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, 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 introduce a task-progressive joint pretraining strategy that sequentially targets T2I, editing, 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 remaining competitive with several server-side models. By employing step distillation, we further reduce denoising processing to just 4 steps, enabling our DreamLite could generate or edit a 1024 x 1024 image in less than 1s on a Xiaomi 14 smartphone. To the best of our knowledge, DreamLite is the first unified on-device diffusion model that supports both image generation and image editing.
69cb3e8f22f9e9dce3fe1fe0
Kailai Feng
admin_assigned
2026-03-31T09:48:01.811Z
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kailai feng
carlofkl
user
carlofkl
69cb3e8f22f9e9dce3fe1fe1
Yuxiang Wei
claimed_verified
2026-03-31T09:25:25.733Z
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Yuxiang Wei
csyxwei
user
csyxwei
69cb3e8f22f9e9dce3fe1fe2
Bo Chen
69cb3e8f22f9e9dce3fe1fe3
Yang Pan
69cb3e8f22f9e9dce3fe1fe4
Hu Ye
69cb3e8f22f9e9dce3fe1fe5
Songwei Liu
69cb3e8f22f9e9dce3fe1fe6
Chenqian Yan
69cb3e8f22f9e9dce3fe1fe7
Yuan Gao
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, 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 introduce a task-progressive joint pretraining strategy that sequentially targets T2I, editing, 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 remaining competitive with several server-side models. By employing step distillation, we further reduce denoising processing to just 4 steps, enabling our DreamLite could generate or edit a 1024 x 1024 image in less than 1s on a Xiaomi 14 smartphone. To the best of our knowledge, DreamLite is the first unified on-device diffusion model that supports both image generation and image editing.
DreamLite is a compact unified on-device diffusion model that supports both text-to-image generation and text-guided image editing with efficient training and inference.
diffusion models text-to-image generation text-guided image editing mobile U-Net in-context spatial concatenation task-progressive joint pretraining step distillation denoising processing GenEval ImgEdit