Search is not available for this dataset
label class label 3
classes | latent sequence |
|---|---|
1dog | [
[
[
0.674001932144165,
0.5220367312431335,
-0.08538233488798141,
0.3434295356273651,
1.421834945678711,
-0.5711292028427124,
0.750150203704834,
0.022528884932398796,
0.8383235335350037,
-0.14081640541553497,
2.334258794784546,
1.13108885288... |
1dog | [
[
[
-0.5210975408554077,
0.2799062132835388,
-1.3457638025283813,
0.042385704815387726,
0.2252449095249176,
-0.6940844655036926,
-0.7056360244750977,
0.8889088034629822,
-0.5546013712882996,
0.9920197129249573,
-0.921320378780365,
0.2313560... |
1dog | [[[0.9299851655960083,-0.3545624613761902,-0.11646638065576553,1.080183506011963,0.9336830973625183,(...TRUNCATED) |
1dog | [[[0.4643545150756836,1.159124732017517,0.4591493308544159,1.2618976831436157,0.8688426613807678,1.5(...TRUNCATED) |
1dog | [[[0.02184654027223587,1.164801001548767,2.3916497230529785,1.8131107091903687,1.737846851348877,2.1(...TRUNCATED) |
1dog | [[[0.7214303612709045,1.6001919507980347,0.15799014270305634,0.3384092450141907,0.3359840512275696,0(...TRUNCATED) |
1dog | [[[1.4456974267959595,1.1459866762161255,1.0163121223449707,1.1947654485702515,1.0975418090820312,1.(...TRUNCATED) |
1dog | [[[1.3393735885620117,1.0888593196868896,1.169750690460205,1.2294034957885742,1.153085470199585,0.95(...TRUNCATED) |
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) |
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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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