Training Stable Diffusion with Dreambooth using 🧨 Diffusers

Dreambooth is a technique to teach new concepts to Stable Diffusion using a specialized form of fine-tuning. Some people have been using it with a few of their photos to place themselves in fantastic situations, while others are using it to incorporate new styles. 🧨 Diffusers provides a Dreambooth training script. It doesn’t take long to train, but it’s hard to select the right set of hyperparameters and it’s easy to overfit. We conducted a lot of experiments to analyze […]

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Sentiment Analysis on Encrypted Data with Homomorphic Encryption

It is well-known that a sentiment analysis model determines whether a text is positive, negative, or neutral. However, this process typically requires access to unencrypted text, which can pose privacy concerns. Homomorphic encryption is a type of encryption that allows for computation on encrypted data without needing to decrypt it first. This makes it well-suited for applications where user’s personal and potentially    

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Accelerating Document AI

Enterprises are full of documents containing knowledge that isn’t accessible by digital workflows. These documents can vary from letters, invoices, forms, reports, to receipts. With the improvements in text, vision, and multimodal AI, it’s now possible to unlock that information. This post shows you how your teams can use open-source models to build custom solutions for free! Document AI includes many data science tasks from image classification, image to text, document question answering, table question answering, and visual question answering. […]

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Director of Machine Learning Insights [Part 4]

If you’re interested in building ML solutions faster visit: hf.co/support today! 👋 Welcome back to our Director of ML Insights Series! If you missed earlier Editions you can find them here: 🚀 In this fourth installment, you’ll hear what the following top Machine Learning Directors say about Machine Learning’s impact on their respective industries: Javier Mansilla, Shaun Gittens, Samuel Franklin, and Evan Castle.    

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