Instructions to use elisabethdiml/sigtwill-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use elisabethdiml/sigtwill-lora 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("elisabethdiml/sigtwill-lora") prompt = "sigtwill" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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("elisabethdiml/sigtwill-lora")
prompt = "sigtwill"
image = pipe(prompt).images[0]sigtwill lora
Model description
FLUX LoRA trained on Eton-style luxury menswear shirts with editorial and outdoor campaign imagery.
Trigger words
You should use sigtwill 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 elisabethdiml/sigtwill-lora
Base model
black-forest-labs/FLUX.1-dev