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:
- d888a7bbf2717996f11b8effde6324c4bd36500d7a54d113b15032d412ab9a8a
- Size of remote file:
- 3.29 MB
- SHA256:
- 2a52e337b0e949443eea859916913378a8b002d24d73704a5bc80ca14de45f05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.