Exploring MLLM-Diffusion Information Transfer with MetaCanvas

2025-12-12

AI Summary

Current multimodal large language models (MLLMs) excel at visual understanding but remain limited to serving as global text encoders for diffusion models in image/video generation, hindering fine-grained spatial and spatiotemporal reasoning and planning within latent spaces. To address this, we propose MetaCanvas—a novel framework that pioneers the direct use of MLLMs as latent-space planners for diffusion models. It introduces latent-space instruction injection and cross-modal alignment mechanisms to enable layout control, attribute binding, and knowledge-guided generation. Lightweight and model-agnostic, MetaCanvas seamlessly integrates with mainstream diffusion backbones including SDXL, SVD, and AnimateDiff. Evaluated across six tasks—text/image-to-image/video generation, editing, and contextual video generation—it consistently outperforms global conditioning baselines, achieving significant improvements in structural fidelity and generation controllability. This work effectively bridges the long-standing capability gap between multimodal understanding and generative modeling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Multimodal learning has rapidly advanced visual understanding, largely via multimodal large language models (MLLMs) that use powerful LLMs as cognitive cores. In visual generation, however, these powerful core models are typically reduced to global text encoders for diffusion models, leaving most of their reasoning and planning ability unused. This creates a gap: current multimodal LLMs can parse complex layouts, attributes, and knowledge-intensive scenes, yet struggle to generate images or videos with equally precise and structured control. We propose MetaCanvas, a lightweight framework that lets MLLMs reason and plan directly in spatial and spatiotemporal latent spaces and interface tightly with diffusion generators. We empirically implement MetaCanvas on three different diffusion backbones and evaluate it across six tasks, including text-to-image generation, text/image-to-video generation, image/video editing, and in-context video generation, each requiring precise layouts, robust attribute binding, and reasoning-intensive control. MetaCanvas consistently outperforms global-conditioning baselines, suggesting that treating MLLMs as latent-space planners is a promising direction for narrowing the gap between multimodal understanding and generation.

Problem

• Bridging multimodal understanding and precise visual generation gaps
• Enabling MLLMs to plan in spatial latent spaces for diffusion models
• Achieving structured control in image and video generation tasks

Innovation

• MLLMs plan in latent spaces for diffusion
• Lightweight framework enables precise multimodal control
• Latent-space planning bridges understanding-generation gap