[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f3d5p8y1ugh1mm":3},{"code":4,"message":5,"data":6,"timestamp":26},10000,"success",{"full_name":7,"position":8,"description":9,"language":10,"stars":11,"forks":12,"stars_gained":13,"age_days":14,"hn_threads":15,"one_liner":16,"summary_md":17,"topic":18,"github_topics":21,"license":22,"homepage":23,"updated_at":24,"history":25},"Kruk2\u002Fjasna",11,"Video restoration tool","Python",3573,274,1469,279,[],"Detects and restores mosaic-covered regions in videos and images, using a local GPU, instead of rebuilding the whole frame.","## The problem\n\nRemoving censor mosaics from a video is usually a manual job: mask each frame, paint over it, re-encode. Frame-by-frame segmentation and tracking is tedious, and generic video inpainting tools treat the whole frame as the target, which risks altering parts that should stay untouched. Jasna automates the loop: it decodes on the GPU, detects mosaics per frame, groups each mosaic into clips, restores each clip with a sequence-aware model, and blends only the restored patch back into the original.\n\n## What's different\n\nThe project is built around *scoped* restoration rather than whole-frame enhancement. Detection, tracking and clip boundaries come first, so the model only ever sees a 256x256 patch and the untouched pixels keep their original quality. Restoration is split into two tiers: a free BasicVSR++ path and a slower diffusion-based LTX path for supporters, with optional secondary upscaling (UNet 4x, RTX Super Res, Topaz TVAI). Compared with general-purpose video inpainting frameworks, the trade-off is narrowness: it is purpose-built for one task and one GPU stack, not a platform you can retarget. A preview player that restores live without writing a file is a genuinely useful workflow detail that most pipelines lack.\n\n## Who it's for\n\nIndividual users with an NVIDIA GTX 16-series\u002FRTX 20-series or newer (AMD RX 7000\u002F9000 is labelled experimental) who process local video files and want a GUI plus a CLI. It is not for CPU-only machines, older GPUs, cloud\u002Fnetwork storage, or paths containing non-English characters. Teams needing batch orchestration, reproducible pipelines, or permissive licensing should look elsewhere. Anyone uncomfortable with a paid-license gate on the better models should stick to the free tier or skip it.\n\n## Cost of adoption\n\nHardware is the real barrier: NVIDIA compute capability 7.5+, recent drivers, SSD storage on NTFS or a Linux filesystem, and for LTX at least 8 GB VRAM and 16 GB RAM. The first run spends roughly 15-60 minutes compiling models for your GPU, and that `model_weights` folder must be carried forward on upgrades. The app is free, but the sharper UNet 4x, SD 1.5 image model and LTX are unlocked by a one-time supporter payment ($15-$25), tied to a license code valid on 3 machines with online activation. The license is AGPL-3.0, so redistributing a modified version has source obligations. Installation is by downloading a release rather than a package manager; follow the README.\n\n## Verdict\n\nJasna is a young project, created in early 2026, with about 3.5k stars and a single weekly board appearance of +1,469, so treat current attention as a spike rather than a proven track record. The engineering approach is coherent and the GUI\u002FCLI split is more polished than most single-purpose GPU tools, but the supporter-license model and strict hardware matrix are real commitments. Adopt it if you have the right GPU, a local workflow and this specific need; otherwise the free tier is a cheap way to test the waters. Expect churn and read the licensing terms carefully before building anything around it.",{"slug":19,"name":20},"media","Media & Design",[],"AGPL-3.0",null,"2026-10-24T18:06:42Z",[],1792879354]