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Naive-Progress4549 t1_j507af0 wrote

I think that if you go in the guided_diffusion repository you can see that the super resolution network condition the output by concatenating the low resolution image. There are also other ways to condition, like the gradients during sampling.

I am trying to adapt the guided_diffusion repository for some other task since a couple of months now...I have to say I am facing quite some difficulties overall!

I hope this helps

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CurrentlyJoblessFML OP t1_j508inw wrote

Hi! Thanks for the response. I’ll try my luck by just concatenating my noisy input with yt along the channel dimension and see if that works. In the SR3 paper, the authors also mention that they tried using a different way to condition the model but they found that simply concatenating it gave them the same generation quality so they just stuck with that.

Good luck with your project and HMU if you ever want to discuss this. I’ve been breaking my head on these diffusion models for the past couple of days so I feel your struggle.

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