orpatashnik/StyleCLIP

Health v2 · maintenance only

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4121 stars · 564 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official implementation of StyleCLIP, a method for text-driven manipulation of StyleGAN-generated imagery using CLIP. It provides three approaches: CLIP-based latent optimization, a latent mapper network, and global style-space directions.

Use cases

• edit images with text prompts
• manipulate StyleGAN latent vectors using natural language
• change facial attributes of generated faces via text
• explore CLIP-guided image editing
• reproduce StyleCLIP paper results
• run text-to-face editing in Colab

When to choose

• you want text-guided editing of StyleGAN images
• you need the reference implementation of the StyleCLIP paper
• you work with GAN latent space manipulation research

When to avoid

• you need general-purpose image editing of arbitrary photos without a StyleGAN inversion step
• you want a production-ready maintained product
• you need modern diffusion-based text-to-image editing

Facets

library · maturity maintenance

machine-learning image-processing llm-inference deep-learning computer-vision image-processing artificial-intelligence python cross-platform stylegan clip text-driven-image-editing image-manipulation generative-models research-code iccv-2021 gpu

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markdown · JSON · MCP: product_card(name="orpatashnik/StyleCLIP")