Instructions to use gokaygokay/Flux-White-Background-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gokaygokay/Flux-White-Background-LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("gokaygokay/Flux-White-Background-LoRA") prompt = "grid of 4 3D game assets, pixar, in the middle, white background" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Usage
your prompt + , in the middle ,white background for best results
This LoRA is trained with FAL Fast LoRA Trainer .

- Prompt
- grid of 4 3D game assets, pixar, in the middle, white background

- Prompt
- dog, in the middle, white background

- Prompt
- two warriors fighting, in the middle, white background

- Prompt
- rome colosseum, in the middle, white background

- Prompt
- tea cup, in the middle, white background

- Prompt
- hat, in the middle, white background
Citation and attribution
This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.
@software{aydogan2024flux_white_background_lora,
author = {Aydoğan, Gökay},
title = {{Flux-White-Background-LoRA}},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/gokaygokay/Flux-White-Background-LoRA},
note = {Model repository; cite the base model and upstream datasets as required.}
}
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Model tree for gokaygokay/Flux-White-Background-LoRA
Base model
black-forest-labs/FLUX.1-dev