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
File size: 822 Bytes
f2c0505 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # 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.
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