File size: 2,089 Bytes
8fdcb1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | ---
license: apache-2.0
base_model: Tongyi-MAI/Z-Image-Turbo
pipeline_tag: text-to-image
library_name: diffusers
tags:
- z-image
- nunchaku
- svdquant
- int4
- quantization
---
# Z-Image-Turbo Nunchaku Lite INT4 r32
Diffusers-loadable conversion of:
- Base model: `Tongyi-MAI/Z-Image-Turbo`
- Source repo: `nunchaku-ai/nunchaku-z-image-turbo`
- Source checkpoint: `svdq-int4_r32-z-image-turbo.safetensors`
The transformer uses `quant_method: nunchaku_lite`, INT4 SVDQ, group size 64, rank 32, and 238 quantized targets. Packed fused QKV and SwiGLU projections are split in logical layout into the stock Z-Image graph. The Qwen3 text encoder is packaged with BitsAndBytes 4-bit NF4.
## Benchmark
| Checkpoint | Latency | Max VRAM |
| --- | ---: | ---: |
| Converted Diffusers Nunchaku Lite INT4 r32 + BNB4 Qwen3 | 6.03 s (stdev 0.00 s) | 12.01 GiB |
RTX 5090, 1024×1024, 9 scheduler steps (8 DiT forwards), guidance scale 0, seed 42, one warmup and three measured runs. VRAM is peak total device usage sampled through `nvidia-smi`. The native row uses the base model's BF16 Qwen3 encoder.
## Output Comparison
Both images use the same prompt, seed, scheduler, resolution, and step count. Native Nunchaku 1.x selects NVFP4 kernels on Blackwell, so the comparison uses native NVFP4 as a visual reference only (cross-precision MAE 23.37, RMSE 30.81).
## Run
Requires the Hugging Face `kernels` package and an NVIDIA Turing, Ampere, Ada, or Blackwell (not Hopper) GPU.
```python
import torch
from diffusers import ZImagePipeline
pipe = ZImagePipeline.from_pretrained(
"lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder",
torch_dtype=torch.bfloat16,
).to("cuda")
image = pipe(
prompt='A cinematic portrait of a red fox in a misty forest at sunrise, detailed fur, volumetric light',
height=1024,
width=1024,
num_inference_steps=9,
guidance_scale=0.0,
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("output.png")
```
|