Instructions to use RadonDong/LUAD_white_formalin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RadonDong/LUAD_white_formalin with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("RadonDong/LUAD_white_formalin") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 9a1e3a12d72ea64c7dd54403eb0dab3d06c5b6aa855021207396661d918b37d2
- Size of remote file:
- 571 kB
- SHA256:
- 8dc6506800956ef94601171f10c144d49988adf1ff7c8f352e09b49c09ad30f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.