It's a a pretty big step from "high similarity scores" to concluding that was the reason for failure. High is relative in this context, and the example sentences are arguably more similar to each other than to most other sentences (the definition of similarity I is another topic). From the vector similarity, we don't know much about the actual vector embeddings of the respective sentences, nor about how they impact the model as a whole. Obviously, something goes wrong for the given examples, but I don't think you can pin the problem down to one particular layer in a huge model like Dall-e.