Update README.md

README.md

@@ -48,7 +48,7 @@ You can load the pre-trained Emuru VAE using Diffusers’ `AutoencoderKL` interf
 
 ```python
 from diffusers import AutoencoderKL
-model = AutoencoderKL.from_pretrained("vpippi/emuru_vae")
+model = AutoencoderKL.from_pretrained("blowing-up-groundhogs/emuru_vae")
 ```
 
 Below is an example code snippet that demonstrates how to load an image directly from a URL, process it, encode it into the latent space, decode it back to image space, and save the reconstructed image.
@@ -63,7 +63,7 @@ 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")
+model = AutoencoderKL.from_pretrained("blowing-up-groundhogs/emuru_vae")
 
 # Function to preprocess an RGB image:
 # Loads the image, converts it to RGB, and transforms it to a tensor normalized to [0, 1].
@@ -80,7 +80,7 @@ def postprocess_tensor(tensor):
 
 # Example: Encode and decode an image.
 # Replace the following line with your image path.
-image_path = hf_hub_download(repo_id="vpippi/emuru_vae", filename="samples/lam_sample.jpg")
+image_path = hf_hub_download(repo_id="blowing-up-groundhogs/emuru_vae", filename="samples/lam_sample.jpg")
 
 input_image = preprocess_image(image_path)