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README.md

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----
-dataset_info:
-  features:
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: '0'
-          1: '1'
-          2: '2'
-          3: '3'
-          4: '4'
-          5: '5'
-          6: '6'
-          7: '7'
-          8: '8'
-          9: '9'
-          10: a
-          11: b
-          12: c
-          13: d
-          14: e
-          15: f
-  - name: latent
-    sequence:
-      sequence:
-        sequence: float32
-  splits:
-  - name: test
-    num_bytes: 106824288
-    num_examples: 6312
-  - name: train
-    num_bytes: 2029441460
-    num_examples: 119915
-  download_size: 2082210019
-  dataset_size: 2136265748
----
-# Dataset Card for "latent_lsun_church_256px"
-
-This is derived from https://huggingface.co/datasets/tglcourse/lsun_church_train
-
-Each image is cropped to 256px square and encoded to a 4x32x32 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
-
-```
-

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