Instructions to use black-forest-labs/FLUX.1-Fill-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-Fill-dev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import FluxFillPipeline from diffusers.utils import load_image image = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup.png") mask = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup_mask.png") # switch to "mps" for apple devices pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", dtype=torch.bfloat16, device_map="cuda") image = pipe( prompt="a white paper cup", image=image, mask_image=mask, height=1632, width=1232, guidance_scale=30, num_inference_steps=50, max_sequence_length=512, generator=torch.Generator("cpu").manual_seed(0) ).images[0] image.save(f"flux-fill-dev.png") - Diffusion Single File
How to use black-forest-labs/FLUX.1-Fill-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
- Notebooks
- Google Colab
- Kaggle
object-removal?
#13
by Clement - opened
Is it possible to perform object removal with this model just by tweaking the inference params (I couldn't make it work so far), or would this task require fine-tuning?
Clement changed discussion title from object-removal to object-removal?
Also curious
You can use segmentation tool like SAM to create a mask of the object you want to remove and then fill it with FLUX.