# Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
## 2 Related Work  
Here, we review the related works in three areas: Disentanglement in generative models, compositional image synthesis and layout-to-image generation. It is worth noting that disentanglement and composition are inter-related and a work may be relevant to both aspects. To ensure clarity, we categorize the models that involve a compositional step into the compositional group, and those that do not into the disentanglement group. To enhance comprehension, the reviewed works are summarized in Table 2.  
**Disentanglement in Generative Models. ** Disentanglement refers to the property of a learned representation being sensitive to changes in one generating factor while being relatively insensitive to changes in other factors [13]. Some studies promote disentanglement by regularizing the distribution of latent codes to be factorized or independent [14; 15]. This regularization encourages the latent code to control specific attributes of the generated image. Other studies introduce additional information loss to enhance the influence of factorized latent codes [16; 17]. However, due to the lack of explicit inductive bias, the disentanglement achieved by these models is not predictable and the phenomenon is often not apparent. In contrast, our model incorporates an explicit inductive bias that considers an object as a composition of several parts, with each part defined in a supervised manner and assigned a semantic meaning. As a result, our model achieves significantly improved disentanglement, compared to these existing models. Some studies have focused on achieving disentanglement between specific attribute pairs, such as style and content [18; 19], identity and pose [19], identity and domain [20], shape and physical attribute [21] and appearance and geometry [22; 23; 24; 25; 26; 27]. In contrast, our model aims to disentangle semantic parts defined by humans. Another line of research aims to disentangle representations in the hierarchy dimension by incorporating structure priors into the model [28; 29; 30; 31] or by utilizing structured noise codes [32]. However, as the disentanglement in these models is achieved without supervision, the resulting disentangled factors may not align well with human understanding. In contrast, the disentangled factors in our model align closely with human conception. **Compositional Image Synthesis. ** Composition reflects the concept that an object is composed of its components and the goal of compositional learning is to explicitly model these components and their interactions [33]. Some researchers have directly performed compositional image synthesis in pixel space [34; 35; 36]. However, these models often generate floating objects over the background or are only effective on simple datasets, due to the limited representational power of the pixel space. In contrast, our model achieves high-fidelity image synthesis on real-image datasets. It is important to note that our world is inherently three-dimensional. Several models with a 3D prior have recently been proposed in the literature [37; 38; 39; 40; 41; 42; 43; 44]. These models typically encode a 3D prior by incorporating explicit 3D operations such as camera view direction, rotation and translation. However, these models are still designed to decompose the image at the object level and are unable to fully capture the fine-grained part-level decomposition. Some studies have explored compositional learning by decomposing objects into shape and appearance representations [45; 46]; however, these are still not as fine-grained as that in our model. To the best of our knowledge, only a limited number of studies have achieved compositional learning at the semantic part level [12; 47]. Among them, SemanticStyleGAN [12] stands out for its high-quality compositional image synthesis ability. However, SemanticStyleGAN sacrifices efficiency for performance by employing separate local generators for each semantic part. Additionally, it requires explicit factorization of the latent code into base,