Instructions to use sbessot/armelle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sbessot/armelle 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("sbessot/armelle") prompt = "RMLL" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
armelle
Model description
Trigger words
You should use RMLL to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/flux-lora-fast-training.
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Model tree for sbessot/armelle
Base model
black-forest-labs/FLUX.1-dev