FLUX.2-klein-4B-mflux-4bit

4-bit quantized weights of [FLUX.2 [klein] 4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B) by Black Forest Labs, optimized for [mflux](https://github.com/filipstrand/mflux) on Apple Silicon. pip install mflux mflux-generate-flux2 \ --model RunPod/FLUX.2-klein-4B-mflux-4bit \ --prompt "A cute robot standing in a field of flowers, digital art" \ --width 1024 \ --height 1024 \ --steps 4 \ --seed 42 \ --output output.png from mflux import Flux2 flux = Flux2( model="RunPod/FLUX.2-klein-4B-mflux-4bit", base_model="flux2-klein-4b", ) image = flux.generate_image( prompt="A cute robot standing in a field of flowers, digital art", width=1024, height=1024, num_inference_steps=4, seed=42, ) image.save("output.png") - **Base model**: [black-forest-labs/FLUX.2-klein-4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B) (Apache 2.0) - **Quantization**: 4-bit via MLX `nn.quantize` (group_size=64), created with `mflux-save --quantize 4` - **Requirements**: [mflux](https://github.com/filipstrand/mflux) v0.16.0+, Apple Silicon Mac - **Performance**: ~11s for 512x512 (4 steps) on M3 Pro 18GB pip install mflux mflux-save \ --path ./FLUX.2-klein-4B-mflux-4bit \ --model flux2-klein-4b \ --quantize 4 FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer by [Black Forest Labs](https://bfl.ai) for fast image generation and editing. It delivers state-of-the-art quality with sub-second inference on consumer hardware. - Ultra-fast inference (4 steps) - Text-to-image and multi-reference image editing - Apache 2.0 — fully open for commercial use - [Blog post](https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence) | [GitHub](https://github.com/black-forest-labs/flux2) Apache 2.0, inherited from the [original model](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B). - **Model**: [Black Forest Labs](https://blackforestlabs.ai) - **mflux**: [Filip Strand](https://github.com/filipstrand/mflux) - **Quantization & upload**: [Runpod](https://runpod.io) / [OpenClaw2Go](https://github.com/runpod-workers/openclaw2go)