Update README.md

README.md

@@ -37,4 +37,33 @@ dataset_info:
 ---
 # Dataset Card for "latent_lsun_church_256px"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+This is derived from https://huggingface.co/datasets/tglcourse/lsun_church_train
+
+Each image is cropped to 256px square and encoded to a 32x32x4 latent representation using the same VAE as that employed by Stable Diffusion
+
+Decoding
+```python
+from diffusers import AutoencoderKL
+from datasets import load_dataset
+from PIL import Image
+import numpy as np
+import torch
+
+# load the dataset
+dataset = load_dataset('tglcourse/latent_lsun_church_256px')
+
+# Load the VAE (requires access - see repo model card for info)
+vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")
+
+latent = torch.tensor([dataset['train'][0]['latent']]) # To tensor (bs, 4, 32, 32)
+latent = (1 / 0.18215) * latent # Scale to match SD implementation
+with torch.no_grad():
+    image = vae.decode(latent).sample[0] # Decode 
+image = (image / 2 + 0.5).clamp(0, 1) # To (0, 1)
+image = image.detach().cpu().permute(1, 2, 0).numpy() # To numpy, channels lsat
+image = (image * 255).round().astype("uint8") # (0, 255) and type uint8
+image = Image.fromarray(image) # To PIL
+image # The resulting PIL image
+
+```
+