Multimodal LLMs for Image Generation: EasyRef Sets New Standards

Personalized image generation with diffusion models has recently made remarkable progress. Traditional tuning-free methods typically encode reference images by averaging their image embeddings. However, this image-independent approach does not allow for interaction between the images to capture consistent visual elements across multiple references. Tuning-based methods like Low-Rank Adaptation (LoRA) can effectively extract such consistent elements, but they require specific fine-tuning for each individual image group.

EasyRef presents a new approach: a plug-and-play adaptation method that allows diffusion models to respond to multiple reference images and text prompts. To effectively utilize consistent visual elements in multiple images, EasyRef relies on the multimodal understanding and instruction-following capabilities of Multimodal Large Language Models (MLLMs). The MLLM is instructed to capture consistent visual elements based on the instructions. Embedding the MLLM representations into the diffusion process via adapters enables generalization to unknown domains and the extraction of consistent visual elements from unseen data.

Efficiency and Detail Fidelity through Innovative Strategies

To reduce computational cost and improve detail fidelity, EasyRef introduces an efficient reference aggregation strategy and a progressive training scheme. Reference aggregation allows for effective summarization of visual information from multiple reference images. The progressive training scheme optimizes the learning process by gradually considering increasingly complex aspects of image generation.

MRBench: A New Benchmark for Multi-Reference Image Generation

MRBench introduces a new benchmark for multi-reference image generation. This benchmark allows for a comprehensive evaluation of EasyRef's performance compared to other methods. The results show that EasyRef outperforms both tuning-free methods like IP-Adapter and tuning-based methods like LoRA, achieving superior aesthetic quality and robust zero-shot generalization across various domains.

The Role of Multimodal LLMs

The use of MLLMs is central to the functionality of EasyRef. These models are capable of understanding and processing complex relationships between text and images. By combining text prompts and multiple reference images, EasyRef can leverage the strengths of both information sources, thereby improving the quality and consistency of the generated images.

Applications and Future Prospects

EasyRef opens up new possibilities for personalized image generation in various application areas, including design, art, and e-commerce. The ability to extract consistent visual elements from multiple reference images allows for the creation of images that meet the individual desires and ideas of users. Future research could focus on expanding the functionality of EasyRef, for example, by integrating further modalities such as audio or 3D models.

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