No, I don’t know a specific UNet implementation with a focus on variable input shapes.
However, once you’ve found a “good” implementation you could check which shapes would work, which would fail, and adapt the code if needed.
E.g. transposed conv layers give you the option to specify the output_size in case multiple output sizes would be possible and you could of course add padding etc. based on shape conditions.
Generally I see that models are written for specific use cases and not “universal” workloads. E.g. if the author of a model expects to work with input images using a static shape of 224x224, I don’t think much work will be spent on making the model flexible for other inputs.