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Post-Training Quantization on Diffusion Models (CVPR 2023) This is the demonstration video of our paper “DapQ-DiT: Distribution-Aware Introduction about Towards Accurate Post-Training Quantization for Vision Transformer (ACM MM 2022) Shrink your models and speed up inference — all without retraining! This video'll explore step-by-step Try Voice Writer - speak your thoughts and let AI handle the grammar: Four techniques to optimize the speed ... On this AI Research Roundup, host Alex dives into a fascinating paper tackling model efficiency: SVDQuant: Absorbing Outliers by ...
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[ICCV 2025] DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization
Xiuyu Li - Q-Diffusion: Quantizing Diffusion Models
ACM ICMR 2026 DapQ-DiT: Distribution Aware Post-Training Quantization for Efficient Generative Tasks
Get Started Post-Training Dynamic Quantization | AI Model Optimization with Intel® Neural Compressor
Introduction about Towards Accurate Post-Training Quantization for Vision Transformer (ACM MM 2022)
CVPR2026 - Sampling-Aware Quantization for Diffusion Models
From FP32 to INT8: Post-Training Quantization Explained in PyTorch
On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks (Apr 2026)
Recipes for Post-training Quantization of Deep Neural Networks (Abstract)
Diffusion Adversarial Post-Training for One-Step Video Generation
Quantization vs Pruning vs Distillation: Optimizing NNs for Inference
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Last Updated: June 7, 2026
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