Question about hyperparameters

[pseudo-usama]

I've quite a lot of questions about this model. I've successfully trained latent diffusion on AFHQ dataset. But i'm having a hard time understanding many hyperparameters in yaml files.

In Autoencoder yaml:

• embed_dim: Why we are using embeddings in an autoencoder?
• n_embed: What is this?
• double_z: What is the purpose of this? I've noticed that it's True for KL autoencoder & False for VQ autoencoder. Why?
• ch: I know it means channels. But how does this changes model architecture?
• ch_mult: How does this work?
• lossconfig.target: This is set to taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator is it using a discriminator (like in a GAN)? Why an autoencoder needs an Discriminator?
• lossconfig.params.disc_weight: Is that related to discriminator in VQLPIPSWithDiscriminator & how does it influences it?
• lossconfig.params.codebook_weight: What is a codebook weight in a VQ autoencoder?

In Latent Diffusion yaml:

• first_stage_key: What is this? In every yaml file it's set to image.
• num_timesteps_cond: What does this do? In every file it's set to 1.
• log_every_t: How does this work?

I would be grateful for any form of assistance. Thank you!

[bhosalems]

Hi, Did you figure these out?

[pseudo-usama]

@bhosalems No, please update here if you find any answers. Thanks