[New post] How to use safetensors with automatic1111?

How to use safetensors with automatic1111?

Andre Wagner

May 9

How to use safetensors with automatic1111? If you're working with machine learning models, it's important to ensure the safety and privacy of your data. One way to do this is by using SafeTensors, a library that provides a secure environment for running computations on sensitive data. In this article, we'll explore how to use SafeTensors with Automatic1111, an open-source platform for building conversational AI.

What are SafeTensors?

SafeTensors is a library developed by OpenMined, a community-driven organization that aims to make the world's data more accessible and secure. It provides a way to perform computations on encrypted data without revealing the raw data to the computation. This makes it possible to train machine learning models on sensitive data while preserving privacy.

The key idea behind SafeTensors is homomorphic encryption. Homomorphic encryption is a technique that allows computations to be performed directly on encrypted data, without decryption. This means that the data remains encrypted throughout the computation, and the result is also encrypted. The owner of the data can then decrypt the result using a secret key.

Using SafeTensors with Automatic1111

Now that we understand what SafeTensors is, let's take a look at how to use it with Automatic1111. Automatic1111 is an open-source platform that makes it easy to build conversational AI agents. It uses GPT-3a state-of-the-art language model developed by OpenAI, to generate responses to user input.

To use SafeTensors with Automatic1111, we need to modify our code to work with encrypted data. Here's an example of how to do this:

Import SafeTensors and other necessary libraries:

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import torch  import syft as sy  sy.load("numpy")  from sympc.session import Session  from sympc.tensor import MPCTensor    hook = sy.TorchHook(torch)

Set up a session for the computation:

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alice = sy.VirtualWorker(hook, id="alice")    session = Session(parties=[alice])

Create an MPCTensor object for our data:

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data = [1, 2, 3, 4, 5]  xt = torch.tensor(data).share(alice, alice, crypto_provider=alice)  mpc_data = MPCTensor(secret=xt, shape=(5,), session=session)

In this example, we create a list of numbers and convert it to a PyTorch tensor. We then encrypt the tensor using the share() function, which splits the tensor into shares and distributes them between two workers: alice (who holds one share) and bob (who holds the other share). We also specify that alice should be the crypto provider, which means that she will perform all the cryptographic operations.

Finally, we create an MPCTensor object from the encrypted tensor by passing it to the constructor along with the shape of the tensor and the session object.

Use the MPCTensor in our computation:

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result = mpc_data + mpc_data

Here, we perform a simple addition operation on the MPCTensor. The result is also an MPCTensor, so it remains encrypted.

Decrypt the result:

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result = result.reconstruct()  print(result)

Finally, we call the reconstruct() function to decrypt the result and print it out. In this case, the result will be a PyTorch tensor containing the sum of our original data: [2, 4, 6, 8, 10].

Conclusion

In conclusion, SafeTensors is a powerful library that allows us to perform computations on encrypted data without compromising privacy. By using SafeTensors with Automatic1111, we can build conversational AI agents that operate on sensitive data in a secure and privacy-preserving manner.

While the process of working with encrypted data can be more complex than working with unencrypted data, it's essential for protecting the confidentiality of our users' information.

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