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latents sequencelengths 4 4 | label_latent int64 0 999 |
|---|---|
[
[
[
2.1326520442962646,
4.022431373596191,
2.503732919692993,
3.529430866241455,
1.780723214149475,
0.5165610909461975,
4.273795127868652,
5.5315985679626465,
4.491783142089844,
1.7778517007827759,
3.0758378505706787,
5.2626543045043945,
... | 726 |
[
[
[
1.426426649093628,
0.6210892200469971,
4.963717460632324,
-0.33825254440307617,
3.559312582015991,
1.4125216007232666,
-4.890995502471924,
-0.43694841861724854,
-3.7673261165618896,
-3.0983283519744873,
1.8089739084243774,
-2.6937899589... | 917 |
[[[7.844578266143799,3.911954641342163,6.265392780303955,1.486894965171814,9.750190734863281,4.48775(...TRUNCATED) | 13 |
[[[1.864761233329773,10.804437637329102,1.0170234441757202,4.269654750823975,1.2092145681381226,1.26(...TRUNCATED) | 939 |
[[[-0.5518589615821838,5.776851654052734,-1.8749537467956543,0.9443236589431763,-2.1623244285583496,(...TRUNCATED) | 6 |
[[[-5.766753673553467,-0.3822507858276367,0.025632312521338463,-4.1985182762146,-3.683450698852539,8(...TRUNCATED) | 983 |
[[[4.933608055114746,4.588385105133057,3.6943459510803223,3.9011635780334473,4.860240936279297,4.346(...TRUNCATED) | 655 |
[[[5.3907151222229,6.166732311248779,7.227005481719971,6.848473072052002,6.387278079986572,5.1693058(...TRUNCATED) | 579 |
[[[4.769820213317871,6.574337482452393,3.1444427967071533,2.533736228942871,6.514801502227783,7.0421(...TRUNCATED) | 702 |
[[[7.381595611572266,14.06937026977539,17.47007179260254,14.849177360534668,17.10161781311035,14.999(...TRUNCATED) | 845 |
End of preview. Expand in Data Studio
Better latent: I advise you to use another dataset https://huggingface.co/datasets/cloneofsimo/imagenet.int8 which is already compressed (5Go only) and use a better latent model (SDXL)
This dataset is the latent representation of the imagenet dataset using the stability VAE stabilityai/sd-vae-ft-ema.
Every image_latent is of shape (4, 32, 32).
If you want to retrieve the original image you have to use the model used to create the latent image :
vae_model = "stabilityai/sd-vae-ft-ema"
vae = AutoencoderKL.from_pretrained(vae_model)
vae.eval()
The images have been encoded using :
images = [DEFAULT_TRANSFORM(image.convert("RGB")) for image in examples["image"]]
images = torch.stack(images)
images = vaeprocess.preprocess(images)
images = images.to(device="cuda", dtype=torch.float)
with torch.no_grad():
latents = vae.encode(images).latent_dist.sample()
With DEFAULT_TRANSFORM being :
DEFAULT_IMAGE_SIZE = 256
DEFAULT_TRANSFORM = transforms.Compose(
[
transforms.Resize((DEFAULT_IMAGE_SIZE, DEFAULT_IMAGE_SIZE)),
transforms.ToTensor(),
]
)
The images can be decoded using :
import datasets
latent_dataset = datasets.load_dataset(
"Forbu14/imagenet-1k-latent"
)
latent = torch.tensor(latent_dataset["train"][0]["latents"])
image = vae.decode(latent).sample
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