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Found 2 papers, 1 papers with code
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization
In particular, for the former, we introduce a learnable per-channel dual clipping scheme, which is designed to efficiently identify outliers in the unbalanced activations with fine granularity.
Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models
Specifically, at the calibration sample level, we select calibration samples based on the density and diversity in the latent space, thus facilitating the alignment of their distribution with the overall samples; and at the reconstruction output level, we propose Fine-grained Block Reconstruction, which can align the outputs of the quantized model and the full-precision model at different network granularity.
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