DreamLite: A Lightweight On-Device Unified Model for Image Generation and Editing

Diffusion models have made significant progress in both text-to-image (T2I) generation and text-guided image editing. However, thesemodels are typically built with billions of parameters, leading to high la-tency and increased deployment challenges. While on-device diffusionmodels improve efficiency, they largely focus on T2I generation and lacksupport for image editing. In this paper, we propose DreamLite, a com-pact unified on-device diffusion model (0.39B) that supports bothT2I generation and text-guided image editing within a single network.DreamLite is built on a pruned mobile U-Net backbone and unifies con-ditioning through in-context spatial concatenation in the latent space.It concatenates images horizontally as input, using a (target | blank)configuration for generation tasks and (target | source) for editing tasks.To stabilize the training of this compact model, we introduce a task-progressive joint pretraining strategy that sequentially targets T2I, edit-ing, and joint tasks. After high-quality SFT and reinforcement learning,DreamLite achieves GenEval (0.72) for image generation and ImgEdit(4.11) for image editing, outperforming existing on-device models and re-maining competitive with several server-side models. By employing stepdistillation, we further reduce denoising processing to just 4 steps, en-abling our DreamLite could generate or edit a 1024 × 1024 image in lessthan 3s on iPhone 17 pro. To the best of our knowledge, DreamLiteis the first unified on-device diffusion model that supports both imagegeneration and image editing. Our code and model are available at here.