Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
diffusers-training
Instructions to use mhbkb/base_diffusion_models_nightshade300_visualwrong with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mhbkb/base_diffusion_models_nightshade300_visualwrong with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mhbkb/base_diffusion_models_nightshade300_visualwrong", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 19eb2c9bfb28f52eb628845f1c2428e20535a1fbfda4e0d7e7ac02203aca7fdf
- Size of remote file:
- 6.93 GB
- SHA256:
- f4f0fca0c78b3385a26af35dbdbd217885db229f563e7fd44f7e6aade02d942a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.