Qwen Image Edit 2509 Lightning 4-Step Nunchaku Lite INT4 r32
This directory is a Diffusers-loadable conversion of:
- Base Diffusers model:
Qwen/Qwen-Image-Edit-2509 - Source Nunchaku checkpoint repo:
nunchaku-ai/nunchaku-qwen-image-edit-2509 - Source safetensors checkpoint:
lightning-251115/svdq-int4_r32-qwen-image-edit-2509-lightning-4steps-251115.safetensors - Lightning LoRA fused by source checkpoint:
Qwen-Image-Edit-2509-Lightning-4steps-V1.0
The transformer is packaged for the Diffusers Nunchaku Lite loader:
quant_method:nunchaku_lite- SVDQ precision:
int4 - SVDQ group size:
64 - SVDQ rank:
32 - SVDQ targets:
720 - AWQ targets:
120
The text encoder is saved with BitsAndBytes 4-bit NF4:
quant_method:bitsandbytesload_in_4bit:truebnb_4bit_quant_type:nf4bnb_4bit_compute_dtype:bfloat16
The scheduler config is the lightning scheduler used by the Nunchaku 4/8-step example, with base_shift = max_shift = log(3).
Benchmark
Measured with 4 inference steps on a 1248x832 input image. Latency is the mean of 3 measured runs after 1 warmup. Max VRAM is peak CUDA allocated memory during generation.
| Checkpoint | Latency | Max VRAM |
|---|---|---|
| Converted Diffusers Nunchaku Lite INT4 r32 + BNB4 text encoder | 12.60 s | 21.21 GiB |
| Original Nunchaku INT4 r32 safetensors | 11.27 s | 35.13 GiB |
Original baseline: nunchaku-ai/nunchaku-qwen-image-edit-2509/lightning-251115/svdq-int4_r32-qwen-image-edit-2509-lightning-4steps-251115.safetensors loaded through the native Nunchaku transformer after patching native precision selection to use the checkpoint filename.
Output Comparison
The comparison uses the same input image, prompt, seed, scheduler, and 4-step settings for this converted checkpoint and its original Nunchaku safetensors baseline.
Run
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline
model_path = "lite-infer/qwen-image-edit-2509-lightning-4steps-nunchaku-lite-int4_r32-bnb4-text-encoder"
image_path = "input.png"
output_path = "output.png"
pipe = QwenImageEditPlusPipeline.from_pretrained(model_path, torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = Image.open(image_path).convert("RGB")
prompt = "Change the image to watercolor style."
result = pipe(
image=image,
prompt=prompt,
generator=torch.Generator(device="cuda").manual_seed(1),
true_cfg_scale=1.0,
num_inference_steps=4,
num_images_per_prompt=1,
)
result.images[0].save(output_path)