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Found 5 papers, 3 papers with code
TextCraftor: Your Text Encoder Can be Image Quality Controller
Our findings reveal that, instead of replacing the CLIP text encoder used in Stable Diffusion with other large language models, we can enhance it through our proposed fine-tuning approach, TextCraftor, leading to substantial improvements in quantitative benchmarks and human assessments.
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds
We achieve so by introducing efficient network architecture and improving step distillation.
Rethinking Vision Transformers for MobileNet Size and Speed
With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to optimize the performance and complexity of ViTs to enable efficient deployment on mobile devices.
Real-Time Neural Light Field on Mobile Devices
Nevertheless, to reach a similar rendering quality as NeRF, the network in NeLF is designed with intensive computation, which is not mobile-friendly.
EfficientFormer: Vision Transformers at MobileNet Speed
Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance.
