Planning a motion in a cluttered environment is a recurring task autonomous agents need to solve. This paper presents a first attempt to learn generative models for collision-free trajectory generation based on conditioned score-based models. Given multiple navigation tasks, environment maps and collision-free trajectories pre-computed with a sample-based planner, using a signed distance function loss we learn a vision encoder of the map and use its embedding to learn a conditioned score-based model for trajectory generation. A novelty of our method is to integrate in a temporal U-net architecture, using a cross-attention mechanism, conditioning variables such as the latent representation of the environment and task features. We validate our approach in a simulated 2D planar navigation toy task, where a robot needs to plan a path that avoids obstacles in a scene.