Nirman
hajar001

hajar001/stylegan2-ffhq-128

Architecture: StyleGAN - Training: Lazy R1 regularization + Path Length Regularization - Dataset: FFHQ (Flickr-Faces-HQ) - Resolution: 128×1...

Model Documentation

Generate Faces using the app : [https://stylegan-face-generation.streamlit.app/]



This model has been pushed to the Hub using the PytorchModelHubMixin integration:
  • Code: https://github.com/HajarHAMDOUCH01/Face-Generator-StyleGAN-PyTorch
  • Paper: [https://arxiv.org/abs/1812.04948]
  • Paper: [https://arxiv.org/abs/1912.04958]
  • Dataset(128*128 , 65k from FFHQ Dataset): [https://www.kaggle.com/datasets/hajarhamdouch/ffhq-128128-65k]


    • StyleGAN Face Generator (128×128)



    This model generates realistic human faces at 128×128 resolution using a StyleGAN1 architecture trained with StyleGAN2 regularization techniques.

    Model Description



  • Architecture: StyleGAN
  • Training: Lazy R1 regularization + Path Length Regularization
  • Dataset: FFHQ (Flickr-Faces-HQ)
  • Resolution: 128×128 pixels
  • Latent dimension: 512
  • Features: Style mixing, noise injection, truncation trick


    • Usage



    Quick Start



    python
import torch
from torchvision.utils import save_image
from huggingface_hub import hf_hub_download
import sys, os

    Download and load model
    
model_file = hf_hub_download(
    repo_id="hajar001/stylegan2-ffhq-128",
    filename="style_gan.py"
)
sys.path.insert(0, os.path.dirname(model_file))
from style_gan import StyleGAN

    model = StyleGAN.from_pretrained("hajar001/stylegan2-ffhq-128")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()

    Generate a single face
    
with torch.no_grad():
    z = torch.randn(1, 512, device=device)
    images = model.generate(z, truncation_psi=0.7)

    Denormalize from [-1, 1] to [0, 1]
    
images = (images + 1) / 2
images = torch.clamp(images, 0, 1)

    save_image(images, "generated_face.png")
print("Generated face saved to generated_face.png")


    Generate Multiple Faces



    python

    Generate 16 faces in a 4×4 grid
    
with torch.no_grad():
    z = torch.randn(16, 512, device=device)
    images = model.generate(z, truncation_psi=0.7)

    images = (images + 1) / 2
images = torch.clamp(images, 0, 1)

    save_image(images, "generated_faces_grid.png", nrow=4)
print("Generated 16 faces")


    Truncation Trick



    The truncation_psi parameter controls the trade-off between quality and diversity:
  • 1.0: Maximum diversity, lower quality
  • 0.7: Balanced (recommended)
  • 0.5: Higher quality, less diversity


    • python

    High quality, less diverse
    
images = model.generate(z, truncation_psi=0.5)

    More diverse, slightly lower quality
    
images = model.generate(z, truncation_psi=1.0)


    Style Mixing



    python

    Generate two random latent codes
    
z1 = torch.randn(1, 512, device=device)
z2 = torch.randn(1, 512, device=device)

    Mix styles (coarse features from z1, fine details from z2)
    
with torch.no_grad():
    w1 = model.mapping(z1)
    w2 = model.mapping(z2)
    
    
    Create mixed w: first 4 layers from w1, rest from w2
    
    w_mixed = torch.cat([
        w1.unsqueeze(1).expand(-1, 4, -1),
        w2.unsqueeze(1).expand(-1, 8, -1)
    ], dim=1)
    
    mixed_image = model.synthesis(w_mixed)

    mixed_image = (mixed_image + 1) / 2
save_image(mixed_image, "style_mixed.png")


    Training Details



  • Epochs: 38+ (ongoing training)
  • Optimizer: Adam (β₁=0.0, β₂=0.99)
  • Learning Rates: G=0.0002, D=0.0002
  • Regularization:
  • R1 gamma: 5.0 (applied every 16 iterations)
  • Path Length Regularization: 1.0 (applied every 16 iterations)
  • Batch Size: 32
  • Style Mixing Probability: 0.8


    • Limitations



  • Resolution limited to 128×128


    • Technical Notes



    This is a hybrid approach: StyleGAN1 architecture (AdaIN-based style modulation) trained with StyleGAN2 regularization techniques (R1 + PLR). This combines the simplicity of StyleGAN1 with the improved training stability of StyleGAN2.

    Citation



    If you use this model, please cite the original StyleGAN papers:

    bibtex
@article{karras2018stylebased,
  title={A Style-Based Generator Architecture for Generative Adversarial Networks},
  author={Karras, Tero and Laine, Samuli and Aila, Timo},
  journal={arXiv preprint arXiv:1812.04948},
  year={2018}
}

    @article{karras2019stylegan2,
  title={Analyzing and Improving the Image Quality of StyleGAN},
  author={Karras, Tero and Laine, Samuli and Aittala, Miika and Hellsten, Janne and Lehtinen, Jaakko and Aila, Timo},
  journal={arXiv preprint arXiv:1912.04958},
  year={2019}
}


    Resources



  • Code Repository: https://github.com/HajarHAMDOUCH01/Face-Generator-StyleGAN-PyTorch
  • StyleGAN Paper: https://arxiv.org/abs/1812.04948
  • StyleGAN2 Paper: https://arxiv.org/abs/1912.04958
  • FFHQ Dataset: https://github.com/NVlabs/ffhq-dataset


    • License



    MIT License
  • See LICENSE file for details

    • Files & Weights

    FilenameSizeAction
    model.safetensors 0.07 GB