Hong Gu

SSR not only resolves destructive parameter collisions in LoRA merging but also guarantees mathematical optimality, setting a new standard for efficiency in diffusion model training.

GPTQ-style quantization can be significantly improved by directly aligning quantized layer outputs with the original full-precision model's output, rather than the compensated weights, and accounting for "compensation-aware error."