Please refer to Qwen-Image-Lightning github to learn how to use the models.

use with diffusers 🧨:

make sure to install diffusers from main (pip install git+https://github.com/huggingface/diffusers.git)

from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
import torch 
import math

# From https://github.com/ModelTC/Qwen-Image-Lightning/blob/342260e8f5468d2f24d084ce04f55e101007118b/generate_with_diffusers.py#L82C9-L97C10
scheduler_config = {
 "base_image_seq_len": 256,
 "base_shift": math.log(3), # We use shift=3 in distillation
 "invert_sigmas": False,
 "max_image_seq_len": 8192,
 "max_shift": math.log(3), # We use shift=3 in distillation
 "num_train_timesteps": 1000,
 "shift": 1.0,
 "shift_terminal": None, # set shift_terminal to None
 "stochastic_sampling": False,
 "time_shift_type": "exponential",
 "use_beta_sigmas": False,
 "use_dynamic_shifting": True,
 "use_exponential_sigmas": False,
 "use_karras_sigmas": False,
}
scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
pipe = DiffusionPipeline.from_pretrained(
 "Qwen/Qwen-Image", scheduler=scheduler, torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights(
 "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors"
)

prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition."
negative_prompt = " "
image = pipe(
 prompt=prompt,
 negative_prompt=negative_prompt,
 width=1024,
 height=1024,
 num_inference_steps=8,
 true_cfg_scale=1.0,
 generator=torch.manual_seed(0),
).images[0]
image.save("qwen_fewsteps.png")

Base model