Qwen-Image Technical Report
Chenfei Wu, Jiahao Li, Jingren Zhou +36 authors
Qwen-Image, an image generation model, advances text rendering and image editing through a comprehensive data pipeline, progressive training, and dual-encoding mechanism.
Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
From Editor to Dense Geometry Estimator
JiYuan Wang, Chunyu Lin, Lei Sun +6 authors
FE2E, a framework using a Diffusion Transformer for dense geometry prediction, outperforms generative models in zero-shot monocular depth and normal estimation with improved performance and efficiency.
Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning
Yibin Wang, Zhimin Li, Yuhang Zang +6 authors
Pref-GRPO, a pairwise preference reward-based GRPO method, enhances text-to-image generation by mitigating reward hacking and improving stability, while UniGenBench provides a comprehensive benchmark for evaluating T2I models.
PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity
Kwanyoung Kim, Byeongsu Sim
PLADIS leverages sparse attention in cross-attention layers to enhance pre-trained text-to-image diffusion models, improving text alignment and human preference without additional training.
WithAnyone: Towards Controllable and ID Consistent Image Generation
Hengyuan Xu, Wei Cheng, Peng Xing +8 authors
A diffusion-based model addresses copy-paste artifacts in text-to-image generation by using a large-scale paired dataset and a contrastive identity loss to balance identity fidelity and variation.
Alchemist: Turning Public Text-to-Image Data into Generative Gold
Valerii Startsev, Alexander Ustyuzhanin, Alexey Kirillov +2 authors
A new method using a pre-trained generative model helps construct a high-impact SFT dataset, Alchemist, which improves the generative quality of text-to-image models while maintaining diversity.
DanceOPD: On-Policy Generative Field Distillation
Wei Zhou, Xiongwei Zhu, Zelin Xu +8 authors
A novel on-policy generative field distillation framework called DanceOPD is proposed to unify text-to-image generation, local editing, and global editing capabilities in flow-matching models through capability-specific routing and velocity-based training.
RewardDance: Reward Scaling in Visual Generation
Jie Wu, Yu Gao, Zilyu Ye +9 authors
RewardDance is a scalable reward modeling framework that aligns with VLM architectures, enabling effective scaling of RMs and resolving reward hacking issues in generation models.
ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
Junying Chen, Zhenyang Cai, Pengcheng Chen +5 authors
ShareGPT-4o-Image and Janus-4o enable open research in photorealistic, instruction-aligned image generation through a large dataset and multimodal model.
An Empirical Study of GPT-4o Image Generation Capabilities
Sixiang Chen, Jinbin Bai, Zhuoran Zhao +16 authors
An empirical study of GPT-4o's image generation capabilities across multiple tasks reveals its strengths and limitations compared to other models, highlighting the importance of architectural design and data scaling in unified generative frameworks.
MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation
Weimin Wang, Jiawei Liu, Zhijie Lin +9 authors
MagicVideo-V2 generates high-fidelity and smooth videos from text using an integrated pipeline that includes text-to-image, video motion generation, and frame interpolation modules, outperforming existing systems in user evaluations.
Kandinsky 3.0 Technical Report
Vladimir Arkhipkin, Andrei Filatov, Viacheslav Vasilev +6 authors
Kandinsky 3.0, a large-scale text-to-image model based on latent diffusion, improves quality and realism through a larger architecture and advanced text understanding.
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
Yaohui Wang, Xinyuan Chen, Xin Ma +17 authors
LaVie, a text-to-video framework integrating cascaded video latent diffusion models, achieves state-of-the-art video generation by leveraging temporal self-attentions and joint image-video fine-tuning.
ChatAnything: Facetime Chat with LLM-Enhanced Personas
Yilin Zhao, Xinbin Yuan, Shanghua Gao +4 authors
A framework for generating anthropomorphized personas with diverse voices and appearances from text descriptions using LLMs and generative models, with improved face landmark detection for automatic animation.
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Chong Mou, Xintao Wang, Jiechong Song +2 authors
DragonDiffusion, a novel image editing method, enables precise manipulation of generated or real images using classifier guidance and multi-scale attention mechanisms without requiring fine-tuning.
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model
Lingmin Ran, Xiaodong Cun, JiaWei Liu +5 authors
X-Adapter upgrades text-to-image diffusion models like SDXL to work with existing plugins without retraining, through feature remapping and a null-text training strategy.
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