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Jiazi Bu

7 accepted papers

2026

DiCache: Let Diffusion Model Determine Its Own Cache

ICLR 2026poster

Recent years have witnessed the rapid development of acceleration techniques for diffusion models, especially caching-based acceleration methods. These studies seek to answer two fundamental questions: _"When to cache"_ and _"How to use cache"_, typically relying on predefined empirical laws or data…

Cited by 0SourcecodeScholar
2026

Fine-Grained GRPO for Precise Preference Alignment in Flow Models

CVPR 2026

The incorporation of online reinforcement learning (RL) into diffusion and flow-based generative models has recently gained attention as a powerful paradigm for aligning model behavior with human preferences. By leveraging stochastic sampling via Stochastic Differential Equations (SDEs) during the d

Cited by 0SourcecodeScholar
2025

ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way

CVPR 2025poster

The text-to-video (T2V) generation models, offering convenient visual creation, have recently garnered increasing attention. Despite their substantial potential, the generated videos may present artifacts, including structural implausibility, temporal inconsistency, and a lack of motion, often resul…

2025

HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance

NeurIPS 2025poster

Text-to-image (T2I) diffusion/flow models have drawn considerable attention recently due to their remarkable ability to deliver flexible visual creations. Still, high-resolution image synthesis presents formidable challenges due to the scarcity and complexity of high-resolution content. Recent appro…

Cited by 0SourceScholar
2025

MotionClone: Training-Free Motion Cloning for Controllable Video Generation

ICLR 2025poster

Motion-based controllable video generation offers the potential for creating captivating visual content. Existing methods typically necessitate model training to encode particular motion cues or incorporate fine-tuning to inject certain motion patterns, resulting in limited flexibility and generaliz…

Cited by 34SourcePDFScholar
2024

Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance

NeurIPS 2024poster

The trade-off between cost and performance has been a longstanding and critical issue for deep neural networks. One key factor affecting the computational cost is the width of each layer. However, in practice, the width of layers in a neural network is mostly empirically determined. In this paper,…

Cited by 1SourcePDFScholar
2024

ViTree: Single-Path Neural Tree for Step-Wise Interpretable Fine-Grained Visual Categorization

AAAI 2024technical

As computer vision continues to advance and finds widespread applications across various domains, the need for interpretability in deep learning models becomes paramount. Existing methods often resort to post-hoc techniques or prototypes to explain the decision-making process, which can be indirect…