some more readme info

README.md

@@ -11,12 +11,19 @@ This node "advanced > auto merge block weighted" takes two models, merges indivi
 
 ### Search Depth
 To calculate ratios to test, the node branches out from powers of 0.5
+
 - A depth of 2 will examine 0.0, 0.5, 1.0
 - A depth of 4 will examine 0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0
 - A depth of 6 will examine 33 different ratios
 
+There are 25 blocks to examine. If you use a depth of 4 and create 2 samples each, `25 * 9 * 2 = 450` images will be generated.
+
 ### Classifier
-The classifier models have been taken from the sdweb-auto-MBW repo
+The classifier models have been taken from the sdweb-auto-MBW repo.
+
+- [Laion Aesthetic Predictor](https://huggingface.co/spaces/Geonmo/laion-aesthetic-predictor)
+- [Waifu Diffusion 1.4 aesthetic model](https://huggingface.co/hakurei/waifu-diffusion-v1-4)
+- [Cafe Waifu](https://huggingface.co/cafeai/cafe_waifu) and [Cafe Aesthetic](https://huggingface.co/cafeai/cafe_aesthetic)
 
 ### Notes
 - --highvram flag recommended - both models will be kept in VRAM and the process is much faster
@@ -25,3 +32,4 @@ The classifier models have been taken from the sdweb-auto-MBW repo
 - the final model is saved in the models/checkpoints directory with a timestamped name
 - the resulting model will contain the text encoder and VAE sent to the node
 - the unet will (probably) be fp16 and the rest fp32. that's how they're sent to the node
+- - see: `model_management.should_use_fp16()`