How should I adjust the hyperparameters to train a strong DDPM model based on CelebA for human face inpainting task?

[LinWeiJeff]

I'm very grateful for your work about this github repository!!! However, I have some questions about the hyperparameters to want to ask you.
I know your repository is heavily based on guided-diffusion, and I want to use guided-diffusion to train a DDPM model based on CelebA dataset consisting of 202,599 align&cropped human face images (each image is 218(height)x178(width) pixels) for human face image synthesis task and then use the saved model to perform human face image inpainting task.

Could you give me some suggestions about how to adjust the hyperparameters used in guided-diffusion to train a DDPM model as strong as models provided by guided-diffusion for human face image synthesis and then inpainting task?
I wonder why the sizes of the models provided by guided-diffusion are so big, especially why the size of 256x256 diffusion (not class conditional) is so big (about 2.1GB) and how it is trained.
By the way, for dataset, should I need to resize the images in my dataset to 256x256 firstly?

I know your repository adds two extra hyperparameters (one is p2_gamma and the another is p2_k), but I still want to use guided-diffusion to train my own DDPM model firstly because I want to learn DDPM from base and then advance, hence, I come here to ask you how to adjust the other hyperparameters except for p2_gamma and p2_k.

I hope the trained strong DDPM model learns the features of human face well so it can be used for human face image synthesis task and human face image inpainting task (i.e. recovering the masked parts of a masked human face image).

I want to know how to adjust the values of the hyperparameters in diffusion_defaults() of script_util.py, model_and_diffusion_defaults() of script_util.py and create_argparser() of image_train.py in guided-diffusion to train a strong denoising model in DDPM based on my CelebA dataset.

I have tried some combinations of the hyperparameters used in guided-diffusion to train, however, the human face image inpainting results of the saved model files ema_0.9999_XXX.pt and modelXXX.pt are both bad.
I mainly used RePaint to perform sampling for human face image inpainting task, as described in the README of RePaint, it uesd the pretrained model celeba256_250000.pt (The model is downloaded from download.sh and based on guided-diffusion) to perform sampling for human face image inpainting task, and the size of the model is big (about 2.1GB) and its (celeba256_250000.pt) sampling results are not bad. However, I don't know why the model's size is so big and how it is trained.

In addition, because of the limitation of my GPU memory, I set the value of the hyperparameter num_channels only 64, I want to know if this hyperparameter affects the performance of the traind DDPM model. Should I try to set it larger?

In conclusion, I hope you can give me some suggestions about how to adjust the hyperparameters used in guided-diffusion to let me get a strong DDPM model for human face image inpainting task.

Thanks a lot for your any help!!!
p.s. I directly and manually set up the values of the hyperparameters in the codes of guided-diffusion not through any flag.