Instructions to use argmaxinc/mlx-FLUX.1-schnell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- DiffusionKit
How to use argmaxinc/mlx-FLUX.1-schnell with DiffusionKit:
# Pipeline for Flux from diffusionkit.mlx import FluxPipeline pipeline = FluxPipeline( shift=1.0, model_version=argmaxinc/mlx-FLUX.1-schnell, 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 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-FLUX.1-schnell argmaxinc/mlx-FLUX.1-schnell
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Create README.md
#1
by awni - opened
No description provided.
I added the MLX tag to the library so people see this model when they look for MLX library compatible models on HF. Could help with discovery.. but up to you all if you want to include it!
Thanks! Merging now.
arda-argmax changed pull request status to merged