Unsloth Diffusion GGUFs
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Find GGUFs and other variants of diffusion based Qwen-Image and FLUX models.
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Read our How to Run Qwen-Image-2512 Guide! ๐
This is a GGUF quantized version of Qwen-Image-2512.
unsloth/Qwen-Image-2512-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance.
- Important layers are upcasted to higher precision.
- To use the model, read our guides for ComfyUI or stable-diffusion.cpp.
- Uses tooling from ComfyUI-GGUF by city96.
Samples
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Introduction
We are excited to introduce Qwen-Image-2512, the December update of Qwen-Imageโs text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base Qwen-Image model released in August, Qwen-Image-2512 features the following key improvements:
- Enhanced Huamn Realism Qwen-Image-2512 significantly reduces the โAI-generatedโ look and substantially enhances overall image realism, especially for human subjects.
- Finer Natural Detail Qwen-Image-2512 delivers notably more detailed rendering of landscapes, animal fur, and other natural elements.
- Improved Text Rendering Qwen-Image-2512 improves the accuracy and quality of textual elements, achieving better layout and more faithful multimodal (text + image) composition.
Model Performance
We conducted over 10,000 rounds of blind model evaluations on AI Arena, and the results show that Qwen-Image-2512 is currently the strongest open-source modelโwhile remaining highly competitive even among closed-source models.
Quick Start
Install the latest version of diffusers
pip install git+https://github.com/huggingface/diffusers
The following contains a code snippet illustrating how to use Qwen-Image-2512:
from diffusers import DiffusionPipeline
import torch
model_name = "Qwen/Qwen-Image-2512"
# Load the pipeline
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
device = "cuda"
else:
torch_dtype = torch.float32
device = "cpu"
pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype).to(device)
# Generate image
prompt = '''A 20-year-old East Asian girl with delicate, charming features and large, bright brown eyesโexpressive and lively, with a cheerful or subtly smiling expression. Her naturally wavy long hair is either loose or tied in twin ponytails. She has fair skin and light makeup accentuating her youthful freshness. She wears a modern, cute dress or relaxed outfit in bright, soft colorsโlightweight fabric, minimalist cut. She stands indoors at an anime convention, surrounded by banners, posters, or stalls. Lighting is typical indoor illuminationโno staged lightingโand the image resembles a casual iPhone snapshot: unpretentious composition, yet brimming with vivid, fresh, youthful charm.'''
negative_prompt = "ไฝๅ่พจ็๏ผไฝ็ป่ดจ๏ผ่ขไฝ็ธๅฝข๏ผๆๆ็ธๅฝข๏ผ็ป้ข่ฟ้ฅฑๅ๏ผ่กๅๆ๏ผไบบ่ธๆ ็ป่๏ผ่ฟๅบฆๅ
ๆป๏ผ็ป้ขๅ
ทๆAIๆใๆๅพๆททไนฑใๆๅญๆจก็ณ๏ผๆญๆฒใ"
# Generate with different aspect ratios
aspect_ratios = {
"1:1": (1328, 1328),
"16:9": (1664, 928),
"9:16": (928, 1664),
"4:3": (1472, 1104),
"3:4": (1104, 1472),
"3:2": (1584, 1056),
"2:3": (1056, 1584),
}
width, height = aspect_ratios["16:9"]
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=50,
true_cfg_scale=4.0,
generator=torch.Generator(device="cuda").manual_seed(42)
).images[0]
image.save("example.png")
Showcase
Enhanced Huamn Realism
In Qwen-Image-2512, human depiction has been substantially refined. Compared to the August release, Qwen-Image-2512 adds significantly richer facial details and better environmental context. For example:
A Chinese female college student, around 20 years old, with a very short haircut that conveys a gentle, artistic vibe. Her hair naturally falls to partially cover her cheeks, projecting a tomboyish yet charming demeanor. She has cool-toned fair skin and delicate features, with a slightly shy yet subtly confident expressionโher mouth crooked in a playful, youthful smirk. She wears an off-shoulder top, revealing one shoulder, with a well-proportioned figure. The image is framed as a close-up selfie: she dominates the foreground, while the background clearly shows her dormitoryโa neatly made bed with white linens on the top bunk, a tidy study desk with organized stationery, and wooden cabinets and drawers. The photo is captured on a smartphone under soft, even ambient lighting, with natural tones, high clarity, and a bright, lively atmosphere full of youthful, everyday energy.
