Update README.md

README.md

@@ -3,4 +3,12 @@ license: agpl-3.0
 datasets:
 - nkp37/OpenVid-1M
 - TempoFunk/webvid-10M
----
+---
+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.