From c8ed3d11285b430ee029d0ac70222666587019a0 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Oct 2023 06:29:58 +0200
Subject: [PATCH] Update README.md
---
README.md | 29 +++++++++++++++++++++++------
1 file changed, 23 insertions(+), 6 deletions(-)
diff --git a/README.md b/README.md
index cc3b15b..18ffb4b 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,22 @@ Without the interposer, the two latent spaces are incompatible:
## Training
-The training script should spit out a working model, nn layout is probably not optimal but I'm pretty short on VRAM to trial and error a better layout. PRs welcome.
+Most of the training/preprocessing code is a 1:1 mirror from my latent upscaler. The folder layout it expects is also the same.
+
+### Interposer v3.1
+This is basically a complete rewrite. Replaced the mediocre bunch of conv2d layers with something that looks more like a proper neural network. No VGG loss because I still don't have a better GPU.
+
+Training was done on combined Flickr2K + DIV2K, with each image being processed into 6 1024x1024 segments. Padded with some of my random images for a total of 22,000 source images in the dataset.
+
+I think I got rid of most of the XL artifacts, but the color/hue/saturation shift issues are still there. I actually saved the optimizer state this time so I might be able to do 100K steps with visual loss on my P40s. Hopefully they won't burn up.
+
+v3.0 was 500k steps at a constant LR of 1e-4, v3.1 was 1M steps using a CosineAnnealingLR to drop the learning rate towards the end. Both used AdamW.
+
+
+
+### Older versions
+
+Interposer v1.1
### Interposer v1.1
This is the second release using the "spaceship" architecture. It was trained on the Flickr2K dataset and was continued from the v1.0 checkpoint.
@@ -28,6 +43,10 @@ Overall, it seems to perform a lot better, especially for real life photos. I al
+Interposer v1.0
+
### Interposer v1.0
Not sure why the training loss is so different, it might be due to the """highly curated""" dataset of 1000 random images from my Downloads folder that I used to train it.
@@ -35,12 +54,10 @@ I probably should've just grabbed LAION.
I also trained a v1-to-v2 mode, before realizing v1 and v2 shared the same latent space. Oh well.
-
- Loss graphs for v1.0 models
+
- 
-
- 
+
+