Diffusion-driven text-to-image (T2I) generation has achieved remarkable advancements in recent years. To further improve T2I models’ capability in numerical and spatial reasoning, layout is employed as an intermedium to bridge large language models and layout-based diffusion models. However, these methods often rely on closed-source, large-scale LLMs for layout prediction, limiting accessibility and scalability. They also struggle with generating images from prompts with multiple objects and complicated spatial relationships. To tackle these challenges, we introduce a divide-and-conquer approach which decouples the generation task into multiple subtasks. First, the layout prediction stage is divided into numerical & spatial reasoning and bounding box visual planning, enabling even lightweight LLMs to achieve layout accuracy comparable to large-scale models. Second, the layout-to-image generation stage is divided into two steps to synthesize objects from easy ones to difficult ones. Experiments are conducted on the HRS and NSR-1K benchmarks and our method outperforms previous approaches with notable margins. In addition, visual results and user study demonstrate that our approach significantly improves the perceptual quality, especially when generating multiple objects from complex textural prompts.

Large-scale low earth orbit (LEO) satellite constellations have become important to support ubiquitous communications. The vast computational resources embedded within satellite networks are driving the rapid development of space computing and satellite Cloud-native technologies, with various satellite artificial intelligence (AI) applications. However, a single satellite only has limited communication and computation resources, especially when dealing with remote sensing images by deep learning methods. To address this issue, this paper proposed a pipeline inference approach for Cloud-native satellites: PipeSIA, which enables multiple satellites to execute inference tasks cooperatively. A satellite constellation was designed for PipeSIA, whose network topology was also organized as a pipeline for efficient communication. Different from conventional computation offloading schemes, the proposed scheme jointly considers the node sequence and task allocation problem. A joint optimization problem is formulated as a series multi-arms bandit (SMAB). Then$\epsilon$-greedy algorithm and deep Q-network (DQN) are applied to optimize the node selection and task allocation. Simulation results show that the proposed scheme can effectively reduce the overall delay.