NSFW JS vs Stable Diffusion Webgpu
Side-by-side comparison · Updated May 2026
| Description | The NSFWJS model empowers users to perform client-side indecent content checking by providing tools for monitoring and filtering inappropriate material directly on the client-side. Incorporating features such as Camera and Blur Protection enhances detection accuracy. Mechanisms are in place to manage false positives, ensuring legitimate content is not blocked erroneously. The model is efficient for client-side deployment with 93% accuracy and a size of 4.2MB. Additional resources are available through various Github repositories and blog posts. | The Stable Diffusion WebGPU service allows users to run the Stable Diffusion image generation model directly in their browser using GPU acceleration. It requires the latest version of Chrome with specific experimental flags enabled, and provides customizable settings for generating images. Users can download the model directly to their browser cache and adjust settings such as prompt, negative prompt, number of inference steps, guidance scale, and more. Support is available for troubleshooting common errors and issues. |
| Category | Security Application | Image Generation |
| Rating | No reviews | No reviews |
| Pricing | Pricing unavailable | Pricing unavailable |
| Starting Price | N/A | N/A |
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| Tags | indecent content checkingclient-side deploymentCamera ProtectionBlur Protectionfalse positives | WebGPUStable Diffusionimage generationbrowserGPU acceleration |
| Features | ||
| Client-side indecent content checking | ||
| 93% accuracy | ||
| 4.2MB model size | ||
| Camera and Blur Protection | ||
| False positive handling mechanisms | ||
| Loading indicator | ||
| Efficient client-side deployment | ||
| Resource availability (Github repositories, blog) | ||
| Owned by Infinite Red, Inc. | ||
| Suitable for live camera feeds | ||
| GPU acceleration in-browser | ||
| Customizable image generation settings | ||
| Direct model download to browser cache | ||
| Support for experimental WebAssembly flags | ||
| Ability to run VAE after each inference step | ||
| Error troubleshooting via FAQ | ||
| Ported StableDiffusionPipeline from Python to JavaScript | ||
| Large memory allocation support with onnxruntime and emscripten+binaryen | ||
| FP16 support with recent Chrome versions | ||
| Seamless integration with web technologies | ||
| View NSFW JS | View Stable Diffusion Webgpu | |
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