Instructions to use ecccho/lumi-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ecccho/lumi-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ecccho/lumi-diffusion", 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
| # Introdution | |
| This model is fine-tuned based on AbyssOrangeMix2 using DreamBooth. | |
| Learning rate = 1e-6 | |
| Steps = 9000 | |
| Training dataset and prior preservation loss dataset is also uploaded in this account. | |
| If you want to create the object lumi, you can use the prompt **lun** | |