Instructions to use m28yhtd/Remove_object_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use m28yhtd/Remove_object_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("m28yhtd/Remove_object_lora", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a96ce8b440ca0dd6ae4c534bc81328e0005304a5039cf9238b2b663018cc1a85
- Size of remote file:
- 6.59 MB
- SHA256:
- b65f16e7360846dcebacb74a79c8e2a0e7c91ed5254e87b70be9aa1ce00ec546
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.