Training code

[kylechang523]

Hi,

I'm super interested in this repo, is it possible to release the training code for learning purposes?

Best,
Kaiyu

[Sci-Epiphany]

How is the kernel model of the article trained? I tried to use guided-diffusion to train the model I generated, and found that the following problems occurred when exporting the model:
RuntimeError: Error(s) in loading state_dict for UNetModel:
Missing key(s) in state_dict: "input_blocks.2.0.in_layers.0.weight", "input_blocks.2.0.in_layers.0.bias", "input_blocks.2.0.in_layers.2.weight", "input_blocks.2.0.in_layers.2.bias", "input_blocks.2.0.emb_layers.1.weight", "input_blocks.2.0.emb_layers.1.bias", "input_blocks.2.0.out_layers.0.weight", "input_blocks.2.0.out_layers.0.bias", "input_blocks.2.0.out_layers.3.weight", "input_blocks.2.0.out_layers.3.bias", "input_blocks.4.0.in_layers.0.weight", "input_blocks.4.0.in_layers.0.bias", "input_blocks.4.0.in_layers.2.weight", "input_blocks.4.0.in_layers.2.bias", "input_blocks.4.0.emb_layers.1.weight", "input_blocks.4.0.emb_layers.1.bias", "input_blocks.4.0.out_layers.0.weight", "input_blocks.4.0.out_layers.0.bias", "input_blocks.4.0.out_layers.3.weight", "input_blocks.4.0.out_layers.3.bias", "input_blocks.6.0.in_layers.0.weight", "input_blocks.6.0.in_layers.0.bias", "input_blocks.6.0.in_layers.2.weight", "input_blocks.6.0.in_layers.2.bias", "input_blocks.6.0.emb_layers.1.weight", "input_blocks.6.0.emb_layers.1.bias", "input_blocks.6.0.out_layers.0.weight", "input_blocks.6.0.out_layers.0.bias", "input_blocks.6.0.out_layers.3.weight", "input_blocks.6.0.out_layers.3.bias", "input_blocks.8.0.in_layers.0.weight", "input_blocks.8.0.in_layers.0.bias", "input_blocks.8.0.in_layers.2.weight", "input_blocks.8.0.in_layers.2.bias", "input_blocks.8.0.emb_layers.1.weight", "input_blocks.8.0.emb_layers.1.bias", "input_blocks.8.0.out_layers.0.weight", "input_blocks.8.0.out_layers.0.bias", "input_blocks.8.0.out_layers.3.weight", "input_blocks.8.0.out_layers.3.bias", "input_blocks.10.0.in_layers.0.weight", "input_blocks.10.0.in_layers.0.bias", "input_blocks.10.0.in_layers.2.weight", "input_blocks.10.0.in_layers.2.bias", "input_blocks.10.0.emb_layers.1.weight", "input_blocks.10.0.emb_layers.1.bias", "input_blocks.10.0.out_layers.0.weight", "input_blocks.10.0.out_layers.0.bias", "input_blocks.10.0.out_layers.3.weight", "input_blocks.10.0.out_layers.3.bias", "output_blocks.1.1.in_layers.0.weight", "output_blocks.1.1.in_layers.0.bias", "output_blocks.1.1.in_layers.2.weight", "output_blocks.1.1.in_layers.2.bias", "output_blocks.1.1.emb_layers.1.weight", "output_blocks.1.1.emb_layers.1.bias", "output_blocks.1.1.out_layers.0.weight", "output_blocks.1.1.out_layers.0.bias", "output_blocks.1.1.out_layers.3.weight", "output_blocks.1.1.out_layers.3.bias", "output_blocks.3.2.in_layers.0.weight", "output_blocks.3.2.in_layers.0.bias", "output_blocks.3.2.in_layers.2.weight", "output_blocks.3.2.in_layers.2.bias", "output_blocks.3.2.emb_layers.1.weight", "output_blocks.3.2.emb_layers.1.bias", "output_blocks.3.2.out_layers.0.weight", "output_blocks.3.2.out_layers.0.bias", "output_blocks.3.2.out_layers.3.weight", "output_blocks.3.2.out_layers.3.bias", "output_blocks.5.1.in_layers.0.weight", "output_blocks.5.1.in_layers.0.bias", "output_blocks.5.1.in_layers.2.weight", "output_blocks.5.1.in_layers.2.bias", "output_blocks.5.1.emb_layers.1.weight", "output_blocks.5.1.emb_layers.1.bias", "output_blocks.5.1.out_layers.0.weight", "output_blocks.5.1.out_layers.0.bias", "output_blocks.5.1.out_layers.3.weight", "output_blocks.5.1.out_layers.3.bias", "output_blocks.7.1.in_layers.0.weight", "output_blocks.7.1.in_layers.0.bias", "output_blocks.7.1.in_layers.2.weight", "output_blocks.7.1.in_layers.2.bias", "output_blocks.7.1.emb_layers.1.weight", "output_blocks.7.1.emb_layers.1.bias", "output_blocks.7.1.out_layers.0.weight", "output_blocks.7.1.out_layers.0.bias", "output_blocks.7.1.out_layers.3.weight", "output_blocks.7.1.out_layers.3.bias", "output_blocks.9.1.in_layers.0.weight", "output_blocks.9.1.in_layers.0.bias", "output_blocks.9.1.in_layers.2.weight", "output_blocks.9.1.in_layers.2.bias", "output_blocks.9.1.emb_layers.1.weight", "output_blocks.9.1.emb_layers.1.bias", "output_blocks.9.1.out_layers.0.weight", "output_blocks.9.1.out_layers.0.bias", "output_blocks.9.1.out_layers.3.weight", "output_blocks.9.1.out_layers.3.bias".
Unexpected key(s) in state_dict: "input_blocks.2.0.op.weight", "input_blocks.2.0.op.bias", "input_blocks.4.0.op.weight", "input_blocks.4.0.op.bias",

I really hope the author can see this question. Your answer is very important to me.

[Drangonliao123]

How is the kernel model of the article trained? I tried to use guided-diffusion to train the model I generated, and found that the following problems occurred when exporting the model: RuntimeError: Error(s) in loading state_dict for UNetModel: Missing key(s) in state_dict: "input_blocks.2.0.in_layers.0.weight", "input_blocks.2.0.in_layers.0.bias", "input_blocks.2.0.in_layers.2.weight", "input_blocks.2.0.in_layers.2.bias", "input_blocks.2.0.emb_layers.1.weight", "input_blocks.2.0.emb_layers.1.bias", "input_blocks.2.0.out_layers.0.weight", "input_blocks.2.0.out_layers.0.bias", "input_blocks.2.0.out_layers.3.weight", "input_blocks.2.0.out_layers.3.bias", "input_blocks.4.0.in_layers.0.weight", "input_blocks.4.0.in_layers.0.bias", "input_blocks.4.0.in_layers.2.weight", "input_blocks.4.0.in_layers.2.bias", "input_blocks.4.0.emb_layers.1.weight", "input_blocks.4.0.emb_layers.1.bias", "input_blocks.4.0.out_layers.0.weight", "input_blocks.4.0.out_layers.0.bias", "input_blocks.4.0.out_layers.3.weight", "input_blocks.4.0.out_layers.3.bias", "input_blocks.6.0.in_layers.0.weight", "input_blocks.6.0.in_layers.0.bias", "input_blocks.6.0.in_layers.2.weight", "input_blocks.6.0.in_layers.2.bias", "input_blocks.6.0.emb_layers.1.weight", "input_blocks.6.0.emb_layers.1.bias", "input_blocks.6.0.out_layers.0.weight", "input_blocks.6.0.out_layers.0.bias", "input_blocks.6.0.out_layers.3.weight", "input_blocks.6.0.out_layers.3.bias", "input_blocks.8.0.in_layers.0.weight", "input_blocks.8.0.in_layers.0.bias", "input_blocks.8.0.in_layers.2.weight", "input_blocks.8.0.in_layers.2.bias", "input_blocks.8.0.emb_layers.1.weight", "input_blocks.8.0.emb_layers.1.bias", "input_blocks.8.0.out_layers.0.weight", "input_blocks.8.0.out_layers.0.bias", "input_blocks.8.0.out_layers.3.weight", "input_blocks.8.0.out_layers.3.bias", "input_blocks.10.0.in_layers.0.weight", "input_blocks.10.0.in_layers.0.bias", "input_blocks.10.0.in_layers.2.weight", "input_blocks.10.0.in_layers.2.bias", "input_blocks.10.0.emb_layers.1.weight", "input_blocks.10.0.emb_layers.1.bias", "input_blocks.10.0.out_layers.0.weight", "input_blocks.10.0.out_layers.0.bias", "input_blocks.10.0.out_layers.3.weight", "input_blocks.10.0.out_layers.3.bias", "output_blocks.1.1.in_layers.0.weight", "output_blocks.1.1.in_layers.0.bias", "output_blocks.1.1.in_layers.2.weight", "output_blocks.1.1.