feat(diffusers/pipelines): add pipelines and required modules of QwenImage in Diffusers Master

[Dong1017]

What does this PR do?

Adds

1. QwenImage Pipelines and Required Modules

(Comparable with Diffusers Master)

a. Pipelines

• mindone.diffusers.QwenImagePipeline
• mindone.diffusers.QwenImageImg2ImgPipeline
• mindone.diffusers.QwenImageInpaintPipeline
• mindone.diffusers.QwenImageEditPipeline
• mindone.diffusers.QwenImageEditInpaintPipeline

b. Modules

• mindone.diffusers.models.AutoencoderQwenImage
• mindone.diffusers.models.QwenImageTransformer2DModel
• mindone.diffusers.loaders.QwenImageLoraLoaderMixin

2. add UTs of pipelines

• All UTs were setup according to Diffusers Master, accessed in Sep 17, 2025.
  • tests/diffusers_tests/pipelines/qwenimage/test_qwenimage.py
  • tests/diffusers_tests/pipelines/qwenimage/test_qwenimage_img2img.py
  • tests/diffusers_tests/pipelines/qwenimage/test_qwenimage_inpaint.py
  • tests/diffusers_tests/pipelines/qwenimage/test_qwenimage_edit.py
• Using MindSpore 2.7.0 can pass both fp32 and bf16 UTs.
• Using MindSpore 2.6.0 can pass bf16 UTs, while fp32 will happen to TypeError.

Usage

• QwenImagePipeline

import mindspore as ms 
from mindone.diffusers import QwenImagePipeline 

pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", mindspore_dtype=ms.bfloat16) 
prompt = "A cat holding a sign that says hello world" 
# Depending on the variant being used, the pipeline call will slightly vary. 
# Refer to the pipeline documentation for more details. 
image = pipe(prompt, num_inference_steps=50)[0][0] 
image.save("qwenimage.png") 

• QwenImageImg2ImgPipeline

import mindspore as ms 
from mindone.diffusers import QwenImageImg2ImgPipeline
from mindone.diffusers.utils import load_image

pipe = QwenImageImg2ImgPipeline.from_pretrained("Qwen/Qwen-Image", mindspore_dtype=mindspore.bfloat16)
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
init_image = load_image(url).resize((1024, 1024))
prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney"
images = pipe(prompt=prompt, negative_prompt=" ", image=init_image, strength=0.95)[0][0]
images.save("qwenimage_img2img.png")

• QwenImageInpaintPipeline

import mindspore as ms 
from mindone.diffusers import QwenImageInpaintPipeline 
from mindone.diffusers.utils import load_image 

pipe = QwenImageInpaintPipeline.from_pretrained("Qwen/Qwen-Image", mindspore_dtype=ms.bfloat16) 
prompt = "Face of a yellow cat, high resolution, sitting on a park bench" 
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" 
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" 
source = load_image(img_url) 
mask = load_image(mask_url) 
image = pipe(prompt=prompt, negative_prompt=" ", image=source, mask_image=mask, strength=0.85)[0][0] 
image.save("qwenimage_inpainting.png") 

• QwenImageEditPipeline

import mindspore as ms 
from PIL import Image 
from mindone.diffusers import QwenImageEditPipeline 
from mindone.diffusers.utils import load_image 

pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", mindspore_dtype=ms.bfloat16) 
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png").convert("RGB") 
prompt = ("Make Pikachu hold a sign that says 'Qwen Edit is awesome', yarn art style, detailed, vibrant colors") 
# Depending on the variant being used, the pipeline call will slightly vary. 
# Refer to the pipeline documentation for more details. 
image = pipe(image, prompt, num_inference_steps=50)[0][0] 
image.save("qwenimage_edit.png") 

• QwenImageEditInpaintPipeline

import mindspore as ms 
from PIL import Image
from mindone.diffusers import QwenImageEditInpaintPipeline
from mindone.diffusers.utils import load_image

pipe = QwenImageEditInpaintPipeline.from_pretrained("Qwen/Qwen-Image-Edit", mindspore_dtype=mindspore.bfloat16)
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
source = load_image(img_url)
mask = load_image(mask_url)
image = pipe(prompt=prompt, negative_prompt=" ", image=source, mask_image=mask, strength=1.0, num_inference_steps=50)[0][0]
image.save("qwenimage_inpainting.png")

Performance

Experiments are tested on Ascend Atlas 800T A2 machines with MindSpore 2.7.0

Pipeline	Weight Loading Time	Mode	Speed
QwenImagePipeline	15m21s	Pynative	9.93 s/it
QwenImageImg2ImgPipeline	14m57s	Pynative	9.56 s/it
QwenImageInpaintPipeline	10m10s	Pynative	4.80 s/it
QwenImageEditPipeline	13m57s	Pynative	13.25 s/it
QwenImageEditInpaintPipeline	13m20s	Pynative	13.98 s/it

Limitation

QwenImageEditPipeline and QwenImageEditInpaintPipeline will load modules from Qwen-Image-Edit. The use of these two pipes requires manually changing image_processor_type from Qwen2VLImageProcessorFast to Qwen2VLImageProcessor in Qwen-Image-Edit/processor/preprocessor_config.json

Notes

1. require transformers==4.52.1
2. The produced pictures are nearly identical to those by Torch, when setting consistent random seed and hidden states from the text encoder.
3. TODO: jit mode; LORA test; UTs of modules

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline?
• Did you make sure to update the documentation with your changes? E.g. record bug fixes or new features in What's New. Here are the
  documentation guidelines
• Did you build and run the code without any errors?
• Did you report the running environment (NPU type/MS version) and performance in the doc? (better record it for data loading, model inference, or training tasks)
• Did you write any new necessary tests?

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@xxx

[SamitHuang]

How to fix the requirement of transformers==4.52.1?

[SamitHuang]

Can add an inference example and lora fine-tune example in examples folder, which helps introduce QwenImage

[Dong1017]

How to fix the requirement of transformers==4.52.1?

The main reason for using transformers==4.52.1 rather than transformers==4.50.0 is to avoid AttributeError and keep consistent with the requirements from Qwen-Image.
Using transformers==4.50.0 will raise the following AttributeError:

../../transformers/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py:1517: in __init__
    super().__init__(config)
../../transformers/src/transformers/modeling_utils.py:1898: in __init__
    self.generation_config = GenerationConfig.from_model_config(config) if self.can_generate() else None
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        decoder_config = model_config.get_text_config(decoder=True)
        if decoder_config is not model_config:
            default_generation_config = GenerationConfig()
>           decoder_config_dict = decoder_config.to_dict()
                                  ^^^^^^^^^^^^^^^^^^^^^^
E           AttributeError: 'dict' object has no attribute 'to_dict'

../../transformers/src/transformers/generation/configuration_utils.py:1287: AttributeError

Upgrading transformers from 4.50.0 to 4.52.1 or highr version will solve this error.