🎨 SyntheticGen

Mitigating Long-Tail Bias in LoveDA via Prompt-Controlled Diffusion Augmentation

Addressing class imbalance in remote sensing datasets through controlled synthetic generation

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🌟 Overview

SyntheticGen tackles the long-tail distribution problem in LoveDA by generating synthetic imagery with explicit control over class ratios. You can specify exactly what proportion of each land cover class should appear in the output.

✨ Highlights

• Two-stage pipeline: ratio-conditioned layout D3PM + ControlNet image synthesis.
• Full or sparse ratio control (e.g., building:0.4).
• Config-first workflow for reproducible experiments.

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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

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

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📚 Usage

Training Pipeline (Configs)

Stage A: Train Layout Generator (D3PM)

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

(Optional) Ratio Prior for Sparse Conditioning

python src/scripts/compute_ratio_prior.py \
  --config configs/compute_ratio_prior_loveda_train.yaml

Stage B: Train Image Generator (ControlNet)

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

Inference / Sampling (Configs)

End-to-end sampling (layout -> image):

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

Override config parameters via CLI if needed:

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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⚙️ Configuration

All experiments are driven by YAML/JSON config files in configs/.

Task	Script	Example Config
Layout Training	src/scripts/train_layout_d3pm.py	configs/train_layout_d3pm_masked_sparse_80k.yaml
Ratio Prior	src/scripts/compute_ratio_prior.py	configs/compute_ratio_prior_loveda_train.yaml
ControlNet Training	src/scripts/train_controlnet_ratio.py	configs/train_controlnet_ratio_loveda_1024.yaml
Sampling / Inference	src/scripts/sample_pair.py	configs/sample_pair_ckpt40000_building0.4.yaml

Config tips

• Examples live in configs/.
• To resume training, set resume_from_checkpoint: "checkpoint-XXXXX" in your config.
• Dataset roots and domains are centralized in configs; edit once, reuse everywhere.
• CLI flags override config values for quick experiments.

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📁 Data Format

LoveDA Dataset Structure

LoveDA/
  Train/
    Train/            # some releases include this extra nesting
      Urban/
        images_png/
        masks_png/
      Rural/
        images_png/
        masks_png/
    Urban/
      images_png/
      masks_png/
    Rural/
      images_png/
      masks_png/
  Val/
    ...

Generic Dataset Structure

your_dataset/
  images/
    image_001.png
  masks/
    image_001.png   # label map with matching stem

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📦 Pre-Generated Datasets

We provide synthetic datasets used in the paper: https://drive.google.com/drive/folders/14cMpLTgvcLdXhRY0kGhFKpDRMvpok90h?usp=sharing

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🧾 Outputs

• Checkpoints include training_config.json and class_names.json.
• Sampling writes image.png, layout.png, and metadata.json.

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📄 Citation

@misc{wijenayake2026mitigating,
      title={Mitigating Long-Tail Bias in LoveDA 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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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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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

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