You can deploy this model and start earning money today!

puruchinera/
AnimeFaces256_AEKL

Name: AnimeFaces256_AEKL
Availability: InStock

safetensors

puruchinera/anime-faces-256

puruchinera/anime-pixel-art-animefacesv2

puruchinera/anime-faces-256px-v2

apache-2.0

Views

No views yet

AnimeFaces256-AEKL

This repository contains an AutoencoderKL (AEKL) trained from scratch for latent-space image synthesis experiments on anime face datasets. This model is designed specifically for latent-space synthesis rather than direct image generation.

Model Details

Architecture: AutoencoderKL (AEKL)
Input Resolution: 256×256
Latent Space: (4, 16, 16)
Components: Encoder–Decoder with KL divergence regularization
Features: Includes attention mechanisms in latent feature extraction and uses EMA (Exponential Moving Average) weights during training for improved stability.
VAE Scale Factor: Fixed at 0.04706926643848419 (required for correct latent normalization)
Author: Yoel Pilier (ORCID) (GitHub)

Intended Purpose

Latent-space image synthesis research
KL-regularized autoencoders exploration
Perceptual learning (LPIPS) evaluation
Foundation model experimentation
Attention-based VAE studies

Results (Epoch 99)

Reconstruction

Latent Interpolation

Usage

Installation & Setup

To train or use the model from the source repository:

bash

git clone https://github.com/YoelPilier/AnimeFaces256_AEKL
cd AnimeFaces256_AEKL
pip install -r requirements.txt

Loading the Model

python

from Models import VAE
import torch

vae = VAE.from_pretrained(checkpoint="best", use_ema=True)
vae = vae.to("cuda")
vae.eval()

Encoding Images into Latents

python

with torch.no_grad():
    x = batch_images.to("cuda")
    z_mean, z_log_var = vae.encoder(x)
    z = vae.reparameterize(z_mean, z_log_var)
    latents = z * vae.scaling_factor

Decoding Latents into Images

python

with torch.no_grad():
    z = latents / vae.scaling_factor
    images = vae.decoder(z)

Core VAE Implementation

For reference, the core architecture is structured as follows:

python

class VAE(nn.Module):
    REPO_ID = "puruchinera/AnimeFaces256_AEKL"

    def __init__(self, input_dim=3, hidden_dim=64, latent_dim=4, compression_factor=4):
        super().__init__()

        self.encoder = Encoder(
            input_dim,
            hidden_dim,
            num_downsamples=compression_factor,
            latent_size=latent_dim,
        )

        self.decoder = Decoder(
            input_dim,
            hidden_dim,
            num_upsamples=compression_factor,
            latent_size=latent_dim,
        )

        self.scaling_factor = 0.04706926643848419
        self.latent_dim = latent_dim

    def forward(self, x):
        z_mean, z_log_var = self.encoder(x)
        z = self.reparameterize(z_mean, z_log_var)
        reconstructed = self.decoder(z)
        return reconstructed, z_mean, z_log_var

    def reparameterize(self, mean, log_var):
        log_var = torch.tanh(log_var) * 5
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        return mean + eps * std

Training Details

To train the model yourself using accelerate:

accelerate launch train_vae.py

Hardware & Time

Framework: PyTorch (trained from scratch)
Hardware: NVIDIA A6000
Total Training: 100 epochs
Epoch Duration: ~1.5 hours
Total Training Time: ~150 hours

Optimizer & Learning Rate Schedule

python

optimizer = torch.optim.AdamW(
    model.parameters(),
    lr=3e-4,
    weight_decay=1e-5
)

Epoch < 74: lr = 3e-4
Epoch ≥ 74: lr = 3e-5
Epoch ≥ 85: lr = 5e-6

Losses Evaluated

Reconstruction loss
KL Divergence
LPIPS perceptual loss

Final Training Metrics (Epoch 99)

Metric	Training Value	Validation Value
Loss	0.1967	0.2022
PSNR	22.1035	22.1793
SSIM	0.7837	0.7853
KL Divergence	219.7652	229.9244
LPIPS	0.1533	0.1611
Reconstruction	0.0862	0.0814

Raw Logs

How to Cite

If you use this repository or its code in your research or projects, please cite it as:

bibtex

@misc{pilier2026animefaces256aekl,
  author       = {Yoel Pilier},
  title        = {AnimeFaces256-AEKL: AutoencoderKL trained for latent-space image synthesis on anime face datasets},
  year         = {2026},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/YoelPilier/AnimeFaces256_AEKL}}
}