To further improve HiDiffusion for generating even higher-resolution images with better quality and efficiency, several enhancements can be considered:
Progressive Upsampling: Implement a progressive upsampling strategy where the resolution is gradually increased in multiple stages. This approach can help maintain image quality and details while scaling to extremely high resolutions.
Dynamic Feature Adjustment: Develop a more advanced feature adjustment mechanism in RAU-Net that can adaptively resize feature maps based on the complexity of the image content. This dynamic adjustment can help preserve important details during the generation process.
Enhanced Attention Mechanism: Refine the MSW-MSA by incorporating more sophisticated attention mechanisms, such as hierarchical attention or cross-modal attention, to capture long-range dependencies and improve image coherence at higher resolutions.
Fine-tuning Strategies: Explore fine-tuning strategies that can optimize the performance of HiDiffusion for specific tasks or datasets, ensuring better convergence and higher-quality image synthesis.
Parallel Processing: Implement parallel processing techniques to distribute the computational load across multiple GPUs or devices, enabling faster generation of high-resolution images without compromising quality.