Instructions to use nitrosocke/mo-di-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/mo-di-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("nitrosocke/mo-di-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
Would love some more info on how the model was trained
#21
by PHUNSTER - opened
Amazing work!
I'd love to learn how to train my own model on a style and would love some guidance.
- What codebase did you use to train this model? TheLastBen or AUTO1111-Dreambooth extension?
- How many images did you use to train? Did you use captions? Were they autogenerated captions?
- Did you use Concept/Regularization images? If so what kind?
Any points is much appreciated!
The author of this model wrote a dreambooth style training guide: https://github.com/nitrosocke/dreambooth-training-guide
Keep in mind that this is slightly outdated, since then we've started using higher learning rates and lr-schedulers, meaning less images are needed to converge. However, the guide does explain (I believe) how this model was trained.