Added notes to README

README.md

@@ -1,7 +1,47 @@
 jit-models
 ==========
-
 This project exists to turn PyTorch models into Torch JIT, which can be run in
 any language with libtorch bindings.
 
+How to develop
+--------------
+The best way is to import the *inference* code and work backwards. 
+
+Try to comment out all of the model so `forward` only returns the input, then begin
+opening things up.
+
+Scripting vs tracing
+--------------------
+Scripting transforms the code. It can contain branches.
+
+Tracing evaluates the codepath with example data, fixing the branches taken.
+
+The two techniques can be combined in the various nodes in the module tree.
+
+Model-specific details
+----------------------
+
+**glow-tts notes**
+
+Only two changes!
+
+`FlowGenerator.forward` (the root module) needs the following changes:
+
+Hardcode a few constructor args. These come from `inference.py`'s arg set:
+
+```
+def forward(self, x, x_lengths, y=None, y_lengths=None, g=None, gen=False, noise_scale=1., length_scale=1.):
+	y = None
+	y_lengths = None
+	noise_scale = .667
+	length_scale = 1.0 
+	gen = True
+```
+
+Return only the mel output at the end of the `forward` branch used in tracing:
+
+```
+#return (y, y_m, y_logs, logdet), attn, logw, logw_, x_m, x_logs 
+return y
+```