Instructions to use AlexanderLab/kftn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlexanderLab/kftn 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("AlexanderLab/kftn") prompt = "KFTN" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 966abcc11cf17f57dcfffce75e2a9e6dc89906914d785df90ab218ae4b496c3b
- Size of remote file:
- 344 MB
- SHA256:
- 5a97ab49902cd3ae860420a6f5d24b363cefaa4d67513c8c29bd048c0a34c3c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.