For the same prompt, Qwen-Image-2512 yields notably more lifelike facial features, and background objectsโe.g., the desk, stationery, and beddingโare rendered with significantly greater clarity than in Qwen-Image.
A 20-year-old East Asian girl with delicate, charming features and large, bright brown eyesโexpressive and lively, with a cheerful or subtly smiling expression. Her naturally wavy long hair is either loose or tied in twin ponytails. She has fair skin and light makeup accentuating her youthful freshness. She wears a modern, cute dress or relaxed outfit in bright, soft colorsโlightweight fabric, minimalist cut. She stands indoors at an anime convention, surrounded by banners, posters, or stalls. Lighting is typical indoor illuminationโno staged lightingโand the image resembles a casual iPhone snapshot: unpretentious composition, yet brimming with vivid, fresh, youthful charm.
Here, hair strands serve as a key differentiator: Qwen-Imageโs August version tends to blur them together, losing fine detail, whereas Qwen-Image-2512 renders individual strands with precision, resulting in a more natural and realistic appearance.
Another case:
An East Asian teenage boy, aged 15โ18, with soft, fluffy black short hair and refined facial contours. His large, warm brown eyes sparkle with energy. His fair skin and sunny, open smile convey an approachable, friendly demeanorโno makeup or blemishes. He wears a blue-and-white summer uniform shirt, slightly unbuttoned, made of thin breathable fabric, with black headphones hanging around his neck. His hands are in his pockets, body leaning slightly forward in a relaxed pose, as if engaged in conversation. Behind him lies a summer school playground: lush green grass and a red rubber track in the foreground, blurred school buildings in the distance, a clear blue sky with fluffy white clouds. The bright, airy lighting evokes a joyful, carefree adolescent atmosphere.
In this example, Qwen-Image-2512 better adheres to semantic instructionsโfor instance, the prompt specifies โbody leaning slightly forward,โ and Qwen-Image-2512 accurately captures this posture, unlike its predecessor.
An elderly Chinese couple in their 70s in a clean, organized home kitchen. The woman has a kind face and a warm smile, wearing a patterned apron; the man stands behind her, also smiling, as they both gaze at a steaming pot of buns on the stove. The kitchen is bright and tidy, exuding warmth and harmony. The scene is captured with a wide-angle lens to fully show the subjects and their surroundings.
This comparison starkly highlights the gap between the August and December models. The original Qwen-Image struggles to accurately render aged facial features (e.g., wrinkles), resulting in an artificial โAI look.โ In contrast, Qwen-Image-2512 precisely captures age cues, dramatically boosting realism.
Finer Natural Detail
Qwen-Image-2512โs enhanced detail rendering extends beyond humansโto landscapes, wildlife, and more. For instance:
A turquoise river winds through a lush canyon. Thick moss and dense ferns blanket the rocky walls; multiple waterfalls cascade from above, enveloped in mist. At noon, sunlight filters through the dense canopy, dappling the river surface with shimmering light. The atmosphere is humid and fresh, pulsing with primal jungle vitality. No humans, text, or artificial traces present.
Side-by-side, Qwen-Image-2512 exhibits superior fidelity in water flow, foliage, and waterfall mistโand renders richer gradation in greens. Another example (wave rendering):
At dawn, a thin mist veils the sea. An ancient stone lighthouse stands at the cliffโs edge, its beacon faintly visible through the fog. Black rocks are pounded by waves, sending up bursts of white spray. The sky glows in soft blue-purple hues under cool, hazy lightโevoking solitude and solemn grandeur.
Fur detail is another highlightโhere, a golden retriever portrait:
An ultra-realistic close-up of a golden retriever outdoors under soft daylight. Hair is exquisitely detailed: strands distinct, color transitioning naturally from warm gold to light cream, light glinting delicately at the tips; a gentle breeze adds subtle volume. Undercoat is soft and dense; guard hairs are long and well-defined, with visible layering. Eyes are moist, expressive; nose is slightly damp with fine specular highlights. Background is softly blurred to emphasize the dogโs tangible texture and vivid expression.
