24 citations · 35 across the 7 of their papers we have counts for
8 papers
WithEveryone: Unified Planning and Identity Grounding for Group Image Generation
Hengyuan Xu, Qixun Wang, Yiji Cheng +5
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every refe…
InstantCharacter: Personalize Any Characters with a Scalable Diffusion Transformer Framework
Jiale Tao, Yanbing Zhang, Qixun Wang +9
Current learning-based subject customization approaches, predominantly relying on U-Net architectures, suffer from limited generalization ability and compromised image quality. Mea…
InstantIR: Blind Image Restoration with Instant Generative Reference
Jen-Yuan Huang, Haofan Wang, Qixun Wang +4
Handling test-time unknown degradation is the major challenge in Blind Image Restoration (BIR), necessitating high model generalization. An effective strategy is to incorporate pri…
CSGO: Content-Style Composition in Text-to-Image Generation
Peng Xing, Haofan Wang, Yanpeng Sun +5
The diffusion model has shown exceptional capabilities in controlled image generation, which has further fueled interest in image style transfer. Existing works mainly focus on tra…
InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
Haofan Wang, Peng Xing, Renyuan Huang +3
Style transfer is an inventive process designed to create an image that maintains the essence of the original while embracing the visual style of another. Although diffusion models…
InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
Haofan Wang, Matteo Spinelli, Qixun Wang +3
Tuning-free diffusion-based models have demonstrated significant potential in the realm of image personalization and customization. However, despite this notable progress, current…
InstantID: Zero-shot Identity-Preserving Generation in Seconds
Qixun Wang, Xu Bai, Haofan Wang +5
There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by…
1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop
Qixun Wang, Xiaofeng Guo, Haofan Wang
Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes. Existing PSG methods utilize one-…