MtArtGPT: A Multi-Task Art Generation System With Pre-Trained Transformer
Cong Jin, R. Zhu, Zixing Zhu, Lu Yang, Yang Min, Jiebo Luo
Abstract
Instruction tuning large language models are making rapid advances in the field of artificial intelligence where GPT-4 models have exhibited impressive multi-modal perception capabilities. Such models have been used as the core assistant for many tasks including art generation. However, high-quality art generation relies heavily on human prompt engineering which is in general uncontrollable. To address these issues, we propose a multi-task AI generated content (AIGC) system for art generation. Specifically, a dense representation manager is designed to process multi-modal user queries and generate dense and applicable prompts to GPT. To enhance artistic sophistication of the whole system, we fine-tune the GPT model by a meticulously collected prompt-art dataset. Furthermore, we introduce artistic benchmarks for evaluating the system based on professional knowledge. Experiments demonstrate the advantages of our proposed MtArtGPT system.