RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
Researchers have developed RobuQ, a new framework designed to significantly reduce the computational and memory costs associated with Diffusion Transformers (DiTs) for image generation. This method focuses on robust activation quantization, enabling DiTs to operate at extremely low bit settings, specifically achieving stable image generation on ImageNet-1K with activations quantized to an average of 2 bits. The framework introduces novel techniques like RobustQuantizer and an Activation-only Mixed-Precision Network pipeline to overcome the challenges of quantizing DiT activations. AI
IMPACT Enables more efficient deployment of Diffusion Transformers for image generation, potentially lowering hardware requirements.