# Fast Fourier Asymmetric Context Aggregation Network:
## 6 Discussion  
Although our method has shown promising advancements in controllability and the quality of generated images, there are still several factors worth discussing regarding both the strengths and limitations of our approach. One of the key advantages of the method we propose is its ability to provide fine-grained control over generated clothing images, which represents a significant improvement over the traditional methods. By leveraging texture blocks and design sketches as driving forces, we are able to achieve high customization while maintaining consistency in the generated content. This level of control is particularly important for practical applications in fashion design, where ensuring semantic coherence and meeting design specifications are crucial. However, this enhanced controllability can, in some cases, limit the model's generative capabilities. Specifically, while our method excels in controlled scenarios, it may not be as effective in generating highly diverse clothing styles as other methods based on pre-trained generative models, which are designed to produce a wider range of outputs. This limitation highlights the challenge of balancing control and diversity in generative models. Another key contribution of our work is the introduction of the ACAM components, which have played a crucial role in enhancing the model's performance. The ACAM focuses on heterogeneous feature modeling, which helps to establish contextual relationships in the generated images, leading to more realistic and semantically consistent clothing representations. The exploration of such methods is a focal point in this field as contextual modeling is often critical in computer vision tasks. However, despite its advantages, the ACAM still faces some challenges. In certain cases, especially when the design sketch is not sufficiently detailed or when complex clothing features are involved, the model may struggle to generate highly accurate or realistic textures and patterns. This suggests that further improvements to the ACAM component may be needed to enhance the model's performance in these more challenging scenarios. At the same time, time cost is also a key concern for generative models. Currently, our method's training and inference time costs are on par with the existing methods and, in some cases, slightly higher. While we believe that a modest increase in time cost for significant performance improvement is acceptable, it could still impact the future development of the model. Therefore, how to significantly reduce time cost while improving performance remains a direction worth exploring. In summary, our method still holds significant potential for future improvements. Potential future research will focus on, but is not limited to, the following areas:  
* Optimizing the acquisition process of sketch and texture data to enhance the practical value of the method;
* Improving its synthesis performance under more extreme guidance conditions to further broaden its applicability;
* Improving time efficiency without sacrificing performance or while enhancing performance;
* Exploring cross-applications with related tasks such as virtual try-on, clothing retrieval, and recommendation systems to expand its use cases. Moving forward, we will continue to investigate clothing image synthesis technologies based on artificial intelligence, building upon these aspects. **Author Contributions:** Conceptualization, H.L. and Y.H. ; methodology, H.L. and Y.H. ; software, Y.H. ; validation, Y.H., M.D., and Z.L. ; formal analysis, G.L. ; investigation, Y.H. ; resources, H.L. ; data curation, Y.H. ; writing--original draft preparation, Y.H. ; writing--review and editing, H.L. ; visualization, H.L. ; supervision, H.L. ; project administration, M.W. ; funding acquisition, H.L. and G.L. All authors have read and agreed to the published version of the manuscript.