Welcome to the repository dedicated to housing a refined and enhanced rendition of the illustrative code exemplified in the scholarly work titled "Red-Teaming the Stable Diffusion Safety Filter." This repository underscores the implementation of the Stable Diffusion NSFW Filter

Kindly Note If your inclination is primarily directed towards engaging with the model interactively or employing |it within a production environment, the provided links above grant immediate access to those avenues.

The essence of this model is encapsulated within a Cog model paradigm, which manifests as a tool that orchestrates the encapsulation of machine learning models into standardized containers. This approach signifies a pivotal advancement in the deployment and operationalization of machine learning prowess.

Inaugurating the journey towards leveraging this model entails a preliminary step of acquiring the Cog framework. This accomplishment can be realized by acquiring Cog from its official repository and installing it within your computational environment. Subsequently, the journey unfolds by sourcing the pre-trained model weights, which can be seamlessly acquired through Hugging Face's authentication token protocol. This orchestrated interplay sets the stage for the model's capabilities to be harnessed effectively.

Once the infrastructure is assembled, invoking the predictive prowess of the model unfurls through the act of executing cog commands. Specifically, one is required to harness the power of the command:

cog run script/download-weights <your-hugging-face-auth-token>

This command harmonizes the convergence of the model's architecture with the acquired pre-trained weights, thereby endowing it with an evolved sense of intelligence. Subsequently, the model becomes ready to undertake predictions, thus unveiling its remarkable ability to decipher and discriminate image content with astute precision. To initiate this process, the command orchestration to be invoked stands as follows:

cog predict -i image=@/path/to/image.jpg

With this, the model sets forth on its mission of perceptual analysis, providing insightful assessments and predictions concerning the image provided as input. The entire journey underscores the synergy between cutting-edge technology, machine learning acumen, and the meticulous orchestration facilitated by the Cog framework.

References: Paper: Red-Teaming the Stable Diffusion Safety Filter, LAION, @m1guelpf