README.md

metadata

license: apache-2.0
pipeline_tag: image-to-image
tags:
  - Diffusion
  - Augmentation
  - PromptControlledDiffusion
  - semanticsegmentation
  - synthetic-data
  - remote-sensing

🎨 SyntheticGen

Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation

Addressing class imbalance in remote sensing datasets through controlled synthetic generation

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🌟 Overview

SyntheticGen is the official implementation for the paper Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation. It tackles the long-tail distribution problem in remote-sensing datasets (specifically LoveDA) by generating synthetic imagery with explicit control over class ratios.

✨ Highlights

• Two-stage pipeline: Ratio-conditioned layout D3PM + ControlNet image synthesis.
• Controllable Augmentation: Specify exact proportions of each land cover class (e.g., building:0.4).
• Data-Centric Strategy: Improves segmentation performance by adding the right samples to the training set.

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🚀 Quick Start

Installation

git clone https://github.com/Buddhi19/SyntheticGen.git
cd SyntheticGen
pip install -r requirements.txt

Generate Your First Synthetic Image

To generate a synthetic image-label pair using a specific configuration:

python src/scripts/sample_pair.py \
  --config configs/sample_pair_ckpt40000_building0.4.yaml

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📚 Usage

Training Pipeline

Stage A: Train Layout Generator (D3PM)

python src/scripts/train_layout_d3pm.py \
  --config configs/train_layout_d3pm_masked_sparse_80k.yaml

Stage B: Train Image Generator (ControlNet)

python src/scripts/train_controlnet_ratio.py \
  --config configs/train_controlnet_ratio_loveda_1024.yaml

Inference / Sampling

Override config parameters via CLI:

python src/scripts/sample_pair.py \
  --config configs/sample_pair_ckpt40000_building0.4.yaml \
  --ratios "building:0.4,forest:0.3" \
  --save_dir outputs/custom_generation

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📄 Citation

@misc{wijenayake2026mitigating,
      title={Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation}, 
      author={Buddhi Wijenayake and Nichula Wasalathilake and Roshan Godaliyadda and Vijitha Herath and Parakrama Ekanayake and Vishal M. Patel},
      year={2026},
      eprint={2602.04749},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.04749}, 
}

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🙏 Acknowledgments

• LoveDA dataset creators for high-quality annotated remote sensing data.
• Hugging Face Diffusers for diffusion model infrastructure.
• ControlNet authors for controllable generation.