The insights from HiDiffusion can be applied to other generative models beyond diffusion models to improve their scalability and efficiency in the following ways:
Feature Adjustment: Implement a feature adjustment mechanism similar to RAU-Net in other generative models to dynamically resize feature maps and reduce object duplication, enhancing the quality of generated images.
Efficient Attention Mechanisms: Integrate efficient attention mechanisms like MSW-MSA into other models to accelerate inference speed without compromising image quality, enabling faster generation of high-resolution images.
Progressive Generation: Incorporate a progressive generation strategy in other models to gradually increase the resolution, allowing for better preservation of details and improved image quality at higher resolutions.
Fine-tuning-Free Approaches: Explore tuning-free approaches in other generative models to leverage pretrained models effectively and scale image generation to higher resolutions without the need for extensive retraining.
By applying these insights, other generative models can benefit from improved scalability, efficiency, and quality in high-resolution image synthesis tasks.