# Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
## 5 Conclusions  
In this paper, we introduced SSSGAN, an efficient model for fine-grained compositional image synthesis. SSSGAN achieves improved efficiency by utilizing a shared generator to generate all of the semantic components. In order to accomplish this, we needed to address the challenges associated with entanglement and mode collapse. To overcome these challenges, we presented a novel method that explicitly models the relationships between the noise codes. Each noise code in SSSGAN controls the generation of a specific semantic part, which are then combined to form a complete object. Our model demonstrated impressive performance in generating high-quality images, providing a high level of control over the generation process for each semantic part, albeit with a slightly increased level of entanglement. We believe that efficiency is crucial for large-scale,  
Figure 11: Sequential part composition results on DeepFashion dataset.  
fine-grained compositional image synthesis and our work presents a fundamental methodology for advancing this field. However, there are still several challenges that need to be addressed in future research. First, improving the disentanglement performance while maintaining image diversity is crucial. Incorporating style-mixing techniques into our method (which is currently incompatible) could potentially achieve this goal. Second, our current model supports the generation of images with a fixed component count. However, it is important to extend its capability to handle dynamic component counts, which are common in real-world objects and scenes. This can potentially be achieved by explicitly modeling the component count in the composition process. Finally, similar to Semantic-StyleGAN, SSSGAN relies on full supervision, and so would be valuable for developing models with similar performance having lower supervision requirements.  
Conceptualization, Z.W. and Z.L.; methodology, Z.W.; software, Z.W.; validation, Z.W.; formal analysis, Z.W.; investigation, Z.W. and Z.L.; resources, Z.L.; data curation,Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W. and Z.L.; visualization, Z.W.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China grant number 62032019 and 61732019.  
Not applicable.  
The code will be made publicly available upon acceptance at [[https://gitee.com/zongtao_wang/sssgan-pytorch](https://gitee.com/zongtao_wang/sssgan-pytorch)]([https://gitee.com/zongtao_wang/sssgan-pytorch](https://gitee.com/zongtao_wang/sssgan-pytorch)), accessed on 23 April 2023.  
We would like to express our gratitude to the anonymous reviewers for their valuable feedback, which greatly contributed to improving our research paper. Additionally, we acknowledge the GPU support provided by the Center for Research and Innovation in Software Engineering (RISE).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GAN & Generative Adversarial Network \\ MLP & Multi-Layer Perceptron \\ SSSGAN & Single-Generator SemanticStyleGAN \\ SPC & Sequential Part Composition \\ SMSE & Sequential Mean Square Error \\ LSTM & Long Short-Term Memory \\ FID & Frechet Inception Distance \\ IS & Inception Score \\ \end{tabular}