Suggestion for Sequential Interpolation

"Another of my lazily made article (because of some AI-made text). Might be helpful."

Interpolating between models is a powerful technique in machine learning and generative systems. By blending models incrementally, we can create a range of outputs that transition smoothly from one source to another.

Guide Table

By following this gradual interpolation method, the process ensures a smooth, controlled progression while allowing for customization and exploration of new traits. Each step serves as a bridge, offering incremental changes that enable precise refinement of the final output.

Advantages of this Interpolation Process

1. Gradual Transition

By blending models in small increments (e.g., 95% of the previous model + 5% of Model D), each new model is a gradual transformation from the previous one. This allows for smooth transitions without sudden or drastic changes, providing more control over the process.

2. Customizable Blending

The approach allows flexibility in adjusting the interpolation ratio. By tweaking the blend between models A, B, and D, you can fine-tune how each model influences the final result. This customization makes it suitable for various use cases, from artistic creations to machine learning applications.

3. Exploration of Intermediate Traits

The intermediate models (E to L) serve as useful exploration points, offering insights into how the model's traits evolve over time. By observing these transitional models, you can identify emergent patterns or characteristics that wouldn't be evident in the initial models.

My Summary

"Everything is summarized in an image of Guide table. I see model D as a stretching tool to model A (generated images are made of extreme weight values, so if you have a 'broken model', it might be used like this). You can see that model L is almost same as model B. Models of A, E-K, and B (or L) creates illusion of linear and even-spaced variety.

Here is my image of madeup graph that demonstrates my idea:"

Tip: Set modifier for Model C to 0,95, keep modifier for Model D to 1. Why? It's more linear.