Comments (2)
Yep, that is the price to pay for having a faster model, we lose out on quality.
Implementing ESRGAN tricks is on the todo list, but I will imagine that it wouldn't be close to the original ESRGAN because our model is much smaller.
The goal for me for this project was to do better than traditional upsampling, which it does.
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Gotcha. Yeah I agree it is fast...however the memory requirements cause it to still not be viable on a simple cpu for inference :/
I am going to try splitting the image to see if that works
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Related Issues (20)
- How to predict a single image HOT 1
- Image was white until I made this change HOT 2
- is there any plan to port the SR model onto a mobile phone? HOT 1
- feeding Low Res images to Generator without downsampling High Res images HOT 1
- infer.py not working in the same dependencies... HOT 1
- Training for Higher Upsampling Levels HOT 2
- ValueError: Input 0 is incompatible with layer model_2: expected shape=(None, 96, 96, 3), found shape=(None, 480, 640, 3) from infer.py HOT 9
- pre-trained model D (Discriminator) not found HOT 1
- VGG19 for grayscale images HOT 1
- how to process sr in real time speed for developing video cam HOT 1
- Models for higher resolution HOT 1
- Any plans making ML Upscaler for gaming/3D? HOT 1
- Running the model in Real time with webcam HOT 1
- logs HOT 1
- SRGAN beginner HOT 6
- Incompatible shape HOT 4
- ValueError: Exception encountered when calling layer "model" (type Functional) HOT 4
- Input 0 of layer "model_2" is incompatible with the layer: expected shape=(None, 96, 96, 3), found shape=(None, 384, 384, 3) HOT 1
- Training requirements? HOT 1
- The model file does not match your network HOT 1
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