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latent
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[[["-0.1885","0.031","-0.1641","0.5586","1.648","-0.8203","1.242","0.617","0.3848","2.938","-0.2578"(...TRUNCATED) |
[[["1.031","1.023","-1.211","2.234","1.516","0.9062","2.031","-0.9688","2.047","0.547","3.86","2.703(...TRUNCATED) |
[[["0.867","0.375","-1.125","0.418","0.508","-0.66","-0.0908","-0.6836","1.953","0.8984","0.2812","1(...TRUNCATED) |
[[["1.242","3.203","2.375","-0.455","-0.7617","0.664","1.008","1.891","-2.188","3.14","-0.4102","-0.(...TRUNCATED) |
[[["-1.375","-1.719","1.297","1.352","0.1914","1.109","-0.6406","-1.047","-1.242","-1.477","0.5273",(...TRUNCATED) |
[[["0.2178","0.3086","0.5938","-1.227","-1.07","-0.05566","1.094","1.258","-0.11426","-0.91","-4.25"(...TRUNCATED) |
[[["2.61","-1.578","-1.32","-0.2676","-0.7188","-0.04004","-0.621","0.4219","-0.5312","1.586","2.094(...TRUNCATED) |
[[["3.984","6.156","2.297","-0.1406","0.789","-0.1396","1.102","1.023","5.562","5.47","1.4375","1.82(...TRUNCATED) |
[[["2.922","0.3848","-0.1084","1.242","-0.629","0.06104","0.9727","0.3223","0.621","0.3965","0.504",(...TRUNCATED) |
[[["-0.8438","-0.9297","0.0996","0.5938","1.086","1.367","2.812","0.2363","-0.785","-1.6875","0.0796(...TRUNCATED) |
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FFHQ Dataset (pravsels/FFHQ_1024) encoded using the dc-ae-f64c128-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_f64", split="train")
dc_ae = DCAE("dc-ae-f64c128-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,
)
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