1 citations · 2 across the 7 of their papers we have counts for
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Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
Natalia Frumkin, Diana Marculescu
Text-to-image diffusion models are computationally intensive, often requiring dozens of forward passes through large transformer backbones. For instance, Stable Diffusion XL genera…
Jumping through Local Minima: Quantization in the Loss Landscape of Vision Transformers
Natalia Frumkin, Dibakar Gope, Diana Marculescu
Quantization scale and bit-width are the most important parameters when considering how to quantize a neural network. Prior work focuses on optimizing quantization scales in a glob…
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers
Natalia Frumkin, Dibakar Gope, Diana Marculescu
When considering post-training quantization, prior work has typically focused on developing a mixed precision scheme or learning the best way to partition a network for quantizatio…