22 citations · 34 across the 14 of their papers we have counts for
14 papers
WanSong v1.0 Technical Report
Binghui Chen, Pandeng Li, Yu Liu +1
Music generation foundation models have recently attracted significant industry attention. However, achieving efficient generation and high-fidelity long-form audio while supportin…
Video = World + Event Stream
Lianghua Huang, Zhi-Fan Wu, Yupeng Shi +24
We present Wan-Streamer v0.3, which reframes our native-streaming interaction model under a single organizing view: a video is a world plus an event stream. The world is the persis…
Wan-Streamer v0.2: Higher Resolution, Same Latency
Lianghua Huang, Zhi-Fan Wu, Yupeng Shi +23
We present Wan-Streamer v0.2, a latency-preserving upgrade of the native-streaming, end-to-end audio-visual interaction model. v0.2 keeps the v0.1 modeling formulation, but raises…
Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation
Tianhe Wu, Kun Yan, Zikai Zhou +23
Few-step distillation has emerged as a critical component in the development of advanced visual generative foundation models, substantially reducing inference overhead while enabli…
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…
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…
ChatDiT: A Training-Free Baseline for Task-Agnostic Free-Form Chatting with Diffusion Transformers
Lianghua Huang, Wei Wang, Zhi-Fan Wu +7
Recent research arXiv:2410.15027 arXiv:2410.23775 has highlighted the inherent in-context generation capabilities of pretrained diffusion transformers (DiTs), enabling them to seam…
In-Context LoRA for Diffusion Transformers
Lianghua Huang, Wei Wang, Zhi-Fan Wu +6
Recent research arXiv:2410.15027 has explored the use of diffusion transformers (DiTs) for task-agnostic image generation by simply concatenating attention tokens across images. Ho…
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…
Group Diffusion Transformers are Unsupervised Multitask Learners
Lianghua Huang, Wei Wang, Zhi-Fan Wu +6
While large language models (LLMs) have revolutionized natural language processing with their task-agnostic capabilities, visual generation tasks such as image translation, style t…
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
Yujie Wei, Shiwei Zhang, Zhiwu Qing +6
Customized generation using diffusion models has made impressive progress in image generation, but remains unsatisfactory in the challenging video generation task, as it requires t…
I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models
Shiwei Zhang, Jiayu Wang, Yingya Zhang +6
Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accurac…
Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following
Yutong Feng, Biao Gong, Di Chen +3
Existing text-to-image (T2I) diffusion models usually struggle in interpreting complex prompts, especially those with quantity, object-attribute binding, and multi-subject descript…
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…