PTQD: Accurate Post-Training Quantization for Diffusion Models Y efei He

Neural Information Processing Systems 

Diffusion models have recently dominated image synthesis and other related generative tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models less practical for low-latency and scalable real-world applications.

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