Instructions to use MdEndan/stable-diffusion-lora-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MdEndan/stable-diffusion-lora-fine-tuned with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("MdEndan/stable-diffusion-lora-fine-tuned") 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:
- a2297dd39f4f8f7a8bf5055b484cd5c665efab0749cbb9106125f5d0e24df89c
- Size of remote file:
- 3.29 MB
- SHA256:
- 833df39d8d8496aee024d2f08db2c83e278df72e7fe0dedfd7f3cab3ad0072c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.