Dense Text-to-Image Generation with Attention Modulation (2308.12964v1)

Abstract: Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions, where each text prompt provides a detailed description for a specific image region. To address this, we propose DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle such dense captions while offering control over the scene layout. We first analyze the relationship between generated images' layouts and the pre-trained model's intermediate attention maps. Next, we develop an attention modulation method that guides objects to appear in specific regions according to layout guidance. Without requiring additional fine-tuning or datasets, we improve image generation performance given dense captions regarding both automatic and human evaluation scores. In addition, we achieve similar-quality visual results with models specifically trained with layout conditions.

Dense Text-to-Image Generation with Attention Modulation: An Overview

This paper introduces DenseDiffusion, a novel approach for improving text-to-image generation using a training-free method for attention modulation. The focus lies on enhancing the fidelity of pre-trained diffusion models, specifically in handling dense captions and enabling user control over image layouts.

Core Contributions

DenseDiffusion addresses critical challenges in existing text-to-image models, such as handling dense captions and offering spatial control without costly fine-tuning. The approach focuses on the relationship between image layouts and the pre-trained model’s attention maps. By modulating these attention maps in real-time, the method guides the generation process in accordance with textual descriptions and predefined layouts.

Methodology

1. Attention Modulation:
   • The technique involves augmenting intermediate cross-attention and self-attention maps to align with layout specifications. The modulation is adaptive, taking into account the original score range and segment areas, thus preserving the integrity of the pre-trained model's capabilities.
2. Adaptive Techniques:
   • Value-range Adapting: Adjusts modulation intensity based on the original attention values to minimize performance degradation.
   • Mask-area Adapting: Calibrates modulation considering the area of each segment, which is essential for handling objects of varying sizes.
3. Implementation:
   • DenseDiffusion employs Stable Diffusion and conducts experiments over 50 DDIM denoising steps, leveraging textual encodings for distinct text segments to improve clarity when closely related objects are present.

Results and Evaluation

Quantitative and qualitative evaluations reveal that DenseDiffusion surpasses existing methods in adhering to both textual and layout conditions. The evaluation metrics include CLIP-Score, SOA-I score, and IoU, combined with human preference studies, consistently favoring DenseDiffusion over other baselines.

1. Comparative Analysis:
   • The method outperformed baselines like SD-Pww and Structure Diffusion, showing superior capability in faithfully rendering detailed descriptions and maintaining spatial alignment.
2. Layout-conditioned Features:
   • DenseDiffusion’s training-free modulation equaled, and at times surpassed, models specifically trained for layout adherence.

Implications and Future Directions

By eliminating the need for extensive retraining, DenseDiffusion offers a practical solution for integrating detailed textual information and spatial control within pre-trained models. This method not only streamlines computational resources but also enhances flexibility in adapting to new user-defined conditions.

Potential future developments could focus on refining attention modulation techniques to handle finer granularity in layout details, thus broadening application scope. Additionally, integrating more robust segmentation models could further empower the synthesis process.

Conclusion

DenseDiffusion provides a significant contribution to the field of text-to-image generation by improving model fidelity to dense textual prompts while maintaining a user-friendly, layout-aware interface. This approach marks a step forward in computational efficiency and practical applicability for AI-driven image synthesis.