This homework aims to implement a few different techniques that require to manipulate images on the manifold of natural images.
(1) Results for different Lp loss, Preceptual loss and regularization loss are shown below.
Different Preceptual loss (latent = z):
Different Lp loss (latent = z):
Different regularization loss (perc_wgt = 1, l1_wgt = 1, latent = z):
(2) different generative models and (3) different latent space
perc_wgt = 1, l1_wgt = 1
The smaller Preceptual weight gives better resultlarger which is closer to the target image. The smaller L1 Weight alsohe give better result, as the L1 loss is used to ensure that the image is a close match to the target. For different GANs, the StyleGAN results give better color than Vanilla GAN results. For different latent spaces, the w and w+ takes more time, but give results with better color and details like the eyes.
So from the results, I think the combiniation of StyleGAN, perc_wgt = 1, l1_wgt = 1 and latent w+ space has the best performance and is the closest to the target image. This takes about 10.23s to run on my laptop. The result of latent space w with the same parameters looks nice as well.
These results are generated using StyleGAN. The input images which is denser has better performance in color, while image which is sparser is harder to guidanceet the information of the color correct. The third image is the input of my own drawing, but does not work well to get the color.
The prompt I use is "Grumpy cat reimagined with blue eyes" to change the eye color.
The result of 2 different classifier-free guidance strength values, with 500 noise steps:
The result of 2 different classifier-free guidance strength values, with guidance strength of 35:
Another input image and the corresponding result with strength = 15 and 200 noise steps is shown below:
As we can see, with the higher the guidance strengths, the prompt has more impact on the output, and the eye color of the cat is closer to blue. And for more noises added to the image, the result image looks more different than the input. As you can see from the experiment, the color of the whole cat becomes different.