Search is not available for this dataset
label
class label
16 classes
latent
sequence
55
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66
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44
[[[-0.047935035079717636,1.7416795492172241,1.4909287691116333,1.63616144657135,1.857316017150879,1.(...TRUNCATED)
10a
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11
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14e
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22
[[[0.059687864035367966,0.3399297893047333,0.7353955507278442,0.441192090511322,0.18219979107379913,(...TRUNCATED)
13d
[[[1.326835036277771,1.188739538192749,1.0723780393600464,0.8247081637382507,0.7801157236099243,1.25(...TRUNCATED)
11
[[[1.615001916885376,1.2819876670837402,1.3419922590255737,1.6463745832443237,1.6021764278411865,1.3(...TRUNCATED)

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

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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