2 citations · 3 across the 9 of their papers we have counts for
10 papers · 1 filter
Autoregressive Visual Generation Needs a Prologue
Bowen Zheng, Weijian Luo, Guang Yang +2
In this work, we propose Prologue, an approach to bridging the reconstruction-generation gap in autoregressive (AR) image generation. Instead of modifying visual tokens to satisfy…
Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation
Bowen Zheng, Weijian Luo, Guang Yang +2
Most discrete visual tokenizers rely on a default design: every position in the sequence shares the same codebook. Researchers try to scale the codebook size to get better reco…
TDM-R1: Reinforcing Few-Step Diffusion Models with Non-Differentiable Reward
Yihong Luo, Tianyang Hu, Weijian Luo +1
While few-step generative models have enabled powerful image and video generation at significantly lower cost, generic reinforcement learning (RL) paradigms for few-step models rem…
Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation
Yihong Luo, Tianyang Hu, Weijian Luo +2
This paper addresses the challenge of achieving high-quality and fast image generation that aligns with complex human preferences. While recent advancements in diffusion models and…
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…
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…
Flow Generator Matching
Zemin Huang, Zhengyang Geng, Weijian Luo +1
In the realm of Artificial Intelligence Generated Content (AIGC), flow-matching models have emerged as a powerhouse, achieving success due to their robust theoretical underpinnings…
One-Step Diffusion Distillation through Score Implicit Matching
Weijian Luo, Zemin Huang, Zhengyang Geng +2
Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples. This has motivated the…
David and Goliath: Small One-step Model Beats Large Diffusion with Score Post-training
Weijian Luo, Colin Zhang, Debing Zhang +1
We propose Diff-Instruct* (DI*), a data-efficient post-training approach for one-step text-to-image generative models to improve its human preferences without requiring image data.…
Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences
Weijian Luo
One-step text-to-image generator models offer advantages such as swift inference efficiency, flexible architectures, and state-of-the-art generation performance. In this paper, we…