Downloads · 30 days
12
10% of all-time downloads
AsadIsmail/Wan2.2-TI2V-5B-ternary
Wan2.2-TI2V-5B-ternary is a text-to-video model from AsadIsmail. Use it when you need video from a text prompt. It is set up for diffusers. The card lists the license as apache-2.0.
Packed ternary version available. The packed/ folder now contains the real 2-bit packed transformer (1.78x smaller: 5.5 GB vs 9.8 GB FP16) that runs on the ternary-quant packed runtime. Load it with packed/loadpackedd…
Downloads · 30 days
12
10% of all-time downloads
All-time downloads
123
Public
Parameters
5B
28.3 GB on disk
Likes
1
Public
Click a slice to open those files.
.safetensors22.8 GB · 80%
How the weights are stored.
F165B · 100%
From the Hugging Face model README
Packed ternary version available. The
packed/folder now contains the real 2-bit packed transformer (1.78x smaller: 5.5 GB vs 9.8 GB FP16) that runs on theternary-quantpacked runtime. Load it withpacked/load_packed_dit.py(pip install ternary-quant). The root files remain FP16 so stockdiffusersstill works. Quality note: the packed transformer was quantized without activation calibration (memory-minimal streaming), so treat it as experimental.
First publicly available ternary-quantized Wan 2.2 model on HuggingFace.
Ternary-quantized version of Wan-AI/Wan2.2-TI2V-5B-Diffusers — Alibaba's latest text-image-to-video DiT model (5B, 572 likes on original).
| Property | Value |
|---|---|
| Base Model | Wan-AI/Wan2.2-TI2V-5B-Diffusers |
| Architecture | WanTransformer3DModel (DiT) |
| Transformer Params | 5.00B |
| Quantization | tritplane3 (306 linear layers) |
| Text Encoder (UMT5-XXL) | FP16 (preserved) |
| VAE (WanVAE) | FP16 (preserved) |
| License | Apache 2.0 |
| Method | Transformer Size |
|---|---|
| FP16 (original) | 10.02 GB |
| Ternary tritplane3 (theoretical packed) | ~5.0 GB |
| In this repo (dequantized FP16) | 9.4 GB |
import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video
pipe = WanPipeline.from_pretrained(
"AsadIsmail/Wan2.2-TI2V-5B-ternary",
torch_dtype=torch.bfloat16,
)
pipe.to("mps") # or "cuda"
output = pipe(
prompt="a cat walking on green grass",
num_frames=81,
num_inference_steps=30,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
Part of ternary-models.