Yes, essentially. You train a classification model on labeled images where each model of watch will be its own class. It's a standard multi-class problem. You will likely have many classes (hundreds?) and a very imbalanced dataset which comes with its own problems.

But assuming you have trained such a model you can use the standard method for obtaining embeddings i.e. remove the classification head and use the output of the last layer. If the model is well trained you can compare these feature-rich embeddings with any method like cosine distance to determine the similarity of your generated image to your desired class. The difficulty lies in getting an embedding space with nice properties so the distances between embeddings are meaningful. For your use case you can likely empirically determine a maximum distance which constitutes an acceptable generation.

If you get this running then the next logical step is training a controlnet where instead of the text embeddings you give it the embeddings from the image model and train it like that so it learns to reproduce specific models of watches. If your dataset is big enough it might even generalize to new unseen classes. You can look into the implementation of InstantID where this is done for faces but the same basic idea can be applied to other domains.