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GiLDM-open

Lightweight public repository for GiLDM, organized into two parts:

  • Method code: gildm/, scripts/, configs/, preprocessing/
  • Result processing: evaluation/ (scripts only, no large experiment snapshots)

Repository Layout

  • gildm/: core model, training, and sampling code
  • scripts/train_gildm.py: unified training entry (stage1, stage2, or all)
  • configs/: selected v6 and ablation configs
  • preprocessing/: dataset preprocessing utilities
  • evaluation/: evaluation and plotting scripts
  • data/sample_TIs/: small sample training images

Quick Start

  1. Install dependencies
pip install -r requirements.txt
  1. Prepare data
  • Sample files are provided in data/sample_TIs/.
  • Full datasets (.npy) should be placed in dataset/.
  1. Train
python scripts/train_gildm.py --config configs/ldm_v6_channel.yaml --stage all

Resume stage 2 only:

python scripts/train_gildm.py --config configs/ldm_v6_channel.yaml --stage stage2 --resume
  1. Evaluate
python evaluation/evaluate_full.py --config configs/ldm_v6_channel.yaml --ckpt stage2-10.ckpt --out-dir evaluation/full_eval_channel_v6
python evaluation/evaluate_full_delta.py --config configs/ldm_v6_delta.yaml --ckpt stage2-10.ckpt --out-dir evaluation/full_eval_delta_v6

Data and Checkpoints

Large datasets and checkpoints are distributed externally via Zenodo.

Released files in the assets record:

  • gildm_checkpoints_v1.0.0.tar.gz
  • gildm_datasets_v1.0.0.tar.gz
  • SHA256SUMS.txt

Integrity (SHA256)

  • gildm_checkpoints_v1.0.0.tar.gz: dfc90983963e0fa842dcc27ba9f50bcf2fe1549c2a4640e8d52468803fbc84ae
  • gildm_datasets_v1.0.0.tar.gz: b3fcb29e6f5e6bc9143358f6bbb42949835a1a1c05a3a3f990afab4849518390

Verify and extract:

shasum -a 256 -c SHA256SUMS.txt
tar -xzf gildm_checkpoints_v1.0.0.tar.gz -C .
tar -xzf gildm_datasets_v1.0.0.tar.gz -C .

Notes

  • The codebase uses the gildm namespace.
  • .gitignore excludes large artifacts such as *.ckpt, *.npy, wandb/, and *.raylink.downloading.

About

Official PyTorch implementation of "Geostatistics-Informed Latent Diffusion Models for Conditional Sedimentary Facies Simulation".

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