Why is the weighted combination of estimated input image ( \hat(x0) ) and noisy image (x_t) used?

[hemanth2090]

Hello,

Your paper is very interesting. Thank you for sharing your code. I have a question regarding Line 218 in image_editor_zecon.py file
https://github.com/YSerin/ZeCon/blob/51e679ef49d3875893eb956411abc802d9d8fc4c/optimization/image_editor_zecon.py#L218C1-L219C1

From what I understand, out['pred_xstart'] is \hat(x_0(x_t)). Since we have x0 available, the loss can be computed between out['pred_xstart'] and x0. Why have you taken a weighted combination in Line 218 to obtain x_t and y_t before computing the loss.

Also, could you please point me to the part of the paper that discusses the formulation of the 'fac' term in line 218.

regards,
hemanth

[kothari1997narayan]

+1
I also have the same question..
Please let us know..
Thanks..

[Xiaoda-Zhong]

Same question.