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NeDS Model Card (RSE 2025)
This repository hosts a NeDS (Neural disaster simulation for transferable building damage assessment) checkpoint trained on xView2 tier3 with 512x512 crops.
Default: follow the official implementation
For the primary and recommended workflow, follow the official NeDS codebase:
- Official repo:
https://github.com/Z-Zheng/pytorch-change-models - NeDS model source there:
torchange/models/neds.py
If you are reproducing paper behavior or training/evaluation procedures, use the official repository first.
Extra in this repo: Diffusers quick start
In addition to the official path, this folder provides a self-contained Diffusers demo that does not require importing pytorch-change-models.
Included files:
neds_diffusers.py: customNeDS+NeDSPipelinefor Diffusersinfer_neds.py: end-to-end inference script- converted controlnet checkpoints:
nds_v1_tier3_512_diffusers_bf16nds_v1_tier3_512_diffusers_fp32
The demo loads through native Diffusers DiffusionPipeline.from_pretrained(...) with custom_pipeline.
Quick start (DiffusionPipeline demo)
import torch
from pathlib import Path
from diffusers import DiffusionPipeline
from neds_diffusers import NeDS
dtype = torch.bfloat16
controlnet = NeDS.from_pretrained("./nds_v1_tier3_512_diffusers_bf16", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
"sd2-community/stable-diffusion-2-1",
custom_pipeline=str(Path("neds_diffusers.py").resolve()),
controlnet=controlnet,
torch_dtype=dtype,
safety_checker=None,
requires_safety_checker=False,
).to("cuda")
# See infer_neds.py for complete preprocessing and call arguments.
Citation
@article{zheng2025neural,
title={Neural disaster simulation for transferable building damage assessment},
author={Zheng, Zhuo and Zhong, Yanfei and Wan, Zijing and Zhang, Liangpei and Ermon, Stefano},
journal={Remote Sensing of Environment},
volume={331},
pages={114979},
year={2025},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.rse.2025.114979},
url = {https://www.sciencedirect.com/science/article/pii/S0034425725003839},
}
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