eo-tts

TerraDiT-Ω-XL, 100 Euler steps, same tiles and noise: fp32 (top) vs bf16 autocast (bottom). Mean PSNR 54.2 dB; bf16 is 1.7× faster on an RTX 3060 (6.3 vs 10.8 s per image).

Test-time scaling for Earth observation diffusion transformers: inference-only search (Best-of-N, then particle-based search with Rollover Budget Forcing) over pretrained satellite image generators, guided by remote-sensing verifiers.

Base model: TerraDiT-Ω-XL (code, weights, ECCV 2026), pinned as a git submodule at commit 8d90f0d and used unmodified.

Status

Stage	State
Paper-protocol baseline (TerraDiT-Ω-XL, random + spatial)	code ready
Best-of-N, N ∈ {1, 4, 8, 16}, 2–3 verifiers	next
Rollover Budget Forcing with custom allocation rules	later

Reproducing the paper baseline

The baseline calls TerraDiT's own generate.py and eval/metrics.py in-process. The only substitution is where imagery comes from: instead of downloading all of Git-10M (394 GB, 787 shards), Git10MSubset keeps exactly the rows a test split uses, checked against the split metadata by img_name, so pixels and captions are byte-identical.

Split	Tiles	Git-10M shards needed	Download
random	14,932	151 of 787	29.3 GB
spatial	14,387	2 of 787	0.3 GB

Shards are fetched one at a time and deleted after extraction; the stored subset is ~0.2 GB per split.

Paper targets (TerraDiT-Ω-XL, condition Ω, from Table 1 and Table S3) are in configs/paper_targets.json; every result file stores them next to our numbers.

Split	FID ↓	LPIPS ↓	SSIM ↑	CLIP (cos) ↑
random	9.25	0.3438	0.2665	0.2812
spatial	17.54	0.3614	0.3421	0.2761

Differences between the paper's stated protocol and the released code, recorded in each result:

• Batch size: the paper says 32, the repo defaults to 16. Noise is drawn per batch, so this changes individual samples, not the distribution. Use --batch-size 32 if it fits.
• Seed: not reported in the paper; we use the repo default, 42.
• CLIP: the repo's metrics.py reports the CLIP logit (cosine × 100); the paper reports cosine. Results carry both clip_score and clip_cos.
• sFID: in the paper, not computed by the repo; not reported here.
• KID: not in the paper; added because it is unbiased on small subsets (Best-of-N).

Setup (Windows 11, NVIDIA GPU)

git clone --recursive https://github.com/Redmonkeycloud/eo-tts.git
cd eo-tts
conda create -n eotts python=3.11 -y
conda activate eotts
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
pip install -e "third_party/TerraDiT[eval]"
pip install -e ".[dev]"
huggingface-cli login            # optional, avoids Hub rate limits on the 29 GB download
python scripts/00_check_env.py

If you cloned without --recursive: git submodule update --init.

Run order

python scripts/01_download_assets.py                        # omega_xl weights + test split files (~2.5 GB)
python scripts/02_build_git10m_subset.py --split spatial    # ~0.3 GB, about a minute
python scripts/03_benchmark.py                              # speed, VRAM, fp32 vs bf16 (~10 min)
python scripts/04_run_baseline.py --split spatial --limit 64   # smoke test
python scripts/02_build_git10m_subset.py --split random     # ~29 GB download, resumable
python scripts/04_run_baseline.py --split random            # the paper number (long run, resumable)
python scripts/04_run_baseline.py --split spatial

Generation resumes from the images already on disk, so an interrupted run is restarted with the same command. Results land in runs/baseline/<run>/results.json.

Layout

configs/paper_targets.json      paper numbers we compare against
eotts/data/git10m_subset.py     exact local subset of Git-10M (shard math, verify, read)
eotts/baselines/                paper-protocol baseline runner
eotts/eval/kid.py               KID (extra metric for small subsets)
eotts/provenance.py             code versions, environment, GPU, written into every result
scripts/00..04                  the steps above, in order
third_party/TerraDiT            upstream code, pinned, unmodified
tests/                          subset builder tests (no GPU, no network)

Licences

Code licence for this repo: to be decided. TerraDiT code is Apache-2.0; its weights are CC-BY-NC-4.0 and Git-10M imagery is CC-BY-NC-ND-4.0, so this work is non-commercial research only. Git-10M data is never redistributed by this repo; it is downloaded from the original repository.