Kruk2/jasnaPublic

Video restoration tool

AI summary: GPU-accelerated video restoration tool targeting mosaic removal with BasicVSR++ and diffusion models.

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PythonAGPL-3.0Created Jan 18, 2026Last push 2d agoLatest release v0.11.2+489 stars this week+489 this month

Quick answers

What is jasna?
GPU-accelerated video restoration tool targeting mosaic removal with BasicVSR++ and diffusion models.
What does jasna do?
Jasna is a video restoration application that utilizes GPU acceleration to remove mosaics from video frames. It functions by decoding video on the GPU, detecting mosaic areas, tracking them across frames, and restoring those specific areas. The tool offers options for using BasicVSR++ for faster restoration or LTX diffusion models for more detailed outputs. It ensures seamless transitions by overlapping restored clips and only blending the altered mosaic area back into the original frame.
Who is jasna for?
Users with modern NVIDIA or AMD GPUs looking to automate video de-mosaicing.
How do I get started with jasna?
jasna --input input.mp4 --output output.mkv
How popular is jasna on GitHub?
Kruk2/jasna has 3,353 stars and 252 forks on GitHub, and gained 489 stars in the last 7 days.
What license does jasna use?
Kruk2/jasna is released under the AGPL-3.0 license.

Star history

since Oct 19, 2026
01K2K3KOct 2026Oct 2026Oct 2026Oct 2026
3.4K stars as of Oct 21, 2026. Measured daily since Oct 19, 2026; GitHub no longer exposes earlier star timestamps.

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Signals and awards

derived from tracked data
  • Rising fast

    +489 stars this week

  • Very active

    999 commits in 52 weeks

  • Outside contributions

    94% of recent commits from the community

  • Repeat trending

    3 trending appearances

What jasna does

Jasna is a video restoration application that utilizes GPU acceleration to remove mosaics from video frames. It functions by decoding video on the GPU, detecting mosaic areas, tracking them across frames, and restoring those specific areas. The tool offers options for using BasicVSR++ for faster restoration or LTX diffusion models for more detailed outputs. It ensures seamless transitions by overlapping restored clips and only blending the altered mosaic area back into the original frame.

Users with modern NVIDIA or AMD GPUs looking to automate video de-mosaicing.

  • Hardware decoding: Decodes video directly on the GPU using NVDEC or AMF.
  • Mosaic detection: Identifies mosaic regions within each frame using models like rfdetr-v6.
  • Clip tracking: Groups tracked mosaic frames into continuous clips that end at scene cuts.
  • Targeted restoration: Restores only the detected mosaic areas using BasicVSR++ or LTX diffusion.
  • Secondary enhancement: Enlarges and sharpens restored pictures using UNet 4x or external scalers.
  • Hardware encoding: Encodes the final output via GPU while preserving original audio streams.

Where teams use it

Video mosaic removal

Restore censored or obfuscated areas in video files to reveal underlying details.

High resolution processing

Process up to 4K or 8K VR videos using advanced restoration and secondary upscaling.

Batch restoration

Process entire folders of videos automatically via the command line interface.

Selective restoration

Define segments and large mosaics to only apply computationally expensive LTX processing where necessary.

Getting started: jasna --input input.mp4 --output output.mkv

README

main branch

The only places to get a genuine License code for the supporter models are Buy Me a Coffee, Unifans, and for China the seller in the Chinese README.

支持者模型的正版许可证码只能通过 Buy Me a Coffee、Unifans 或 闲鱼 获取。

English | 日本語 | 中文

Jasna

Jasna restores mosaics in videos and images. It is free, has a simple app and a command line, and runs on your GPU.

image

Contents

Start here

Advanced

What Jasna does

  • Finds mosaics in a video and restores them. The rest of the picture is not touched.
  • Two restoration models: fast BasicVSR++ (free) and slow but more detailed LTX (for supporters, NVIDIA only).
  • Restores only the parts you choose, with a preview, in the Segment Editor.
  • Works with VR180 videos and still images (SD 1.5, for supporters).
  • Lets you watch a video restored live in the Video Player, without making a file.

What you need

  • NVIDIA GTX 16-series / RTX 20-series or newer, with a recent driver (610+ on Windows, 580+ on Linux). GTX 10-series and older don't work.
  • Or AMD Radeon RX 7000/9000 (experimental).
  • A folder on a local disk, with no Chinese, Japanese, accented or other non-English characters in its path.
More details
  • NVIDIA: your GPU needs compute capability 7.5 or higher in NVIDIA's GPU table.
  • AMD: a GPU supported by ROCm (Radeon RX 7000/9000, Radeon 780M/760M/740M, Ryzen AI). On Windows, AMD Adrenalin driver 26.8.1 or newer. Secondary restoration and LTX are NVIDIA only.
  • Disk format: NTFS on Windows; ext4, Btrfs, XFS or ZFS on Linux. Network disks are not supported. Use an SSD; on a hard disk (HDD) Jasna starts and loads models slowly.
  • LTX needs an NVIDIA RTX GPU, at least 8 GB VRAM and 16 GB RAM. See What LTX needs.

Quick start

  1. Download the release for your system and GPU.
  2. Unpack it into a folder with no Chinese, Japanese, accented or other non-English characters in the path.
  3. Start the app:
    • Windows: double-click jasna.exe.
    • Linux NVIDIA: run the jasna file.
    • Linux AMD: run run_jasna_amd.sh.
  4. Add a video, choose settings and click the ▶ button at the bottom.

Your first job is slow. On NVIDIA, Jasna first prepares the models for your exact GPU. This takes about 15–60 minutes, only once. Close other apps and don't use the PC during this time. When you update Jasna, copy the model_weights folder to the new version, so you don't wait again.

Every setting has a tooltip: move the mouse over the ⓘ icon. Out of GPU memory (VRAM)? Lower Max Clip Size, for example from 90 to 60. See Tuning.

Help and community

  • App guide: the queue, presets, output names, the Video Player and activation.
  • SLS Discord: examples, help and settings. Jasna has a new channel; older posts stay in the old channel (archived). Please don't be too weird.
  • Problem? Click Logs at the bottom of the app, then Export to save a log file. Attach it when you ask for help.

Supporting the project

Jasna is free. Your support pays for training new models, mostly GPU rental. Supporters get a License code for the supporter models:

Support Models
$15 USD or more in total UNet 4x: makes restored areas sharper.
SD 1.5: experimental model for still images.
$25 USD UNet 4x and SD 1.5, plus
LTX: much slower than BasicVSR++, but more detail. NVIDIA only. Includes future updates of this LTX model.

Supporters who gave $15 USD or more on or before 14 October 2026 get LTX for free.

Before you choose LTX:

  • Look at these LTX samples: 1, 2, 3, 4, 5, 6, 7, 8. Explore the new channel and the old channel (archived) on SLS Discord for more samples.
  • Run LTX in Trial mode (speed test) on your PC. It is free and shows if your GPU can run LTX and how long it takes. The video is not restored. See What LTX needs.

More examples: UNet 4x, SD 1.5, more SD 1.5.

Where to support

License codes come only from here:

Anyone else selling Jasna, codes or modified files is a scam: their keys don't work, and their files can run hidden code on your PC. Download Jasna only from GitHub.

How to activate

  1. Support on one of the pages above. You get a License code.
  2. In Jasna, open Activate Jasna, enter the License code and press Activate online.
  3. No internet on that PC, or it fails? Choose Manual, see Activate supporter models.

One License code works on 3 PCs or cloud workspaces. After activation, Jasna works offline. Rented GPUs: see Cloud GPUs.

What counts as a new PC: on Windows, a new motherboard; on Linux, a fresh Linux install. Each uses one of your 3 slots. Reinstalling Windows or changing the GPU, disk or RAM does not: just activate again with the same License code. A few Windows PCs have no real motherboard ID; on those, a new system disk also counts as a new PC.

Have an email and key from Jasna 0.10? They no longer work. Get a new License code from where you bought it.

Advanced

How Jasna works

Almost everything runs on the GPU, from reading the video to saving it.

