Is fitting a model in a for loop equivalent to using epochs>1?

I'm using tensorflow to train a network to do an image segmentation task, and I have a question about the behavior of model.fit between epochs, specifically:

Is there any difference between calling model.fit with 512 epochs, and calling model.fit 512 times?

Here's a simplified version of my code, in case it helps. First, some setup:

# Create image generators for dataset augmentation
imgGen = ImageDataGenerator(**data_augmentation_parameters)
maskGen = ImageDataGenerator(**data_augmentation_parameters)
seed = random.randint(0, 1000000000)
imgIterator = imgGen.flow(img, seed=seed, shuffle=False, batch_size=batch_size)
maskIterator = maskGen.flow(mask, seed=seed, shuffle=False, batch_size=batch_size)

# Load network structure from model.py file
network = unet(net_scale = 1)

# Calculate # of iterations
steps_per_epoch = int(num_samples / batch_size)

The two methods of iteratively fitting:

Fit method #1:

network.fit(
    ((imgBatch, maskBatch) for imgBatch, maskBatch in zip(imgIterator, maskIterator)),
    steps_per_epoch=steps_per_epoch,
    epochs=512,
)

Fit method #2:

for epoch in range(512):
    network.fit(
        ((imgBatch, maskBatch) for imgBatch, maskBatch in zip(imgIterator, maskIterator)),
        steps_per_epoch=steps_per_epoch,
        epochs=1,
    )

I think this question is the same as mine, but I don't understand how the one answer applies to the question - I simply want to know if there is some internal difference between specifying an epoch number 1 and running model.fit in a for loop.

Thank you!