Wuli-art/Qwen-Image-2512-Turbo-LoRA
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<div align="center"> <img src="https://img.alicdn.com/imgextra/i4/O1CN011xTzi6280yXyGmk9b_!!6000000007871-0-tps-3072-938.jpg" width="50%" /> </div>
Qwen-Image-2512-Turbo-LoRA is a 4 or 8-steps turbo LoRA for Qwen Image 2512 trained by Wuli Team. This LoRA matches the original model's ouput quality but is over 20x faster⚡️, 2x from CFG-distillation and others from reduced number of inference steps.
For users in Chinese mainland, you can directly try this model in our website: https://wuli.art/generate, getting four images with 2k resolution generated by Qwen Image 2512 Turbo with only 5 seconds.
You can also try this model on this DEMO webiste: https://modelscope.cn/studios/kelseye/Qwen-Image-2512-Turbo-LoRA-Demo
News
- Jan 8, 2026: Rlease V3.0 with further improved image color and details, please check the differences between V1.0, V2.0, and V3.0 in the first table below. This version is already ComfyUI compatible.
- Jan 2, 2026: Release V2.0 with improved image color and details. This version is already ComfyUI compatible.
- Dec 31, 2025: Release V1.0 and its ComfyUI compatible version (thanks to @System36), we will soon release V2.0 with enhanced performance.
Result Visualization
Differences between different versions
Differences between our turbo LoRA and the original model
Quick start with Diffsynth-Engine
- Diffsynth-Engine:https://github.com/modelscope/DiffSynth-Engine
- Example Code:
import math
from diffsynth_engine import fetch_model, QwenImagePipeline, QwenImagePipelineConfig
# Create pipeline
config = QwenImagePipelineConfig.basic_config(
model_path=fetch_model("Qwen/Qwen-Image-2512", path="transformer/*.safetensors"),
encoder_path=fetch_model("Qwen/Qwen-Image-2512", path="text_encoder/*.safetensors"),
vae_path=fetch_model("Qwen/Qwen-Image-2512", path="vae/*.safetensors"),
offload_mode="cpu_offload",
)
pipe = QwenImagePipeline.from_pretrained(config)
# Load our turbo LoRA
pipe.load_lora(
path=fetch_model("Wuli-Art/Qwen-Image-2512-Turbo-LoRA", path="Wuli-Qwen-Image-2512-Turbo-LoRA-4steps-V1.0-bf16.safetensors"),
scale=1.0,
fused=True,
)
# Change scheduler config
scheduler_config = {
"exponential_shift_mu": math.log(2.5),
"use_dynamic_shifting": True,
"shift_terminal": None
}
pipe.apply_scheduler_config(scheduler_config)
# Sample image
output = pipe(
prompt="a young girl with flowing long hair, wearing a white halter dress and smiling sweetly. The background features a blue seaside where seagulls fly freely.",
cfg_scale=1,
num_inference_steps=4, # 8 is also recommended
seed=42,
width=1328,
height=1328
)
output.save("output.png")Existing problem
- Generated image quality may degrade when handling extremely complex details, you can increase the value of
num_inference_steps.

























