diff --git a/TTS/tts/models/vits.py b/TTS/tts/models/vits.py
index 0b72fbd6..0e91c1f3 100644
--- a/TTS/tts/models/vits.py
+++ b/TTS/tts/models/vits.py
@@ -304,7 +304,12 @@ class Vits(BaseTTS):
 
         if args.use_sdp:
             self.duration_predictor = StochasticDurationPredictor(
-                args.hidden_channels, 192, 3, args.dropout_p_duration_predictor, 4, cond_channels=self.embedded_speaker_dim
+                args.hidden_channels,
+                192,
+                3,
+                args.dropout_p_duration_predictor,
+                4,
+                cond_channels=self.embedded_speaker_dim,
             )
         else:
             self.duration_predictor = DurationPredictor(
diff --git a/docs/source/models/glow_tts.md b/docs/source/models/glow_tts.md
new file mode 100644
index 00000000..66171abd
--- /dev/null
+++ b/docs/source/models/glow_tts.md
@@ -0,0 +1,22 @@
+# Glow TTS
+
+Glow TTS is a normalizing flow model for text-to-speech. It is built on the generic Glow model that is previously
+used in computer vision and vocoder models. It uses "monotonic alignment search" (MAS) to fine the text-to-speech alignment
+and uses the output to train a separate duration predictor network for faster inference run-time.
+
+## Important resources & papers
+- GlowTTS: https://arxiv.org/abs/2005.11129
+- Glow (Generative Flow with invertible 1x1 Convolutions): https://arxiv.org/abs/1807.03039
+- Normalizing Flows: https://blog.evjang.com/2018/01/nf1.html
+
+## GlowTTS Config
+```{eval-rst}
+.. autoclass:: TTS.tts.configs.glow_tts_config.GlowTTSConfig
+    :members:
+```
+
+## GlowTTS Model
+```{eval-rst}
+.. autoclass:: TTS.tts.models.glow_tts.GlowTTS
+    :members:
+```
diff --git a/docs/source/models/vits.md b/docs/source/models/vits.md
new file mode 100644
index 00000000..5c0e92f6
--- /dev/null
+++ b/docs/source/models/vits.md
@@ -0,0 +1,33 @@
+# VITS
+
+VITS (Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
+) is an End-to-End (encoder -> vocoder together) TTS model that takes advantage of SOTA DL techniques like GANs, VAE,
+Normalizing Flows. It does not require external alignment annotations and learns the text-to-audio alignment
+using MAS as explained in the paper. The model architecture is a combination of GlowTTS encoder and HiFiGAN vocoder.
+It is a feed-forward model with x67.12 real-time factor on a GPU.
+
+## Important resources & papers
+- VITS: https://arxiv.org/pdf/2106.06103.pdf
+- Neural Spline Flows: https://arxiv.org/abs/1906.04032
+- Variational Autoencoder: https://arxiv.org/pdf/1312.6114.pdf
+- Generative Adversarial Networks: https://arxiv.org/abs/1406.2661
+- HiFiGAN: https://arxiv.org/abs/2010.05646
+- Normalizing Flows: https://blog.evjang.com/2018/01/nf1.html
+
+## VitsConfig
+```{eval-rst}
+.. autoclass:: TTS.tts.configs.vits_config.VitsConfig
+    :members:
+```
+
+## VitsArgs
+```{eval-rst}
+.. autoclass:: TTS.tts.models.vits.VitsArgs
+    :members:
+```
+
+## Vits Model
+```{eval-rst}
+.. autoclass:: TTS.tts.models.vits.Vits
+    :members:
+```