QwenImage

LoRA

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
Qwen-Image-Edit Plus Qwen/Qwen-Image-Edit-2509

[!TIP] 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 ```py 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") ```

[!TIP] 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.

Multi-image reference with QwenImageEditPlusPipeline

With [QwenImageEditPlusPipeline], one can provide multiple images as input reference.

import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline
from diffusers.utils import load_image

pipe = QwenImageEditPlusPipeline.from_pretrained(
    "Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16
).to("cuda")

image_1 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/grumpy.jpg")
image_2 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peng.png")
image = pipe(
    image=[image_1, image_2], 
    prompt='''put the penguin and the cat at a game show called "Qwen Edit Plus Games"''', 
    num_inference_steps=50
).images[0]

QwenImagePipeline

[[autodoc]] QwenImagePipeline

QwenImageImg2ImgPipeline

[[autodoc]] QwenImageImg2ImgPipeline

QwenImageInpaintPipeline

[[autodoc]] QwenImageInpaintPipeline

QwenImageEditPipeline

[[autodoc]] QwenImageEditPipeline

QwenImageEditInpaintPipeline

[[autodoc]] QwenImageEditInpaintPipeline

QwenImageControlNetPipeline

[[autodoc]] QwenImageControlNetPipeline

QwenImageEditPlusPipeline

[[autodoc]] QwenImageEditPlusPipeline

QwenImagePipelineOutput

[[autodoc]] pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput