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Recalibrating the CFG scale in response to CIM noise can restore over 87% of generation quality lost due to nonidealities in Diffusion Transformers.
Hybrid-LUT slashes LUT storage by two-thirds while enhancing image denoising quality, outperforming existing methods by a significant margin.
Accepting selective mismatches in autoregressive decoding can boost throughput by over 15% without any additional training or model adjustments.
