art transfer tutorial uses incomplete model for training

I read the A neural Algorithm of artistic style recently and while trying to implement I referred pytorch's tutorial but it used only top layers rather than full model as suggested by paper

From same input images :

Style image

Context image:

This is result from tutorial :

This is the result after full model :

As you can see my results captured context as well as style better than current version
the algorithm is expected to work with transfer learning from VGG19 model which will have 19 conv layers but the current tutorial only have 5 layers which is why results aren't up to the mark.

Changes:

• Changed in implantation of model
• max pool to avg pool as suggested by author in paper
• VGG takes 224*224 input image which is not the case with current tutorial.
• multiplying individual loss by a factor
• due to normalisation on gram matrix style loss tends to zero

I would like to contribute to this issue

Start with the existing neural style transfer tutorial and compare its model and loss setup with the linked paper's VGG19 approach. Review the proposed pooling, input-size, loss-weighting, and Gram-matrix normalization changes. Done means the tutorial uses the intended model configuration and produces style-transfer results that better preserve both context and style.