🔥 Awesome Controllable Generative Models
Published:
I’m maintaining a curated collection of recent research papers on controllable generative models!
About This Collection
A continuously updated collection of recent (2023–2025) research papers on controllable generative models, with a special focus on both UNet-based diffusion models and Transformer-based diffusion architectures.
This list emphasizes core advances in:
- 🧭 Control mechanisms – including condition injection, adapters, multi-modal control
- 👁️ Attention interpretation – revealing what diffusion models focus on
- 🎛️ Frequency-based control – using spectral domain knowledge to guide generation
- 🔁 Alignment & knowledge transfer – enabling more coherent, faithful, and data-efficient synthesis
- 🧑🎨 Image-to-image (I2I) editing – flexible, structure-preserving transformation across domains
Check it out!
👉 GitHub Repository: Awesome-Controllable-Generative-Models-Papers
⭐ 36 stars and growing!
The list covers major conferences (CVPR, ICCV, ECCV, ICML, NeurIPS, ICLR, etc.) and recent arXiv preprints. Perfect for researchers and developers exploring controllable synthesis.
💡 Contributions are welcome! If you know a paper we missed or are working on a new controllable generation method, feel free to submit a pull request or open an issue.