In this paper, we propose DreamLite, a compact unified on-device diffusion model (0.39B) that supports both T2I generation and text-guided image editing within a single network. DreamLite is built on a pruned mobile U-Net backbone and unifies conditioning through In-Context spatial concatenation in the latent space. To stabilize the training of this compact model, we introduce a Task-Progressive Joint pretraining strategy that sequentially targets T2I, editing, and joint tasks. After SFT and RL, DreamLite outperforms existing on-device models and remaining competitive with several server-side models in both generation and editing tasks. By employing step distillation, we further achieve 4-step inferencing, enabling our DreamLite could generate or edit a 1024 × 1024 image in less than 5s on a iPhone17 pro.

Our contributions are summarized as follows:

• We propose, to the best of our knowledge, the first unified on-device model that supports both text-to-image generation and text-based image editing, eliminating the need to deploy two separate models.
• We introduce an in-context conditioning mechanism for UNet to unify generation and editing, and propose a task-progressive joint pretraining scheme (i.e., T2I → Edit → Unified Joint Training) to stably train the model.
• DreamLite achieves competitive performance on standard benchmarks and consistently outperforms prior mobile models. After deployment on mobile device, DreamLite could generate or edit a 1024 × 1024 image in less than 5s.