Instructions to use argmaxinc/mlx-FLUX.1-schnell-4bit-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- DiffusionKit
How to use argmaxinc/mlx-FLUX.1-schnell-4bit-quantized with DiffusionKit:
# Pipeline for Flux from diffusionkit.mlx import FluxPipeline pipeline = FluxPipeline( shift=1.0, model_version=argmaxinc/mlx-FLUX.1-schnell-4bit-quantized, low_memory_mode=True, a16=True, w16=True, )
# Image Generation HEIGHT = 512 WIDTH = 512 NUM_STEPS = 4 CFG_WEIGHT = 0 image, _ = pipeline.generate_image( "a photo of a cat", cfg_weight=CFG_WEIGHT, num_steps=NUM_STEPS, latent_size=(HEIGHT // 8, WIDTH // 8), )
- MLX
How to use argmaxinc/mlx-FLUX.1-schnell-4bit-quantized with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-FLUX.1-schnell-4bit-quantized argmaxinc/mlx-FLUX.1-schnell-4bit-quantized
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- af05c0f5a208961cc35c1cf1014e6718ae4378d0f9aa745752b3e95525c9a02f
- Size of remote file:
- 6.69 GB
- SHA256:
- f32230ebfcd5e49a2e91d71e00e18e807ab8c8382375cbcdf3d21a50ae275d08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.