Maintainer: rinongal

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Last updated 6/11/2024
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Model overview

stylegan-nada is a CLIP-guided domain adaptation method that allows shifting a pre-trained generative model, such as StyleGAN, to new domains without requiring any images from the target domain. Leveraging the semantic power of large-scale Contrastive Language-Image Pre-training (CLIP) models, the method can adapt a generator across a multitude of diverse styles and shapes through natural language prompts. This is particularly useful for adapting image generators to challenging domains that would be difficult or outright impossible to reach with existing methods.

The method works by training two paired generators - one held constant and one adapted. The adaptation process aligns the generated images' direction in CLIP space with the given text prompt. This allows for fine-grained control over the generated output without the need for extensive dataset collection or adversarial training. The approach maintains the latent-space properties that make generative models appealing for downstream tasks.

Some similar models include gfpgan, which focuses on practical face restoration, stylegan3-clip that combines StyleGAN3 and CLIP, styleclip for text-driven manipulation of StyleGAN imagery, and stable-diffusion - a latent text-to-image diffusion model.

Model inputs and outputs


  • Input image: The input image to be adapted to a new domain.
  • Style List: A comma-separated list of models to use for style transfer. Only accepts models from the predefined output_style list.
  • Output Style: The desired output style, such as "joker", "anime", or "modigliani". Selecting "all" will generate a collage of multiple styles.
  • Generate Video: Whether to generate a video instead of a single output image. If multiple styles are used, the video will interpolate between them.
  • With Editing: Whether to apply latent space editing to the generated output.
  • Video Format: The format of the generated video, either GIF (for display in the browser) or MP4 (for higher-quality download).


  • Output image: The adapted image in the desired style.
  • Output video: If the "Generate Video" option is selected, a video interpolating between the specified styles.


stylegan-nada can adapt pre-trained generative models like StyleGAN to a wide range of diverse styles and shapes through simple text prompts, without requiring any images from the target domain. This allows for the generation of high-quality images in challenging or unconventional styles that would be difficult to achieve with other methods.

What can I use it for?

The stylegan-nada model can be used for a variety of creative and artistic applications, such as:

  • Fine art and illustration: Adapting a pre-trained model to generate images in the style of famous artists or art movements (e.g., Impressionism, Abstract Expressionism).
  • Character design: Generating character designs in diverse styles, from cartoons to hyperrealistic.
  • Conceptual design: Exploring design concepts by adapting a model to unusual or experimental styles.
  • Visual effects: Generating stylized elements or textures for use in visual effects and motion graphics.

The model's ability to maintain the latent-space properties of the pre-trained generator also makes it useful for downstream tasks like image editing and manipulation.

Things to try

One interesting aspect of stylegan-nada is its ability to adapt a pre-trained model to stylistic extremes, such as generating highly abstract or surreal imagery from a realistic starting point. Try experimenting with prompts that push the boundaries of the model's capabilities, like "a photorealistic image of a cartoon character" or "a landscape painting in the style of a child's drawing".

Additionally, the model's support for video generation and latent space editing opens up possibilities for dynamic, evolving visual narratives. Try creating videos that seamlessly transition between different artistic styles or use the latent space editing features to explore character transformations and other creative concepts.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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