Instructions to use APRIL-AIGC/T3-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use APRIL-AIGC/T3-Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("APRIL-AIGC/T3-Video", 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
Add library_name and clean up model card
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding
library_name: diffusersto the metadata, which enables the "How to use" widget on the model page with adiffuserscode snippet. - Removing a redundant YAML frontmatter block from the markdown content, as all metadata is already (or now will be) correctly placed in the top-level YAML block.
APRIL-AIGC changed pull request status to merged
update REAMDE