diffusers/docs/source/en/api/pipelines/qwenimage.md

QwenImage

Qwen-Image from the Qwen team is an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.

Qwen-Image comes in the following variants:

model type	model id
Qwen-Image	Qwen/Qwen-Image
Qwen-Image-Edit	Qwen/Qwen-Image-Edit

Caching may also speed up inference by storing and reusing intermediate outputs.

LoRA for faster inference

Use a LoRA from lightx2v/Qwen-Image-Lightning to speed up inference by reducing the number of steps. Refer to the code snippet below:

Code

from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
import torch 
import math

ckpt_id = "Qwen/Qwen-Image"

# 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(
    ckpt_id, 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")

The guidance_scale parameter in the pipeline is there to support future guidance-distilled models when they come up. Note that passing guidance_scale to the pipeline is ineffective. To enable classifier-free guidance, please pass true_cfg_scale and negative_prompt (even an empty negative prompt like " ") should enable classifier-free guidance computations.

QwenImagePipeline

autodoc QwenImagePipeline

• all
• call

QwenImageImg2ImgPipeline

autodoc QwenImageImg2ImgPipeline

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• call

QwenImageInpaintPipeline

autodoc QwenImageInpaintPipeline

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• call

QwenImageEditPipeline

autodoc QwenImageEditPipeline

• all
• call

QwenImaggeControlNetPipeline

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• call

QwenImagePipelineOutput

autodoc pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput