Multimodal Diffusion Language Models for Thinking-Aware Editing and Generation
MMaDA-Parallel: Parallel Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation 🌌 Introduction Demo: Parallel text-image generation in action. While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxically degrade performance due to error propagation. To systematically analyze this issue, we propose ParaBench, a new benchmark designed to evaluate both text and image output modalities. Our analysis using ParaBench reveals that this performance degradation is strongly correlated with poor alignment between the generated reasoning and the final image. To resolve this, we propose a parallel multimodal diffusion…
