label
class label 3
classes | latent
sequence |
|---|---|
1dog
| [
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1dog
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1dog
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1dog
| [[[0.7214303612709045,1.6001919507980347,0.15799014270305634,0.3384092450141907,0.3359840512275696,0(...TRUNCATED) |
1dog
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1dog
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1dog
| [[[0.041221506893634796,0.1172712966799736,0.518294095993042,-0.38741397857666016,1.5991599559783936(...TRUNCATED) |
1dog
| [[[1.015040397644043,0.38717252016067505,0.8832125663757324,0.1732202023267746,0.26710712909698486,0(...TRUNCATED) |
End of preview. Expand
in Dataset Viewer.
Dataset Card for "latent_afhqv2_256px"
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_afhqv2_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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