Instructions to use Boogu/Boogu-Image-0.1-Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Boogu/Boogu-Image-0.1-Edit 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("Boogu/Boogu-Image-0.1-Edit", 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
why rely on FLUX 1 VAE ? wouldn't it be beter to upgrade to FLUX2 VAE ?
#4
by LeFourbe - opened
what the title says.
also i didn't see any mentions about FLUX2 ?
anyways, thanks you for the release of Boogu !
best regards
what's done is done .. you can't switch VAEs mid training :D
Thanks for reaching out! Currently, Boogu-Image is a research project, and during our development, we've also run into many of those same issues with the FLUX 1 VAE.
Your suggestion is perfectly aligned with our next steps! We plan to continuously optimize Boogu-Image in future updates, and improving the VAE setup is a big part of that.
We highly welcome any feedback and thoughts from the open-source community. Thank you so much for your support on Boogu-Image! ✨