Merging, in the context of generative AI, refers to the combination or fusion of different AI models or their characteristics. Similar to creating a collage, the best or desired features of multiple models are united into a new model.
A practical example is the merging of different Stable Diffusion models, where one model’s ability to generate faces might be combined with another model’s expertise in creating landscapes. This allows users to utilize the strengths of different models without having to switch between them.
During the merging process, the neural weights of the source models are mathematically combined according to specific rules. The result is a new model that ideally incorporates the desired properties of the original models. However, merging is a complex process, and it’s not always predictable how well the various characteristics will work together in the resulting model.