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
Commit History
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Add `clip_sample=False` to scheduler to make model compatible with DDIM. (#20)
- Add `clip_sample=False` to scheduler to make model compatible with DDIM. (f39ca1003b86defcbe2a12e64f86148c8e39306d) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>0f29764
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Add Diffusers model (#2)
- Add Diffusers model (f7dcf4749f2225291f1631b291fbb39a68cf910f)e721b74