172 citations · 199 across the 16 of their papers we have counts for
8 papers · 1 filter
Conditional Text-to-Image Generation with Reference Guidance
Taewook Kim, Ze Wang, Zhengyuan Yang +4
Text-to-image diffusion models have demonstrated tremendous success in synthesizing visually stunning images given textual instructions. Despite remarkable progress in creating hig…
Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization
Zichen Miao, Zhengyuan Yang, Kevin Lin +4
Recent advancements in timestep-distilled diffusion models have enabled high-quality image generation that rivals non-distilled multi-step models, but with significantly fewer infe…
AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition
Minheng Ni, Chenfei Wu, Huaying Yuan +6
With the advancement of generative models, the synthesis of different sensory elements such as music, visuals, and speech has achieved significant realism. However, the approach to…
MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities
Weihao Yu, Zhengyuan Yang, Lingfeng Ren +7
MM-Vet, with open-ended vision-language questions targeting at evaluating integrated capabilities, has become one of the most popular benchmarks for large multimodal model evaluati…
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation
Yuanhao Zhai, Kevin Lin, Linjie Li +7
Significant advances have been made in human-centric video generation, yet the joint video-depth generation problem remains underexplored. Most existing monocular depth estimation…
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
Zecheng Tang, Chenfei Wu, Zekai Zhang +8
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model…
A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization
Jiazhao Zhang, Ying Hung, Chung-Ching Lin +1
Choosing appropriate hyperparameters plays a crucial role in the success of neural networks as hyper-parameters directly control the behavior and performance of the training algori…
Bring Metric Functions into Diffusion Models
Jie An, Zhengyuan Yang, Jianfeng Wang +4
We introduce a Cascaded Diffusion Model (Cas-DM) that improves a Denoising Diffusion Probabilistic Model (DDPM) by effectively incorporating additional metric functions in training…