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Jasna_engines – AI Model by mouii | AlphaNeural AI

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mouii/
Jasna_engines

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video-restoration

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Jasna TensorRT Engines - RTX 5080 (Blackwell SM 12.0)

Pre-compiled TensorRT engine files for Jasna video restoration tool, built on NVIDIA RTX 5080.

Compatibility

These engines are ONLY compatible with GPUs sharing the same architecture:

GPU Architecture: Blackwell (SM 12.0)
Compatible GPUs: RTX 5070 Ti, RTX 5080, RTX 5090
TensorRT: 10.14.1.48
CUDA: 13.1
PyTorch: 2.10.0a0+nv26.01
Driver: 590.48.01+
Jasna: 0.4.1+

Will NOT work on Ada Lovelace (RTX 4090), Ampere (RTX 3090), or older GPUs.

Files

lada_mosaic_restoration_model_generic_v1.2_clip10.trt_fp16.linux.engine (250 MB) - BasicVSR++ restoration model (clip_size=10, FP16)
lada_mosaic_restoration_model_generic_v1.2_clip40.trt_fp16.linux.engine (967 MB) - BasicVSR++ restoration model (clip_size=40, FP16)
rfdetr-v3.bs4.fp16.linux.engine (77 MB) - RF-DETR detection model (batch_size=4, FP16)

Build Environment

GPU: NVIDIA GeForce RTX 5080 (16 GB VRAM)
OS: Ubuntu 22.04 (Docker container)
Platform: Vast.ai
NVIDIA Driver: 590.48.01
CUDA: 13.1 (V13.1.115)
TensorRT: 10.14.1.48
PyTorch: 2.10.0a0+a36e1d39eb.nv26.01

Usage

Download the engine files and place them in jasna/model_weights/, then run:

./jasna --input "input.mp4" --output "output.mp4" --fp16 --max-clip-size 40 --log-level info

Notes

clip10 engine uses less VRAM (~4 GB), suitable for GPUs with limited memory
clip40 engine uses more VRAM (~8 GB) but processes faster
clip90 compilation failed with OOM on 16 GB VRAM; may work with more swap space
If engines don't work on your setup, delete them and let Jasna recompile automatically

Known Issues

On Vast.ai, multi-GPU hosts with single GPU allocation may cause NVDEC/NVENC failures due to nvidia-container-toolkit#1249 (https://github.com/NVIDIA/nvidia-container-toolkit/issues/1249). Use single-GPU hosts as a workaround.