While recent Flow Matching models avoid the reconstruction bottlenecks of latent autoencoders by operating directly in pixel space, the lack of semantic continuity in the pixel manifold severely intertwines optimal transport paths. This induces severe trajectory conflicts near intersections, yielding sub-optimal solutions. Rather than bypassing this issue via information-lossy latent representations, we directly untangle the pixel-space trajectories by proposing Waypoint Diffusion Transformers (WiT). WiT factorizes the continuous vector field via intermediate semantic waypoints projected from pre-trained vision models. It effectively disentangles the generation trajectories by breaking the optimal transport into prior-to-waypoint and waypoint-to-pixel segments. Specifically, during the iterative denoising process, a lightweight generator dynamically infers these intermediate waypoints from the current noisy state. They then continuously condition the primary diffusion transformer via the Just-Pixel AdaLN mechanism, steering the evolution towards the next state, ultimately yielding the final RGB pixels. Evaluated on ImageNet 256x256, WiT beats strong pixel-space baselines, accelerating JiT training convergence by 2.2x. Code will be publicly released at https://github.com/hainuo-wang/WiT.git.

翻译：摘要：尽管近期流匹配模型通过直接在像素空间中操作避免了潜在自编码器的重建瓶颈，但像素流形中语义连续性的缺失严重交织了最优传输路径。这会在交叉点附近引发严重轨迹冲突，导致次优解。我们并未通过有损潜在表示绕过此问题，而是提出路径点扩散Transformer（WiT）直接解耦像素空间轨迹。WiT通过预训练视觉模型投影的中间语义路径点分解连续向量场，将最优传输分解为"先验至路径点"与"路径点至像素"两阶段，从而有效解耦生成轨迹。具体而言，在迭代去噪过程中，轻量级生成器从当前噪声状态动态推断这些中间路径点，并通过"仅像素自适应层归一化"机制持续调控主扩散Transformer的演化方向，引导其向下一状态演进，最终生成RGB像素。在ImageNet 256×256数据集上的评估表明，WiT击败了强像素空间基线方法，将JiT训练收敛速度提升2.2倍。代码将开源至https://github.com/hainuo-wang/WiT.git。