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Clarity about losses

See original GitHub issue

Hi Guys,

Thanks for the amazing work. How many losses we have overall.

MSE( BaseModel(x_noisy), x)
MSE( SuperResModel1(x_noisy), x) # 64 ----> 256
MSE( SuperResModel2(x_noisy), x) # 256 ----> 1024

So is it mean(L1 + L2 + L3) ?

Or update based on each loss separately. I am little confused.

Issue Analytics

State:
Created a year ago
Reactions:1
Comments:5 (3 by maintainers)

Top GitHub Comments

1reaction

lucidrainscommented, Jun 12, 2022

Yup, each stage can be trained separately, which is actually one of the main benefits of cascading ddpm!

1reaction

s4sarathcommented, Jun 12, 2022

Oh 😦 . I thought this was all chained together conditioned on the output of previous one. So as per your comment, we can train a diffusion model ( unet0 ) conditioned on text separately.

Then unet1 can be trained separately so on. Is this right. I thought in Cascading DDPM paper, they conditioned on the output of first diffusion model ( unet0 ). May be I was wrong

Thanks a lot.

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