# Choosing Models ## Detection model The detection model finds the mosaics in each frame. - **`rfdetr-v6`** (default, included): fast and good for most videos. Use this one if you are not sure. - **`rfdetr-v6-large`**: better detection, but slower. Can be better for 4K. It is a separate download. Take the file for your GPU and put it in `model_weights/`. Jasna finds it automatically. - NVIDIA: [rfdetr-v6-large.onnx](https://github.com/Kruk2/jasna/releases/download/0.1/rfdetr-v6-large.onnx) - AMD: [rfdetr-v6-large.pt](https://github.com/Kruk2/jasna/releases/download/0.1/rfdetr-v6-large.pt) - **`rfdetr-vr-v1`** (included): best for VR180 videos. - **`zelefans-vr-yolo-v2`** (separate download): another VR180 model. - **Lada YOLO** models: can be better for 2D animation. - The old `rfdetr-v5` still works. On AMD, RF-DETR is slower than on NVIDIA. If speed is more important than detection quality, use `lada-yolo-v4`. Each model has its own default detection threshold (`rfdetr-v6`: 0.35, `rfdetr-v6-large` and `rfdetr-vr-v1`: 0.40). Change it with **Detection Threshold** in **Basic Processing**, or with `--detection-score-threshold`. ```bash jasna --input input.mp4 --output output.mkv --detection-model rfdetr-v6 ``` In the [Segment Editor](segments.md) you can choose another detection model for each video. ## Restoration model | | **BasicVSR++** (default) | **LTX** | | --- | --- | --- | | Speed | Fast | Much slower (minutes per minute of video) | | Quality | Good | More detail, more natural | | GPU | Every supported GPU | NVIDIA only | | Cost | Free | For supporters ([Supporting the project](../../README.md#supporting-the-project)) | Use LTX for the videos or scenes you care about most. Before you choose it, look at these samples: [1](https://discord.com/channels/1196376491815092265/1199059436199759943/1556346988486856826), [2](https://discord.com/channels/1196376491815092265/1199059436199759943/1556337300814958684), [3](https://discord.com/channels/1196376491815092265/1199059436199759943/1555612574643388598), [4](https://discord.com/channels/1196376491815092265/1556960822578909244/1559341521575084063), [5](https://discord.com/channels/1196376491815092265/1556960822578909244/1559295317529202780), [6](https://discord.com/channels/1196376491815092265/1556960822578909244/1559205894959403121), [7](https://discord.com/channels/1196376491815092265/1556960822578909244/1559197341473574973), [8](https://discord.com/channels/1196376491815092265/1556960822578909244/1558798127195226112) (explore the [new channel](https://discord.com/channels/1196376491815092265/1556960822578909244) and the [old channel (archived)](https://discord.com/channels/1196376491815092265/1199059436199759943) on SLS Discord for more samples), and run **Trial mode** to see if your PC can run it and how long it takes. Pick it in the **Restoration Model** section, or: ```bash jasna --input input.mp4 --output output.mkv --restoration-model-name ltx ``` ### What LTX needs - **License**: a License code that includes LTX ($25 USD, also gives UNet 4x and SD 1.5, see [Supporting the project](../../README.md#supporting-the-project)). Without one, the app offers **Trial mode** instead (see below); the CLI stops with an error. - **GPU**: NVIDIA RTX 30, 40 or 50-series. RTX 20-series should work, but only the RTX 2080 Ti was tested. GTX 16-series is untested. AMD is not supported. - **VRAM**: 8 GB minimum. More VRAM is faster: on smaller cards parts of the model are kept in system RAM. - **System RAM**: 16 GB minimum (LTX uses about 10 GB). - **Disk**: the model is not bundled. Jasna asks before downloading it the first time you start an LTX job, and downloads only the model you picked: | Model | Download | | --- | ---: | | Standard | about 15 GB | | Fast mode | about 9 GB | | Best, on top of Standard | about 13 GB more | If Hugging Face can't be reached (for example in China), Jasna uses hf-mirror.com automatically. Download still fails? See [Downloading LTX yourself](#downloading-ltx-yourself). LTX does not work with VR180 side-by-side videos or streaming. Secondary restoration and denoising are turned off with LTX. Using ESET? If LTX won't start and the message names `ebehmonl.dll` or `ebehmoni.dll`, add the Jasna folder to ESET's Deep Behavioral Inspection exclusions. Add it again when you put a new Jasna version in a new folder. ### Downloading LTX yourself If the download in Jasna keeps failing, get the files in your browser from [huggingface.co/Kruk2/ltx-restore](https://huggingface.co/Kruk2/ltx-restore/tree/main) (or [hf-mirror.com](https://hf-mirror.com/Kruk2/ltx-restore/tree/main)). You need 3 files: one for the model you picked, plus the two VAE files. | You picked | Files | | --- | --- | | Standard | `ltx-restore-alpha1-distill8-int8.safetensors.enc` | | Fast mode | `ltx-restore-alpha1-distill8-nvfp4.safetensors.enc` | | Best | `ltx-restore-alpha1-int8.safetensors.enc` | | Best + Fast mode | `ltx-restore-alpha1-nvfp4.safetensors.enc` | | Always | `vae.safetensors.enc` and `vae-decoder.safetensors.enc` | Put all 3 in `model_weights/ltx-restore` inside your Jasna folder (create the `ltx-restore` folder if it is missing). Keep the file names exactly as they are, including `.enc`. Start the LTX job again; Jasna will not ask to download. ### LTX