https://github.com/zer0int/clip-generative-adversarial

Projected Gradient Descent (PGD), inverted and amplified -> prompt & generate images with CLIP
https://github.com/zer0int/clip-generative-adversarial

adversarial adversarial-attacks adversarial-examples ai clip generative pgd vision-transformer xai

Last synced: 6 days ago
JSON representation

Projected Gradient Descent (PGD), inverted and amplified -> prompt & generate images with CLIP

• Host: GitHub
• URL: https://github.com/zer0int/clip-generative-adversarial
• Owner: zer0int
• Created: 2024-07-27T11:24:34.000Z (4 months ago)
• Default Branch: CLIP-vision
• Last Pushed: 2024-07-28T18:15:11.000Z (4 months ago)
• Last Synced: 2024-07-28T19:33:41.807Z (4 months ago)
• Topics: adversarial, adversarial-attacks, adversarial-examples, ai, clip, generative, pgd, vision-transformer, xai
• Language: Python
• Homepage:
• Size: 14.4 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## CLIP-generative-adversarial

### Who needs a Diffusion U-Net or Transformer if you can just use the 'text encoder'...? 😉

![examples](https://github.com/user-attachments/assets/e9d233f6-3690-4ee9-b6ea-52db40e85577)

- Uses Projected Gradient Descent (PGD), which is commonly used in adversarial robustness training

- Inverts objective and amplifies perturbations for human perception

- Perturbation towards (!) the prompt by default, making CLIP a self-generative AI 🙃

- Also plots results 'success' (original vs. perturbated cosine similarity)

- You can change the default behavior to "classic" adversarial example generation

- See code comments for details and instructions

------

- If you ever wondered why some strange word in a prompt for e.g. Stable Diffusion works - now you can find out!

- For Stable Diffusion V1, the sole text encoder "guide" is CLIP ViT-L/14 (the model set by default in my code).

- The diff between *this* and feature activation max visualization: We're using the whole model (output) to guide towards a text prompt.

- To visualize indivual 'neurons' (features) in CLIP ViT, see my other repo: [zer0int/CLIP-ViT-visualization](https://github.com/zer0int/CLIP-ViT-visualization)

- To get a CLIP opinion of what CLIP 'thinks' of an image, see: [zer0int/CLIP-XAI-GUI](https://github.com/zer0int/CLIP-XAI-GUI)

------

Requires: torch, torchvision, numpy, PIL, matplotlib, OpenCV, (skimage).

Requires [OpenAI/CLIP](https://github.com/openai/CLIP).