Key Points

1. DiffusionGPT is a unified text-to-image generation system that leverages Large Language Models (LLM) to seamlessly handle diverse input prompts and integrates domain-expert models for high-quality image generation.

2. Diffusion models, such as Stable Diffusion (SD) and SDXL, have revolutionized image generation, but there are challenges with diverse inputs and model limitations which are addressed by DiffusionGPT.

3. DiffusionGPT uses a Large Language Model (LLM) as the cognitive engine to process diverse inputs, offers compatibility with a wide range of diffusion models, and achieves higher accuracy through the incorporation of the Tree-of-Thought (ToT) and human feedback.

4. DiffusionGPT outperforms traditional stable diffusion models, excels in generating more realistic results with finer details, and exhibits potential for community development in image generation.

5. DiffusionGPT constructs a Tree-of-Thought (ToT) structure based on prior knowledge and human feedback to guide the selection of an appropriate model for generating the desired image.

6. The system incorporates a Model Tree based on the concept of Tree-of-Thought (ToT) and uses human feedback to align the model selection process with human preferences, achieving higher accuracy and effectiveness in image generation.

7. DiffusionGPT introduces a Prompt Extension Agent to enrich input prompts, enhancing the quality and detail of the generated outputs.

8. The system includes human feedback and advantage databases to align the model selection process with human preferences, achieving improvements in image aesthetics and alignment with user preferences.

9. Diffusion-GPT provides an efficient and effective pathway for community development in the field of image generation and aims to address challenges through feedback-driven optimization, expansion of model candidates, and broader application of its capabilities.

Summary

The paper introduces DiffusionGPT, a new text-to-image generation system that addresses the challenges faced by current stable diffusion models in realistic scenarios. The proposed system leverages Large Language Models (LLM) to accommodate various types of prompts and integrate domain-expert models for output. It uses the Tree-of-Thought (ToT) structure to guide model selection and Advantage Databases for human feedback. DiffusionGPT showcases potential for pushing the boundaries of image synthesis in diverse domains.

The paper highlights the challenges faced by current stable diffusion models and the limitations in handling diverse inputs and prompt types. It also discusses the contributions of DiffusionGPT, including its use of LLM, the ToT structure, and Advantage Databases to address these challenges and drive the text-to-image generation system. The paper provides a detailed overview of the DiffusionGPT workflow, including the Prompt Parse, Model Building and Searching, Model Selection, and Execution Generation stages.

Additionally, it discusses the integration of Large Language Models (LLMs) and various domain-expert generative models from open-source communities, and the use of human feedback and Advantage Databases in model selection. The paper also includes a comprehensive review and analysis of the proposed system's effectiveness through experimental results and comparisons with existing models. Furthermore, it outlines the limitations of the system and proposes future plans for feedback-driven optimization, expansion of model candidates, and applicability to broader tasks. The paper includes detailed experimental results, user studies, and visual analysis to validate the effectiveness of DiffusionGPT.

Overall, the paper provides a comprehensive overview of DiffusionGPT and its contributions to the field of text-to-image generation.

Reference: https://arxiv.org/abs/2401.10061