Instructions to use black-forest-labs/FLUX.1-Kontext-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use black-forest-labs/FLUX.1-Kontext-dev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Diffusion Single File
How to use black-forest-labs/FLUX.1-Kontext-dev with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Inference
- Notebooks
- Google Colab
- Kaggle
Multilingual powerhouse — testing for mobile deployment
#86
by 3morixd - opened
This model covers Arabic, Finnish, Hebrew, Thai — exactly the kind of multilingual capability we need for global mobile AI.
At Dispatch AI (FZE, UAE), we're building mobile AI that works for everyone. Models like this are the foundation.
We benchmark multilingual models on our 40-phone farm (Snapdragon 865) to see which maintain quality across languages when quantized to 4-bit. Results vary wildly — some lose 30% quality in non-English after quantization.
Would love to see multilingual eval at different quantization levels.
- Dispatch AI (FZE), Sharjah UAE