9 papers
KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers
Yann Bouquet, Alireza Khodamoradi, Kristof Denolf +1
Post-training quantization (PTQ) of diffusion transformers (DiTs) to W4A4 severely degrades output quality, because activations entering each linear layer contain outliers that 4-b…
LoRIF: Low-Rank Influence Functions for Scalable Training Data Attribution
Shuangqi Li, Hieu Le, Jingyi Xu +1
Training data attribution (TDA) identifies which training examples most influenced a model's prediction. Influence function methods are a theoretically grounded family of TDA metho…
LoRaQ: Optimized Low Rank Approximation for 4-bit Quantization
Yann Bouquet, Alireza Khodamoradi, Sophie Yáng Shen +2
Post-training quantization (PTQ) is essential for deploying large diffusion transformers on resource-constrained hardware, but aggressive 4-bit quantization significantly degrades…
Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling
Sooyoung Ryu, Mathieu Salzmann, Saqib Javed
Post-training quantization (PTQ) is a practical path to deploy large diffusion models, but quantization noise can accumulate over the denoising trajectory and degrade generation qu…
Learning to Weight Parameters for Training Data Attribution
Shuangqi Li, Hieu Le, Jingyi Xu +1
We study gradient-based data attribution, aiming to identify which training examples most influence a given output. Existing methods for this task either treat network parameters u…
FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation
Pierre Ancey, Andrew Price, Saqib Javed +1
Estimating the 6-degrees-of-freedom (6DoF) pose of a spacecraft from a single image is critical for autonomous operations like in-orbit servicing and space debris removal. Existing…