4 papers
Relational Feature Caching for Accelerating Diffusion Transformers
Byunggwan Son, Jeimin Jeon, Jeongwoo Choi +1
Feature caching approaches accelerate diffusion transformers (DiTs) by storing the output features of computationally expensive modules at certain timesteps, and exploiting them fo…
GrowTAS: Progressive Expansion from Small to Large Subnets for Efficient ViT Architecture Search
Hyunju Lee, Youngmin Oh, Jeimin Jeon +2
Transformer architecture search (TAS) aims to automatically discover efficient vision transformers (ViTs), reducing the need for manual design. Existing TAS methods typically train…
AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models
Seunghoon Lee, Jeongwoo Choi, Byunggwan Son +3
We present in this paper a novel post-training quantization (PTQ) method, dubbed AccuQuant, for diffusion models. We show analytically and empirically that quantization errors for…
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
Jeimin Jeon, Youngmin Oh, Junghyup Lee +4
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training stra…