  1. Decode the video on the GPU (NVDEC on NVIDIA, AMF on AMD).
  2. Detect mosaics in each frame (default model rfdetr-v6).
  3. Track each mosaic over time and group its frames into clips (up to Max Clip Size frames). Clips end at scene cuts, so two different shots are never mixed.
  4. Restore each clip as a 256x256 picture with BasicVSR++. Clips overlap a little, so there are no visible jumps between them.
  5. Optional secondary restoration (UNet 4x, RTX Super Res or Topaz TVAI) makes the restored picture bigger and sharper.
  6. Blend the restored picture back into the original frame. Only the mosaic area changes.
  7. Encode with the GPU encoder (NVENC on NVIDIA, AMF on AMD). Audio is copied (encoded again only if the output format can't hold it).

The first job is slow because the models for steps 2 and 4 (and UNet 4x, if used) are prepared for your exact GPU (NVIDIA only). The result is saved in model_weights and used again later. You can copy it to a newer Jasna version.

LTX works differently. It goes over the video in 4 passes: find mosaics, prepare, restore with a diffusion model, save. It is slower but can add more real detail. See Choosing models.

Command line

The command line can do most of what the app does (not the Video Player, LTX seed previews, mask suggestions or interactive image mode).

# One video
jasna --input input.mp4 --output output.mkv

# One image
jasna --input photo.png --output restored.png

# A whole folder
jasna --input input_folder --output output_folder

jasna --help lists all options. See the CLI reference.

All guides

Benchmarks

Time to restore a whole 3:22 video with BasicVSR++ on an RTX 5090 (Linux). Lower is better.

Input Lada 0.11.0 Jasna v0.9.1
720p H.264 01:47 00:32 (3.4x faster)
1080p H.264 02:02 00:34 (3.6x faster)
2160p H.264 04:56 01:03 (4.7x faster)
GPU memory, older versions and how it was measured

GPU memory used (typical / peak, in GB). — means not measured. Lada uses less GPU memory than Jasna.

Input Lada v0.4.1 v0.5.0 v0.9.0 v0.9.1
720p H.264 8-bit 1.8/3.1 17.6/19.9 8.6/9.0 8.6/9.1 4.2/4.6
1080p H.264 8-bit 2.0/3.3 18.9/21.4 9.5/10.2 9.3/10.1 4.8/5.6
2160p H.264 8-bit 2.6/4.1 26.0/30.5 12.9/15.0 11.4/15.4 7.2/10.9
8K VR HEVC 60 fps (15 s) — — — 17.1/18.3 —

Times for older versions (v0.4.1: 01:35 / 01:46 / 03:23; v0.5.0: 00:45 / 00:47 / 01:22; v0.9.0: 00:34 / 00:39 / 01:15 for 720p / 1080p / 2160p; 8K VR: 00:34), the test settings, RAM use and every codec are in the full benchmark report. Older full-length videos: legacy report.

Licensing

Jasna's public application source is AGPL-3.0. Models and bundled dependencies keep their own terms. See licensing and source availability, model notices and the release source correspondence.

Acknowledgments

  • Lada (Codeberg): Jasna is inspired by Lada and in some places based on it. The mosaic_restoration_1.2 model used by Jasna was trained by ladaapp, the author of Lada.
  • ZeLeFans: for the VR detection model and the detailed analysis of mosaic shapes and VR projection.
View on GitHub

Recent activity

commits and pull requests

Releases and announcements

38 total
  1. v0.11.2v0.11.2Oct 17, 202623.1K downloads

    ## Read [previous](https://github.com/Kruk2/jasna/releases/tag/v0.11.1) release notes for new model. Support on [Buy Me a Coffee](https://buymeacoffee.com/kruk2) or [Unifans](https://app.unifans.io/c/kruk2). License codes from anywhere else are fake. [More info](https://github.com/Kruk2/jasna#supporting-the-project) 中国用户请通过[闲鱼](https://github.com/Kruk2/jasna/blob/main/README.zh.md#在闲鱼购买)支持。其他渠道都是骗子。 Remember that first run is slower on new version. **You can copy ltx model from model_weights directory to avoid large downloads.** ## Changes - **faster LTX by around 13% on long videos (longer than 1 minute) - applies to all GPU**. Its exposed as new experimental flag that is enabled by default on Windows. It changes a bit output but I didn't notice worse quality. Linux has to enable it explicitly in GUI or CLI. You can run 1 min clip and do A/B tests to see if you spot a quality degradation. [Feedback thread](https://github.com/Kruk2/jasna/issues/489) - many fixes for Windows to improve startup/runtime issues and better diagnostic - fixes for 8GB VRAM and low RAM configurations - warning when Jasna runs on HDD (its slow, prefer SSD) - updated attention kernels (

