Update README.md

README.md

@@ -52,10 +52,11 @@ Below is an example code snippet that demonstrates how to load an image directly
 ### Code Example
 
 ```python
-from diffusers import AutoencoderKL
 import torch
-from torchvision.transforms.functional import to_tensor, to_pil_image
 from PIL import Image
+from diffusers import AutoencoderKL
+from huggingface_hub import hf_hub_download
+from torchvision.transforms.functional import to_tensor, to_pil_image
 
 # Load the pre-trained Emuru VAE from Hugging Face Hub.
 model = AutoencoderKL.from_pretrained("vpippi/emuru_vae")
@@ -74,8 +75,9 @@ def postprocess_tensor(tensor):
     return to_pil_image(tensor)
 
 # Example: Encode and decode an image.
-# Replace with your image path.
-image_path = "/path/to/image"
+# Replace the following line with your image path.
+image_path = hf_hub_download(repo_id="vpippi/emuru_vae", filename="samples/lam_sample.jpg")
+
 input_image = preprocess_image(image_path)
 
 # Encode the image to the latent space.
@@ -96,20 +98,4 @@ reconstructed_image = postprocess_tensor(reconstructed)
 
 # Save the reconstructed image.
 reconstructed_image.save("reconstructed_image.png")
-```
-
-## Additional Information
-
-If you'd like to test with images hosted directly on the Hugging Face Hub, consider:
-- **Including sample images in your repository:** Place them in a folder (e.g., `samples/`) and reference them directly.
-- **Using the `huggingface_hub` API:** For example:
-
-  ```python
-  from huggingface_hub import hf_hub_download
-  from PIL import Image
-
-  # Replace 'vpippi/emuru_vae' and 'samples/lam_sample.jpg' with your details.
-  image_path = hf_hub_download(repo_id="vpippi/emuru_vae", filename="samples/lam_sample.jpg")
-  sample_image = Image.open(image_path).convert("RGB")
-  sample_image.show()
-  ```
+```