NAMI: Efficient Image Generation via Bridged Progressive Rectified Flow Transformers

Understand

Flow-based Transformer models have achieved state-of-the-art image generation performance, but often suffer from high inference latency and computational cost due to their large parameter sizes.

• To improve inference efficiency without compromising quality, we propose Bridged Progressive Rectified Flow Transformers (NAMI), which decompose the generation process across temporal, spatial, and architectural demensions.
• We divide the rectified flow into different stages according to resolution, and use a BridgeFlow module to connect them.
• Fewer Transformer layers are used at low-resolution stages to generate image layouts and concept contours, and more layers are progressively added as the resolution increases.