Instructions to use ApacheOne/Wan2.2-Animate-2-14B-OrbitQuant-W4A4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ApacheOne/Wan2.2-Animate-2-14B-OrbitQuant-W4A4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ApacheOne/Wan2.2-Animate-2-14B-OrbitQuant-W4A4", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
| from .source_quant import build_packed_from_official_source | |
| from .packed_linear import OrbitQuantPackedLinear, OrbitQuantW4A4Engine | |
| from .loader_official import build_packed_official_transformer, place_transformer | |
| from .attention_integration import install_dense_attention_acceleration | |
| from .kv_cache_streamer import Animate2KVCacheCPUOffloader | |
| __all__ = [ | |
| 'build_packed_from_official_source', 'OrbitQuantPackedLinear', 'OrbitQuantW4A4Engine', | |
| 'build_packed_official_transformer', 'place_transformer', | |
| 'install_dense_attention_acceleration', 'Animate2KVCacheCPUOffloader', | |
| ] | |