Instructions to use anonymous728/VORTA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use anonymous728/VORTA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("anonymous728/VORTA", 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
VORTA: Efficient Video Diffusion via Routing Sparse Attention
TL;DR - VORTA accelerates video diffusion transformers by sparse attention and dynamic routing, achieving speedup with negligible quality loss.
Quick Start
- Download the checkpoints into the
./resultsdirectory under the VORTA GitHub code repository.
git lfs install
git clone git@hf.co:anonymous728/VORTA
# mv VORTA/<model_name> results/, <model_name>: wan-14B, hunyuan; e.g.
mv VORTA/wan-14B results/
Other alternative methods to download the models can be found here.
- Follow the
README.mdinstructions to run the sampling with speedup. 🤗
- Downloads last month
- -
Model tree for anonymous728/VORTA
Base model
Wan-AI/Wan2.1-T2V-14B-Diffusers