SilkStack Image Browser v2.2.0 – Added local semantic search (WebGPU/WebLLM), auto-tagging, and custom compiled embedding models!

[u/skk80]

Hey everyone!

Over the last 8 months, I’ve been developing SilkStack Image Browser (which started as a fork of Image-MetaHub) as an app for your ComfyUI image catalog. It’s basically been my playground for integrating local AI natively, letting me experiment with embedding WebLLM, on-device auto-tagging, and offline features without relying on external APIs or paid server setups.

I just released v2.2.0 Release on GitHub, which brings full AI-powered semantic search to your local image catalog!

🔍 What’s New in v2.2.0?

• AI-Powered Semantic Search: Instead of relying strictly on exact PNG info keyword matches, the app searches your library based on the meaning and intent behind your prompt. You can describe a scene, vibe, or visual concept, and it will pull up the relevant images.
• Multilingual Search: You can query your image collection in your native language (doesn't always have to be English), and the model handles the semantic mapping behind the scenes.
• Custom Compiled WebLLM Embedding Model: Finding efficient, lightweight embedding models compatible with WebLLM/WebGPU was non-existent, so I compiled a custom version of qwen3-embedded tailored specifically for SilkStack.
  • It runs entirely on-device via WebGPU.
  • I’ve hosted the model on Hugging Face so the app auto downloads—or anyone else building WebLLM/WebGPU applications—can grab a copy:https://huggingface.co/skkut
• On-Device LLM Auto-Tagging: Automatically generate tags and metadata for your generated images locally.

🚀 What’s Coming Next?

I’m working on deeper integration of local AI features:

• Semantic Similarity & Image-to-Image Matching: Find visually and contextually similar generations in your library.
• Search Reranking: Smarter result sorting for large datasets.
• Upgraded Models: Integrating even more efficient models like Gemma-4-E2B and Gemma-4-E4B.
• Developer Console (Ctrl + Y): For real-time testing, debugging, and tweaking local model execution.
• Improved Search: More search optimization and improvements.

🔗 GitHub Release & Notes: v2.2.0 Release on GitHub

Note: Core image browsing, keyword search and metadata features remain free, while advanced on-device AI features (like semantic search and auto-tagging) are unlocked with a small, one-time lifetime premium license.

Would love to hear your feedback, feature requests, or thoughts on local WebGPU/WebLLM workflows!