5 papers · 1 filter
CentriQ: Calibration-Free Quantization of Diffusion Transformers via Exact Mean Centering
Nataša Jovanović, Mathieu Salzmann, Saqib Javed
Diffusion transformers (DiTs) achieve state-of-the-art image generation, but their sampling cost limits deployment. Quantizing both weights and activations to 4 bits reduces this c…
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…
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…
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…