Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32

4 bit (UINT4 with SVD rank 32) quantization of Tongyi-MAI/Z-Image-Turbo using SDNQ.

Usage:

pip install git+https://github.com/Disty0/sdnq

import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers

pipe = diffusers.ZImagePipeline.from_pretrained("Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    num_inference_steps=9,
    guidance_scale=0.0,
    generator=torch.manual_seed(42),
).images[0]
image.save("z-image-turbo-sdnq-uint4-svd-r32.png")

Original BF16 vs SDNQ quantization comparison:

Quantization	Model Size	Visualization
Original BF16	12.3 GB
SDNQ UINT4	3.5 GB