Command line¶

synapse-sr INPUT.tif OUTPUT.tif [options]

In a terminal you get a live progress bar (stages, tile count, elapsed time and ETA), then a summary table:

                    synapse-sr result
model          synapse-pro-v2
output         640 × 640 px · 2 m · bands B04 B03 B02 B08
backend        pytorch · bfloat16 · tile 32 halo 16
consistency    B04 0.91τ  B03 0.80τ  B02 0.68τ  B08 0.99τ
support        HIGH 22%  MEDIUM 48%  LOW 29%
invalid input  0.0%
time           80.3 s
done wrote OUTPUT.tif

Common tasks¶

Task	Command
Default (Pro, best available device)	`synapse-sr scene.tif scene_2m.tif`
Fast model on a CPU	`synapse-sr scene.tif scene_2m.tif --model flash --device cpu`
Offline, local weights	`synapse-sr scene.tif scene_2m.tif --weights synapse-pro-v2.safetensors`
Explicit cloud mask	`synapse-sr scene.tif scene_2m.tif --scl scene_SCL_20m.tif`
No cloud masking	`synapse-sr scene.tif scene_2m.tif --scl none`
Raw DN from baseline ≥ 04.00 without a tag	`synapse-sr scene.tif scene_2m.tif --offset -1000`
Four bands only (for a GIS)	`synapse-sr scene.tif scene_2m.tif --no-confidence`
Low GPU memory	`synapse-sr scene.tif scene_2m.tif --batch 2`
Scripts and pipelines	`synapse-sr scene.tif scene_2m.tif --json` (one JSON line on stdout, no bars)
Diagnose the machine	`synapse-sr --env`
What models exist	`synapse-sr --models`

All options¶

Option	Default	Meaning
`--model`	`pro`	`pro`, `flash`, or a registered name such as `pro-v1`
`--weights`		local `.safetensors` checkpoint; no network access needed
`--device`	CUDA if available	`cuda`, `cuda:1`, `cpu`, `mps`
`--scl`	`auto`	scene classification raster; `auto` uses `<input>_scl.tif` when it exists; `none` disables
`--offset`	tag or 0	DN offset added before dividing by 10 000
`--tile`	64 GPU scan / 32 PyTorch / 128 Flash	tile size in 10 m pixels
`--halo`	16	context around each tile, in 10 m pixels
`--batch`	8 CUDA / 1 CPU	tiles per forward pass
`--no-confidence`		write B04 B03 B02 B08 only
`--json`		machine-readable summary on stdout
`--quiet`		no progress bar or summary
`--env`, `--models`, `--version`		print and exit (`--env --json` for JSON)

Output file¶

Bands	Content
1-4	B04 B03 B02 B08 surface reflectance, float32, NaN where the input was invalid
5-8	`ERRSCALE_*`: learned per-pixel error scale for each band
9	`SUPPORT`: 2 observation-determined, 1 medium, 0 prior-dominated or invalid

Same CRS and bounds as the input, with a 2 m pixel.

Exit codes¶

0 success. Any error (unreadable file, unsupported grid, missing weights) exits non-zero with a message that says what to change; see Troubleshooting.