TL;DR
DreamLite is a compact, unified on-device diffusion model supporting both image generation and editing, achieving high efficiency and quality on smartphones.
Contribution
It introduces the first unified on-device diffusion model capable of both image generation and editing with a lightweight architecture and task-specific pretraining.
Findings
Achieves GenEval score of 0.72 for image generation.
Attains ImgEdit score of 4.11 for image editing.
Can generate or edit a 1024x1024 image in less than 1 second on a smartphone.
Abstract
Diffusion models have made significant progress in both text-to-image (T2I) generation and text-guided image editing. However, these models are typically built with billions of parameters, leading to high latency and increased deployment challenges. While on-device diffusion models improve efficiency, they largely focus on T2I generation and lack support for image editing. 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. 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…
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