MFLUX v.0.10.0 Release Notes

🎨 Model Improvements

• FLUX.1 Krea [dev] Support!
• FLUX.1-Krea-dev-mflux-4bit Model: Added filipstrand/FLUX.1-Krea-dev-mflux-4bit quantized model to HF
• FLUX.1-Kontext-dev-mflux-4bit Model: Added akx/FLUX.1-Kontext-dev-mflux-4bit quantized model to HF, contributed by @akx

✨ New Features

• 5-bit Quantization Support: Added support for 5-bit quantization as a new option alongside existing 3, 4, 6, and 8-bit quantization levels

🔧 Improvements

• Enhanced Default Inference Steps: Increased default inference steps for dev models from 14 to 25 for improved image quality
• Multiple Model Aliases Support: Improved model configuration system to properly support multiple aliases per model, making model selection more flexible and robust

🐛 Bug Fixes

• LoRA Resume Training: Fixed critical bug where adapters created after training interruption would fail to load for generation with AttributeError: 'list' object has no attribute 'weight'. The issue occurred because the resume loading logic wasn't properly handling layers that are legitimately lists in the transformer architecture (like attn.to_out). (see #224)

🔧 Technical Requirements

• MLX Compatibility: This release assumes MLX 0.27.0 and upwards for optimal performance and compatibility
• MLX Compatibility for test: Fix MLX version to 0.27.1 for image generation tests
• Non-strict Weight Updates: Explicitly added non-strict mode (strict=False) for weight updates to maintain compatibility with later MLX versions that enforce stricter weight validation by default

👩‍💻 Developer Experience

• Streamlined Release Process: Removed TestPyPi publishing step from release workflow for simplified deployment

🙏 Contributors

• @filipstrand - FLUX.1 Krea [dev] model support, 5-bit quantization, enhanced defaults, and various improvements
• @akx - Added 4-bit quantized Kontext model to HF