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Found 4 papers, 1 papers with code
PTQ4DiT: Post-training Quantization for Diffusion Transformers
SSC extends this approach by dynamically adjusting the balanced salience to capture the temporal variations in activation.
On the Faithfulness of Vision Transformer Explanations
In contrast, our proposed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations.
Token Transformation Matters: Towards Faithful Post-hoc Explanation for Vision Transformer
To incorporate the influence of token transformation into interpretation, we propose TokenTM, a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects.
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Diffusion models have achieved remarkable success in image generation tasks, yet their practical deployment is restrained by the high memory and time consumption.
