29 citations · 42 across the 19 of their papers we have counts for
18 papers · 1 filter
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
Dense2MoE: Restructuring Diffusion Transformer to MoE for Efficient Text-to-Image Generation
Youwei Zheng, Yuxi Ren, Xin Xia +2
Diffusion Transformer (DiT) has demonstrated remarkable performance in text-to-image generation; however, its large parameter size results in substantial inference overhead. Existi…
Hyper-Bagel: A Unified Acceleration Framework for Multimodal Understanding and Generation
Yanzuo Lu, Xin Xia, Manlin Zhang +4
Unified multimodal models have recently attracted considerable attention for their remarkable abilities in jointly understanding and generating diverse content. However, as context…
Seedream 4.0: Toward Next-generation Multimodal Image Generation
Team Seedream, :, Yunpeng Chen +48
We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image compositi…
Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis
Yanzuo Lu, Yuxi Ren, Xin Xia +6
Distribution Matching Distillation (DMD) is a promising score distillation technique that compresses pre-trained teacher diffusion models into efficient one-step or multi-step stud…
SeedEdit 3.0: Fast and High-Quality Generative Image Editing
Peng Wang, Yichun Shi, Xiaochen Lian +5
We introduce SeedEdit 3.0, in companion with our T2I model Seedream 3.0, which significantly improves over our previous SeedEdit versions in both aspects of edit instruction follow…
Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation
Shanchuan Lin, Ceyuan Yang, Hao He +6
Existing large-scale video generation models are computationally intensive, preventing adoption in real-time and interactive applications. In this work, we propose autoregressive a…
Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
Team Seawead, Ceyuan Yang, Zhijie Lin +52
This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parame…
Seedream 3.0 Technical Report
Yu Gao, Lixue Gong, Qiushan Guo +28
We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in…
Training-free Diffusion Acceleration with Bottleneck Sampling
Ye Tian, Xin Xia, Yuxi Ren +6
Diffusion models have demonstrated remarkable capabilities in visual content generation but remain challenging to deploy due to their high computational cost during inference. This…
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
Lixue Gong, Xiaoxia Hou, Fanshi Li +25
Rapid advancement of diffusion models has catalyzed remarkable progress in the field of image generation. However, prevalent models such as Flux, SD3.5 and Midjourney, still grappl…
Diffusion Adversarial Post-Training for One-Step Video Generation
Shanchuan Lin, Xin Xia, Yuxi Ren +3
The diffusion models are widely used for image and video generation, but their iterative generation process is slow and expansive. While existing distillation approaches have demon…
ByteEdit: Boost, Comply and Accelerate Generative Image Editing
Yuxi Ren, Jie Wu, Yanzuo Lu +11
Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks. Despite these…
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis
Yuxi Ren, Xin Xia, Yanzuo Lu +5
Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Mod…
UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
Jiacheng Zhang, Jie Wu, Yuxi Ren +9
Latent diffusion models (LDM) have revolutionized text-to-image generation, leading to the proliferation of various advanced models and diverse downstream applications. However, de…
VmambaIR: Visual State Space Model for Image Restoration
Yuan Shi, Bin Xia, Xiaoyu Jin +5
Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks…
ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
Jiaxiang Cheng, Pan Xie, Xin Xia +7
Recent advancement in text-to-image models (e.g., Stable Diffusion) and corresponding personalized technologies (e.g., DreamBooth and LoRA) enables individuals to generate high-qua…
Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
Weifeng Chen, Yatai Ji, Jie Wu +6
Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts. However, extending these techniques to video g…