Know, then Draw: A New Learning Paradigm for Visual Generation

Visual generation requires both high-level semantic knowledge and fine-grained
pixel details. We propose a learning paradigm that assigns these roles to autoregressive pretraining for knowledge acquisition and diffusion post-training for
high-fidelity rendering. Within this framework, we study the objective, representation, and scaling of knowledge acquisition and rendering. Controlled comparisons identify autoregression as the stronger pretraining objective for downstream
rendering, improving both learning efficiency and final generation quality. We
further find that AR-only generation quality does not necessarily correlate with
final performance; semantic quality matters more. This motivates a purely semantic tokenizer trained with language supervision alone, without an image decoder
or reconstruction loss. We establish compute-optimal scaling laws for knowledge
acquisition and rendering, and find that autoregressive pretraining lowers the fitted asymptotic rendering loss compared with pixel diffusion models trained from
scratch. Guided by the laws, we train a 3B-parameter text-to-image model with
only 6,600 H100 GPU-hours, achieving 86 on GenEval and 87 on DPG-Bench
and validating the proposed paradigm at scale.