Closed universewill closed 1 year ago
Hello!
The answer is No, because lama generator has deterministic structure: it's simply a convolutional autoencoder with no noise injections.
I see that there is an option noise_fill_hole
in the trainer. If I enable it would LAMA learns to generate image in a non-deterministic way?
We tried that in our early experiments and it did not lead to any significant diversity: there is a strong supervised reconstruction loss at the core of the method, which penalizes any variations. For pluralistic generation, principally different methods are needed (purely adversarial training like in CoModGAN; or autoregressive MLE models like VQGAN+transformer or diffusion)
By strong supervised reconstruction loss, are you referring to the L1 loss only? I see in some of your config files that the weight given to the L1 loss on missing areas is 0. I think it means that basically L1 loss is not actually effective? I am just wondering if I disable the supervised reconstruction loss(es), could I train LAMA for pluralistic generation?
can lama generate various result for one same pair of mask and image?