change the outcome.

For example, a video upscaling model that looks excellent on one anime clip may produce overly sharp or "dirty" textures on another, and diffusion-based tools like SeedVR2 can be sensitive to sampling steps and noise strength. That's why I'm not declaring a single winner. What matters most is matching the open source video upscaler to your footage and being willing to fine-tune the settings. Below is a practical guide based on what each tool did best in my tests.

Open Source Video Upscaler Hardware Requirements

Open-source video upscalers are free, but they're not lightweight. Unlike cloud-based video upscaler and the majority of paid video upscling software, the entire upscaling process runs on your local PC, so your hardware determines speed, stability, and output quality. That's also why I've listed the official minimum system requirements for each upscaler in this article, because in practice, hardware is the first thing that decides whether the open source video upscaling tool even works.

The GPU Rule

Most open-source AI upscalers rely on NVIDIA CUDA. AMD and Intel GPUs can work through Vulkan/NCNN, but from my tests they are usually slower and more error-prone.

• Recommended: NVIDIA RTX 3060 (12GB VRAM) or higher
• Minimum: NVIDIA GTX 1060 (6GB VRAM)

VRAM Is the Real Bottleneck

Video upscaling is extremely VRAM-heavy. If your GPU runs out of memory, the process will usually crash with a “CUDA Out of Memory” error—something I've encountered even on mid-range cards when targeting 4K.

• 1080p output: 4–6GB VRAM
• 4K output: 8–12GB VRAM

Try This User-Friendly Alternative to Open-Source Video Upscalers

Open-source video upscalers can deliver impressive results, but in real use they often require a lot of technical effort—command-line operations, ComfyUI workflows, model hunting, constant parameter tweaking, and the absent of the real-time preview before export. Even after you get it running, getting a stable result usually means lots of trial and error and a powerful GPU. That's why I also tested a simpler alternative: VideoProc Converter AI. If you want high-quality upscaling without the setup headache, it's one of the most straightforward options I've tried.

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