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taldatech / soft-intro-vae-pytorch

[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
Apache License 2.0
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a question #20

Open mikeyjhom opened 7 months ago

mikeyjhom commented 7 months ago

Dear Mr. Tal Daniel I apologize for taking up your time. I have a question and would like to request your help. I trained the soft-intro_vae model using the code you provided, and the image generated during the training process was very good. However, after the model training was completed, I used the trained model to generate images, but received strange results This is the image generated during the training process

FXF8PMII9)~LRL@0)IWFABF

This is the image generated by calling the trained model

mmexport1711957926710 image_2 Is the method I used to call the trained model incorrect? Think about seeking your advice on a solution. I hope to receive your help. Thank you very much

taldatech commented 7 months ago

Hmmmm I think it has to do with Batch Normalization. I assume you use the standard architecture (and not the style-based one). Try turning BatchNorm on/off (model.train()/model.eval()) and see how it affects the results. The architecture in this model is very out-dated. I would replace all the Batch Normalization with Group Normalization to avoid depending on the batch size statistics.

mikeyjhom commented 7 months ago

Okay, thank you very much for your reply. I will try to solve the problem based on your suggestions