faceforge-dcgan-ffhq64

FaceForge DCGAN-64 — FFHQ

Model description

FaceForge DCGAN-64 is an educational, unconditional PyTorch DCGAN that creates 64 × 64 synthetic face-like images from 128-dimensional random latent vectors. It is the project baseline, not a production portrait model, identity system, or image editor.

• Developed by: Tarek Amr
• Model type: Unconditional DCGAN generator
• Language: English documentation; no text input
• License / terms: mit
• Code: https://github.com/tarekamr737/Realistic-Human-Face-Generation-using-GAN

Files

Do not upload FFHQ image files, an R3GAN checkpoint, generated samples that could be confused with real people, or arbitrary Python pickles to this model repository.

Training

• Dataset: FFHQ, obtained separately under its applicable terms.
• Training resolution: 64 × 64 RGB; source files were 512 × 512 and were resized during loading.
• Architecture: DCGAN generator with a 128-D latent input. Use the exact latent_dim and feature_maps stored in model_metadata.json.
• Objective: BCE-with-logits adversarial objective with Adam optimizers.
• Run: [epochs, batch size, hardware, wall-clock time, date]
• Preprocessing: [confirm resize, crop policy, and normalization from metadata]

Evaluation

Record only measured results from the exported evaluation.json:

Inspect fixed-noise grids for artifacts and collapse. Losses, a small sample grid, and pixel diversity do not demonstrate realism, fairness, privacy, or fitness for a real-world deployment.

Intended use

Suitable only for education, controlled GAN demonstrations, and comparisons with stronger pretrained models. All outputs must be visibly labelled synthetic.

Out-of-scope use

Do not use this model for face recognition, identity inference, impersonation, deception, profiling, biometric decisions, or claims that a generated image depicts a real person. It should not be used as evidence of fairness, privacy, or demographic representation.

Limitations and bias

This is a low-resolution baseline trained on a limited face dataset. Outputs can be blurred, artifacted, repetitive, or unrepresentative. The model can inherit dataset bias and may generate people-like images with unintended visual similarities. It has not received a demographic, safety, privacy, or benchmark evaluation.

Citation and acknowledgements

Credit FFHQ according to its official dataset documentation and terms. For the optional visual reference in the FaceForge app, cite BrownVC R3GAN separately; it is not this model and is not redistributed here.