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Diffusion models have opened up new avenues for the field of imagegeneration, resulting in the proliferation of high-quality models shared onopen-source platforms. However, a major challenge persists in currenttext-to-image systems are often unable to handle diverse inputs, or are limitedto single model results. Current unified attempts often fall into twoorthogonal aspects: i) parse Diverse Prompts in input stage; ii) activateexpert model to output. To combine the best of both worlds, we proposeDiffusionGPT, which leverages Large Language Models (LLM) to offer a unifiedgeneration system capable of seamlessly accommodating various types of promptsand integrating domain-expert models. DiffusionGPT constructs domain-specificTrees for various generative models based on prior knowledge. When providedwith an input, the LLM parses the prompt and employs the Trees-of-Thought toguide the selection of an appropriate model, thereby relaxing input constraintsand ensuring exceptional performance across diverse domains. Moreover, weintroduce Advantage Databases, where the Tree-of-Thought is enriched with humanfeedback, aligning the model selection process with human preferences. Throughextensive experiments and comparisons, we demonstrate the effectiveness ofDiffusionGPT, showcasing its potential for pushing the boundaries of imagesynthesis in diverse domains.

DiffusionGPT: LLM-Driven Text-to-Image Generation System