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cs.CV2026

FocusDiT: Masking Queries in Diffusion Transformers for Fine-grained Image Generation

Xueji Fang, Liyuan Ma, Jianhao Zeng +3

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}) layer…

cs.CV2026

Equilibrated Diffusion: Frequency-aware Textual Embedding for Equilibrated Image Customization

Liyuan Ma, Xueji Fang, Guo-Jun Qi

Image customization learns target subjects from reference concept images and generates conditioned images per text prompts, mainly modifying styles or backgrounds. Prevailing metho…

cs.CV2025

When Images Speak Louder: Mitigating Language Bias-induced Hallucinations in VLMs through Cross-Modal Guidance

Jinjin Cao, Zhiyang Chen, Zijun Wang +3

Vision-Language Models (VLMs) have shown solid ability for multimodal understanding of both visual and language contexts. However, existing VLMs often face severe challenges of hal…

cs.CV2025

Self-Guidance: Boosting Flow and Diffusion Generation on Their Own

Tiancheng Li, Weijian Luo, Zhiyang Chen +2

Proper guidance strategies are essential to achieve high-quality generation results without retraining diffusion and flow-based text-to-image models. Existing guidance either requi…

cs.CV2025

InfLVG: Reinforce Inference-Time Consistent Long Video Generation with GRPO

Xueji Fang, Liyuan Ma, Zhiyang Chen +2

Recent advances in text-to-video generation, particularly with autoregressive models, have enabled the synthesis of high-quality videos depicting individual scenes. However, extend…

cs.CV2025

Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation

Zilyu Ye, Zhiyang Chen, Tiancheng Li +3

Diffusion and flow matching models have achieved remarkable success in text-to-image generation. However, these models typically rely on the predetermined denoising schedules for a…