# Fast Fourier Asymmetric Context Aggregation Network:
## 2 Related Work  
### Image Generation Models  
Image generation models have become a pivotal tool in the field of computer vision research, designed to synthesize realistic or stylized images by learning the underlying distributions from data. These models play a critical role in a wide range of applications, such as creative content generation [19; 20; 21; 22; 23], medical imaging [24; 25; 26; 27; 28], multi-scene imaging optimization [29; 30; 31; 32; 33], and fashion design [34; 35; 36]. In recent years, the evolution of image generation models has been predominantly influenced by two significant methodologies: generative adversarial networks (GANs) [37] and diffusion models [38]. GANs, originally introduced by [PERSON] et al. [39], typically consist of two neural networks: a generator, which learns to produce realistic images that can deceive the discriminator, and a discriminator, which evaluates the authenticity of the generated samples. This adversarial setup encourages the generator to iteratively improve its outputs, achieving highly realistic image synthesis. Over the years, various GAN-based architectures have made impressive strides in areas such as image generation quality, data dependency optimization, and controllability of generated content, significantly advancing the field  
Figure 1: Showcase of garment images generated using our method. The real images are marked with red solid line frames, while the generated images are marked with black solid line frames. of image generation technology. For instance, the StyleGAN [40] series proposed by researchers at NVIDIA has enabled the synthesis of highly detailed high-resolution images, taking GAN-generated content quality to a new level. Similarly, CycleGAN [41], introduced by [PERSON] et al., facilitates image translation under unpaired data conditions, offering a key solution to the problem of training data dependency. Moreover, conditional generative adversarial networks (cGAN) [42], proposed by [PERSON] et al., establish the foundation for GAN controllability by enabling content generation through conditional embedding. However, despite these advancements, GANs still face challenges such as training instability and mode collapse, which somewhat limit their ability to produce diverse and consistent results across different tasks. To optimize the stability of GANs, researchers have undertaken significant efforts, proposing a range of effective solutions. For instance, [PERSON] et al. [43] introduced Wasserstein distance during training, which resulted in smoother and more stable gradients, mitigating the problem of gradient vanishing to some extent and thereby enhancing the training stability of GANs. Similarly, [PERSON] et al. [44] employed the least squares loss function, alleviating the gradient vanishing issue in both the generator and discriminator networks. These contributions have been pivotal in the development and advancement of GANs. Today, GANs have become a representative technology in the field of image generation, widely applied across various generative tasks. Diffusion models, on the other hand, represent a fundamentally different approach to image generation. Inspired by non-equilibrium thermodynamics, these models progressively add noise to input images during training and then learn to reverse this process, effectively generating images from noise. The Denoising Diffusion Probabilistic Model (DDPM) [45] proposed by [PERSON] et al. represents a milestone in the field. By simplifying the model structure and optimizing the training process, the DDPM significantly reduces the training complexity of diffusion models while markedly improving the quality of generated samples. This advancement has demonstrated the immense potential of diffusion models for high-quality and high-resolution image synthesis, sparking widespread academic interest in diffusion-based approaches. Building on this progress, [PERSON] et al.