AFP-GIC: Controllable Generative Image Compression [R]

Hi ML Community,

I am excited to share our latest framework, AFP-GIC, officially published in IEEE Access (2026). We have released the deployment codebase and hosted an interactive visual playground.

• GitHub Repository: https://github.com/yifeipet/AFP_GIC
• Hugging Face Interactive Space: https://huggingface.co/spaces/yifeipet/AFP-GIC
• Paper on arXiv: https://arxiv.org/abs/2605.16817

The Bottlenecks We Solve

At ultra-low bitrates, standard learned image codecs suffer from local distortion, while generative models often introduce unwanted AI hallucinations. AFP-GIC addresses this via an asymmetric Adaptive Fused Prior Transfer pipeline that enables prior-guided texture reconstruction without transmitting the fused prior itself.

Key Technical Highlights (NVIDIA RTX 4090):

• Single-Model Multi-Rate Control: Toggle across 5 target bitrate operating points within one deployable pretrained model.
• 18.1% Lower Decoder Latency: Reduces decoding time to 80.47 ms vs. 98.27 ms for DC-VIC, a state-of-the-art controllable generative image compression model. Latency was measured using 256×256 patches.
• 20.5% Parameter Reduction: Uses 31.1M fewer inference parameters (120.6M vs. 151.7M for DC-VIC).

Open Benchmark Data

We packaged all 2,760 reconstructed images and metric CSVs in our GitHub Releases for direct academic cross-evaluation.

Reconstructed Images and Metrics: https://github.com/yifeipet/AFP_GIC/releases

We would love your feedback and appreciate a Star on GitHub or Like on Hugging Face if this helps your research!