Latent inpainting in diffusion models still relies almost universally on linearly interpolating VAE latents under a downsampled mask. We propose a key principle for compositing image latents: Pixel-Equivalent Latent Compositing (PELC). An equivalent latent compositor should be the same as compositing in pixel space. This principle enables full-resolution mask control and true soft-edge alpha compositing, even though VAEs compress images 8x spatially. Modern VAEs capture global context beyond patch-aligned local structure, so linear latent blending cannot be pixel-equivalent: it produces large artifacts at mask seams and global degradation and color shifts. We introduce DecFormer, a 7.7M-parameter transformer that predicts per-channel blend weights and an off-manifold residual correction to realize mask-consistent latent fusion. DecFormer is trained so that decoding after fusion matches pixel-space alpha compositing, is plug-compatible with existing diffusion pipelines, requires no backbone finetuning and adds only 0.07% of FLUX.1-Dev's parameters and 3.5% FLOP overhead. On the FLUX.1 family, DecFormer restores global color consistency, soft-mask support, sharp boundaries, and high-fidelity masking, reducing error metrics around edges by up to 53% over standard mask interpolation. Used as an inpainting prior, a lightweight LoRA on FLUX.1-Dev with DecFormer achieves fidelity comparable to FLUX.1-Fill, a fully finetuned inpainting model. While we focus on inpainting, PELC is a general recipe for pixel-equivalent latent editing, as we demonstrate on a complex color-correction task.

翻译：扩散模型中的潜在修复仍然几乎普遍依赖于在下采样掩码下对VAE潜在表示进行线性插值。我们提出了图像潜在合成的一个关键原则：像素等效潜在合成（PELC）。等效的潜在合成器应与像素空间中的合成相同。这一原则实现了全分辨率掩码控制和真正的软边缘阿尔法合成，尽管VAE将图像在空间上压缩了8倍。现代VAE捕获了超出块对齐局部结构的全局上下文，因此线性潜在混合无法实现像素等效：它会在掩码接缝处产生大量伪影，并导致全局质量下降和颜色偏移。我们引入了DecFormer，一个拥有770万参数的Transformer模型，它预测每通道混合权重和离流形残差校正，以实现掩码一致的潜在融合。DecFormer的训练目标是确保融合后的解码与像素空间阿尔法合成相匹配，可与现有扩散流程即插即用，无需主干网络微调，仅增加FLUX.1-Dev模型参数的0.07%和3.5%的FLOP开销。在FLUX.1系列模型上，DecFormer恢复了全局颜色一致性、软掩码支持、锐利边界和高保真掩码处理，将边缘区域的误差指标较标准掩码插值降低了高达53%。作为修复先验，在FLUX.1-Dev上结合DecFormer的轻量级LoRA实现了与完全微调的修复模型FLUX.1-Fill相当的保真度。虽然我们专注于修复任务，但PELC是实现像素等效潜在编辑的通用方案，正如我们在复杂色彩校正任务中所展示的。