Comments (7)
Thank you! I will tried this out and give more feedback.
Hi, I am also working on this project recently. Have you successfully applied ConSinGAN in the super resolution task?
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Hi, we haven't tested animation and super-resolution with our model yet, but I would be interested to see how well it works.
For animation I believe it should work just as well as SinGAN, since I think the main idea is to just perturbate z_opt at test time. However, if I remember correctly SinGAN changes some hyperparameters for animation training (different --min_size, different noise padding, possibly others). An easy way to test animation would be to train a ConSinGAN model on the image of interest and then slightly perturbate the input noise z_opt at test time to see results (maybe check how SinGAN perturbates the noise exactly). I think SinGAN does something like 0.95z_opt + 0.05random_noise, but the details might be more involved.
For SR it's a bit more challenging as you observed. One idea to do this in our model would be to upsample the feature maps produced by the final generator (but before applying self.tail()) and feeding them again into the final generator block. This approach could be repeated several times until the desired resolution is reached. But I haven't tried this so not sure how well it will work and you might have to play around a little with the upsampling operation, i.e. by how much you upsample before feeding the upsampled features back into the final generator block.
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Edit: I just added code/examples (see readme) for image animation. Training is the same as for uncondtional image generation. At test time we simply add random noise to z_opt to create minor variations for the animation.
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Thank you! I will tried this out and give more feedback.
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Hi, @tohinz thanks for the great work! I would also be very interested to hear if there are any updates about the super resolution task. Or perhaps someone else has already addressed the issue?
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Hi, we haven't spent any time on testing ConSinGAN for super resolution. There are some ways this could be implemented but we are not planning on working on this. I'm happy to help with any specific questions or if you run into problems with the code though.
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Hi, ok thanks for your response! I might come back to you when I start implementing myself.
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Related Issues (20)
- Image Animation
- fine-tune error HOT 3
- Harmonization IndexError HOT 4
- Harmonization Error HOT 2
- nan problem of SIFID calculation HOT 1
- Where was the article published HOT 1
- How to generate images like SinGan? HOT 2
- Hello, I'm having some problems. RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED HOT 11
- About Learning Rate Scaling HOT 1
- All the generated/Fake samples at each stages are found to be a black image.
- Hi..In my case the generated images are found to be poorer in quality (esp. local structure) unlike SINGAN HOT 1
- Running On Multiple GPUs HOT 5
- How can I trained a Grayscale image? HOT 1
- Running my images HOT 1
- Suggest to loosen the dependency on albumentations
- Hello, I have some problems.
- Reconstruction loss
- Is there a way to save ConSinGAN model training progress? HOT 1
- Is there a way to up the resolution size of the Harmonized Image? HOT 1
- Generate g higher resolution images HOT 3
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