CFW training

[JackeyDeng]

hello, I'm confused about the CFW trianing.
Firstly, Latent codes (4D tensors) of HR images is needed to train CFW, but in your paper (4.1. Implementation Details) it says : "Then we adopt the fine-tuned diffusion model to generate the corresponding latent codes Z0 given the above LR images as conditions.", so what is needed to train the CFW? I think the paper is right. So why do we need the HR image latents rather than LR image latents?
Secondly, how does fusion_weight work when training? should we select fusion_w randomly in range [0, 1]? But I cannot find it in the code.

[IceClear]

Hi.
First, CFW is used for further improve the quality of the latents from diffusion Unet. The info of LR is introduced by the encoder of autoencoder.
Second, w is set to 1 during training. We may add this detail in the paper.

[JackeyDeng]

Hi I am still a little confused.
Why is the latent of HR image is needed when training CFW rather than the latent of LR image. I think the latter is compatible with the inference process and is more plausible.
here is the paper says which I think is right:

but here is what we need to train CFW which I think is wrong:
latents
└── 00000001.npy # Latent codes (N, 4, 64, 64) of HR images generated by the diffusion U-net, saved in .npy format.