Instructions to use wangjian21/N_AS2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wangjian21/N_AS2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("wangjian21/N_AS2") prompt = "nude sexual erotic bather body art" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 2367e7d9aeec67aeb0968902121e6d74e44f8bafb741a18b33970291f35d5ab2
- Size of remote file:
- 526 kB
- SHA256:
- 858d60228245d8a707de4a49a8e59bd2ab227c30662a65e923fc2f00fa478e54
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.