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
| # The demo service. One card holds VGGT-Omega + VAE + the finetuned transformer + the DMD LoRA (~77 GiB). | |
| # CARD=0 service/run.sh --port 8412 | |
| export CUDA_VISIBLE_DEVICES=${CARD:-0} | |
| export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True | |
| export PYTORCH_ALLOC_CONF=expandable_segments:True | |
| cd "$(dirname "$0")/.." | |
| # Exit code 3 = the service found its CUDA context poisoned and asked to be reloaded; anything else stops. | |
| while :; do | |
| python service/app.py "$@" | |
| [ $? = 3 ] || break | |
| sleep 2 | |
| done | |