diff --git a/README.md b/README.md
index 7938456..4a234f7 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ This repository is an updated version of [stylegan2-ada-pytorch](https://github.
 - General improvements: reduced memory usage, slightly faster training, bug fixes.
 
 Compatibility:
-- Compatible with old network pickles created using [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada) and [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch).
+- Compatible with old network pickles created using [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada) and [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch).  (Note: running old StyleGAN2 models on StyleGAN3 code will produce the same results as running them on stylegan2-ada/stylegan2-ada-pytorch.  To benefit from the StyleGAN3 architecture, you need to retrain.)
 - Supports old StyleGAN2 training configurations, including ADA and transfer learning. See [Training configurations](./docs/configs.md) for details.
 - Improved compatibility with Ampere GPUs and newer versions of PyTorch, CuDNN, etc.
 
@@ -56,7 +56,7 @@ While new generator approaches enable new media synthesis capabilities, they may
 * 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.
 * 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.
 * CUDA toolkit 11.1 or later.  (Why is a separate CUDA toolkit installation required?  See [Troubleshooting](./docs/troubleshooting.md#why-is-cuda-toolkit-installation-necessary)).
-- GCC 7 or later (Linux) or Visual Studio (Windows) compilers.  Recommended GCC version depends on CUDA version, see for example [CUDA 11.4 system requirements](https://docs.nvidia.com/cuda/archive/11.4.1/cuda-installation-guide-linux/index.html#system-requirements).
+* GCC 7 or later (Linux) or Visual Studio (Windows) compilers.  Recommended GCC version depends on CUDA version, see for example [CUDA 11.4 system requirements](https://docs.nvidia.com/cuda/archive/11.4.1/cuda-installation-guide-linux/index.html#system-requirements).
 * Python libraries: see [environment.yml](./environment.yml) for exact library dependencies.  You can use the following commands with Miniconda3 to create and activate your StyleGAN3 Python environment:
   - `conda env create -f environment.yml`
   - `conda activate stylegan3`