1 citations · 3 across the 21 of their papers we have counts for
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Learning Quantized Adaptive Conditions for Diffusion Models
Yuchen Liang, Yuchuan Tian, Lei Yu +4
The curvature of ODE trajectories in diffusion models hinders their ability to generate high-quality images in a few number of function evaluations (NFE). In this paper, we propose…
Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation
Yuchuan Tian, Jianhong Han, Hanting Chen +5
Due to the unaffordable size and intensive computation costs of low-level vision models, All-in-One models that are designed to address a handful of low-level vision tasks simultan…
U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers
Yuchuan Tian, Zhijun Tu, Hanting Chen +3
Diffusion Transformers (DiTs) introduce the transformer architecture to diffusion tasks for latent-space image generation. With an isotropic architecture that chains a series of tr…
DiJiang: Efficient Large Language Models through Compact Kernelization
Hanting Chen, Zhicheng Liu, Xutao Wang +2
In an effort to reduce the computational load of Transformers, research on linear attention has gained significant momentum. However, the improvement strategies for attention mecha…
PanGu- Pro:Rethinking Optimization and Architecture for Tiny Language Models
Yehui Tang, Kai Han, Fangcheng Liu +7
The power of large language models (LLMs) has been demonstrated through numerous data and computing resources. However, the application of language models on mobile devices is faci…