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Jasna is a JAV mosaic restoration tool with a simple GUI, a CLI, a GPU-only processing pipeline, TensorRT support, optional secondary restoration models, still-image restoration, and streaming support.

It is inspired by, and in some places based on, Lada. The mosaic_restoration_1.2 restoration model used by Jasna was trained by ladaapp, the Lada author.

Jasna is free. Supporters get a key that unlocks the extra models trained for this project: the unet-4x secondary upscaler and the experimental SD 1.5 image restoration model. See Supporting the project.

• What Jasna Does
• Community
• Requirements
• Quick Start
• Post-export Actions
• First Run
• Choosing Models
• Tuning Quality and VRAM
• Streaming
• Benchmarks
• Supporting the Project
• Current Limitations and TODO
• Running from Source

• Restores mosaics in video files.
• Restores mosaics in still images with the experimental SD 1.5 image model.
• Detects mosaics with RF-DETR models by default; Lada YOLO models are also available.
• Reduces clip-boundary flicker with temporal overlap and crossfade.
• Can use secondary restoration through unet-4x, RTX Super Resolution, or Topaz Video AI.
• Can stream restored video to the built-in browser player or a supported Stash fork.

Join the SLS Discord for examples, support, and settings discussion. Please don't be too weird.

• A modern Nvidia GPU with compute capability 7.5 or newer.
• Rough GPU guide: GTX 16-series, RTX 20-series, RTX 30-series, RTX 40-series, RTX 50-series, and newer workstation/data-center cards.
• Too old: GTX 10-series, including GTX 1050/1060/1070/1080.
• For exact GPU lookup, check NVIDIA's CUDA GPU compute capability table.
• Up-to-date Nvidia drivers. Tested driver: 591.67. The 59x driver family is the minimum expected family.
• An install path that uses ASCII characters only.
• Windows release package: bundled with ffmpeg, ffprobe, and mkvmerge.
• Linux release package: bundled with ffmpeg, ffprobe, and mkvmerge.

Jasna automatically manages VRAM. When GPU VRAM runs low, frames waiting in the processing queue are temporarily moved to system RAM and moved back when needed. This requires no configuration.

1. Download the latest Windows or Linux release package.
2. Unzip it into a folder with ASCII-only characters in the path.
3. Start the app:
   • Windows: double click jasna.exe.
   • Linux: run the jasna file.
4. Add a video or image in the GUI, choose settings, and start processing.

You can also use Jasna from the command line:

jasna --input input.mp4 --output output.mkv

For still images, no image-specific flag is needed:

jasna --input photo.png --output restored.png

For folder input, both --input and --output must be folders. Jasna processes images first, then videos, shows an overall [current/total] file counter, and writes <name>_out<ext> into the output folder by default.

jasna --input input_folder --output output_folder

Folder batches can also use the same {original} filename template style as the GUI:

jasna --input input_folder --output output_folder --output-pattern "{original}_restored.mp4"

Images keep their source extension, while videos use the template extension when one is provided. Jasna checks the planned folder outputs before processing and exits with an error if the template maps multiple inputs to the same output file.

The GUI can run an action after the whole queue finishes: None, Shutdown PC, or Custom Command. The same feature is available in the CLI on Windows and Linux:

jasna --input input.mp4 --output output.mkv --post-export-action shutdown

Custom commands run through the system shell after all exports finish:

jasna --input input_folder --output output_folder --post-export-action command --post-export-command "echo done"

The first run is slow because TensorRT engines are compiled for your GPU. Compilation usually takes 15-60 minutes.

Close other applications, including browsers, and avoid using the PC during compilation. Engines are cached in model_weights and reused on later runs. You can copy engine files and folders from an older Jasna version to a newer one.

If you run out of VRAM during processing, reduce max clip size first, for example from 180 to 60. Disabling BasicVSR++ compilation also lowers peak VRAM, but processing will be slower.

In general, use the latest RF-DETR model. Lada YOLO models are also available and can work better for 2D animations.

CLI option:

jasna --input input.mp4 --output output.mkv --detection-model rfdetr-v5

Jasna and Lada restore a 256x256 crop of each mosaic region. Large mosaic regions, close-ups, and 4K videos can therefore look blurry after the primary restoration model. A secondary restoration model can upscale the restored crop to 512x512 or 1024x1024 before blending it back.

Supported secondary models:

• unet-4x: supporter model. Faster than TVAI with similar quality in current testing. Trained on an in-domain JAV dataset and visually close to TVAI iris-2. See unet-4x / secondary restoration examples on SLS Discord. Unlock it with a supporter key; see Supporting the project. If you hit quality problems, open a GitHub issue.
• RTX Super Resolution: very fast, free, and has no extra dependencies. Quality is okay. Some videos may flicker, so test on a short clip first.
• TVAI: better than RTX Super Resolution and comparable to unet-4x in current testing, but very slow. Requires Topaz Video, which is paid and Windows-only. Recommended model: iris-2.

CLI option:

jasna --input input.mp4 --output output.mkv --secondary-restoration unet-4x

For TVAI, --tvai-args can customize the Topaz model parameters. The default model is iris-2. Configure these environment variables for Topaz Video:

VRAM and time usage:

Restoration examples are available on SLS Discord.

For still images, Jasna can use a fine-tuned Stable Diffusion 1.5 inpaint model instead of the video pipeline. It detects mosaics, inpaints each region at 512x512, and blends the result back.

• CLI: jasna --input photo.png --output out.png
• GUI: add an image to the queue. Image jobs route to SD 1.5 automatically.
• Tuning options: --sd15-steps, --sd15-strength (clamped to <= 0.7), --sd15-freeu / --no-sd15-freeu, --sd15-seed, and --sd15-variants N.
• The image model is selected with --image-restoration-model-name. The default and only current value is sd-15-jav.
• --restoration-model-name is for video only.

The SD 1.5 model is not bundled and is about 6.9 GB. It belongs in model_weights/sd-15-jav/. You can place the bundle there yourself or let Jasna fetch it from huggingface.co/Kruk2/sd-15-jav. Jasna asks before downloading, either through the CLI prompt or the GUI Download model button.

The checkpoint is currently available only to supporters and uses the same key as unet-4x. See Supporting the project.

The SD 1.5 path is experimental. Results vary by scene, but some images can work very well. Try several --sd15-variants values and keep the best result. Expect about 7 GB VRAM during inference, and a bit more for large 4K images.

Examples are available on SLS Discord and more SD 1.5 examples.

Temporal overlap reduces flicker at clip boundaries. Larger overlap increases processing time but can reduce flicker. Going above 20 usually does not help much.

Recommended starting point:

• Use the highest max clip size your GPU can handle.
• Set temporal overlap between 8 and 20.
• Keep crossfade enabled with --enable-crossfade.

Limited testing guidance:

4K videos use more VRAM. A lower clip size may produce similar quality and process faster. Clip sizes below 60 can work on some videos, but 60 is preferred even if you need to disable model compilation.

CLI example:

jasna --input input.mp4 --output output.mkv --max-clip-size 90 --temporal-overlap 8 --enable-crossfade

The restoration model is compiled into TensorRT sub-engines. Compilation improves speed but uses more VRAM. You can opt out at the cost of performance:

jasna --input input.mp4 --output output.mkv --no-compile-basicvsrpp

Compiled engine VRAM only, not total processing VRAM:

Streaming lets you watch restored video on the fly without processing the whole file first.

Streaming mode is CLI-only for now. It opens an HLS player in a browser window. Pick a video file and start watching. Seeking is supported.

jasna --stream

On Windows, streaming uses the same file as the app: jasna.exe --stream. There may be no separate jasna-cli.exe.

Jasna can be used inside Stash through a custom Stash fork. Play a scene and Stash launches Jasna automatically, processing as you watch. Seeking works.

Custom fork: Stash v0.30.1-jasna

Setup:

1. Download the Stash fork from the link above.
2. Set environment variables before starting Stash:
   • JASNA_CLI_PATH: full path to jasna.exe, unless you renamed it.
   • JASNA_WORKING_DIR: full path to the folder containing that executable.
3. Important: Before using Stash, run streaming once on a short video with the same settings you plan to use in Stash. This precompiles TensorRT engines and avoids the first health-check timeout.
4. Start Stash and play a scene.

If Stash logs timeout waiting for jasna-cli to become healthy, check JASNA_CLI_PATH first, then precompile as above.

RTX 5090 + i9 13900k:

Support pays for training extra models, mainly GPU rental and compute time for larger datasets. Supporters get a key that unlocks:

• unet-4x secondary upscaler for sharper 256->1024 restoration.
• SD 1.5 image restoration, the experimental still-image model.

Example results:

• unet-4x / secondary restoration examples on SLS Discord
• SD 1.5 image restoration examples on SLS Discord and more SD 1.5 examples

How to get a key:

1. Contribute $15 USD or more in total, across any number of contributions and at any time.
2. After your contribution is processed, your supporter key is sent automatically:
   • Unifans: sent by platform message. There might be a slight delay.
   • Buy Me a Coffee, including crypto: sent to the email or handle used for the contribution. The key is tied to that email or handle.

Jasna is in early development. The main goals are improving restoration quality, mosaic detection, speed, and VRAM usage, in that order. The project is currently aimed at more technical users, so some workflows may still be rough. Pull requests are welcome.

Current TODO:

• Proper VR support.
• SeedVR support.
• Continued performance and VRAM improvements.

Python requirement from pyproject.toml: Python 3.13 or newer.

The public source checkout does not include the protection module. Running from source is fine for development and free models, but supporter-only models such as unet-4x and SD 1.5 image restoration will not be available from a plain source checkout.

Install runtime dependencies:

uv pip install . --no-build-isolation

For Nvidia library builds, you also need:

• VS Build Tools 2022 with C++ support.
• CUDA 13.0 installed on the system.
• cmake and ninja:

uv pip install cmake ninja

Developer setup also requires:

• ffmpeg and ffprobe on PATH; ffmpeg major version must be 8.
• mkvmerge, from MKVToolNix.
• The two libraries below installed into your Python environment:
  • https://codeberg.org/Kruk2/vali
  • https://codeberg.org/Kruk2/PyNvVideoCodec

Then install Jasna in editable mode:

uv pip install -e .[dev]