Instructions to use Benevolent/NGNegative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Benevolent/NGNegative 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-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Benevolent/NGNegative") prompt = "masterpiece, best quality, 1girl, pink eyes, long hair, black hair, (grapefruit), (temple in background), sitting, kimono, medium breasts, light smile, arms behind back," image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
NGNegative
,%20(temple%20in%20background),%20sitting,%20kimono,%20mediu.png)
- Prompt
- masterpiece, best quality, 1girl, pink eyes, long hair, black hair, (grapefruit), (temple in background), sitting, kimono, medium breasts, light smile, arms behind back,
- Negative Prompt
- NG_DeepNegative_V1_75T,(worst quality, low quality:1.4), logo, text, monochrome
Trigger words
You should use NG_DeepNegative_V1_75T to trigger the image generation.
Download model
Weights for this model are available in PyTorch format.
Download them in the Files & versions tab.
- Downloads last month
- -
Model tree for Benevolent/NGNegative
Base model
stabilityai/stable-diffusion-xl-base-1.0