Generation of Charaters outside of total_chn.txt
Opened this issue · 5 comments
The model is a inspiring one. When I am using your model, I found that we can only generate those characters inside the total_chn.txt, i.e., inside the dataset. When I try to generate those characters outside of the list, the torch dimension (the embedding layer) does not match. I am not sure how the content in the character attributes encoder being encoded.
I would like to ask if you have any ways to input those characters that are not including in total_chn.txt and generate those characters, without re-training the whole model. Thank you.
The model is a inspiring one. When I am using your model, I found that we can only generate those characters inside the total_chn.txt, i.e., inside the dataset. When I try to generate those characters outside of the list, the torch dimension (the embedding layer) does not match. I am not sure how the content in the character attributes encoder being encoded. I would like to ask if you have any ways to input those characters that are not including in total_chn.txt and generate those characters, without re-training the whole model. Thank you.
hello, can you tell me where to get the model, I did not find any trained models released by the author
Hello, I would like to know what is in the . /char_stroke.txt' file in test_cfg.yaml?Looking forward to your reply!
Hello, I would like to know what is in the . /char_stroke.txt' file in test_cfg.yaml?Looking forward to your reply!
The stroke encoding, i.e. chinese_stroke.txt if you are generating Chinese characters.
The model is a inspiring one. When I am using your model, I found that we can only generate those characters inside the total_chn.txt, i.e., inside the dataset. When I try to generate those characters outside of the list, the torch dimension (the embedding layer) does not match. I am not sure how the content in the character attributes encoder being encoded. I would like to ask if you have any ways to input those characters that are not including in total_chn.txt and generate those characters, without re-training the whole model. Thank you.
hello, can you tell me where to get the model, I did not find any trained models released by the author
I trained by myself.
Hello, I would like to know what is in the . /char_stroke.txt' file in test_cfg.yaml?Looking forward to your reply!
The stroke encoding, i.e. chinese_stroke.txt if you are generating Chinese characters.
Thank you very much for your reply! May I ask if you have used this model to generate other texts such as Arabic and English, the cpkt I have trained using DG-Font gets errors like the following when I apply it to this:Traceback (most recent call last):
File "/home/llll/PycharmProjects/Fontdiffproject/Font-diff/train.py", line 122, in
main()
File "/home/llll/PycharmProjects/Fontdiffproject/Font-diff/train.py", line 56, in main
model.sty_encoder.load_state_dict(tmp_dict)
File "/home/llll/anaconda3/envs/fontdiffuser/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1671, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for StyleEncoder:
Missing key(s) in state_dict: "features.0.weight", "features.0.bias", "features.1.weight", "features.1.bias", "features.1.running_mean", "features.1.running_var", "features.4.weight", "features.4.bias", "features.5.weight", "features.5.bias", "features.5.running_mean", "features.5.running_var", "features.8.weight", "features.8.bias", "features.9.weight", "features.9.bias", "features.9.running_mean", "features.9.running_var", "features.11.weight", "features.11.bias", "features.12.weight", "features.12.bias", "features.12.running_mean", "features.12.running_var", "features.15.weight", "features.15.bias", "features.16.weight", "features.16.bias", "features.16.running_mean", "features.16.running_var", "features.18.weight", "features.18.bias", "features.19.weight", "features.19.bias", "features.19.running_mean", "features.19.running_var", "features.22.weight", "features.22.bias", "features.23.weight", "features.23.bias", "features.23.running_mean", "features.23.running_var", "features.25.weight", "features.25.bias", "features.26.weight", "features.26.bias", "features.26.running_mean", "features.26.running_var", "cont.weight", "cont.bias".
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