As GBY mentioned, they use the same implementation.

There is a slight difference in the parameters.

For tf.nn.conv2d:

filter: A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]

For tf.layers.conv2d:

filters: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).

I would use tf.nn.conv2d when loading a pretrained model (example code: https://github.com/ry/tensorflow-vgg16), and tf.layers.conv2d for a model trained from scratch.

For convolution, they are the same. More precisely, tf.layers.conv2d (actually _Conv) uses tf.nn.convolution as the backend. You can follow the calling chain of: tf.layers.conv2d>Conv2D>Conv2D.apply()>_Conv>_Conv.apply()>_Layer.apply()>_Layer.\__call__()>_Conv.call()>nn.convolution()...

As others mentioned the parameters are different especially the "filter(s)". tf.nn.conv2d takes a tensor as a filter, which means you can specify the weight decay (or maybe other properties) like the following in cifar10 code. (Whether you want/need to have weight decay in conv layer is another question.)

kernel = _variable_with_weight_decay('weights',
                                     shape=[5, 5, 3, 64],
                                     stddev=5e-2,
                                     wd=0.0)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')

I'm not quite sure how to set weight decay in tf.layers.conv2d since it only take an integer as filters. Maybe using kernel_constraint?

On the other hand, tf.layers.conv2d handles activation and bias automatically while you have to write additional codes for these if you use tf.nn.conv2d.