Getting it Right: Improving Spatial Consistency in Text-to-Image Models
(2404.01197)Abstract
One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. In this paper, we offer a comprehensive investigation of this limitation, while also developing datasets and methods that achieve state-of-the-art performance. First, we find that current vision-language datasets do not represent spatial relationships well enough; to alleviate this bottleneck, we create SPRIGHT, the first spatially-focused, large scale dataset, by re-captioning 6 million images from 4 widely used vision datasets. Through a 3-fold evaluation and analysis pipeline, we find that SPRIGHT largely improves upon existing datasets in capturing spatial relationships. To demonstrate its efficacy, we leverage only ~0.25% of SPRIGHT and achieve a 22% improvement in generating spatially accurate images while also improving the FID and CMMD scores. Secondly, we find that training on images containing a large number of objects results in substantial improvements in spatial consistency. Notably, we attain state-of-the-art on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on <500 images. Finally, through a set of controlled experiments and ablations, we document multiple findings that we believe will enhance the understanding of factors that affect spatial consistency in text-to-image models. We publicly release our dataset and model to foster further research in this area.
Overview
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A new comprehensive approach to enhance spatial consistency in text-to-image (T2I) synthesis includes the creation of the SPRIGHT dataset and novel training techniques.
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The SPRIGHT dataset is designed to address the lack of spatial relationship representation in existing datasets by re-captioning images to focus on spatial details.
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Enhancements in spatial accuracy and image fidelity were observed by fine-tuning Stable Diffusion models with SPRIGHT, demonstrating the dataset's effectiveness.
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Research findings emphasize the importance of diverse training data, detailed descriptions, and the fine-tuning of the CLIP text encoder for improving spatial consistency.
Improving Spatial Consistency in Text-to-Image Models through SPRIGHT Dataset and Efficient Training Techniques
Introduction
Spatial relationships in text prompts present a significant challenge in the field of text-to-image (T2I) synthesis. Despite advancements in diffusion models like Stable Diffusion and DALL-E, generating images that adhere to specified spatial relationships remains an area needing improvement. Addressing this, a recent study introduces a comprehensive approach encompassing new datasets, training methodologies, and extensive analyses aimed at enhancing the generation of spatially consistent images from textual descriptions.
Creation of the SPRIGHT Dataset
A core finding of the research is the identification of a lack in representation of spatial relationships in existing vision-language datasets. To tackle this, the authors introduce the SPRIGHT dataset, a spatially focused collection created by re-captioning approximately 6 million images from four extensively used vision datasets. SPRIGHT focuses on detailed spatial relationships, significantly surpassing the spatial representation in existing datasets. This is confirmed through a detailed analysis highlighting an overwhelming increase in the presence of spatial relationships in SPRIGHT compared to original datasets.
Methodological Advancements
The paper demonstrates the efficacy of SPRIGHT through remarkable improvements in spatial consistency in generated images. By fine-tuning Stable Diffusion models with a fraction of SPRIGHT, the study reports a 22% improvement in spatial accuracy, alongside significant enhancements in image fidelity metrics such as FID and CMMD scores. Moreover, a novel training strategy that emphasizes images with a high object count is introduced, leading to state-of-the-art performance in spatial consistency benchmarks like the T2I-CompBench.
Diverse Analyses and Findings
Extensive analyses conducted by the researchers shed light on various factors influencing spatial consistency in T2I models. Key findings include:
- Training Data Composition: Models trained with a balanced mix of spatial and general captions from SPRIGHT demonstrate optimal performance, underscoring the value of diverse training data.
- Impact of Caption Length: Longer, spatially focused captions lead to better performance, highlighting the importance of detailed descriptions in training data.
- Exploring the CLIP Text Encoder: Fine-tuning the CLIP text encoder with spatially enriched captions from SPRIGHT results in enhanced semantic understanding of spatial relationships, offering insights into model improvements at a granular level.
Future Directions and Concluding Remarks
The study presents a pivotal step towards understanding and improving spatial consistency in text-to-image generation. The SPRIGHT dataset, accompanied by the identified efficient training methodologies, sets a new benchmark in the generation of spatially accurate images. It opens avenues for further research into the intricate dynamics of language and visual representation in AI models.
The open release of the SPRIGHT dataset by the authors not only invites further exploration in this specific area but also supports broader research initiatives aimed at enhancing the capabilities of generative AI in understanding and visualizing spatial relationships.
In conclusion, the paper articulates the complex challenge of spatial consistency in generative AI, presenting a solid foundation of resources and insights for future advancements in the field. The SPRIGHT dataset, alongside the illustrated training techniques and comprehensive analyses, mark significant milestones in the journey towards more sophisticated text-to-image models that can faithfully interpret and visualize the spatial nuances embedded in textual descriptions.