Instructions to use AlayaLab/Evoke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlayaLab/Evoke with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AlayaLab/Evoke", 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
Update metadata and add paper link
This PR updates the model card for EVOKE:
- Changes the pipeline tag from
text-to-videotoimage-to-videoto accurately reflect the model's primary conditioning mode. - Adds
library_name: diffusersto enable appropriate library integration and discoverability. - Adds a direct link to the paper on the Hugging Face Hub in the header.
No other modifications are made; the existing usage instructions, notes, and citation remain as provided by the authors.
Thanks @nielsr ! The paper link was a good catch β it's live on main now,
along with fixes to the arXiv badge, the GitHub URL, and the download
commands.
I'm keeping pipeline_tag: text-to-video though: EVOKE runs t2v / i2v / v2v
and t2v is a first-class mode, so no single tag really fits β both are
already in tags. And I'd rather hold off on declaring
library_name: diffusers, since the repo has no root model_index.json
and needs a diffusers fork that isn't on PyPI, so the generated snippet
wouldn't run.
Closing since main has moved past this branch. Thanks again!