Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor
It addresses the problem of fine-grained visual understanding in multimodal AI for researchers and practitioners, offering an incremental improvement by integrating diffusion features with existing methods.
This work tackles the limitation of CLIP in multimodal large language models by exploring pre-trained text-to-image diffusion models as task-aware visual encoders, finding they capture fine-grained details and enable better image-text alignment, and demonstrates improved performance on vision-centric tasks requiring spatial and compositional reasoning.
Recent advances in multimodal large language models (MLLMs) have enabled image-based question-answering capabilities. However, a key limitation is the use of CLIP as the visual encoder; while it can capture coarse global information, it often can miss fine-grained details that are relevant to the input query. To address these shortcomings, this work studies whether pre-trained text-to-image diffusion models can serve as instruction-aware visual encoders. Through an analysis of their internal representations, we find diffusion features are both rich in semantics and can encode strong image-text alignment. Moreover, we find that we can leverage text conditioning to focus the model on regions relevant to the input question. We then investigate how to align these features with large language models and uncover a leakage phenomenon, where the LLM can inadvertently recover information from the original diffusion prompt. We analyze the causes of this leakage and propose a mitigation strategy. Based on these insights, we explore a simple fusion strategy that utilizes both CLIP and conditional diffusion features. We evaluate our approach on both general VQA and specialized MLLM benchmarks, demonstrating the promise of diffusion models for visual understanding, particularly in vision-centric tasks that require spatial and compositional reasoning. Our project page can be found https://vatsalag99.github.io/mustafar/.