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Post-Training Quantization
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
Published:6/25/2024
Diffusion Model QuantizationPost-Training QuantizationDiffusion TransformerDynamic Activation QuantizationImageNet Dataset
The paper introduces QDiT, a method for accurate quantization of Diffusion Transformers (DiTs), addressing spatial and temporal variance in weights and activations. By combining automatic quantization and samplewise dynamic activation quantization, QDiT reduces computational c
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OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
Published:1/23/2025
LLM QuantizationOrthogonal and Scaling TransformationsQuantization Space Utilization RateKL-Top Loss FunctionPost-Training Quantization
The OSTQuant method optimizes large language model quantization using orthogonal and scaling transformations, addressing uneven and heavytailed data distributions. Introducing Quantization Space Utilization Rate (QSUR) effectively assesses quantizability, while the KLTop loss f
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