Breaking the Accuracy-Speed Barrier: How MXFP4/6 Quantization Revolutionizes Image and Video Generation
Source: https://github.com/ROCm/rocm-blogs/blob/b3bf907bd73c596c49c77b1d178c7c41bcd2713c/blogs/artificial-intelligence/mxfp-t2i-t2v/README.md
Attribution: Hongyi Yao, Zhe Li, Han Wang, Xuanwu Yin, Dong Li, Steve Reinhardt, Ephrem Wu, Emad Barsoum; Copyright (c) 2025 Advanced Micro Devices, Inc. (AMD)
Licence: MIT
Licence terms: https://github.com/ROCm/rocm-blogs/blob/b3bf907bd73c596c49c77b1d178c7c41bcd2713c/blogs/artificial-intelligence/mxfp-t2i-t2v/README.md#L28-L48
Evidence: https://github.com/ROCm/rocm-blogs/blob/b3bf907bd73c596c49c77b1d178c7c41bcd2713c/blogs/artificial-intelligence/mxfp-t2i-t2v/README.md#L28-L48
Changes: Selected only verified original or explicitly rerun measurements; mapped exact release labels; normalized percentages by 100 where recorded; retained raw values and protocol limits; added extraction and attribution metadata. No measurements rerun or estimated by whatmodel.sh.
Scope: Only the retained original measurements and documentation within the explicitly licensed artifact. No licence inferred for external leaderboards, copied third-party scores, model weights, generated media, or benchmark inputs.

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