move notes from python to README

README.md

@@ -0,0 +1,25 @@
+Auto-MBW for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) loosely based on [sdweb-auto-MBW](https://github.com/Xerxemi/sdweb-auto-MBW)
+
+### Purpose
+This node "advanced > auto merge block weighted" takes two models, merges individual blocks together at various ratios, and automatically rates each merge, keeping the ratio with the highest score. The resulting model will contain the text encoder and VAE sent to the node, without modification. Whether this is a good idea or not is anyone's guess. In practice this makes models that make images the classifier says are good.
+
+### Settings
+- Prompt: to generate sample images to be rated
+- Sample Count: number of samples per ratio per block to generate
+- Search Depth: number of branches to take while choosing ratios to test
+- Classifier: model used to rate images
+
+### 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
+
+### Classifier
+Currently this only supports [cafeai](https://huggingface.co/cafeai) "aesthetic" and "waifu" models.
+
+### Notes
+- --highvram flag recommended - both models will be kept in VRAM and the process is much faster
+- many hardcoded settings are arbitrary - such as the sampler and block processing order
+- generated images are not saved
+- the final model is saved in the models/checkpoints directory with a timestamped name

__init__.py

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-# Automatic Merge Block Weighted for ComfyUI
-# put this file in ComfyUI/custom_nodes/
-
-# pseudo README.md:
-
-# does not work with --highvram flag. this will be fixed
-
-# sampler settings (ddim, 20 steps, cfg7) are arbitrary and hard-coded
-# so is BLOCK_ORDER. no testing was done to show these settings are better
-# clip and vae are not modified. resulting model filename is bad, sorry
-
-# increasing search_depth increases processing time exponentially
-# increasing sample_count increases processing time linearly
-
-# modifying defaults don't necessarily improve results. results are 99%
-# dependent on the cafe classifer models from HF
-
 import math
 import pathlib
 import time