  2. v0.11.1v0.11.1Oct 15, 202629.7K downloads

    ## New model: LTX LTX restores much more detail than BasicVSR++. It is slower and works on NVIDIA only (for now). [LTX feedback thread](https://github.com/Kruk2/jasna/issues/489) [Samples on Discord](https://github.com/Kruk2/jasna/blob/main/docs/en/models.md#restoration-model) - Trial mode is free, so you can check how fast it runs on your GPU. [What LTX needs](https://github.com/Kruk2/jasna/blob/main/docs/en/models.md#what-ltx-needs) - Requirements: NVIDIA RTX 30/40/50, 8 GB VRAM, 16 GB RAM (LTX uses about 10 GB). RTX 20 should work, but I only tested 2080 Ti. **LTX is for supporters** - **Current supporters get LTX for free but if you feel like buying me another coffee - THANKS**. If you didn't get the new key please write on platform you bought on. - Support with $25 to get LTX, UNet 4x and SD 1.5 ($15 gives UNet 4x and SD 1.5). - Your support pays for GPU time to train new models and my coffee supply. Support on [Buy Me a Coffee](https://buymeacoffee.com/kruk2) or [Unifans](https://app.unifans.io/c/kruk2). License codes from anywhere else are fake. [More info](https://github.com/Kruk2/jasna#supporting-the-project) 中国用户请通过[闲鱼](https://github.com/Kruk2/ja

  3. v0.10.0v0.10.0Aug 11, 2026104.2K downloads

    ### 🚀 If you want to support this project [Buy me a coffee](https://buymeacoffee.com/kruk2) or [Unifans](https://app.unifans.io/c/kruk2) 🚀 #### By supporting you will get access to [two models](https://github.com/Kruk2/jasna#supporting-the-project) ### ⚠ The only places to get a genuine key for the paid models are [Buy Me a Coffee](https://buymeacoffee.com/kruk2) and [Unifans](https://app.unifans.io/c/kruk2). ### ⚠ 付费模型的正版密钥只能通过 [Buy Me a Coffee](https://buymeacoffee.com/kruk2) 或 [Unifans](https://app.unifans.io/c/kruk2) 获取。 #### You can copy .engine files from previous version to avoid engine recompilation (just copy entire model_weights folder and override). **[How to use Jasna](https://github.com/Kruk2/jasna#quick-start)** Pick proper OS/GPU vendor, download all parts and unzip. ### v0.10.0 **New features:** - High performance Video Player. You can click the button at the top or add multiple files to Queue and play them - Custom command execution after video is completed. [Read more](https://github.com/Kruk2/jasna/blob/main/docs/en/advanced_processing.md#post-export-actions) - Context menu for videos in queue (open location, copy path, requeue) - Explicit

  4. v0.9.1v0.9.1Jul 28, 202629.6K downloads

    ### 🚀 If you want to support this project [Buy me a coffee](https://buymeacoffee.com/kruk2) or [Unifans](https://app.unifans.io/c/kruk2) 🚀 #### By supporting you will get access to [two models](https://github.com/Kruk2/jasna#supporting-the-project) #### You can copy .engine files from previous version to avoid engine recompilation (just copy entire model_weights folder and override). **[How to use Jasna](https://github.com/Kruk2/jasna#quick-start)** Pick proper OS/GPU vendor, download all parts and unzip. ## Check also v0.9.0 [release notes](https://github.com/Kruk2/jasna/releases/tag/v0.9.0) ### v0.9.1 **Improvements:** - **Massive VRAM usage reduction when using compiled restoration model. 4.5GB less on average for 180 clip size and 2.8GB less on 90 clip size** [Check benchmark table](https://github.com/Kruk2/jasna#benchmarks) - Increasing clip size still increases VRAM usage but at **much lower rate** - Compilation is now clip-size agnostic which means that you can change it and model won't recompile - Allow for up to 720 clip size in GUI - **More speed optimizations (~12% faster on average)** **Fixes:** - more AMD fixes - more Windows scaling fi

