Dense Prediction with Image Generation Models
Zanyi Wang · hf · 2026-07-13
This paper proposes a novel approach adapting pre-trained text-to-image diffusion Transformers for dense prediction.
The core idea is to bypass RGB image generation entirely. Instead, tokens are mapped directly to the task's native outputs, enabling pixel-level dense regression or classification. The authors note that this method requires minimal additional parameters yet achieves state-of-the-art performance across multiple dense prediction tasks.
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