Comments (3)
Do I need to train different models from scratch if I want to obtain some images with different styles?
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And how to change a style of a generated image if I trained a model with VAE?
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To produce outputs with different styles, you need to train with VAE by using --use_vae
flag. It it was not trained with VAE, it cannot generate different styles.
The pretrained models of COCO, ADE20K and Cityscapes are all without VAE, because we actually didn't want random generation of styles, in order to keep the evaluation metric reproducible.
As you know. for GauGAN video, we trained with VAE.
Once you finish training with VAE, to produce different styles for the same semantic layout input, simply run the model multiple times. It will always generate different results.
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Related Issues (20)
- test.py how to use Style Images,I have some Style Images,but I dont know how to use. HOT 2
- How can I run this using RGB label as input ? HOT 4
- CUDA error: device-side assert triggered HOT 1
- Two update_learning_rate with same name
- RuntimeError: cublas runtime error : an access to GPU memory space failed at
- Converting to onnx HOT 2
- Choice between multi-scale discriminator and progrssive growing of GANs ?
- How many categories have you predicted for flickr data using deeplab?
- How "label_nc" in the setting works? HOT 2
- CUDA Error?? HOT 4
- Public API
- Extend to instance segmentation
- IndexError with custom dataset HOT 2
- errors with custom dataset HOT 3
- how to prepare the custom datasets?
- Issue a black background is added to the transparent image HOT 1
- GauGAN2 Paper HOT 1
- add --tf_log , program can not run HOT 1
- any pretrained models on ade20k or cityscapes with use_vae?
- Online demo app no longer on nvidia site?
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