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Hello HAT team,
Firstly, I would like to extend my gratitude for the insightful work you have done with the HAT model. It's truly inspiring to see such advancements in the field.
As someone who …
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Hello. For the image super-resolution task, in your code, you just save the low-resolution image and the inversion results without using Eq.(7) for optimization. Could you please tell me how Eq.(7) is…
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Hi @rromb, @ablattmann, @pesser, and thank you for making your great work publicly available.
Could you please supply the code for the class `ldm.data.openimages.SuperresOpenImagesAdvancedTrain`/`V…
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In your paper, you mentioned User-Interactive Super-resolution, how can I manually increasing and de-creasing the scale of blur kernel or manually increasing and decreasing the level of noise?
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I wonder if it would be possible to add code to do inference using an image/task pair?
E.g. super-resolution given an input image.
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Hello! First of all, congratulations on your paper getting the best ICCV paper, thank you for your contribution to GAN network research, and share your code. I have had the honor to study your paper a…
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Google researches published paper with details of implementation of their superresolution: https://arxiv.org/abs/1905.03277
It seems not trivial but implementable. Algorithm introduces new approach…
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The pytorch version of the code reproduces results that are different from those in your paper, and lua is rarely used nowadays. Can you please provide the super-resolved results of each dataset?
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Hello Michael.
I am reading your code recently and get confused about how the super resolution works.
In file DeBayerKernels.cu,
line 398 to 402 writes
```
float posX = ((float)x + 0.5f + dimX /…
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Hello, I had a lot of questions when I applied your work to the super-resolution model, such as Local_Base and so on. If possible, could you give a concrete example to show us how to apply it?