options - **Model**: **Standard** (default) or **Best**. Best is slightly better and about 1.5x slower. CLI: `--restoration-model-name ltx-undistilled`. - **Fast mode** (RTX 50-series only): about 1.4x faster, slightly less detail, smaller download. CLI: `--ltx-fast`. - **Sharper large mosaics**: more detail on big mosaics (close-ups, 4K), about 3x slower on those. Needs about 12 GB of free VRAM (16 GB cards for Segment Editor ranges); otherwise Jasna restores them normally. CLI: `--ltx-large-canvas`. - **Less overlap (experimental, faster)**: on by default, about 13% faster when a mosaic stays on screen for more than 4 seconds. Turn it off for the older mode with more overlap. CLI: `--no-experimental-ltx-less-overlap`. - **Seed**: a different seed gives a slightly different result; the same seed gives the same result. CLI: `--ltx-seed 7`. - **Trial mode**: runs LTX at full speed without the model or a license, so you can see how fast it is on your PC. The video is **not** restored. CLI: `--ltx-trial`. - **LTX only for big mosaics**: big mosaics get LTX, the rest the faster BasicVSR++. Saves a lot of time on long videos. See [Restoring only parts of a video](segments.md#cli---ltx-auto-split). To preview LTX, open the [Segment Editor](segments.md), select a range, choose LTX and turn on **Restore preview**. The Video Player always uses BasicVSR++. ### How long it takes Rough time for **1 minute of 1080p video with mosaics on screen the whole time** (Standard model). Scenes without mosaics are much quicker. Best takes about 1.5x as long. | GPU | Time | With Fast mode | | --- | ---: | ---: | | RTX 5090 | about 5 min | about 3 min | | RTX 4090 | about 7 min | not available | | RTX 5080 | about 10 min | about 7 min | | RTX 3090 | about 15 min | not available | | RTX 2080 Ti | about 45 min | not available | - The first LTX run takes longer while Jasna prepares it for your GPU. - Progress shows four steps: finding mosaics, preparing, restoring, saving. The first LTX video on your GPU, and the first after each update, starts with a short speed test. After that, the time left is accurate from the end of step 1. - LTX slows down when other apps use the GPU. Close games and browsers. - **Windows**: LTX fails with "Access is denied" while Windhawk runs inside Jasna. Exit Windhawk or add `jasna.exe` to its excluded processes. Jasna warns about this when it starts. ## Secondary restoration Jasna restores each mosaic as a 256x256 picture. On big mosaics, close-ups and 4K video this can look blurry. A secondary model makes this picture bigger (512x512 or 1024x1024) before it goes back into the frame, so the result is sharper. NVIDIA only. - **UNet 4x** (for supporters): quality close to TVAI `iris-2`, and faster than TVAI. See the [examples on SLS Discord](https://discord.com/channels/1196376491815092265/1199059436199759943/1516497879684874260) and [Supporting the project](../../README.md#supporting-the-project). - **RTX Super Res** (free): very fast, nothing to install. Quality is OK. Some videos flicker, so test a short clip first. - **Topaz TVAI**: similar to UNet 4x, but very slow. Needs [Topaz Video](https://www.topazlabs.com/topaz-video), which is paid and Windows only. Use the `iris-2` model. Not used with LTX. ```bash jasna --input input.mp4 --output output.mkv --secondary-restoration unet-4x ``` For TVAI, set the environment variables `TVAI_MODEL_DATA_DIR` and `TVAI_MODEL_DIR` to your Topaz Video model folders, like below. `--tvai-args` changes other Topaz settings. Topaz Video environment variables ### Speed and VRAM comparison Time and VRAM for two 1080p videos: | Secondary type | CAWD 1080p | KV-109 1080p | | ------------------------ | -----------------:| -----------------:| | No secondary | 22s / 10.0 GB VRAM | 11s / 10.7 GB VRAM | | UNet 4x | 29s / 12.5 GB VRAM | 14s / 12.6 GB VRAM | | RTX Super Res | 25s / 11.7 GB VRAM | 13s / 11.4 GB VRAM | | TVAI (2 workers, Iris-2) | 52s / 12.1 GB VRAM | 24s / 12.4 GB VRAM | ## Still-image restoration (SD 1.5) For images, Jasna uses a different model: a trained Stable Diffusion 1.5 model. Add an image to the queue (or give it to the CLI) and Jasna uses this model automatically: ```bash jasna --input photo.png --output restored.png ``` - For supporters. One License code can include SD 1.5, UNet 4x and LTX. See [Supporting the project](../../README.md#supporting-the-project). - Not included in the download. It is about **6.9 GB**. Jasna asks before it downloads it from [huggingface.co/Kruk2/sd-15-jav](https://huggingface.co/Kruk2/sd-15-jav). - Needs about **7 GB of VRAM**, a bit more for big 4K images. This model is experimental. Results change a lot from image to image, but some are very good. Make a few versions and keep the best one: ```bash jasna --input photo.png --output restored.png --sd15-variants 4 ``` To try seeds one by one in the GUI, use [Process images interactively](gui.md#images-one-by-one). All options (`--sd15-steps`, `--sd15-strength`, `--sd15-seed`, ...) are in the [CLI reference](cli.md#sd-15-image-restoration). Examples: [SD 1.5 examples on SLS Discord](https://discord.com/channels/1196376491815092265/1199059436199759943/1492139124348420106) and [more](https://discord.com/channels/1196376491815092265/1199059436199759943/1516571355317800990).