LoRWeB: Spanning the Visual Analogy Space with a Weight Basis of LoRAs

Visual analogy learning enables image manipulation through demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words. Given a triplet {a, a', b}, the goal is to generate b' such that a : a' :: b : b'.

LoRWeB specializes the model for each analogy task at inference time through dynamic composition of learned transformation primitives. It introduces a learnable basis of LoRA modules to span the space of different visual transformations and a lightweight encoder that dynamically selects and weighs these basis LoRAs based on the input analogy pair.

Hila Manor^1,2,  Rinon Gal²,  Haggai Maron^1,2,  Tomer Michaeli¹,  Gal Chechik^2,3

¹Technion - Israel Institute of Technology    ²NVIDIA    ³Bar-Ilan University

Given a prompt and an image triplet {a, a', b} that visually describe a desired transformation, LoRWeB dynamically constructs a single LoRA from a learnable basis of LoRA modules, and produces an editing result b' that applies the same analogy to the new image.

🛠 Sample Usage

To perform inference using the LoRWeB weights, use the inference.py script from the official GitHub repository:

python inference.py \
  -w "path/to/lorweb_model.safetensors" \
  -c "config/your_config.yaml" \
  -a "data/path_to_a_img.jpg" \
  -t "data/path_to_atag_img.jpg" \
  -b "data/path_to_b_img.jpg" \
  -o "outputs/generated_btag_img_path.jpg"

ℹ️ Additional Information

This model is a reproduction of the original model from the paper. It was trained from scratch using Technion resources. This might introduce differences from the results reported in the paper. Please see the samples directory for examples of this model's outputs on the {a, a', b} triplets from the teaser figure.

Please see our full modelcard and further details in the GitHub Repo.

📚 Citation

If you use this model in your research, please cite:

@article{manor2026lorweb,
    title={Spanning the Visual Analogy Space with a Weight Basis of LoRAs},
    author={Manor, Hila and Gal, Rinon and Maron, Haggai and Michaeli, Tomer and Chechik, Gal},
    journal={arXiv preprint arXiv:2602.15727},
    year={2026}
}

🙏🏻 Acknowledgements

This project builds upon:

• FLUX.1-Kontext by Black Forest Labs
• Diffusers by Hugging Face
• PEFT by Hugging Face
• AI-Toolkit for training infrastructure