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# SD-Latent-Upscaler
Upscaling stable diffusion latents using a small neural network.
Very similar to my [latent interposer](https://github.com/city96/SD-Latent-Interposer/tree/main), this small model can be used to upscale latents in a way that doesn't ruin the image. I mostly explain some of the issues with upscaling latents in [this issue](https://github.com/city96/SD-Advanced-Noise/issues/1#issuecomment-1678193121). Think of this as an ESRGAN for latents, except severely undertrained.
**Currently, SDXL has some minimal hue shift issues.** Because of course it does.
## Installation
### ComfyUI
To install it, simply clone this repo to your custom_nodes folder using the following command: `git clone https://github.com/city96/SD-Latent-Upscaler custom_nodes/SD-Latent-Upscaler`.
Alternatively, you can download the [comfy_latent_upscaler.py](https://github.com/city96/SD-Latent-Upscaler/blob/main/comfy_latent_upscaler.py) file to your ComfyUI/custom_nodes folder as well. You may need to install hfhub using the command pip install huggingface-hub inside your venv.
If you need the model weights for something else, they are [hosted on HF](https://huggingface.co/city96/SD-Latent-Upscaler/tree/main) under the same Apache2 license as the rest of the repo.
### Auto1111
Currently not supported but it should be possible to use it at the hires-fix part.
### Local models
The node pulls the required files from huggingface hub by default. You can create a `models` folder and place the modules there if you have a flaky connection or prefer to use it completely offline, it will load them locally instead. The path should be: `ComfyUI/custom_nodes/SD-Latent-Upscaler/models`
Alternatively, just clone the entire HF repo to it: `git clone https://huggingface.co/city96/SD-Latent-Upscaler custom_nodes/SD-Latent-Upscaler/models`
### Usage/principle
Usage is fairly simple. You use it anywhere where you would upscale a latent. If you need a higher scale factor (e.g. x4), simply chain two of the upscalers.
As part of a workflow - notice how the second stage works despite the low denoise of 0.2. The image remains relatively unchanged.
## Training
### Upscaler v2.0
I decided to do some more research and change the network architecture alltogether. This one is just a bunch of `Conv2d` layers with an `Upsample` at the beginning, similar to before except I reduced the kernel size/padding and instead added more layers.
Trained for 1M iterations on DIV2K + Flickr2K. I changed to AdamW + L1 loss (from SGD and MSE loss) and added a `OneCycleLR` scheduler.
### Upscaler v1.0
This version was still relatively undertrained. Mostly a proof-of-concept.
Trained for 1M iterations on DIV2K + Flickr2K.
<details>
<summary>Loss graphs for v1.0 models</summary>
(Left is training loss, right is validation loss.)
<details>
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