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: 784 Bytes
f2c0505 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import torch
from orbitquant_wan_a2.nibbles import pack_uint4
from orbitquant_wan_a2.reference import w4a4_linear_reference
def test_reference_matches_explicit_bf16_dequant():
torch.manual_seed(3)
m, n, k = 7, 11, 64
cb = torch.linspace(-0.2, 0.2, 16)
ac = torch.randint(0,16,(m,k),dtype=torch.uint8)
wc = torch.randint(0,16,(n,k),dtype=torch.uint8)
a_s = torch.rand(m) + 0.1
w_s = torch.rand(n) + 0.1
bias = torch.randn(n, dtype=torch.bfloat16)
out = w4a4_linear_reference(pack_uint4(ac), a_s, pack_uint4(wc), w_s, cb, k, bias)
a = (cb[ac.long()] * a_s[:,None]).to(torch.bfloat16)
w = (cb[wc.long()] * w_s[:,None]).to(torch.bfloat16)
ref = (a.float() @ w.float().T + bias.float()).to(torch.bfloat16)
assert torch.equal(out, ref)
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