Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
FluxKontextPipeline
image-generation
flux
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
OOM Error
#80
by feel-123 - opened
I'm encountering an Out-of-Memory (OOM) error while running the FLUX.1-Kontext-dev model. I understand this model has high GPU memory requirements.
Could you please suggest an alternative version or a lighter variant of the FLUX.1-Kontext-dev model that offers comparable performance but can run efficiently on GPUs with ≤16 GB of VRAM, without significant loss in accuracy?
You can try the INT4 quantized version(just search for it in the model tree). I previously managed to run it successfully on a 24GB GPU without enabling offload, so you might want to try using it with offload enabled.