In this article, we present a novel approach to text-to-image synthesis called Grounded Semantic Fusion (GSF). Our model combines the strengths of two existing methods: Self-Attention based Image Synthesis (SAIS) and Layout Diffusion (LD). GSF introduces a caption input that provides semantic coherence requirements, enabling the model to generate more accurate and visually appealing images.
The proposed method consists of three stages: 1) grounded text representation, 2) image synthesis, and 3) controllable semantic coherence. The grounded text stage involves representing the input caption as a set of grounded texts, each corresponding to a specific semantic concept. The image synthesis stage generates an image feature map using a self-attention mechanism based on the grounded texts. Finally, the controllable semantic coherence stage combines the image feature map with the caption to control the semantic coherence between objects in the generated image.
We evaluate our method through several qualitative and quantitative experiments. The results demonstrate that GSF outperforms existing methods in terms of both image quality and semantic coherence. We also show that our model can handle challenging scenarios, such as generating images with multiple objects or complex layouts.
Key takeaways
- Grounded Semantic Fusion (GSF) is a novel approach to text-to-image synthesis that combines the strengths of SAIS and LD.
- GSF introduces a caption input that provides semantic coherence requirements, enabling the model to generate more accurate and visually appealing images.
- The proposed method consists of three stages: grounded text representation, image synthesis, and controllable semantic coherence.
- Experimental results demonstrate that GSF outperforms existing methods in terms of both image quality and semantic coherence.
- GSF can handle challenging scenarios, such as generating images with multiple objects or complex layouts.
