Instructions to use diffusers-internal-dev/flux2-bnb-4bit-modular with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers-internal-dev/flux2-bnb-4bit-modular 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("diffusers-internal-dev/flux2-bnb-4bit-modular", 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] - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
license: other
license_name: flux-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.2-dev/blob/main/LICENSE.txt
extra_gated_prompt: >-
By clicking "Agree", you agree to the [FLUX [dev] Non-Commercial License
Agreement](https://huggingface.co/black-forest-labs/FLUX.2-dev/blob/main/LICENSE.txt)
and acknowledge the [Acceptable Use
Policy](https://bfl.ai/legal/usage-policy).
tags:
- image-generation
- image-editing
- flux.2
pipeline_tag: image-to-image
library_name: diffusers
This repository contains the NF4 quantized DiT and the text encoders from https://huggingface.co/black-forest-labs/FLUX.2-dev. The VAE is not quantized.
Refer to this blog post to learn more.