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Personalizing Image Generation with Diffusion Models
This chapter explores the techniques for personalizing image generation using diffusion models, focusing on fine-tuning methods on small datasets. The speakers discuss innovative approaches like unique identifier tokens and the Dreambooth method to enhance image fidelity while avoiding overfitting. Additionally, they highlight the impact of large datasets and pre-trained models on performance, examining the balance between generalization and specificity in generative AI.