Poly-OPD：面向能力可选择流模型的异构多教师在线策略蒸馏

摘要

领先的开源文生图模型往往具备互补优势：一个可能在偏好对齐的美学效果上领先，而另一个则能更忠实地遵循构图指令。然而，由于各模型的自编码器和噪声调度存在差异，这些优势难以在模型之间迁移。本文提出了 Poly-OPD 框架，能够将异构教师模型的互补优势整合到一个紧凑的流匹配学生模型中。为弥合不同教师模型之间不兼容的潜在空间，Poly-OPD 通过像素桥进行同策略蒸馏。每个学生模型生成的图像经选定教师模型的编码器重新编码，并在该教师模型的噪声调度下从幅度匹配的噪声水平进行细化。所得目标进一步在冻结的 DINOv2 空间与学生模型进行匹配，从而在不兼容的潜在空间之间实现监督。为在无跨教师干扰的情况下保留互补能力，Poly-OPD 使用梯度兼容性诊断来组织其适配器：注意力 LoRA 模块在教师模型之间共享，而前馈适配器则保持教师特定。在蒸馏过程中，差距感知课程将更多训练分配给学生模型仍落后于教师模型的构图类别。随着各差距逐渐缩小，训练转向剩余差距更大的类别。通过将 FLUX.1-dev 和 Z-Image 蒸馏到 2.5B 的 SD3.5-Medium 学生模型中，Poly-OPD 将 GenEval 从 67.3 提升到 73.3，超越了两个更大的教师模型，并将 DrawBench HPSv3 从 9.34 提高到 11.35，从而在一个可切换模型中整合了双方的优点。

English

Leading open text-to-image models often carry complementary strengths: one may lead on preference-aligned aesthetics while another follows compositional instructions more faithfully. However, differences in their autoencoders and noise schedules make it difficult to transfer these strengths across models. In this paper, we present Poly-OPD, a framework that can consolidate complementary strengths of heterogeneous teachers into a single compact flow-matching student. To bridge the incompatible latent spaces of different teachers, Poly-OPD performs on-policy distillation through a pixel bridge. Each student-generated image is re-encoded by a selected teacher's encoder and refined from a noise level matched by magnitude under the teacher's noise schedule. The resulting target is further matched to the student in frozen DINOv2 space, enabling supervision across incompatible latent spaces. To retain complementary capabilities without cross-teacher interference, Poly-OPD uses a gradient compatibility diagnostic to organize its adapters: attention LoRA modules are shared across teachers, whereas feed-forward adapters remain teacher-specific. During distillation, a gap-aware curriculum devotes more training to compositional categories where the student still falls short of the teacher. As each gap narrows, training shifts toward categories with larger remaining gaps. By distilling FLUX.1-dev and Z-Image into a 2.5B SD3.5-Medium student, Poly-OPD improves GenEval from 67.3 to 73.3, surpassing both larger teachers, and raises DrawBench HPSv3 from 9.34 to 11.35, consolidating both strengths within a switchable model.