Instructions to use alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta 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("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", torch_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
Citation
#12
by zhuhz22 - opened
Hi, extremely nice work! I just wonder how to cite your work :), could you display a Bibtex format?
I'm very glad that this work can be helpful to you. You can cite it as follows:
@misc {huggingface:flux_contolnet_inpainting,
author = {Alimama Creative},
title = {FLUX.1-dev-Controlnet-Inpainting-Beta},
year = {2024},
publisher = {Hugging Face},
journal = {Model Card},
howpublished = {\url{https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta}}
}
ljp changed discussion status to closed