Four Bits

An exploration of the FLUX.2 [dev] model, a state-of-the-art diffusion model, reveals how 4-bit quantization can be leveraged to optimize image generation with minimal quality loss, achieving a 1.6x speed improvement. This process involves compressing the model's weights, which naturally leads to some information loss, but innovative techniques such as blockwise quantization and static post-training quantization help mitigate these effects. The model's architecture incorporates multiple transformer blocks and matrix multiplications, with a significant focus on optimizing the inference process by using lower precision FP4 computations instead of the standard BFloat16. By quantizing the model's weights and activations, and using a fixed global maximum for scaling, the team manages to maintain image quality while substantially reducing computation time. The effort underscores the potential of custom kernel engineering and inference optimization in enhancing model performance, exemplified by a significant reduction in latency from 2.776s to 1.81s.