Recently, text-to-image diffusion models have demonstrated impressive ability to generate high-quality images conditioned on the textual input. However, these models struggle to accurately adhere to textual instructions regarding spatial layout information. While previous research has primarily focused on aligning cross-attention maps with layout conditions, they overlook the impact of the initialization noise on the layout guidance. To achieve better layout control, we propose leveraging a spatial-aware initialization noise during the denoising process. Specifically, we find that the inverted reference image with finite inversion steps contains valuable spatial awareness regarding the object's position, resulting in similar layouts in the generated images. Based on this observation, we develop an open-vocabulary framework to customize a spatial-aware initialization noise for each layout condition. Without modifying other modules except the initialization noise, our approach can be seamlessly integrated as a plug-and-play module within other training-free layout guidance frameworks. We evaluate our approach quantitatively and qualitatively on the available Stable Diffusion model and COCO dataset. Equipped with the spatial-aware latent initialization, our method significantly improves the effectiveness of layout guidance while preserving high-quality content.

Imitation Learning (IL) is a paradigm in sequential decision making where an agent uses offline expert trajectories to mimic the expert's behavior [1]. While Reinforcement Learning (RL) requires an additional reward signal that can be hard to specify in practice, IL only requires expert trajectories that can be easier to collect. In part due to its simplicity, IL has been applied successfully in several real world tasks, from robotic manipulation [2, 3, 4] to autonomous driving [5, 6]. A key challenge in deploying IL, however, is that the agent may encounter states in the final deployment environment that were not labeled by the expert offline [7]. In applications such as healthcare [8, 9] and robotics [10, 11], online experimentation can be risky (e.g., on human patients) or costly to label (e.g., off-policy robotic datasets can take months to collect).