Instructions to use elicara/vincentlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use elicara/vincentlora 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("elicara/vincentlora") prompt = "Close-up photo of a man<lora:vincent:0.75> vincentlora, looks at camera, simple bedroom, cinematic, <lora:amateurphoto-6version:0.0>" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Vincent

- Prompt
- Close-up photo of a man<lora:vincent:0.75> vincentlora, looks at camera, simple bedroom, cinematic, <lora:amateurphoto-6version:0.0>
Model description
Vincent
Trigger words
You should use vincentlora to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for elicara/vincentlora
Base model
black-forest-labs/FLUX.1-dev