A comprehensive tool for inspecting LoRA (Low-Rank Adaptation) files for machine learning models, particularly Stable Diffusion.
See the different blocks of the different networks. Ideally uses the more common format but↵ still good to see how the weights are effectively. We use Frobenius norm for the magnitude↵ and a vector norm for the strength.
See the different settings for the LoRA file. What model it was trained on. Any VAE. Networ↵ k Dim/Rank and Alpha. Learning rates. Optimizer settings, learning rate schedulers.
Dataset with buckets. Bucket resolutions.
Subsets showing the different subset datasets, image augments, captions.
- Browser-based LoRA file inspection
- Detailed metadata analysis
- Weight and network characteristic visualization
- WebAssembly-powered performance
- No external dependencies (like torch or python)
crates/
: Project sub-modulesinspector/
: Core Rust librarylora-inspector-wasm/
: WebAssembly web frontendlora-inspector/
: CLI application
- Rust
- WebAssembly
- React
- Vite
- Candle (machine learning library)
- lora-inspector-wasm:
- AVA (JavaScript unit tests)
- Playwright (E2E testing)
- inspector: Rust built-in test module
- lora-inspector: Limited test coverage
- Unit tests for key parsing
- E2E browser testing
- Metadata validation
- Cross-browser compatibility
- Rust toolchain
- Node.js
- Yarn 4.9.1+
- wasm-pack
# Clone the repository
git clone https://github.com/rockerBOO/lora-inspector-rs.git
# Navigate to the project directory
cd lora-inspector-rs
# Install dependencies for web frontend
cd crates/lora-inspector-wasm
yarn install --immutable
# Start development server
make dev-wasm
# Build the project
make build
# Run tests
make test
Contributions are welcome! Please check individual crate READMEs for specific guidelines.
- WebAssembly frontend
- Add LR Warmup metadata support
- Improve weight visualization
- Expand metadata parsing capabilities
- Increase test coverage
MIT License - See LICENSE file
- Inspired by the need for better LoRA model inspection tools
- Built with passion for machine learning and open-source development