ReGround: Improving Textual and Spatial Grounding at No Cost
(2403.13589)Abstract
When an image generation process is guided by both a text prompt and spatial cues, such as a set of bounding boxes, do these elements work in harmony, or does one dominate the other? Our analysis of a pretrained image diffusion model that integrates gated self-attention into the U-Net reveals that spatial grounding often outweighs textual grounding due to the sequential flow from gated self-attention to cross-attention. We demonstrate that such bias can be significantly mitigated without sacrificing accuracy in either grounding by simply rewiring the network architecture, changing from sequential to parallel for gated self-attention and cross-attention. This surprisingly simple yet effective solution does not require any fine-tuning of the network but significantly reduces the trade-off between the two groundings. Our experiments demonstrate significant improvements from the original GLIGEN to the rewired version in the trade-off between textual grounding and spatial grounding.
Overview
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ReGround introduces a novel architecture enhancing the coherence between textual and spatial inputs in text-to-image generation, addressing description omission in GLIGEN.
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The paper proposes a network rewiring solution, making gated self-attention and cross-attention modules operate in parallel, improving integration without extra resources.
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Evaluations on MS-COCO and NSR-1K-GPT datasets show significant improvements in balancing textual and spatial grounding, achieving higher CLIP scores and stable YOLO scores.
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ReGround's innovative approach highlights its potential for broader application in text-to-image models, providing a template for future enhancements in multi-input integration.
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.