Update README.md

README.md

@@ -4,20 +4,28 @@
 
 Guitar plugin made with JUCE that uses neural network models to emulate real world hardware.
 
-Checkout the tutorial on [YouTube](https://youtu.be/HrNf6DNRUdU) for creating your own models for the SmartPedal.
+- Checkout the tutorial on [YouTube](https://youtu.be/HrNf6DNRUdU) for creating your own models for the SmartPedal.
+- Visit the GuitarML [ToneLibrary Website](https://guitarml.com/tonelibrary/tonelib-sa.html) to download SmartPedal compatible models.
+
+![app](https://github.com/GuitarML/SmartGuitarPedal/blob/master/resources/app_pic.png)
 
 This plugin uses a WaveNet model to recreate the sound of real world hardware, such as
-a TS9 Tubescreamer or Blues Jr amp. Drive and Level are used for simple ways to
-control the sound. The WaveNet model is effective at emulating distortion style effects or tube amplifiers.
+a TS9 Tubescreamer or Blues Jr amp. Drive and Level adjust the signal gain before and after the 
+WaveNet model processing. As of version 1.5, the SmartPedal can run models conditioned on a single parameter, 
+such as a gain control. When conditioned models are loaded, the LED graphic will change colors 
+from red to blue, and the Drive knob will control the conditioned parameter.
 
-![app](https://github.com/keyth72/SmartGuitarPedal/blob/master/resources/app_pic.png)
+The WaveNet model is effective at emulating distortion style effects or tube amplifiers, but cannot capture
+time based effects such as reverb or delay. You can capture the sound of an amplifier either by recording with 
+a microphone, or direct out from a load box. When running "Direct Out" models, you will need to use an
+Impulse Response plugin to accurately model the amp speaker/cabinet. 
 
-You can create your own models and load them in SmartGuitarPedal using the [PedalNetRT](https://github.com/GuitarML/PedalNetRT) repository.
+You can create your own models and load them in SmartGuitarPedal using the [PedalNetRT](https://github.com/GuitarML/PedalNetRT) repository directly, or
+by using the Capture Utility files (available for download at [GuitarML.com](https://guitarml.com/) with Google Colab and following the [Video Tutorial](https://youtu.be/HrNf6DNRUdU).
 
 Model training is done using PyTorch on pre recorded .wav samples. More info in the above repository. 
 To share your best models, email the json files to [email protected] and they may be included 
-in the latest release as a downloadable zip.
-
+in the latest [ToneLibrary](https://guitarml.com/tonelibrary/tonelib-sa.html) release.
 
 Also see companion plugin, the [SmartGuitarAmp](https://github.com/GuitarML/SmartGuitarAmp)
 
@@ -52,3 +60,6 @@ a different model architecture and will not be compatible. Only WaveNet models t
 This project is licensed under the Apache License, Version 2.0 - see the [LICENSE](LICENSE) file for details.
 
 This project builds off the work done in the [WaveNetVA](https://github.com/damskaggep/WaveNetVA) repository.
+
+### Special Thanks
+Special thanks to Stefan Schmidt for the graphics in SmartPedal version 1.5. These were created from a Blender model and rendered using the Cycles render engine.