Update README.md

2023-08-15 20:22:07 +02:00
parent 58dcbeadd0
commit b4547b3386
1 changed files with 37 additions and 0 deletions
@@ -1,2 +1,39 @@
 # 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-Interposer custom_nodes/SD-Latent-Interposer.
+
+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.
+
+### 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.
+
+![LATENT_UPSCALER_ANI](https://github.com/city96/SD-Latent-Upscaler/assets/125218114/fdc4c74b-f0c3-4d28-ae11-d8970a3f3d7f)
+
+![LATENT_UPSCALER_COLOR](https://github.com/city96/SD-Latent-Upscaler/assets/125218114/ec6997ce-664b-4956-a947-503b8b591f73)
+
+## Training
+
+### Interposer v1.0 
+
+This current version is still relatively undertrained, as with the interposer. Mostly a proof-of-concept but it seems good enough as a base.
+
+Trained for 1M iterations on DIV2K + Flickr2K.
+
+(Left is training loss, right is validation loss.)
+![loss](https://github.com/city96/SD-Latent-Upscaler/assets/125218114/edbc30b4-56b4-4b74-8c0b-3ab35916e963)