Similarly, texture quality improves in depictions of rugged wildlifeโfor example, a male argali sheep:
A male argali stands atop a barren, rocky mountainside. Its coarse, dense grey-brown coat covers a powerful, muscular body. Most striking are its massive, thick, outward-spiraling hornsโa symbol of wild strength. Its gaze is alert and sharp. The background reveals steep alpine terrain: jagged peaks, sparse low vegetation, and abundant sunlightโconveying the harsh yet majestic wilderness and the animalโs resilient vitality.
Improved Text Rendering
Qwen-Image-2512 further elevates text renderingโalready a strength of the originalโby improving accuracy, layout, and multimodal integration.
For instance, this prompt requests a complete PPT slide illustrating Qwen-Imageโs development roadmap (generation and editing tracks):
่ฟๆฏไธๅผ ็ฐไปฃ้ฃๆ ผ็็งๆๆๅนป็ฏ็๏ผๆดไฝ้็จๆทฑ่่ฒๆธๅ่ๆฏใๆ ้ขๆฏโQwen-Imageๅๅฑๅ็จโใไธๆนไธๆกๆฐดๅนณๅปถไผธ็ๅๅ ๆถ้ด่ฝด๏ผ่ฝด็บฟไธญ้ดๅ็โ็ๅพ่ทฏ็บฟโใ็ฑๅทฆไพงๆทก่่ฒๆธๅไธบๅณไพงๆทฑ็ดซ่ฒ๏ผๅนถไปฅ็ฒพ่ด็็ฎญๅคดๆถๅฐพใๆถ้ด่ฝดไธๆฏไธช่็น้่ฟ่็บฟ่ฟๆฅ่ณไธๆน้็ฎ็่่ฒๅ่ง็ฉๅฝขๆฅๆๆ ็ญพ๏ผๆ ็ญพๅ ไธบๆธ ๆฐ็ฝ่ฒๅญไฝ๏ผไปๅทฆๅๅณไพๆฌกๅ็๏ผโ2025ๅนด5ๆ6ๆฅ Qwen-Image ้กน็ฎๅฏๅจโโ2025ๅนด8ๆ4ๆฅ Qwen-Image ๅผๆบๅๅธโโ2025ๅนด12ๆ31ๆฅ Qwen-Image-2512 ๅผๆบๅๅธโ ๏ผๅจๅดๅ ๆๆพ่๏ผๅจไธๆนไธๆกๆฐดๅนณๅปถไผธ็ๅๅ ๆถ้ด่ฝด๏ผ่ฝด็บฟไธญ้ดๅ็โ็ผ่พ่ทฏ็บฟโใ็ฑๅทฆไพงๆทก่่ฒๆธๅไธบๅณไพงๆทฑ็ดซ่ฒ๏ผๅนถไปฅ็ฒพ่ด็็ฎญๅคดๆถๅฐพใๆถ้ด่ฝดไธๆฏไธช่็น้่ฟ่็บฟ่ฟๆฅ่ณไธๆน้็ฎ็่่ฒๅ่ง็ฉๅฝขๆฅๆๆ ็ญพ๏ผๆ ็ญพๅ ไธบๆธ ๆฐ็ฝ่ฒๅญไฝ๏ผไปๅทฆๅๅณไพๆฌกๅ็๏ผโ2025ๅนด8ๆ18ๆฅ Qwen-Image-Edit ๅผๆบๅๅธโโ2025ๅนด9ๆ22ๆฅ Qwen-Image-Edit-2509 ๅผๆบๅๅธโโ2025ๅนด12ๆ19ๆฅ Qwen-Image-Layered ๅผๆบๅๅธโโ2025ๅนด12ๆ23ๆฅ Qwen-Image-Edit-2511 ๅผๆบๅๅธโ
We can even generate a before-and-after comparison slide to highlight the leap from โAI-blurryโ to โphotorealisticโ:
่ฟๆฏไธๅผ ็ฐไปฃ้ฃๆ ผ็็งๆๆๅนป็ฏ็๏ผๆดไฝ้็จๆทฑ่่ฒๆธๅ่ๆฏใ้กถ้จไธญๅคฎไธบ็ฝ่ฒๆ ่กฌ็บฟ็ฒไฝๅคงๅญๆ ้ขโQwen-Image-2512้็ฃ ๅๅธโใ็ป้ขไธปไฝไธบๆจชๅๅฏนๆฏๅพ๏ผ่ง่ง็ฆ็น้ไธญไบไธญ้ด็ๅ็บงๅฏนๆฏๅบๅใๅทฆไพงไธบ้ข้จๅ ๆปๆฒกๆไปปไฝ็ป่็ๅฅณๆงไบบๅ๏ผ่ดจๆๅทฎ๏ผๅณไพงไธบ้ซๅบฆๅๅฎ็ๅนด่ฝปๅฅณๆง่ๅ๏ผ็ฎ่คๅ็ฐ็ๅฎๆฏๅญ็บน็ไธ็ปๅพฎๅ ๅฝฑๅๅ๏ผๅไธๆ นๆ นๅๆ๏ผ็ผ็ธ้ไบฎ๏ผ่กจๆ ่ช็ถ๏ผๆดไฝ่ดจๆๆฅ่ฟๅๅฎๆๅฝฑใไธคๅพๅไน้ดไปฅไธไธช็ปฟ่ฒๆต็บฟๅ็ฎญๅคด้พๆฅใ้ ๅ็งๆๆๅ่ถณ๏ผไธญ้จๆ ๆณจโ2512่ดจๆๅ็บงโ๏ผไฝฟ็จ็ฝ่ฒๅ ็ฒๅญไฝ๏ผๅฑ ไธญๆพ็คบใ็ฎญๅคดไธคไพงๆๅพฎๅผฑๅ ๆๆๆ๏ผๅขๅผบๅจๆๆใๅจๅพๅไธๆน๏ผไปฅ็ฝ่ฒๆๅญๅ็ฐไธ่ก่ฏดๆ๏ผโโ ๆด็ๅฎ็ไบบ็ฉ่ดจๆใๅคงๅน ๅบฆ้ไฝไบ็ๆๅพ็็AIๆ๏ผๆๅไบๅพๅ็ๅฎๆง โ ๆด็ป่ ป็่ช็ถ็บน็ใๅคงๅน ๅบฆๆๅไบ็ๆๅพ็็็บน็็ป่ใ้ฃๆฏๅพ๏ผๅจ็ฉๆฏๅๅป็ปๆด็ป่ ปใโ ๆดๅคๆ็ๆๅญๆธฒๆใๅคงๅน ๆๅไบๆๅญๆธฒๆ็่ดจ้ใๅพๆๆททๅๆธฒๆๆดๅ็กฎ๏ผๆ็ๆดๅฅฝโ
A more complex infographic example:
่ฟๆฏไธๅน ไธไธ็บงๅทฅไธๆๆฏไฟกๆฏๅพ่กจ๏ผๆดไฝ้็จๆทฑ่่ฒ็งๆๆ่ๆฏ๏ผๅ ็บฟๅๅๆๅ๏ผ่ฅ้ ๅบๅท้ใ็ฒพๅ็็ฐไปฃๅทฅไธๆฐๅดใ็ป้ขๅไธบๅทฆๅณไธคๅคงๆฟๅ๏ผๅธๅฑๆธ ๆฐ๏ผ่ง่งๅฑๆฌกๅๆใๅทฆไพงๆฟๅๆ ้ขไธบโๅฎ้ ๅ็็็ฐ่ฑกโ๏ผไปฅๆต ่่ฒๅ่ง็ฉๅฝขๆก็ชๅบๆพ็คบ๏ผๅ ้จๆๅไธไธชๆทฑ่่ฒๆ้ฎๅผๆก็ฎ๏ผ็ฌฌไธไธชๆก็ฎๅฑ็คบไธๅ ๆฃ่ฒ็ฒๆซ็ถๅๆไธๆปด่ฝๆฐดๆปด็ๅพๆ ๏ผๆๅญไธบโๅข่/็ปๅโ๏ผๅ้ข้ ๆ็ปฟ่ฒๅฏน้ฉ๏ผ็ฌฌไบไธชๆก็ฎไธบไธไธช่ฃ ๆ่่ฒๆถฒไฝๅนถๅๅบๆฐๆณก็้ฅๅฝข็ถ๏ผๆๅญไธบโไบง็ๆฐๆณก/็ผบ้ทโ๏ผๅ้ข้ ๆ็ปฟ่ฒๅฏน้ฉ๏ผ็ฌฌไธไธชๆก็ฎไธบไธคไธช็้็้ฝฟ่ฝฎ๏ผๆๅญไธบโ่ฎพๅค่ ่/ๅฌๅๅๅคฑๆดปโ๏ผๅ้ข้ ๆ็ปฟ่ฒๅฏน้ฉใๅณไพงๆฟๅๆ ้ขไธบโใไธไผใๅ็็็ฐ่ฑกโ๏ผไฝฟ็จ็ฑณ้ป่ฒๅ่ง็ฉๅฝขๆกๅ็ฐ๏ผๅ ้จๅไธชๆก็ฎๅ็ฝฎไบๆทฑ็ฐ่ฒ่ๆฏๆนๆกไธญใๅพๆ ๅๅซไธบ๏ผไธ็ป็ฒพๅฏๅฎๅ็้ๅฑ้ฝฟ่ฝฎ๏ผๆๅญไธบโๅๅบๆ็ใๆพ่ๆ้ซใโ๏ผไธๆน่ฆ็้็ฎ็็บข่ฒๅๅท๏ผไธๆๆด้ฝๆๅ็้ๅฑ็ฎกๆ๏ผๆๅญไธบโๆๅๅ ้จใ็ปๅฏนๆ ๆฐๆณก/ๅญ้ใโ๏ผไธๆน่ฆ็้็ฎ็็บข่ฒๅๅท๏ผไธๆกๅๅบ็้ๅฑ้พๆกๆญฃๅจๆฟๅๆๅ๏ผๆๅญไธบโๆๆๅผบๅบฆไธ่ไน ๆงใๅพๅฐๅขๅผบใโ๏ผไธๆน่ฆ็้็ฎ็็บข่ฒๅๅท๏ผไธๅ ่ ่็ๆณๆ๏ผๆๅญไธบโๅ ๅทฅ่ฟ็จใ้ถ่ ่/้ถๅฏๅๅบ้ฃ้ฉใโ๏ผไธๆน่ฆ็้็ฎ็็บข่ฒๅๅทใๅบ้จไธญๅคฎๆไธ่กๅฐๅญๆณจ้๏ผโๆณจ๏ผๆฐดๅ็ๅญๅจ้ๅธธไผๅฏผ่ด่ด้ขๆๅนฒๆฐๆง็็ปๆ๏ผ่้็ๆณๆๅขๅผบ็็ถๆโ๏ผๅญไฝไธบ็ฝ่ฒ๏ผๆธ ๆฐๅฏ่ฏปใๆดไฝ้ฃๆ ผ็ฐไปฃ็ฎ็บฆ๏ผ้ ่ฒๅฏนๆฏๅผบ็๏ผๅพๅฝข็ฌฆๅทๅ็กฎไผ ่พพๆๆฏ้ป่พ๏ผ้ๅ็จไบๅทฅไธๅน่ฎญๆ็งๆฎๆผ็คบๅบๆฏใ
Or even a full educational poster:
่ฟๆฏไธๅน ็ฑๅไบไธชๅๆ ผ็ปๆ็3ร4็ฝๆ ผๅธๅฑ็ๅๅฎๆๅฝฑไฝๅ๏ผๆดไฝๅ็ฐโๅฅๅบท็ไธๅคฉโไธป้ข๏ผ็ป้ข้ฃๆ ผ็ฎๆดๆธ ๆฐ๏ผๆฏไธๅๆ ผ็ฌ็ซๆๆฏๅ็ปไธไบ็ๆดป่ๅฅ็ๅไบ่็ปใ็ฌฌไธ่กๅๅซๆฏโ06:00 ๆจ่ทๅค้่บซไฝโ๏ผ้ข้จ็นๅ๏ผไธไฝๅฅณๆง่บซ็ฉฟ็ฐ่ฒ่ฟๅจๅฅ่ฃ ๏ผ่ๆฏๆฏๅๅ็ๆ้ณไธ่ฑ้็ปฟๆ ๏ผโ06:30 ๅจๆๆไผธๆฟๆดปๅ ณ่โ๏ผๅฅณๆง่บซ็็ไผฝๆๅจ้ณๅฐๅๆจ้ดๆไผธ๏ผ่บซไฝ่ๅฑ๏ผ่ๆฏไธบๆทก็ฒ่ฒๅคฉ็ฉบไธ่ฟๅฑฑ่ฝฎๅป๏ผโ07:30 ๅ่กก่ฅๅ ปๆฉ้คโ๏ผๆกไธๆๆพๅ จ้บฆ้ขๅ ใ็ๆฒนๆๅไธๆฏๆฉๆฑ๏ผๅฅณๆงๅพฎ็ฌ็ๅๅค็จ้ค๏ผโ08:00 ่กฅๆฐดๆถฆ็ฅโ๏ผ้ๆ็ป็ๆฐดๆฏไธญๆตฎๆๆ ๆชฌ็๏ผๅฅณๆงๆๆๆฐดๆฏ่ฝปๅ๏ผ้ณๅ ไปๅทฆไพงๆ็ งๅ ฅๅฎค๏ผๆฏๅฃๆฐด็ ๆป่ฝ๏ผ็ฌฌไบ่กๅๅซๆฏ๏ผโ09:00 