Update README.md

README.md

@@ -53,6 +53,14 @@ This new version provides a **Hugging Face–compatible AutoencoderKL** checkpoi
 
 It offers **plug-and-play compatibility** with diffusion pipelines and can be seamlessly used to build or train new diffusion models.
 
+## ⚡️ Quickstart 
+```python
+from diffusers import AutoencoderKL
+
+vae = AutoencoderKL.from_pretrained("REPA-E/e2e-vavae-hf").to("cuda")
+```
+> Use `vae.encode(...)` / `vae.decode(...)` in your pipeline. (A full example is provided below.)
+
 ## 📦 Requirements
 The following packages are required to load and run the REPA-E VAEs with the `diffusers` library:
 
@@ -62,7 +70,7 @@ pip install torch>=2.3.1
 ```
 
 ## 🚀 Example Usage
-Below is a minimal example showing how to load and use the REPA-E end-to-end trained FLUX-VAE with `diffusers`:
+Below is a minimal example showing how to load and use the REPA-E end-to-end trained VA-VAE with `diffusers`:
 
 ```python
 from io import BytesIO