Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls

Summary

This paper talks about Noise Consistency Training, a new method that allows AI models called one-step generators to learn new controls efficiently without needing to be fully retrained.

What's the problem?

Usually, when AI models want to learn extra controls or conditions to change how they generate content, they require a lot of time and computing power to retrain the whole model, which is expensive and slow.

What's the solution?

The researchers introduced Noise Consistency Training, which works by adding a small adapter to the existing model and training it to maintain consistent behavior when noise is added in a special way during the generation process. This method injects new controls directly into the model without needing the original training data or full retraining.

Why it matters?

This matters because it makes it much faster and cheaper to improve AI content generators with new features, enabling more flexible and controllable content creation in fields like image and video generation.