Benchmarks

Reference throughput benchmarks for VIESUS across presets, the factors that affect performance, and how to size a deployment.

VIESUS is designed to scale across hardware tiers and production volumes. The figures below are baselines under controlled conditions — your real-world results vary with hardware, image content, and the quality vs. speed settings you choose.

These benchmarks are baselines, not guarantees. Always run a representative sample of your own production images on your target hardware before sizing infrastructure.

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Throughput by preset

All configurations are benchmarked on the same reference system. The Standard test set represents average customer images — a realistic spread of sizes and content. With this content, roughly 10% of images trigger 2× AI upscaling, 20% trigger 4×, and 20% trigger Artifact Removal, and vScene runs on every image. Background Removal is measured on a separate set where 100% of images have their background removed.

Each configuration maps to a ready-made preset — see the Presets Gallery.

The figures below are placeholders (TBD) pending a full test run.

Configuration	Image set	Avg time / image	Throughput (images / hour)
Default (base enhancement)	Standard	TBD	TBD
vScene on	Standard	TBD	TBD
2× AI upscaling	Standard	TBD	TBD
4× AI upscaling	Standard	TBD	TBD
Artifact Removal	Standard	TBD	TBD
Background Removal	Background set	TBD	TBD

Reference system: TBD.

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What affects performance

Category	Factors
Image	Source resolution (cost grows with pixel count), content complexity (many faces, dense textures), compression level (heavily compressed JPEGs may trigger AI Artifact Removal as a pre-step)
Configuration	Upscaling factor, quality mode (Fast / Standard / High Quality), AI upscaling intensity, Facial Reconstruction settings
Hardware	GPU model and VRAM (insufficient VRAM causes paging and slowdowns), CPU, storage (SSD I/O for high-batch throughput)
Workflow	Number of parallel instances (scales until the GPU saturates), batch size (amortises model load), surrounding pipeline steps (download, archive, upload)

For how to tune these levers, see Performance Tuning.

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Sizing your deployment

A practical sizing approach:

1

Pick a representative sample of your actual production images (1,000–5,000 is plenty).

2

Run them on the candidate hardware with your target configuration (viesusini.json).

3

Measure end-to-end time including I/O, not just enhancement time.

4

Multiply by your daily volume to estimate capacity and add 30–50% headroom.

For hardware recommendations and the full set of tuning levers, see Performance Tuning.