Instructions to use Skatch/front-profile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Skatch/front-profile 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("Skatch/front-profile") prompt = "n" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
front-profile

- Prompt
- n
- Negative Prompt
- mm
Trigger words
You should use front to trigger the image generation.
You should use body 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 Skatch/front-profile
Base model
black-forest-labs/FLUX.1-dev