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README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+- image-generation
+- gan
+- stylegan
+- stylegan3
+- nvidia
+---
+# Male Faces Generator (StyleGAN3 by NVIDIA)
+
+<img width="679" alt="BroGAN Sample Faces" src="https://huggingface.co/quartzermz/BroGANv1.0.0/blob/main/SampleImages.png">
+
+This is a [StyleGAN3 PyTorch](https://github.com/NVlabs/stylegan3) model trained on 50k faces of men scraped from pinterest and its associated bias.
+
+### Usage
+
+Demo on Spaces is not yet implemented.
+
+If you want to generate your own images, follow the steps on [StyleGAN3 PyTorch](https://github.com/NVlabs/stylegan3) under "Getting started." Run gen_images.py and specify your seed and truncation.
+
+#### Sample Images
+Sample images were generated directly from model with no-postprocessing. 
+Truncation of: '0.7'
+Recommend using [CodeFormer]https://github.com/sczhou/CodeFormer to restore faces and upscale to desired resolution (I like upscaling by 2x to 512x512).
+
+### Dataset & Model Details
+
+Dataset was scraped from pinterest and cropped using dlib. Further symmetry and rotation filtering applied via U2Net and MTCNN.
+
+Training was done locally using StyleGAN3 with an RTX 4090 and cuda 11.8. Starting checkpoint was the ffhq 256x256 pre-trained model. Training took roughly 24 hours but hyperparameter tuning/restarting was modified about halfway through. Roughly at 400 ticks, dataset was filtered and reduced by half to images with greater symmetry.  
+
+- Configuration: `stylegan3-r`
+- GPUs: `1`
+- Batch Size: `32`
+- Gamma: `0.5`
+- Final tick: `575`
+- Image Resolution: '256x256'
+- Final fid50k_full value (this pickle): `13.854398226827872`
+
+### Credits
+
+Please don't forget to give credit if you decide to share/distribute this model. Training these take a lot of time and effort :)