Instructions to use IcelosAI/Wipeoutracer_LoRA_FLUX1_Dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use IcelosAI/Wipeoutracer_LoRA_FLUX1_Dev 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("IcelosAI/Wipeoutracer_LoRA_FLUX1_Dev") prompt = "beautiful romantic photo of wipeoutracer flying through london city, motion blur, extreme depth of field" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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A compact LoRa that enables the generation of anti-gravity racing cars like the WipeOut series.
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It was trained based on 45 images with a total of 2,000 steps. The base model is FLUX1 Dev
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## Trigger words
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A compact LoRa that enables the generation of anti-gravity racing cars like the WipeOut series.
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It was trained based on 45 images with a total of 2,000 steps. The base model is FLUX1 Dev.
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## Trigger words
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