SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models

Abstract: Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of LLMs to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts. The code is released at https://github.com/Qrange-group/SUR-adapter.

• The paper introduces SUR-adapter to enhance semantic understanding in diffusion models via efficient knowledge distillation from large language models.
• A parameter-efficient fine-tuning strategy aligns narrative prompts with complex cues, maintaining high-quality image generation.
• Experiments on the SURD dataset show improved CLIP scores and semantic accuracy, demonstrating enhanced commonsense reasoning in generated images.

Overview of "SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with LLMs"

The paper presents a novel approach to enhance the semantic understanding and reasoning capabilities of text-to-image diffusion models. The proposed method, the Semantic Understanding and Reasoning adapter (SUR-adapter), aims to bridge the gap between simple narrative prompts and complex keyword-based prompts by leveraging LLMs.

Diffusion models have shown significant capability in generating high-quality, content-rich images from textual prompts. However, these models often struggle with semantic understanding and commonsense reasoning, particularly when input prompts are concise narratives. This limitation necessitates complex and elaborate prompt designs to achieve high-quality image generation. The SUR-adapter addresses this limitation by introducing a parameter-efficient fine-tuning strategy, which enhances diffusion models' ability to interpret and reason about narrative prompts without degrading image quality.

Methodology and Dataset

The study introduces a new dataset, SURD, which comprises over 57,000 semantically enriched multimodal samples. Each sample consists of a simple narrative prompt, its corresponding complex prompt, and a high-quality image. This dataset serves as the foundation for transferring semantic and reasoning capabilities to diffusion models.

To facilitate this transfer, the paper proposes the SUR-adapter:

1. Knowledge Distillation: The adapter transfers knowledge from LLMs to diffusion models. This process enhances the text encoder's ability to generate high-quality textual representations for image synthesis.
2. Representation Alignment: The approach aligns the semantic representation of simple prompts with complex prompts using the collected dataset. Knowledge from LLMs is integrated through the adapter to enrich the semantic comprehension of concise narrative text inputs.
3. Performance Maintenance: The model maintains the image quality of pre-trained diffusion models during fine-tuning to prevent degradation in generation performance.

Experimental Results

Experiments leverage multiple LLMs and well-known diffusion models to validate the effectiveness of the SUR-adapter. Key findings include:

• The SUR-adapter significantly enhances the semantic accuracy and commonsense reasoning capabilities of diffusion models across various prompt types. Enhancements are quantitatively validated using metrics such as CLIP scores and semantic accuracy rates for action, color, and counting prompts.
• The method maintains the image generation quality, which is confirmed through analyses involving no-reference image quality assessment metrics and user preference studies.
• Ablation studies reveal that larger LLMs or deeper LLM layers potentially contribute to better diffusion model performance, albeit the current implementation of SUR-adapter can only distill limited semantic information from these models.

Implications and Future Work

The implications of this research are multifaceted. Practically, it offers a pathway to improve user experience in text-to-image generation interfaces by allowing more intuitive and straightforward prompt inputs without compromising image quality. Theoretically, it opens avenues for integrating more advanced reasoning capabilities into multimodal models by leveraging the growing capabilities of LLMs.

The paper also highlights potential limitations, including the challenge of comprehensive semantic alignment and the limited scope of knowledge transfer from LLMs. Addressing these could involve expanding the dataset scope or scaling the adapter's architecture to harness more semantic capabilities effectively.

Overall, the study provides a substantial contribution to the field of text-to-image generation, offering insights that could drive further research into multimodal model enhancement using large-scale LLMs.