Instructions to use gokaygokay/Flux-Digital-Backgrounds-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use gokaygokay/Flux-Digital-Backgrounds-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-Digital-Backgrounds-LoRA") prompt = "dgtlbg, a painting of nature. In this image there are rocks in the river, trees and grass on both sides. The sun is rising from behind these mountains, clouds can be seen above it which has golden sunlight falling upon them, background" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Usage
dgtlbg, <<your prompt>>, background for best results
This LoRA is trained with FAL Fast LoRA Trainer .

- Prompt
- dgtlbg, a painting of nature. In this image there are rocks in the river, trees and grass on both sides. The sun is rising from behind these mountains, clouds can be seen above it which has golden sunlight falling upon them, 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_digital_backgrounds_lora,
author = {Aydoğan, Gökay},
title = {{Flux-Digital-Backgrounds-LoRA}},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/gokaygokay/Flux-Digital-Backgrounds-LoRA},
note = {Model repository; cite the base model and upstream datasets as required.}
}
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Model tree for gokaygokay/Flux-Digital-Backgrounds-LoRA
Base model
black-forest-labs/FLUX.1-dev