High-resolution image synthesis has always been a challenge in digital imagery due to issues such as the emergence of repetitive patterns and structural distortions. While pre-trained diffusion models have been effective, they often result in artifacts when it comes to high-resolution image generation. Despite various attempts, such as enhancing the convolutional layers of these models, a comprehensive solution has been elusive.
Addressing this gap, researchers from institutions like The Chinese University of Hong Kong, Centre for Perceptual and Interactive Intelligence, Sun Yat-Sen University, SenseTime Research, and Beihang University, have developed an innovative method called FouriScale. The unique strategy it employs leverages frequency domain analysis to tackle the intrinsic problems of high-resolution image synthesis. It replaces traditional convolutional layers with an approach that incorporates dilation and low-pass filtering, thereby consistently maintaining structural integrity and decreasing repetitive patterns across various image resolutions.
FouriScale’s approach is a game-changer, as it brings about consistency in structure and scale without needing to retrain models for each new resolution. Essentially, it uses the dilation technique to adjust convolutional layers and a low-pass filter to smooth out high-frequency components that contribute to visual artifacts. As a result, it generates high-quality images of diverse sizes and aspect ratios.
Another feature of FouriScale is its padding-then-cropping strategy, enhancing its flexibility and applicability across different use cases. Its ability to generate high-quality images surpasses that of existing methodologies, marking it a leader in image synthesis.
Notably, FouriScale significantly outperforms existing models in comparative studies. It is capable of producing images at resolutions up to 4096×4096 pixels, avoiding the usual issues of pattern repetition and structural distortion. When generating images at four times the native resolution of pre-trained models, FouriScale achieved an improved Frechet Inception Distance (FID) score, indicating a closer resemblance to real images.
In essence, FouriScale is a major advancement in the digital imagery field, addressing key challenges in high-resolution image synthesis. Its innovative use of frequency domain analysis, dilation, and low-pass filtering sets a new standard. The introduction of FouriScale is instrumental in the production of high-quality images, with incredible fidelity and structural integrity, without the need for extensive model retraining. Therefore, beyond being a significant technical achievement, FouriScale paves the way for a future where the boundaries of image quality and resolution continue to be challenged and expanded.
