Pyroka:

As far as i understand the WeightedRandomSampler makes sure (if correctly used), that in each Batch there is approximatly the same amount of samples for each Class.


That’s one use case, but I wouldn’t say that it’s the only correct one. As the name suggests this sampler is used for weighting the sampling strategy, not only balancing it.



 Pyroka:

Now i can set “replacement” to false, […]


Yes, your description is correct. If you want to have balanced batches, you should not use replacement=False.



 Pyroka:

But that also means it is possible to have the same exact image of class “1” multiple times in my training leading to an at least slightly biased learning.


Yes, that might be the case, but could still perform better than the imbalanced dataset.
You would have to apply the sampler on your use case and compare the model performance metric to see how e.g. the confusion matrix changes and what the acceptable result would be.