Super Resolution Diffusion Model

[dvIdol]

Hello.

I am very interested in the unconditional image generation pipelines. Like in this example here:
https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation

I have trained a network that is 128x128 and it gives very good results for what I need. However the resolution is very low.

The main diffusers readme mentions a super resolution diffusion model that comes after the low resolution model. How do I make this model? There are no examples and everything seems to be turning to stable diffusion. Is there any guide for how to train a low to high resolution diffusion model?

Thank you for making such a library, it is very good.

[patrickvonplaten]

Related to #146

[anton-l]

@patil-suraj IIRC you had plans for an SR example too? I might not have bandwidth in the next few weeks, but can work on SR after, if it's not high on your list.

[patil-suraj]

Yes, that's on my todo list. But if anyone is interested feel free to open a PR, happy to help :)

[pcuenca]

I'm interested in this too, and it's becoming relevant for the on-going fast.ai course. I might have some time to start working on this in a few days and/or help @anton-l and @patil-suraj when they do :)

[patil-suraj]

That's awesome Pedro! I'm looking at implementing SR3 https://iterative-refinement.github.io/ for this task.

[pcuenca]

My thought exactly :)

[duongna21]

@patil-suraj @pcuenca I can spend time implementing SR example this weekend (PyTorch & Flax).

[patil-suraj]

Reopening this issue as it's related to training super-res model.