FaceGen v1
This model is a Deep Convolutional Generative Adversarial Network (DCGAN) trained to generate high-quality 128x128 images of human faces. It was trained for 250 epochs on a curated dataset of feline images, pushing...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 712 MB
- Versão
- vRevision 0a0cf6c
- Atualizado
- 20 de out. de 2026
- Downloads
- 0
- Fonte
- Hugging Face
Descrição da fonte
FaceGen v1 - 128px DCGAN
This model is a Deep Convolutional Generative Adversarial Network (DCGAN) trained to generate high-quality 128x128 images of human faces. It was trained for 250 epochs on a curated dataset of feline images, pushing the boundaries of traditional GAN architectures at this resolution.
Sample
Here's a sample after epoch 200: image
Model Details
- Architecture: DCGAN (Deep Convolutional GAN)
- Resolution: 128x128 pixels (RGB)
- Parameters: ~186M (Generator)
- Training Duration: ~22 hours on NVIDIA RTX 5060 Ti 16GB
- Framework: PyTorch with Mixed Precision (AMP)
Training Hyperparameters
- Batch Size: 128
- Learning Rate: 0.0002
- Optimizer: Adam (Beta1: 0.5, Beta2: 0.999)
- Latent Vector (Z): 128 dimensions
Training details
The full training code can be found as train.py in this repo.
The training data we used was from HF: stable-bias/faces
Intended Use
This model is intended for artistic and research purposes. It demonstrates how GANs can capture complex faces and even eye reflections at medium resolutions.
How to use
To use this model, clone this repository and run the provided inference script. Ensure you have matplotlib, torch and torchvision installed.
python3 inference.py
Sample output: image
Fontes
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Fontes
1 fonteVerificado 20 de out.
Artefatos de modelo
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Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- license: mit tags: - gan - pytorch - vision - dcgan - faces - humans - face metrics: - loss datasets: - SDbiaseval/faces pipeline_tag: unconditional-image-generation --- # FaceGen v1 - 128px DCGAN This model is a Deep Convolutional Generative Adversarial Network (DCGAN) trained to generate high-quality 128x128 images of human faces. It was trained for 250 epochs on a curated dataset of feline images, pushing the boundaries of traditional GAN architectures at this resolution. ## Sample Here's a sample after epoch 200:  ## Model Details - **Architecture:** DCGAN (Deep Convolutional GAN) - **Resolution:** 128x128 pixels (RGB) - **Parameters:** ~186M (Generator) - **Training Duration:** ~22 hours on NVIDIA RTX 5060 Ti 16GB - **Framework:** PyTorch with Mixed Precision (AMP) ## Training Hyperparameters - **Batch Size:** 128 - **Learning Rate:** 0.0002 - **Optimizer:** Adam (Beta1: 0.5, Beta2: 0.999) - **Latent Vector (Z):** 128 dimensions ## Training details The full training code can be found as `train.py` in this repo. The training data we used was from HF: stable-bias/faces ## Intended Use This model is intended for artistic and research purposes. It demonstrates how GANs can capture complex faces and even eye reflections at medium resolutions. ## How to use To use this model, clone this repository and run the provided inference script. Ensure you have `matplotlib`, `torch` and `torchvision` installed. ```bash python3 inference.py ``` **Sample output:** 
Source context: 0 downloads · 3 likes · Pipeline unconditional-image-generation · Repo LH-Tech-AI/FaceGen-v1
Source context: 0 downloads · 2 likes · Pipeline unconditional-image-generation · Repo LH-Tech-AI/FaceGen-v1
