- The paper introduces ReGround, a novel architecture that rewires attention modules to process textual and spatial inputs in parallel, reducing description omission.
- This approach enhances textual grounding while preserving spatial accuracy, as shown by higher CLIP scores with minimal impact on YOLO performance.
- Experimental results on MS-COCO and NSR-1K-GPT confirm its effectiveness in maintaining detailed textual content even when spatial cues are sparse.
ReGround: Enhancing Coherence Between Textual and Spatial Inputs in Image Generation Models
Introduction
Recent advancements in diffusion models have significantly propelled the capabilities of text-to-image (T2I) generation, enabling users to generate images based on textual descriptions. Lately, efforts have concentrated on incorporating spatial instructions, such as bounding boxes, to augment the creativity and controllability of generated images. Among various attempts, GLIGEN stands out for its innovative approach to integrate additional spatial cues into T2I models. However, an analysis of GLIGEN reveals a notable issue: the model often prioritizes spatial grounding over textual grounding, leading to the omission of textual details in the generated images. This paper introduces ReGround, a novel architecture that addresses this limitation. By rewiring the network architecture to process gated self-attention and cross-attention in parallel rather than sequentially, ReGround significantly mitigates the trade-off between textual and spatial grounding without requiring any additional training, parameters, or computational resources.
Gated Self-Attention and Description Omission
Gated self-attention, as introduced by GLIGEN, is a mechanism designed to enhance a pretrained T2I model’s capability to incorporate spatial guidance via bounding boxes. The technique, however, often leads to the omission of important details specified in the input text prompt, a phenomenon we refer to as "description omission." Our investigation indicates that the sequential arrangement of spatial grounding (gated self-attention) and textual grounding (cross-attention) modules in the GLIGEN architecture contributes significantly to this issue.
Network Rewiring
We propose a simple yet effective solution by rewiring the attention modules: making the relationship between gated self-attention and cross-attention parallel rather than sequential. This adjustment enables the model to effectively integrate textual and spatial inputs without compromising the integrity of either. Notably, this modification can be applied to the already pretrained GLIGEN model without any further training or parameter adjustments, demonstrating its simplicity and efficiency.
Experiments
The evaluation on the MS-COCO and NSR-1K-GPT datasets confirmed that ReGround substantially improves upon GLIGEN's performance. It achieves a remarkable balance between spatial and textual grounding, as evidenced by higher CLIP scores (indicating better textual alignment) with minimal impact on YOLO scores (indicating spatial alignment). Furthermore, when tested on a modified version of MS-COCO with randomly removed bounding boxes to heighten the challenge of textual grounding, ReGround demonstrated a significant advantage over GLIGEN, successfully incorporating textual details even when the corresponding spatial cues were absent.
Implications and Future Directions
ReGround not only addresses the description omission issue observed in GLIGEN but also provides a new perspective on designing T2I models that can seamlessly integrate both textual and spatial inputs. The simplicity of the solution — requiring no additional training or resources — underscores its potential for broader application and adaptation in existing T2I generation models. Moving forward, it would be worthwhile to explore the application of ReGround's architecture in other domains where the integration of multiple types of input is crucial for generating coherent and contextually rich outputs.