This is a simplified guide to an AI model called Nsfw-Filter maintained by M1guelpf. If you like these kinds of guides, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter.

Model overview

The nsfw-filter model is a modified implementation of the example code provided in the Red-Teaming the Stable Diffusion Safety Filter paper. This model is designed to run any image through the Stable Diffusion content filter, providing a way to detect and filter out potentially NSFW (Not Safe For Work) content. The model is packaged as a Cog model, a tool that packages machine learning models as standard containers.

Similar models in this space include Stable Diffusion, a latent text-to-image diffusion model capable of generating photo-realistic images, the Stable Diffusion Upscaler for upscaling images, and the Stable Diffusion 2-1-unclip Model.

Model inputs and outputs

The nsfw-filter model takes a single input: an image to be run through the NSFW filter. The output is a JSON object with the filtered image and a boolean value indicating whether the image is considered NSFW.

Inputs

• Image: The image to be run through the NSFW filter.

Outputs

• Image: The filtered image.
• Is NSFW: A boolean value indicating whether the image is considered NSFW.

Capabilities

The nsfw-filter model is capable of detecting and filtering out potentially NSFW content in images. This can be useful for a variety of applications, such as content moderation, image curation, or building safe-for-work environments.

What can I use it for?

The nsfw-filter model can be used to build applications that require content filtering, such as social media platforms, online communities, or image-based services. By integrating this model, you can ensure that your platform or service remains safe and family-friendly.

Things to try

One interesting thing to try with the nsfw-filter model is to experiment with different types of images, from portraits to landscapes, to see how the model performs. You can also try using the model in combination with other Stable Diffusion-based models, such as the Stable Diffusion Upscaler or the Stable Diffusion 2-1-unclip Model, to create a comprehensive image processing pipeline.

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