Title: M-COLOR: MLLM-GUIDED DIFFUSION MODELS FOR IMAGE COLORIZATION
Primary area: generative models
Keywords: language-based image colorization, diffusion models

Abstract: Language-based Image colorization transforms grayscale images into vivid, visually pleasing colorized outputs with semantic guidance. Existing methods often rely on CLIP text embeddings, which may struggle with deep semantic understanding, leading to suboptimal colorization. In this paper, we propose M-Color, a novel diffusion-based framework that leverages multimodal large language models (MLLMs) to enhance language comprehension through an Adaptive Decoding strategy. To maintain structural consistency, we introduce a Luminance-Aware Encoder (LAE) that aligns grayscale images with the colorized output and a Luminance Extraction Module (LEM) to integrate luminance information into the latent generation process. Extensive experiments demonstrate that M-Color achieves superior semantic alignment, improves structural consistency, and outperforms state-of-the-art methods in both quantitative and qualitative evaluations.

TL;DR: The paper proposes M-Color, a diffusion-based method that uses multimodal language models and luminance cues for semantically aligned, structurally consistent image colorization.