Instructions to use mwkldeveloper/chinese-basic-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mwkldeveloper/chinese-basic-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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mwkldeveloper/chinese-basic-lora") 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:
- 4ef8e924450e828370106bef2de8add9b76219138a4bee5543d3324b5f40a007
- Size of remote file:
- 6.59 MB
- SHA256:
- da4f890a62d5fa0ef8ed5304d9c0c8d07c6d9b278748f6a5a7653eb24c0a03c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.