diff --git a/AMD-ROCm.md b/AMD-ROCm.md
index 19fb2a9..46471f5 100644
--- a/AMD-ROCm.md
+++ b/AMD-ROCm.md
@@ -123,7 +123,7 @@ On the other hand, for best performance during generation (but slower startup on
 
 ### Reduce VRAM consumption
 
-If you use the `bf16` data type (*Settings > Compute Settings > Execution Precision > Device precision type*), which is autodetected on RDNA3 and newer cards, there is the chance that VRAM usage will be very high (16+ GB) when decoding the final image and when upscaling with non-latent upscalers. To workaround the problem, ensure to set `Device precision type` as `fp16`, and disable VAE upcasting in *Variable Auto Encoder > VAE upcasting*. 
+If you use the `bf16` data type (*Settings > Compute Settings > Execution Precision > Device precision type*), which is autodetected on RDNA3 and newer cards, there is the chance that VRAM usage will be very high (16+ GB) when decoding the final image and when upscaling with non-latent upscalers. To workaround the problem, ensure to set `Device precision type` as `fp16`, and disable VAE upcasting in *Variational Auto Encoder > VAE upcasting*. 
 Setting `fp16` has also a noticeable impact on performance. 
 
 ### Composable Kernel (CK) Flash attention
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b4db416..a1174af 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -875,7 +875,7 @@ Commit hash: `master: #dcfc9f3` `dev: #935cac6`
   - optimizations: full offload, quantization and tiling support  
   - [TeaCache](https://github.com/ali-vilab/TeaCache/blob/main/TeaCache4LTX-Video/README.md) integration  
 - **VAE**:  
-  - tiling granular options in *settings -> variable auto encoder*  
+  - tiling granular options in *settings -> Variational Auto Encoder*  
 - **UI**:  
   - live preview optimizations and error handling  
   - live preview high quality output, thanks @Disty0  
diff --git a/Home.md b/Home.md
index d980582..20f302c 100644
--- a/Home.md
+++ b/Home.md
@@ -74,7 +74,7 @@ Learn how you can manage your resources better:
 - [Memory Offloading](Offload)  
   includes performance notes for different offload options  
 - [Model Quantization](Quantization)  
-- [Variable Auto-Encoder](VAE)  
+- [Variational Autoencoder](VAE)  
 - [Nunchaku](Nunchaku) how to use for faster inference  
 
 Plus collection of [Benchmarks](Benchmark) to highlight what to expect using different compile and/or device settings: - Compile/device settings  
diff --git a/VAE.md b/VAE.md
index dd61f4c..cc09afd 100644
--- a/VAE.md
+++ b/VAE.md
@@ -1,4 +1,4 @@
-# Variable Auto-Encoder (VAE)
+# Variational Autoencoder (VAE)
 
 VAE is a model that can be used to compress and decompress images.  
 It is a type of autoencoder that learns a latent space representation of the input data. The model is trained to minimize the reconstruction error of the input data, while also learning a latent space that is continuous and smooth. This allows the model to generate new images by sampling from the latent space.