How to Generate Only Safe For Work (SFW) Images

Introduction

As AI technology continues to advance, image-generation models have become increasingly popular for various creative and practical applications. However, with the growing accessibility of these models, there is a pressing need to ensure that the images generated are safe for all audiences. In this blog, we we'll explore how to generate only Safe-for-Work (SFW) images using AI-driven solutions while maintaining content safety and integrity.

Understanding the Importance of SFW Images

Safe-for-Work images are essential to maintaining a respectful and inclusive online environment. Whether for marketing campaigns, educational materials, or social media content, it is crucial to prevent the accidental display of Not Safe-for-Work (NSFW) images that may be explicit, offensive, or inappropriate. Implementing a safety checker for AI-generated images helps protect users from potentially harmful content and upholds the reputation of businesses and platforms.

"key": "API Key",

"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner))",

"negative_prompt": Add negative prompt here

"width": "512",

"height": "512",

"samples": "1",

"num_inference_steps": "20",

"safety_checker": “Yes”, cc

"enhance_prompt": "yes",

"seed": null,

"guidance_scale": 7.5,

"multi_lingual": "no",

"panorama": "no",

"self_attention": "no",

"upscale": "no",

"embeddings_model": null,

"webhook": null,

"track_id": null

Creating a Safety Checker for NSFW Images

While there isn't a comprehensive list of all NSFW images, AI-driven solutions allow us to build safety checkers that can identify and replace such images with suitable alternatives. To achieve this, follow these steps:

Dataset Preparation:

• Compile a diverse dataset of SFW images representing the desired themes and styles.
• Ensure the dataset covers a wide range of subjects to improve the accuracy of the safety checker.

Training an NSFW Classifier:

• Utilize machine learning techniques to train a binary classifier that can distinguish between SFW and NSFW images.
• Use negative prompts: add words like NSFW, nude etc in negative prompt (((special emphasis))), and a wide variety of labelled examples to achieve higher accuracy.

Replacing NSFW Images:

• If the safety checker detects an NSFW image, replace it with an appropriate blank or placeholder image.
• Ensure that the replacement image does not contain any potentially harmful or offensive content.

Fine-Tuning and Continuous Improvement:

• Continuously fine-tune the NSFW classifier to improve accuracy and avoid false positives/negatives.
• Regularly update the safety checker with new data and learning experiences.

Best Practices for SFW Image Generation at Stable Diffusion API

-Promote Diversity and Inclusion:

Ensure that your SFW image dataset includes representation from diverse cultures, backgrounds, and identities.

Encourage AI models to generate images that reflect inclusivity and respect for all users.

-Regular Monitoring: Periodically review the generated images and user feedback to identify any potential issues.

Take immediate action to rectify any false negatives or false positives.

-User-Controlled Filters: Provide users with options to customize their content preferences, including enabling or disabling the safety checker.

Respect user preferences and offer them a safe browsing experience.

Ensure, the negative prompt contains as many NSFW keywords so that the generation will filter out the bad(pornographic) images and it will not generate it

Also, our API supports safety_checker. Once this is passed to yes and the image generated has NSFW content, it will block that image.

 Use this model for creating QR image: Controlnet QR Code

Conclusion

Generating only Safe-for-Work (SFW) images is a crucial step in ensuring content safety in AI-driven solutions. By implementing an NSFW image safety checker, businesses and platforms can create a respectful and inclusive environment for all users. Remember to continuously improve the safety checker, promote diversity in image datasets, and prioritize user feedback for a safer and more enjoyable experience for everyone.