Instructions to use Nesslovver/V_e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Nesslovver/V_e 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("Nesslovver/V_e") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- b9464f0fa31e3bf95c65164d0d3ba2b6969db1ba3575aa51aca7fcd415a4caf0
- Size of remote file:
- 19.3 MB
- SHA256:
- e8946dd9629b6fc28ba305addda12fbb0f781878d3d5eba97779598d6949a8db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.