Qwen-Image-2.1 W4A4 (int4)
Built with Qwen.
Non-commercial use only. An educational artifact under the Qwen Research License, which the base model carries. Commercial use needs a separate licence from the rights holder; publishing this here does not grant one.
4-bit weights, 4-bit activations, for the Qwen-Image-2.1 transformer. Calibrated with SVDQuant, error-compensated with GPTQ, packed for Nunchaku's fused kernel. 4.34 GB against about 10 GB for the same layers in bf16.
The low-rank branch is fused into the 4-bit GEMM, which is where the speed comes from — an unfused branch gives back 24–34% of it.
Hardware
sm_75 through sm_120 — Ada (4090, L40S) and Blackwell alike.
Measured
| card | cap | seconds | vs bf16 | resident GB | LPIPS vs bf16 |
|---|---|---|---|---|---|
| RTX 4090 48GB | sm_89 | 11.576 | 1.84x | 21.35 | 0.231 |
| RTX PRO 6000 Max-Q | sm_120 | 37.616 | 0.35x | 21.29 | 0.1836 |
40 steps, 1024×1024, seed 42, six prompts. LPIPS is AlexNet against the bf16 image of the same prompt on the same card.
GPTQ is worth 6% here: 0.1946 with round-to-nearest against 0.1836 with GPTQ, at the same speed, the same file size and the same resident memory — it only changes which grid point each weight lands on.
Read LPIPS as distance from the bf16 trajectory, not as damage. On a dense-text prompt every build renders the text correctly while scoring 0.24–0.35, and on one prompt the bf16 reference garbles a sign that this checkpoint renders correctly — and is scored worse for it.
Only the transformer is quantized. The Qwen3-VL text encoder stays bf16 and is about 16.3 GB of a 21.5 GB resident total, so it, not this file, decides how many reference images fit on a card.
Text accuracy against bf16
The nvfp4 build of this same recipe scored 15/16 against bf16's 14/16 on a reference-free seed sweep (8 seeds, two text prompts, text read against the prompt) — indistinguishable. This int4 build was not put through that sweep; its LPIPS is 0.1836 against the nvfp4 build's 0.1425, so treat it as the same recipe at slightly lower fidelity, on the far wider set of cards it runs on.
Requirements
Packed for a specific kernel. The layout is an MMA fragment swizzle, so a different Nunchaku build may read it incorrectly — and would produce a wrong image rather than an error. The loader checks and warns.
torch==2.12.1
nunchaku==1.3.0.dev20260306+cu13.0torch2.12
Loading
import torch
from diffusers import QwenImage21Pipeline
from quantize import nunchaku_io
transformer = nunchaku_io.load_checkpoint(precision="int4", device="cuda")
pipe = QwenImage21Pipeline.from_pretrained(
"Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16, transformer=transformer)
How it was built
Smoothing factors from real activation statistics (SmoothQuant, α=0.5), a rank-128 SVD branch kept in bf16, and the residual rounded onto Nunchaku's grid by GPTQ against a calibration Hessian. One Hessian pass, 128 distinct inputs across 224 layers, 24 GB resident, ~5 minutes.
w4a4_build.json records the whole build, including per-layer GPTQ statistics.
License
Derivative of Qwen/Qwen-Image-2.1 under the
Qwen Research License Agreement — non-commercial only. See NOTICE.