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
| # Validation | |
| Package-build-host validation: | |
| - Python compileall: PASS | |
| - pytest: PASS (17 tests) | |
| - direct-source target rule: 40 x 12 = 480 | |
| - nibble pack/unpack: PASS | |
| - direct-source W4 packing unit test: PASS, BF16 row norms | |
| - CPU packed GEMM reference/layout tests: PASS | |
| - runtime static invariants: PASS | |
| - no dense target `weight` Parameter in `OrbitQuantPackedLinear`: PASS | |
| - CPU K/V cache streaming static checks: PASS | |
| - Sol/flex separation static checks: PASS | |
| Not claimable on the build host: | |
| - Triton JIT compilation on the user's Colab GPU | |
| - numerical CUDA parity for d=5120 / 13824 | |
| - full 32.8-GB transformer quantization | |
| - full Wan-Animate-2 generation | |
| These are mandatory gates in `COLAB_ONE_CELL.py`; generation is not allowed to proceed until the actual packed artifact passes the target-GPU kernel gate. | |