FocusDiT: Masking Queries in Diffusion Transformers for Fine-grained Image Generation
Xueji Fang, Liyuan Ma, Jianhao Zeng, Jinjin Cao, Mingyuan Zhou, Guo-Jun Qi
Abstract
Diffusion transformer (DiT) has been widely adopted in the generative diffusion field, advancing the denoising of query tokens through attention and Feed-Forward (\text{FFN}) layers. FFN actually acts as the key-value vocabulary for decoding visual contents where the value embeds the visual semantical knowledge. We present that focusing on critical query tokens corresponding to more complex details and encouraging the model to improve these tokens is essential for fine-grained visual generation. To this end, we propose FocusDiT, which applies a Masking scheme to focus on critical query tokens that are exclusively fed into FFN. The masked queries can retrieve visual tokens from the FFN vocabularies, and use them to decode their visual details. Extensive text-to-image experiments validate the effectiveness of token masking in enhancing generative performance.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.