| | --- |
| | license: cc-by-nc-4.0 |
| |
|
| | extra_gated_prompt: "This model is released for research and educational purposes only. Access to the model is granted upon agreement to use it ethically, responsibly, and in compliance with the stated license (CC BY-NC 4.0)." |
| | extra_gated_fields: |
| | Full Name: text |
| | Institution / Organization: text |
| | Position / Title: text |
| | Company / School: text |
| | Country: country |
| | I want to use this model for: |
| | type: select |
| | options: |
| | - Research |
| | - Education |
| | - label: Other |
| | value: other |
| | I agree to use this model for non-commercial use ONLY: checkbox |
| | I confirm I will not use this model to cause harm to human subjects or the environment: checkbox |
| | --- |
| | |
| | ## NeDS Model Card (RSE 2025) |
| |
|
| | This repository hosts a NeDS 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`: custom `NeDS` + `NeDSPipeline` for Diffusers |
| | - `infer_neds.py`: end-to-end inference script |
| | - converted controlnet checkpoints: |
| | - `nds_v1_tier3_512_diffusers_bf16` |
| | - `nds_v1_tier3_512_diffusers_fp32` |
| |
|
| | The demo loads through native Diffusers `DiffusionPipeline.from_pretrained(...)` with `custom_pipeline`. |
| |
|
| | ### Quick start (DiffusionPipeline demo) |
| |
|
| | ```python |
| | 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 |
| |
|
| | ```bibtex |
| | @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}, |
| | } |
| | ``` |
| |
|
