--- license: agpl-3.0 datasets: - nkp37/OpenVid-1M - TempoFunk/webvid-10M --- # AMD Hummingbird image-to-video Model ⚡️ In this work, we present **AMD Hummingbird-I2V**, a compact and efficient **diffusion-based** I2V model designed for high-quality video synthesis under limited computational budgets. Hummingbird-I2V adopts a lightweight **U-Net** architecture with **0.9B parameters** and a novel two-stage training strategy guided by **reward-based feedback**, resulting in substantial improvements in inference speed, model efficiency, and visual quality. To further improve output resolution with minimal overhead, we introduce a **super-resolution** module at the end of the pipeline. Additionally, we leverage **ReNeg**, an AMD proposed reward-guided framework for learning negative embeddings via gradient descent, to further boost visual quality. As a result, Hummingbird-I2V can generate high-quality 4K video in just **11 seconds** with 16 inference steps on an AMD Radeon™ RX 7900 XTX GPU. Quantitative results on the VBench-I2V benchmark show that Hummingbird-I2V achieves state-of-the-art performance among U-Net-based diffusion models and competitive results compared to significantly larger DiT-based models. We provide a detailed analysis of the model architecture, training methodology, and benchmark performance.