Update README.md

README.md

@@ -20,7 +20,22 @@ Without the interposer, the two latent spaces are incompatible:
 ![LATENT_INTERPOSER_V3 1](https://github.com/city96/SD-Latent-Interposer/assets/125218114/13e2c01f-580e-4ecb-af1f-b6b21699127b)
 
 ## 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.
+
+![INTERPOSER_V3 1](https://github.com/city96/SD-Latent-Interposer/assets/125218114/daff0ae2-4739-4cef-ba54-ac1d156d3388)
+
+### Older versions
+
+<details><summary>Interposer v1.1</summary>
 
 ### 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
 
 ![loss](https://github.com/city96/SD-Latent-Interposer/assets/125218114/e890420f-cebd-4f88-b243-62560b8384e5)
 
+</details>
+
+<details><summary>Interposer v1.0</summary>
+
 ### 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.
 
-<details>
-  <summary>Loss graphs for v1.0 models</summary>
-  
 ![loss](https://github.com/city96/SD-Latent-Interposer/assets/125218114/f92c399b-a823-4521-b09b-8bdc3795f1ea)
   
 ![xl-to-v1_interposer](https://github.com/city96/SD-Latent-Interposer/assets/125218114/0d963bc5-570f-4ebe-95db-16e261f05e48)
   
 </details>
 
+</details>