Instructions to use nyuuzyou/AircraftFLUX-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nyuuzyou/AircraftFLUX-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-schnell", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nyuuzyou/AircraftFLUX-LoRA") prompt = "2015 American Airlines Airbus A320-214, registration EC-LVC, landing against a clear blue sky" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
A LoRA trained on 165K high-resolution aircraft images and their structured captions, fine-tuning FLUX.1 [schnell] for enhanced aircraft image generation. This adaptation specializes in:
- Accurate generation of commercial aircraft with correct proportions and details
- Precise rendering of specific aircraft models (Boeing, Airbus, etc.)
- Realistic airline liveries and sometimes registration markings
Best used for generating photorealistic aircraft imagery from detailed prompts. Maintains FLUX.1's general image generation capabilities while significantly improving aviation-specific output quality and accuracy.
Training data: 165K aircraft images with structured captions
Recommended prompt: Follow caption format:
[year] [airline] [manufacturer] [type], registration [number], [state] [surrounding objects]
Example: 2023 Lufthansa Airbus A321-271NX, registration D-AIEQ, taking off against a clear sky
- Downloads last month
- 18
Model tree for nyuuzyou/AircraftFLUX-LoRA
Base model
black-forest-labs/FLUX.1-schnell