How do you fix token prediction anomalies during the training phase of a text-to-image generator

Question

Can i know How do you fix token prediction anomalies during the training phase of a text-to-image generator?

Answer 1

Token prediction anomalies in a text-to-image generator can be addressed by using better tokenization strategies, fine-tuning on high-quality aligned datasets, applying regularization techniques, and monitoring loss convergence during training.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses well-structured, high-quality text descriptions to minimize ambiguous tokens.
• Applies robust tokenization with padding and truncation to prevent misalignment.
• Implements anomaly detection for loss values (NaN or Inf) during training.

Hence, by using thoughtful tokenization, high-quality input data, and monitoring model behavior, we mitigate token prediction anomalies and improve text-to-image generation stability.