12 citations · 24 across the 14 of their papers we have counts for
15 papers
Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
Chaojie Mao, Chen-Wei Xie, Chongyang Zhong +55
We present Wan-Image, a unified visual generation system explicitly engineered to paradigm-shift image generation models from casual synthesizers into professional-grade productivi…
Wan-Weaver: Interleaved Multi-modal Generation via Decoupled Training
Jinbo Xing, Zeyinzi Jiang, Yuxiang Tuo +15
Recent unified models have made unprecedented progress in both understanding and generation. However, while most of them accept multi-modal inputs, they typically produce only sing…
Unison: A Fully Automatic, Task-Universal, and Low-Cost Framework for Unified Understanding and Generation
Shihao Zhao, Yitong Chen, Zeyinzi Jiang +5
Unified understanding and generation is a highly appealing research direction in multimodal learning. There exist two approaches: one trains a transformer via an auto-regressive pa…
Wan: Open and Advanced Large-Scale Video Generative Models
Team Wan, Ang Wang, Baole Ai +58
This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transfo…
VACE: All-in-One Video Creation and Editing
Zeyinzi Jiang, Zhen Han, Chaojie Mao +3
Diffusion Transformer has demonstrated powerful capability and scalability in generating high-quality images and videos. Further pursuing the unification of generation and editing…
ICE-Bench: A Unified and Comprehensive Benchmark for Image Creating and Editing
Yulin Pan, Xiangteng He, Chaojie Mao +4
Image generation has witnessed significant advancements in the past few years. However, evaluating the performance of image generation models remains a formidable challenge. In thi…
ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling
Chaojie Mao, Jingfeng Zhang, Yulin Pan +4
We report ACE++, an instruction-based diffusion framework that tackles various image generation and editing tasks. Inspired by the input format for the inpainting task proposed by…
ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer
Zhen Han, Zeyinzi Jiang, Yulin Pan +5
Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primari…
SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning
Yuze Zhao, Jintao Huang, Jinghan Hu +10
Recent development in Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) have leverage Attention-based Transformer architectures and achieved superior perfo…
StyleBooth: Image Style Editing with Multimodal Instruction
Zhen Han, Chaojie Mao, Zeyinzi Jiang +2
Given an original image, image editing aims to generate an image that align with the provided instruction. The challenges are to accept multimodal inputs as instructions and a scar…
Locate, Assign, Refine: Taming Customized Promptable Image Inpainting
Yulin Pan, Chaojie Mao, Zeyinzi Jiang +3
Prior studies have made significant progress in image inpainting guided by either text description or subject image. However, the research on inpainting with flexible guidance or c…
Res-Attn : An Enhanced Res-Tuning Approach with Lightweight Attention Mechanism
Chaojie Mao, Zeyinzi Jiang
Res-Tuning introduces a flexible and efficient paradigm for model tuning, showing that tuners decoupled from the backbone network can achieve performance comparable to traditional…
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
Zeyinzi Jiang, Chaojie Mao, Yulin Pan +2
Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that ma…
Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang +5
Parameter-efficient tuning has become a trend in transferring large-scale foundation models to downstream applications. Existing methods typically embed some light-weight tuners in…
Rethinking Efficient Tuning Methods from a Unified Perspective
Zeyinzi Jiang, Chaojie Mao, Ziyuan Huang +3
Parameter-efficient transfer learning (PETL) based on large-scale pre-trained foundation models has achieved great success in various downstream applications. Existing tuning metho…