Instructions to use Viggle/Meridian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Viggle/Meridian with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Viggle/Meridian", 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
Sample clips
Both clips in media/ are from Wikimedia Commons and were released by their uploaders under
CC0 1.0 (public domain dedication). We modified
them: cut to a 73-frame window, scaled to 1280 × 720, re-encoded as H.264, soundtrack removed.
| file | source | uploader | window |
|---|---|---|---|
sp_bouldering_hang.mp4 |
2020-11-28 - IFSC Euros - Combined M-B - Alex Khazanov - Video 3.webm | Voltmetro | from 9.5 s |
sp_bouldering_reach.mp4 |
Anna Stohr JMM 2013 Annecy Bloc.webm | Shev123 | from 13.75 s |
Both show identifiable athletes at public competitions. The CC0 dedication covers the uploader's copyright, not the athletes' personality rights; the clips are here as technical demo inputs only.