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---
dataset_info:
features:
- name: latents
sequence:
sequence:
sequence: float32
- name: label_latent
dtype: int64
splits:
- name: train
num_bytes: 21682470308
num_examples: 1281167
- name: validation
num_bytes: 846200000
num_examples: 50000
- name: test
num_bytes: 1692400000
num_examples: 100000
download_size: 24417155228
dataset_size: 24221070308
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
> [!WARNING]
> **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 :
```python
vae_model = "stabilityai/sd-vae-ft-ema"
vae = AutoencoderKL.from_pretrained(vae_model)
vae.eval()
```
The images have been encoded using :
```python
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 :
```python
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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