Tarek737/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

File	Purpose
generator_best.pt	Generator state dictionary; load only through the matching FaceForge code.
model_metadata.json	Architecture contract, preprocessing settings, and training information.

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:

Metric	Result	Notes
Generator loss	[value]	Training diagnostic, not a quality score.
Discriminator loss	[value]	Training diagnostic, not a quality score.
Diversity diagnostic	[value]	Pixel-space heuristic, not a realism metric.
FID	Not measured	Do not replace with an estimate.

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.