Instructions to use vcollos/camila with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vcollos/camila 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("vcollos/camila") prompt = "Camila no café" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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("vcollos/camila")
prompt = "Camila no café"
image = pipe(prompt).images[0]Camila Guimarães

- Prompt
- Camila no café
Model description
Camila
Trigger words
You should use Camila to trigger the image generation.
Download model
Weights for this model are available in Safetensors,PyTorch format.
Download them in the Files & versions tab.
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Model tree for vcollos/camila
Base model
black-forest-labs/FLUX.1-dev