saii-cldm-synthetic / README.md
mally-2000's picture
Make SAII-CLDM the default pipeline
238403e verified
metadata
library_name: diffusers
pipeline_tag: image-to-image
tags:
  - seismic-inversion
  - impedance-inversion
  - diffusion
  - ddpm
  - cldm
  - overthrust
  - synthetic-data

SAII-CLDM Synthetic Weights

Open In Colab

Quick Start: Click the badge above to run directly in Google Colab (no setup required).

Pretrained synthetic-data model weights for SAII-CLDM in Diffusers format. This repository corresponds to the synthetic-data experiments in the paper. The field-data model was trained separately and is not included here.

Full project code: GeoAI-INV/SAII-CLDM
Paper: arXiv:2506.13529

Load From Hugging Face

Default pipeline: SAII-CLDM.

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "mally-2000/saii-cldm-synthetic",
    custom_pipeline="mally-2000/saii-cldm-synthetic",
    torch_dtype=torch.float32,
    trust_remote_code=True,
).to("cuda")

SAII-LDDPM is also provided as a baseline:

pipe = DiffusionPipeline.from_pretrained(
    "mally-2000/saii-cldm-synthetic",
    custom_pipeline="pipeline_lddpm",
    torch_dtype=torch.float32,
    trust_remote_code=True,
).to("cuda")

The inference algorithms are implemented in codes/pipeline.py. The root pipeline.py, pipeline_cldm.py, and pipeline_lddpm.py files are thin entry points used by Diffusers remote loading.

Run The Bundled Demo

git clone https://huggingface.co/mally-2000/saii-cldm-synthetic
cd saii-cldm-synthetic
pip install -r requirements.txt

python infer.py CLDM  # SAII-CLDM
python infer.py       # SAII-LDDPM baseline

infer.py uses the bundled Overthrust sample and writes outputs under outputs/infer_LDDPM/ or outputs/infer_CLDM/.

Add --eval to run the full bundled Overthrust evaluation:

python infer.py CLDM --eval

Overthrust Results

Impedance-domain metrics on the bundled Overthrust setting:

Method Steps PSNR SSIM PCC RRE
SAII-LDDPM 1000 33.4413 0.9554 0.9957 0.0324
SAII-CLDM 30 33.1312 0.9494 0.9950 0.0342