Input one image, output one upscaled frame image.
Place bin/param file in models folder, then use command to upscale.
./upscayl-bin -m models/ -n 4xLSDIR -s 4 -i 0.jpg -o 01.jpg
./upscayl-bin -m models/ -n 4xLSDIR -s 4 -i input_frames/ -o output_frames/Example below runs on CPU, Discrete GPU, and Integrated GPU all at the same time. Uses 2 threads for image decoding, 4 threads for one CPU worker, 4 threads for another CPU worker, 2 threads for discrete GPU, 1 thread for integrated GPU, and 4 threads for image encoding.
./upscayl-bin -m models/ -n 4xLSDIR -s 4 -i input_frames/ -o output_frames/ -g -1,-1,0,1 -j 2:4,4,2,1:4mkdir input_frames
mkdir output_frames
# find the source fps and format with ffprobe, for example 24fps, AAC
ffprobe input.mp4
# extract audio
ffmpeg -i input.mp4 -vn -acodec copy audio.m4a
# decode all frames
ffmpeg -i input.mp4 input_frames/frame_%08d.png
# upscale 4x resolution
./upscayl-bin -m models/ -n 4xLSDIR -s 4 -i input_frames -o output_frames
# encode interpolated frames in 48fps with audio
ffmpeg -framerate 24 -i output_frames/%08d.png -i audio.m4a -c:a copy -crf 20 -c:v libx264 -pix_fmt yuv420p output.mp4Usage: upscayl-bin -i infile -o outfile [options]...
-h show this help
-i input-path input image path (jpg/png/webp) or directory
-o output-path output image path (jpg/png/webp) or directory
-s scale upscale ratio (can be 2, 3, 4. default=4)
-t tile-size tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
-m model-path folder path to the pre-trained models. default=models
-n model-name model name (default=4xLSDIR, can be 4xLSDIR | spanx2_ch52 | 4xLSDIR | spanx4_ch52)
-g gpu-id gpu device to use (default=auto) can be 0,1,2 for multi-gpu
-c cpu-only use only CPU for upscaling, instead of vulkan
-j load:proc:save thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
-x enable tta mode
-f format output image format (jpg/png/webp, default=ext/png)
-v verbose outputinput-path and output-path accept file directoryload:proc:save = thread count for the three stages (image decoding + upscaling + image encoding), using larger values may increase GPU usage and consume more GPU memory. You can tune this configuration with "4:4:4" for many small-size images, and "2:2:2" for large-size images. The default setting usually works fine for most situations. If you find that your GPU is hungry, try increasing thread count to achieve faster processing.pattern-format = the filename pattern and format of the image to be output, png is better supported, however webp generally yields smaller file sizes, both are losslessly encodedscale = upscale multiplier, must match model.If you encounter a crash or error, try upgrading your GPU driver: