2 code implementations • 12 Mar 2024 • Weijia Wu, Zhuang Li, YuChao Gu, Rui Zhao, Yefei He, David Junhao Zhang, Mike Zheng Shou, Yan Li, Tingting Gao, Di Zhang
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation.
1 code implementation • 25 Feb 2024 • Luoming Zhang, Yefei He, Wen Fei, Zhenyu Lou, Weijia Wu, YangWei Ying, Hong Zhou
Our framework outperforms previous methods by approximately 1\% for 8-bit PTQ and 2\% for 6-bit PTQ, showcasing its superior performance.
1 code implementation • 24 Nov 2023 • Weijia Wu, Zhuang Li, Yefei He, Mike Zheng Shou, Chunhua Shen, Lele Cheng, Yan Li, Tingting Gao, Di Zhang, Zhongyuan Wang
In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation.
no code implementations • 7 Oct 2023 • Luoming Zhang, Wen Fei, Weijia Wu, Yefei He, Zhenyu Lou, Hong Zhou
Fine-grained quantization has smaller quantization loss, consequently achieving superior performance.
1 code implementation • 5 Oct 2023 • Yefei He, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
In this paper, we introduce a data-free and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency.
1 code implementation • NeurIPS 2023 • Weijia Wu, Yuzhong Zhao, Hao Chen, YuChao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, Chunhua Shen
To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.
no code implementations • ICCV 2023 • Yefei He, Zhenyu Lou, Luoming Zhang, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
no code implementations • 14 Nov 2022 • Yefei He, Zhenyu Lou, Luoming Zhang, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
no code implementations • 16 May 2022 • Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
Extensive experiments demonstrate that the proposed method yields surprising performance both in image classification and human pose estimation tasks.
Ranked #1 on Binarization on ImageNet (Top 1 Accuracy metric)
no code implementations • 8 Apr 2022 • Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization.