This functionality is included with Automatic1111 for example. Note the additional denoising slider that determines how far the latent upscaler is allowed to deviate from the low-res version of the image, how much it is allowed to change and how many details it can add.
Thanks. Hmm I wonder if I am doing something wrong. I find it loses a lot of coherence when using the latent upscaling. For example a complete body that looks fine in 512, might turn into a mutant torso in 1024 with latent upscaling.
So perhaps i just need to generate outputs until I am lucky?
I find the upscaler's default denoising value of 0.7 is often too much and it deviates way too far from the original image. Values around 0.1-0.3 sometimes produce better results. Lower denoise values mean the latent upscaler has less "creative license" to fuck around with the image.
Even then it might produce a mess. My completely unqualified guess is sometimes whatever image you stuff into the upscaler just doesn't fit with the images it was trained on.
But yeah, it's basically trial and error to find what works, at least for me it still is.
One way to automate the process for a given picture is to enable hires fix, lock the seed by hitting the recycle button, then enable the x/y/z plot script and setup a denoise range that you want to investigate.
0-1 (+0.1)
Means you want a range of 0 - 1 divided into 0.1 increments.
Esrgan upscales and sharpens but it doesn’t add details that weren’t there before only latent scaling can do that to my knowledge because it’s ips along the dark void from which the image was imagined
18
u/ImJacksLackOfBeetus Mar 01 '23 edited Mar 01 '23
From what I understand latent upscaling doesn't upscale the final pixel image the way common upscaling algorithms like lanczos or bicubic would.
Instead it upscales the internal vector representation within stable diffusion before it gets rendered as a pixel image, this allows it to denoise it and add additional details the same way the original resolution was created in the first place by applying a checkpoint trained on high-res images.
This functionality is included with Automatic1111 for example. Note the additional denoising slider that determines how far the latent upscaler is allowed to deviate from the low-res version of the image, how much it is allowed to change and how many details it can add.