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Conditioning on y_cond #29
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Hi, thanks for this great question, and I think there are two potential considerations for this:
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Thanks for the kind reply. This makes sense, though it is worth trying the second reason. I will post here if I realized something different. |
Feel free to reopen the issue if there is any question. |
@PouriaRouzrokh I have opened a separate issue on it. But I am in urgent need of a solution, so I just wanted to check with you . In my inpainting case, during the inference only the In the
Any idea on how to proceed? |
@Janspiry Why not just set the mask as |
Hi, thanks for the awesome codes :)
One question for the inpainting task:
Looking at the following snippet from your code in networks.py, I cannot understand why you are conditioning your model on y_cond if you are already modifying your y_noisy based on the y_0 image using the expression "y_noisy*mask+(1.-mask)*y_0"?
Shouldn't concatenating with y_cond be redundant in this case? Your model is already seeing the ground truth parts of the image in the modified version of the y_noisy.
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