  5. v0.9.0v0.9.0Jul 27, 20263.7K downloads

    ### 🚀 If you want to support this project [Buy me a coffee](https://buymeacoffee.com/kruk2) or [Unifans](https://app.unifans.io/c/kruk2) 🚀 ### By supporting you will get access to [two models](https://github.com/Kruk2/jasna#supporting-the-project) #### You can copy .engine files from previous version to avoid engine recompilation (just copy entire model_weights folder and override). **Usage** https://github.com/Kruk2/jasna#quick-start Pick proper OS/GPU vendor, download all parts and unzip. ### Changelog ## New models - No new restoration models :) but there are some previews on [Discord](https://discord.com/channels/1196376491815092265/1199059436199759943/1529646104990191617) - **Added two new detection models**: ```rfdetr-v6```and ```rfdetr-v6-large```. Both have similar accuracy but rfdetr-v6 is 2x faster compared to rfdetr-v5. V6-large can handle 4K content better. New models have higher default detection threshold so if you used a custom one then you should review it. v6-large is a separate download (check models docs) - **Added detection model dedicated for VR:** ```rfdetr-vr-v1```. Zelefans VR model is no longer included by default - **Improved mosaic dete

Commits per week

last 52 weeks
1130Week of 2025-10-25: 0 commitsWeek of 2025-11-01: 0 commitsWeek of 2025-11-09: 0 commitsWeek of 2025-11-16: 0 commitsWeek of 2025-11-23: 0 commitsWeek of 2025-11-30: 0 commitsWeek of 2025-12-07: 0 commitsWeek of 2025-12-14: 0 commitsWeek of 2025-12-21: 0 commitsWeek of 2025-12-28: 0 commitsWeek of 2026-01-04: 0 commitsWeek of 2026-01-11: 0 commitsWeek of 2026-01-18: 51 commitsWeek of 2026-01-25: 46 commitsWeek of 2026-02-01: 43 commitsWeek of 2026-02-08: 48 commitsWeek of 2026-02-15: 6 commitsWeek of 2026-02-22: 0 commitsWeek of 2026-03-01: 0 commitsWeek of 2026-03-08: 8 commitsWeek of 2026-03-15: 58 commitsWeek of 2026-03-22: 61 commitsWeek of 2026-03-29: 16 commitsWeek of 2026-04-05: 0 commitsWeek of 2026-04-12: 0 commitsWeek of 2026-04-19: 0 commitsWeek of 2026-04-26: 4 commitsWeek of 2026-05-03: 0 commitsWeek of 2026-05-10: 0 commitsWeek of 2026-05-17: 0 commitsWeek of 2026-05-24: 0 commitsWeek of 2026-05-31: 11 commitsWeek of 2026-06-07: 17 commitsWeek of 2026-06-14: 57 commitsWeek of 2026-06-21: 1 commitsWeek of 2026-06-28: 0 commitsWeek of 2026-07-05: 0 commitsWeek of 2026-07-12: 59 commitsWeek of 2026-07-19: 51 commitsWeek of 2026-07-26: 63 commitsWeek of 2026-08-02: 4 commitsWeek of 2026-08-09: 17 commitsWeek of 2026-08-16: 2 commitsWeek of 2026-08-23: 1 commitsWeek of 2026-08-30: 1 commitsWeek of 2026-09-06: 0 commitsWeek of 2026-09-13: 1 commitsWeek of 2026-09-20: 90 commitsWeek of 2026-09-27: 113 commitsWeek of 2026-10-04: 72 commitsWeek of 2026-10-11: 79 commitsWeek of 2026-10-18: 19 commitsOct 25, 2025Oct 18, 2026
999 commits in the last 52 weeks.

When work happens

weekday and hour
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Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Who is committing

last 52 weeks
Maintainer commits68 (6%)
Community commits980 (94%)

1,048 commits in total over the last year.

DateListRankStars gained
Oct 21, 2026daily#7+498
Oct 20, 2026daily#7+498
Oct 19, 2026daily#7+502