6 citations · 9 across the 19 of their papers we have counts for
19 papers · 1 filter
GeoStream: Toward Precise Camera Controlled Streaming Video Generation
Yizhou Zhao, Yifan Wang, Xiaoyuan Wang +11
Accurate interactive camera control is essential for video-based world models, but most existing approaches learn camera motion implicitly, leading to inaccurate control under out-…
Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation
Xiaomeng Yang, Yanyu Li, Gordon Guocheng Qian +7
While personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end generation, current practice requires training a separa…
EFlow: Fast Few-Step Video Generator Training from Scratch via Efficient Solution Flow
Dogyun Park, Yanyu Li, Sergey Tulyakov +1
Scaling video diffusion transformers is fundamentally bottlenecked by two compounding costs: the expensive quadratic complexity of attention per step, and the iterative sampling st…
S2DiT: Sandwich Diffusion Transformer for Mobile Streaming Video Generation
Lin Zhao, Yushu Wu, Aleksei Lebedev +11
Diffusion Transformers (DiTs) have recently improved video generation quality. However, their heavy computational cost makes real-time or on-device generation infeasible. In this w…
SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
Dongting Hu, Aarush Gupta, Magzhan Gabidolla +12
Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and mem…
Diffusion-DRF: Free, Rich, and Differentiable Reward for Video Diffusion Fine-Tuning
Yifan Wang, Yanyu Li, Gordon Guocheng Qian +3
Video diffusion alignment has been heavily relied on scalar rewards. These rewards are typically derived from learned reward models in human preference datasets, requiring addition…
Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion Transformers
Dogyun Park, Moayed Haji-Ali, Yanyu Li +5
Diffusion Transformers (DiTs) deliver state-of-the-art generative performance but their quadratic training cost with sequence length makes large-scale pretraining prohibitively exp…
Taming Diffusion Transformer for Efficient Mobile Video Generation in Seconds
Yushu Wu, Yanyu Li, Anil Kag +9
Diffusion Transformers (DiT) have shown strong performance in video generation tasks, but their high computational cost makes them impractical for resource-constrained devices like…
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion
Yanyu Li, Pencheng Wan, Liang Han +3
Stable Diffusion has advanced text-to-image synthesis, but training models to generate images with accurate object quantity is still difficult due to the high computational cost an…
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models
Yushu Wu, Yanyu Li, Ivan Skorokhodov +6
Autoencoder (AE) is the key to the success of latent diffusion models for image and video generation, reducing the denoising resolution and improving efficiency. However, the power…
Improving the Diffusability of Autoencoders
Ivan Skorokhodov, Sharath Girish, Benran Hu +5
Latent diffusion models have emerged as the leading approach for generating high-quality images and videos, utilizing compressed latent representations to reduce the computational…
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training
Dongting Hu, Jierun Chen, Xijie Huang +16
Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to…
SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device
Yushu Wu, Zhixing Zhang, Yanyu Li +11
We have witnessed the unprecedented success of diffusion-based video generation over the past year. Recently proposed models from the community have wielded the power to generate c…
Efficient Training with Denoised Neural Weights
Yifan Gong, Zheng Zhan, Yanyu Li +6
Good weight initialization serves as an effective measure to reduce the training cost of a deep neural network (DNN) model. The choice of how to initialize parameters is challengin…
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
Yang Sui, Yanyu Li, Anil Kag +7
Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a…
SF-V: Single Forward Video Generation Model
Zhixing Zhang, Yanyu Li, Yushu Wu +9
Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models requir…
TextCraftor: Your Text Encoder Can be Image Quality Controller
Yanyu Li, Xian Liu, Anil Kag +6
Diffusion-based text-to-image generative models, e.g., Stable Diffusion, have revolutionized the field of content generation, enabling significant advancements in areas like image…
EGAN: Efficient Training of Efficient GANs for Image-to-Image Translation
Yifan Gong, Zheng Zhan, Qing Jin +8
One highly promising direction for enabling flexible real-time on-device image editing is utilizing data distillation by leveraging large-scale text-to-image diffusion models to ge…
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion
Xian Liu, Jian Ren, Aliaksandr Siarohin +6
Despite significant advances in large-scale text-to-image models, achieving hyper-realistic human image generation remains a desirable yet unsolved task. Existing models like Stabl…