StyleCLIP enables text-driven manipulation of StyleGAN-generated imagery by leveraging CLIP's visual-language understanding. It offers three methods for users to edit images based on textual descriptions: latent vector optimization, a trained latent mapper, and global directions in StyleSpace, providing flexible control over image generation and modification.

How It Works

StyleCLIP integrates StyleGAN's generative capabilities with CLIP's text-image alignment. The latent vector optimization method uses a CLIP-based loss to adjust latent vectors according to text prompts. The latent mapper learns to infer text-guided latent vector residuals for faster, more stable edits. Global directions identify input-agnostic manipulations in StyleGAN's style space, allowing interactive, text-driven adjustments.

Quick Start & Requirements

• Installation: Requires Anaconda, CLIP, and specific StyleGAN implementations (PyTorch or TensorFlow).
• Dependencies:
  • CLIP: pip install ftfy regex tqdm gdown git+https://github.com/openai/CLIP.git
  • PyTorch methods: PyTorch 1.7.1, cudatoolkit=<CUDA_VERSION>
  • TensorFlow methods: TensorFlow 1.14 or 1.15 (tensorflow-gpu==1.14)
  • Pretrained StyleGAN2/StyleGAN2-ADA models are required.
  • Mapper method requires facial recognition network weights.
• Setup: Can involve downloading large datasets and pre-trained models.
• Resources: Preprocessing steps for global directions can take several hours.
• Links: Paper, Replicate, Colab Notebooks

Highlighted Details

• Supports three distinct manipulation techniques: latent optimization, latent mapper, and global directions.
• Offers both interactive GUI and programmatic control.
• Compatible with custom StyleGAN2 and StyleGAN2-ADA models and custom images.
• Methods can be applied to both generated and real images (after inversion).

Maintenance & Community

• Last update: October 2022 (added global direction support for PyTorch).
• Primary author: Or Patashnik.
• Citation available for academic use.

Licensing & Compatibility

• The repository itself does not explicitly state a license in the README.
• StyleGAN implementations used may have their own licenses.
• CLIP is typically available under a permissive license.

Limitations & Caveats

• The TensorFlow implementation for global directions requires older TF versions (1.14/1.15), potentially causing compatibility issues with modern environments.
• Preprocessing for global directions can be time-consuming.
• Editing real images requires an inversion step using external tools like e4e.