LLMGA: Multimodal Large Language Model based Generation Assistant

• URL: http://arxiv.org/abs/2311.16500v4
• Date: Sat, 27 Jul 2024 14:22:42 GMT
• Title: LLMGA: Multimodal Large Language Model based Generation Assistant
• Authors: Bin Xia, Shiyin Wang, Yingfan Tao, Yitong Wang, Jiaya Jia,
• Abstract summary: We introduce a Multimodal Large Language Model-based Generation Assistant (LLMGA) to assist users in image generation and editing. We train the MLLM to grasp the properties of image generation and editing, enabling it to generate detailed prompts. Extensive results show that LLMGA has promising generation and editing capabilities and can enable more flexible and expansive applications.
• Score: 53.150283805515926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a Multimodal Large Language Model-based Generation Assistant (LLMGA), leveraging the vast reservoir of knowledge and proficiency in reasoning, comprehension, and response inherent in Large Language Models (LLMs) to assist users in image generation and editing. Diverging from existing approaches where Multimodal Large Language Models (MLLMs) generate fixed-size embeddings to control Stable Diffusion (SD), our LLMGA provides a detailed language generation prompt for precise control over SD. This not only augments LLM context understanding but also reduces noise in generation prompts, yields images with more intricate and precise content, and elevates the interpretability of the network. To this end, we curate a comprehensive dataset comprising prompt refinement, similar image generation, inpainting \& outpainting, and instruction-based editing. Moreover, we propose a two-stage training scheme. In the first stage, we train the MLLM to grasp the properties of image generation and editing, enabling it to generate detailed prompts. In the second stage, we optimize SD to align with the MLLM's generation prompts. Additionally, we propose a reference-based restoration network to alleviate texture, brightness, and contrast disparities between generated and preserved regions during inpainting and outpainting. Extensive results show that LLMGA has promising generation and editing capabilities and can enable more flexible and expansive applications in an interactive manner.

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