---
dataset_info:
  features:
  - name: latent
    sequence:
      sequence:
        sequence: float16
  splits:
  - name: train
    num_bytes: 5878040000
    num_examples: 70000
  download_size: 3475551903
  dataset_size: 5878040000
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- unconditional-image-generation
size_categories:
- 10K<n<100K
---
FFHQ Dataset (pravsels/FFHQ_1024) encoded using the dc-ae-f128c512-mix-1.0 auto encoder.

Example usage

```
import sys
sys.path.append('../dcae') # https://github.com/vladmandic/dcae
from dcae import DCAE

from datasets import load_dataset
import torch
import torchvision

dataset = load_dataset("SwayStar123/FFHQ_1024_DC-AE_f128", split="train")
dc_ae = DCAE("dc-ae-f128c512-mix-1.0", device="cuda", dtype=torch.bfloat16).eval() # Must be bfloat. with float16 it produces terrible outputs.

def denorm(x):
    return (x * 0.5 + 0.5).clamp(0, 1)

latent = next(iter(dataset))["latent"]
latent = torch.tensor(latent, device=DEVICE, dtype=DTYPE)

with torch.no_grad():
        recon = dc_ae.decode(latent.unsqueeze(0)).squeeze(0)

recon = denorm(recon).to(torch.float32)

torchvision.utils.save_image(
    recon,
    "recon.png",
    normalize=False,
)
```