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---
base_model: black-forest-labs/FLUX.1-dev
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
tags:
- autotrain
- spacerunner
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
widget:
- text: a black and white photo of a Rolls Royce car with a hood ornament on top of it. The logo of the car is visible, along with the text "Rolls Royce" written on it.
output:
url: samples/gallery_20240911_164722.png
- text: 멋진 요트를 배경으로 한 롤스로이스, along with the text "Rolls Royce" written on it.
output:
url: samples/gallery_20240911_124736.png
- text: 멋진 자가용 비행기를 배경으로 한 롤스로이스, along with the text "Rolls Royce" written on it.
output:
url: samples/gallery_20240911_124931.png
- text: 롤스로이스 앞에 서있는 테일러스위프, along with the text "Rolls Royce" written on it.
output:
url: samples/gallery_20240911_125109.png
- text: 롤스로이스 실내 인테리어, along with the text "Rolls Royce" written on it.
output:
url: samples/gallery_20240911_130343.png
instance_prompt: car rollsroyce
---
# flux-lora-car-rolls-royce
<Gallery />
## Trigger words
You should use `car rollsroyce` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/seawolf2357/flux-lora-car-rolls-royce/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('seawolf2357/flux-lora-car-rolls-royce', weight_name='flux-lora-car-rolls-royce')
image = pipeline('A person in a bustling cafe car rollsroyce').images[0]
image.save("my_image.png")
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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