I found two articles using WGAN with other losses:

  
      ieeexplore.ieee.org
  
  
    

Single Image Haze Removal Using Conditional Wasserstein Generative... 17



  
  
    
    
  
  



  
      
      sciencedirect.com
  
  
    

Auto-painter: Cartoon image generation from sketch by using conditional... 9

Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be g…


  
  
    
    
  
  


Both of these have available implementations on Github but they seem to simply scale the other losses i.e. VGGloss with a huge constant like 1000. In my experience trying this out, the adversarial Wasserstein loss started out way too small to have much impact on what was being learned until it passes the other losses, at which point the other losses have no impact.
My current solution is to revert back to using a normal GAN.