in_layers.2.bias", "output_blocks.1.1.emb_layers.1.weight", "output_blocks.1.1.emb_layers.1.bias", "output_blocks.1.1.out_layers.0.weight", "output_blocks.1.1.out_layers.0.bias", "output_blocks.1.1.out_layers.3.weight", "output_blocks.1.1.out_layers.3.bias", "output_blocks.3.2.in_layers.0.weight", "output_blocks.3.2.in_layers.0.bias", "output_blocks.3.2.in_layers.2.weight", "output_blocks.3.2.in_layers.2.bias", "output_blocks.3.2.emb_layers.1.weight", "output_blocks.3.2.emb_layers.1.bias", "output_blocks.3.2.out_layers.0.weight", "output_blocks.3.2.out_layers.0.bias", "output_blocks.3.2.out_layers.3.weight", "output_blocks.3.2.out_layers.3.bias", "output_blocks.5.1.in_layers.0.weight", "output_blocks.5.1.in_layers.0.bias", "output_blocks.5.1.in_layers.2.weight", "output_blocks.5.1.in_layers.2.bias", "output_blocks.5.1.emb_layers.1.weight", "output_blocks.5.1.emb_layers.1.bias", "output_blocks.5.1.out_layers.0.weight", "output_blocks.5.1.out_layers.0.bias", "output_blocks.5.1.out_layers.3.weight", "output_blocks.5.1.out_layers.3.bias", "output_blocks.7.1.in_layers.0.weight", "output_blocks.7.1.in_layers.0.bias", "output_blocks.7.1.in_layers.2.weight", "output_blocks.7.1.in_layers.2.bias", "output_blocks.7.1.emb_layers.1.weight", "output_blocks.7.1.emb_layers.1.bias", "output_blocks.7.1.out_layers.0.weight", "output_blocks.7.1.out_layers.0.bias", "output_blocks.7.1.out_layers.3.weight", "output_blocks.7.1.out_layers.3.bias", "output_blocks.9.1.in_layers.0.weight", "output_blocks.9.1.in_layers.0.bias", "output_blocks.9.1.in_layers.2.weight", "output_blocks.9.1.in_layers.2.bias", "output_blocks.9.1.emb_layers.1.weight", "output_blocks.9.1.emb_layers.1.bias", "output_blocks.9.1.out_layers.0.weight", "output_blocks.9.1.out_layers.0.bias", "output_blocks.9.1.out_layers.3.weight", "output_blocks.9.1.out_layers.3.bias". Unexpected key(s) in state_dict: "input_blocks.2.0.op.weight", "input_blocks.2.0.op.bias", "input_blocks.4.0.op.weight", "input_blocks.4.0.op.bias",

I really hope the author can see this question. Your answer is very important to me.

Have you solved your problem yet? Would greatly appreciate it if you could get back to me and share your training code!

[ZHUYMGeo]

I am also interested in the training porcess. The kernel and x are trained by diffsuion model respectively. That means before training diffusion model, we need some paired kernel and clean x? or we need the prior distribution of kernel and x ?