WiT: Waypoint Diffusion Transformers via Trajectory Conflict Navigation
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.
69ba66395d2b9c28ea09f2f7
Hainuo Wang
69ba66395d2b9c28ea09f2f8
Mingjia Li
admin_assigned
2026-03-18T10:02:50.288Z
645dcca4f4f49de580a18479
/avatars/ef263ba876c70cb127b75b75085d24b3.svg
Mingjia Li
lime-j
user
lime-j
69ba66395d2b9c28ea09f2f9
Xiaojie Guo
admin_assigned
2026-03-18T10:02:56.756Z
646ab8f631fbfc5df81f36c6
/avatars/64611016748beda20a28b3bdd4fa1009.svg
Xiaojie Guo
xguo7
user
xguo7
WiT: Waypoint Diffusion Transformers via Trajectory Conflict Navigation
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.
Waypoint Diffusion Transformers address trajectory conflicts in pixel-space flow matching by using semantic waypoints from pre-trained vision models to disentangle generation paths and accelerate training convergence.
Flow Matching pixel space optimal transport trajectory conflicts Waypoint Diffusion Transformers semantic waypoints pre-trained vision models diffusion transformer Just-Pixel AdaLN iterative denoising RGB pixels