從RGB生成到稠密場讀取：基於文本到圖像模型的像素空間稠密預測

摘要

大规模文本到图像模型因其RGB生成预训练能够学习丰富的语义、结构与几何先验，成为密集预测任务中极具吸引力的主干网络。现有生成式与编辑方法通过将密集预测重构为目标生成任务来复用这些先验：深度、法线、Alpha遮罩、掩膜与热力图等标注被编码至基于RGB训练的VAE潜空间，再解码为类似图像的目标输出。我们认为这种方式继承了生成式输出接口中超出密集预测需求的部分——与RGB合成不同，密集预测要求在同一图像平面上生成像素精确、任务原生的场域，而非渲染新的RGB内容。我们的核心观察在于：预训练的DiT已通过从图块到令牌再到图块的晶格结构在图像平面上组织RGB输入，因此每个令牌索引一个固定输出图块，其通道可承载任务原生量而非RGB外观。我们将其实现为ReChannel：保留VAE编码器以适配DiT的输入分布，但移除目标端的解码器，通过任务LoRA微调冻结的DiT，并利用共享的令牌级线性头部（约33K参数，无空间混合）将每个令牌映射至其p×p×K_t像素空间图块。基于FLUX-Klein，我们在六项密集预测任务与十余个基准上进行了评估。这一极简接口在无三元图抠图、KITTI深度与指代分割任务上创下新纪录，并在法线、显著性及姿态估计中保持竞争力。在匹配的4B参数设置下，其精度更高且速度比"编辑+潜解码"方案快2.48倍——密集感知可从生成式预训练中获益，而无需继承其输出接口。

English

Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets. We argue this inherits more of the generative output interface than dense prediction requires: unlike RGB synthesis, dense prediction asks for pixel-correct, task-native fields on the same image plane, not new RGB content to be rendered. Our key observation is that a pretrained DiT already organizes RGB inputs through a patch-to-token-to-patch lattice on the image plane, so each token indexes a fixed output patch whose channels can carry task-native quantities instead of RGB appearance. We instantiate this as ReChannel: we keep the VAE encoder for the DiT's input distribution but drop the target-side decoder, adapt the frozen DiT with task LoRA, and map each token to its p x p x K_t pixel-space patch through a shared token-local linear head--about 33K parameters, no spatial mixing. Using FLUX-Klein, we evaluate on six dense prediction tasks and over a dozen benchmarks. This minimal interface sets new state-of-the-art on trimap-free matting, KITTI depth, and referring segmentation, and stays competitive on normals, saliency, and pose. In a matched 4B setting it is more accurate and 2.48x faster than an edit-plus-latent-decode counterpart--dense perception can benefit from generative pretraining without inheriting its output interface.