Flux Cross Section
![](https://huggingface.co/fofr/flux-cross-section/resolve/main/images/example_4wxycxyo8.png)
- Prompt
- a XSEC exploded illustration of an SLR camera
![](https://huggingface.co/fofr/flux-cross-section/resolve/main/images/example_bngi301gh.png)
- Prompt
- a XSEC exploded illustration of a cyberpunk sports car on white
Run on Replicate:
https://replicate.com/fofr/flux-cross-section
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
Trigger words
You should use XSEC
to trigger the image generation.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('fofr/flux-cross-section', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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Model tree for fofr/flux-cross-section
Base model
black-forest-labs/FLUX.1-dev