1 citations · 1 across the 10 of their papers we have counts for
12 papers
ReasonUni: Reasoning-Augmented Reconstruction Advances Unified Multimodal Models
Feize Wu, Zhiyuan Yan, Yuhuan Hong +10
Image-to-text (I2T) understanding and text-to-image (T2I) generation are dual tasks in unified multimodal models (UMMs). Reconstructive post-training couples these tasks, but exist…
UniWorld-Design: From Pixel Generation to Layer-Native Design
Zongjian Li, Zhiyuan Yan, Chenxu Bai +9
We introduce UniWorld-Design, a framework that redefines image generation from flat pixel synthesis to structured visual composition, with semantic RGBA layers as the atomic units…
Helios: Real Real-Time Long Video Generation Model
Shenghai Yuan, Yuanyang Yin, Zongjian Li +3
We introduce Helios, the first 14B video generation model that runs at 19.5 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching the quality of a str…
iFSQ: Improving FSQ for Image Generation with 1 Line of Code
Bin Lin, Zongjian Li, Yuwei Niu +9
The field of image generation is currently bifurcated into autoregressive (AR) models operating on discrete tokens and diffusion models utilizing continuous latents. This divide, r…
Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
Yuwei Niu, Weiyang Jin, Jiaqi Liao +7
Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation in Unified Multimodal…
Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback
Zongjian Li, Zheyuan Liu, Qihui Zhang +10
Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their abi…
Unified Multimodal Models as Auto-Encoders
Zhiyuan Yan, Kaiqing Lin, Zongjian Li +10
Image-to-text (I2T) understanding and text-to-image (T2I) generation are two fundamental, important yet traditionally isolated multimodal tasks. Despite their intrinsic connection,…
UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
Bin Lin, Zongjian Li, Xinhua Cheng +9
Although existing unified models achieve strong performance in vision-language understanding and text-to-image generation, they remain limited in addressing image perception and ma…
ImgEdit: A Unified Image Editing Dataset and Benchmark
Yang Ye, Xianyi He, Zongjian Li +5
Recent advancements in generative models have enabled high-fidelity text-to-image generation. However, open-source image-editing models still lag behind their proprietary counterpa…
Open-Sora Plan: Open-Source Large Video Generation Model
Bin Lin, Yunyang Ge, Xinhua Cheng +21
We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on vari…
WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model
Zongjian Li, Bin Lin, Yang Ye +4
Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model train…
OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model
Liuhan Chen, Zongjian Li, Bin Lin +6
Variational Autoencoder (VAE), compressing videos into latent representations, is a crucial preceding component of Latent Video Diffusion Models (LVDMs). With the same reconstructi…