# Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Achievement** & **Disadvantage** & **Comparison with Our Model** \\ \hline
[14; 15] & Increased disentanglement by encouraging latent codes to be factorized & Non-obvious, non-predictable disentanglement & Disentanglement factor has semantic meaning \\ \hline
[16; 17] & Use of mutual information loss to boost factorized latent codes for improved disentanglement & Non-obvious, non-predictable disentanglement & Disentanglement factor has semantic meaning \\ \hline
[18; 19] & Style-content disentanglement & Not fine-grained & Part-level disentanglement \\ \hline
[19] & Identity–pose disentanglement & Not fine-grained & Part-level disentanglement \\ \hline
[20] & Identity–domain disentanglement & Not fine-grained & Part-level disentanglement \\ \hline
[21] & Shape and physical attribute disentanglement & Not fine-grained & Part-level disentanglement \\ \hline
[22; 23; 24; 25; 26; 27] & Appearance–geometry disentanglement & Not fine-grained & Part-level disentanglement \\ \hline
[28; 29; 30; 31] & Structure disentanglement by customizing model architecture, unsupervised & Disentanglement factor does not align well with human conception & Disentanglement factor aligns with human conception \\ \hline
[32] & Structure disentanglement by use of structured noise codes, unsupervised & Disentanglement factor does not align well with human conception & Disentanglement factor aligns with human conception \\ \hline
[34; 35; 36] & Compositional image synthesis in pixel space & Floating object over background or feasible on simple dataset & Composition in hidden space, high quality on real-image dataset \\ \hline
[37; 38; 39; 40; 41; 42; 43; 44] & 3D-aware compositional image synthesis & Decomposes images into object and background, not fine-grained & Part-level compositional image synthesis \\ \hline
[45; 46] & Decomposes image into foreground shape, foreground appearance and background & Not fine-grained & Part-level compositional image synthesis \\ \hline
[47] & Part-level compositionality, unsupervised & Decomposition quality is low & With supervision, high-quality decomposition \\ \hline
[12] & Part-level compositionality, high-quality for image and decomposition & Need substantial amount of heavy local generators, inefficient & Uses single local-generator, efficient \\ \hline
[48; 49; 50; 51; 52; 53; 54] & Sketch-to-image mapping & Semantic-agnostic, need sketches as input & Semantic-aware, no need for input \\ \hline
[11; 55; 56; 57; 58] & Semantic layout-to-image mapping, semantic aware & Object-level, need semantic layout as input & Part-level, no need for input, generation from scratch \\ \hline
[59; 60] & Semantic layout-to-image mapping, part-level & Need layout as input & No need for input, generation from scratch \\ \hline
[60; 61; 62] & Semantic layout-to-image mapping, supports both part- or object-level layout & Needs layout as input & No need for input, generation from scratch \\ \hline
[55; 62; 63] & Semantic layout-to-image mapping, utilizing multi-scale methods to better encode semantic component & Needs layout as input & No need for input, generation from scratch \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis and summary of relevant research. shape and texture codes, resulting in a complex and inefficient model. In contrast, our model overcomes these limitations by using a single local generator for all parts and eliminates the need for latent code factorization, making it more efficient and simple. **Layout-to-Image Generation. ** Many studies have focused on mapping layouts to realistic images, which is valuable for controllable image editing and creation. Some studies have utilized sketches as input, which lack semantic meaning [48; 49; 50; 51; 52; 53; 54]. Other studies have employed segmentation masks. The granularity of the semantic component varies from object level [11; 55; 56; 57; 58] to part level [59; 60]. Certain models support both object- and part-level semantic layouts [61; 60; 62].