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
metadata
library_name: diffusionkit
language:
- en
license: apache-2.0
tags:
- text-to-image
- image-generation
- flux
- mlx
FLUX.1-schnell on DiffusionKit MLX!
Check out the original model!
Check out the DiffusionKit github repository!
Note: This checkpoint features 4-bit quantization of the mmdit module using MLX's nn.quantize function with default settings (group_size=64).
Usage
conda create -n diffusionkit python=3.11 -y
conda activate diffusionkit
pip install diffusionkit
diffusionkit-cli --prompt "detailed cinematic dof render of a \
detailed MacBook Pro on a wooden desk in a dim room with items \
around, messy dirty room. On the screen are the letters 'FLUX on \
DiffusionKit' glowing softly. High detail hard surface render" \
--model-version argmaxinc/mlx-FLUX.1-schnell-4bit-quantized \
--height 768 \
--width 1360 \
--seed 1001 \
--step 4 \
--output ~/Desktop/flux_on_mac.png