Researchers from NVIDIA and Tel Aviv University Introduce Perfusion: A Compact 100 KB Neural Network with Efficient Training Time

Text-to-image(T2I)  models have ushered in a new era of technological flexibility, granting users the power to direct the creative process through natural language inputs. However, personalizing these models to align precisely with user-provided visual concepts has proven challenging. T2I personalization encompasses formidable challenges, such as balancing high visual fidelity and creative control, effectively combining multiple personalized ideas within a single image, and optimizing the model's size for efficient performance. A groundbreaking personalization method called 'Perfusion' has been developed to address these challenges. The essence of Perfusion lies in its ability to employ dynamic rank-1 updates to the underlying T2I model.

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