Know, then Draw: A New Learning Paradigm for Visual Generation
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper proposes a two-stage paradigm for visual generation: autoregressive pretraining for semantic knowledge acquisition, followed by diffusion post-training for high-fidelity rendering. Controlled comparisons identify autoregression as the stronger pretraining objective for downstream rendering. The authors find that semantic quality matters more than AR-only generation quality, motivating a semantic tokenizer trained with language supervision alone. They report compute-optimal scaling laws and a 3B-parameter text-to-image model trained with 6,600 H100 GPU-hours, scoring 86 on GenEval and 87 on DPG-Bench.
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- Primary sourceHugging Face Daily Papers
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The paper proposes a two-stage paradigm for visual generation: autoregressive pretraining for semantic knowledge acquisition, followed by diffusion post-training for high-fidelity rendering. Controlled comparisons identify autoregression as the stronger pretraining objective for downstream rendering. The authors find that semantic quality matters more than AR-only generation quality, motivating a semantic tokenizer trained with language supervision alone. They report compute-optimal scaling laws and a 3B-parameter text-to-image model trained with 6,600 H100 GPU-hours, scoring 86 on GenEval and 87 on DPG-Bench.
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Key findings from the stored brief
- Autoregressive pretraining is reported as a stronger objective than pixel diffusion from scratch for downstream rendering.
- AR-only generation quality does not necessarily correlate with final performance; semantic quality matters more.
- A purely semantic tokenizer trained with language supervision alone is proposed, without an image decoder or reconstruction loss.
- A 3B text-to-image model trained with 6,600 H100 GPU-hours scores 86 on GenEval and 87 on DPG-Bench.
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Why this may matter
The work suggests semantic knowledge and pixel rendering can be learned in separate stages, potentially lowering compute needed for strong text-to-image models. The semantic-tokenizer finding could simplify visual generation pipelines.
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