# Dataset Card for "latent_afhqv2_512px"

Each image is cropped to 512px square and encoded to a 4x64x64 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, 64, 3264
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
```
region:us
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "cat", "1": "dog", "2": "wild"}}}}, {"name": "latent", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 1052290164, "num_examples": 15803}], "download_size": 1038619876, "dataset_size": 1052290164}}
TAGS
#region-us 

# Dataset Card for "latent_afhqv2_512px"

Each image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion

Decoding

# Dataset Card for "latent_afhqv2_512px"

Each image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion

Decoding
TAGS
#region-us 
 # Dataset Card for "latent_afhqv2_512px"

Each image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion

Decoding
passage: TAGS
#region-us 
# Dataset Card for "latent_afhqv2_512px"

Each image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion

Decoding