BlindDPS/blind-dps

Training code

kylechang523 opened this issue · 2 comments

kylechang523 commented

Hi,

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

Best,
Kaiyu

Sci-Epiphany commented

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 commented

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!