10 papers
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…
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…
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…
Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes
Saqib Javed, Ahmad Jarrar Khan, Corentin Dumery +2
Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make…
All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds
Shuangqi Li, Hieu Le, Jingyi Xu +1
Text-to-image diffusion models have demonstrated remarkable capability in generating realistic images from arbitrary text prompts. However, they often produce inconsistent results…
6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction
Théo Gieruc, Marius Kästingschäfer, Sebastian Bernhard +1
Current 3D reconstruction techniques struggle to infer unbounded scenes from a few images faithfully. Specifically, existing methods have high computational demands, require detail…