ไธๆณจ้ซๆๅทฅไฝโ๏ผๅฅณๆงไธๆณจๆฒๅป้ฎ็๏ผๅฑๅนๆพ็คบ็ฎๆด็้ข๏ผ่บซๆๆพๆไธๆฏๅๅกไธไธ็็ปฟๆค๏ผโ12:00 ้ๅฟ้ ่ฏปๆถๅ โ๏ผๅฅณๆงๅๅจไนฆๆกๅ็ฟป้ ็บธ่ดจไนฆ็ฑ๏ผๅฐ็ฏๆฃๅๆๅ ๏ผไนฆ้กตๆณ้ป๏ผๆๆพๅๆฏ็บข่ถ๏ผโ12:30 ๅๅ่ฝปๆพๆผซๆญฅโ๏ผๅฅณๆงๅจๆ่ซ้ไธๆผซๆญฅ๏ผ่ธ้จ็นๅ๏ผโ15:00 ่ถ้ฆไผดๅๅโ๏ผๅฅณๆง็ซฏ็้ชจ็ท่ถๆฏ็ซๅจ็ช่พน๏ผ็ชๅคๆฏๅๅธ่กๆฏไธ้ฃๅจไบๆต๏ผ่ถ้ฆ่ข ่ข ๏ผ็ฌฌไธ่กๅๅซๆฏ๏ผโ18:00 ่ฟๅจ้ๆพๅๅโ๏ผๅฅ่บซๆฟๅ ๏ผๅฅณๆงๆญฃๅจ็ปไน ็ไผฝ๏ผโ19:00 ็พๅณๆ้คโ๏ผๅฅณๆงๅจๅผๆพๅผๅจๆฟไธญๅ่๏ผ็ งๆฟไธๆ็ช่ไธ้ๆค๏ผ้ ไธญ็ญๆฐๅ่ พ๏ผ็ฏๅ ๆธฉๆ๏ผโ21:00 ๅฅๆณๅฉ็ โ๏ผๅฅณๆง็่ ฟๅๅจๆ่ฝฏๅฐๆฏฏไธๅฅๆณ๏ผๅๆ่ฝปๆพ่ไธ๏ผ้ญ็ฎๅฎ้๏ผโ21:30 ่ฟๅ ฅ็ก็ โ๏ผๅฅณๆง่บบๅจๅบไธไผๆฏใๆดไฝ้็จ่ช็ถๅ ็บฟไธบไธป๏ผ่ฒ่ฐไปฅๆ็ฝไธ็ฑณ็ฐไธบๅบ่ฐ๏ผๅ ๅฝฑๅฑๆฌกๅๆ๏ผ็ป้ขๅ ๆปกๆธฉ้ฆจ็็ๆดปๆฐๆฏไธ่งๅพ็่ๅฅๆใ
These are the core enhancements in this update. We hope you enjoy using Qwen-Image-2512!
Citation
If Qwen-Image-2512 proves helpful in your research, weโd greatly appreciate your citation ๐ :)
@misc{wu2025qwenimagetechnicalreport,
title={Qwen-Image Technical Report},
author={Chenfei Wu and Jiahao Li and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kun Yan and Sheng-ming Yin and Shuai Bai and Xiao Xu and Yilei Chen and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhengyi Wang and An Yang and Bowen Yu and Chen Cheng and Dayiheng Liu and Deqing Li and Hang Zhang and Hao Meng and Hu Wei and Jingyuan Ni and Kai Chen and Kuan Cao and Liang Peng and Lin Qu and Minggang Wu and Peng Wang and Shuting Yu and Tingkun Wen and Wensen Feng and Xiaoxiao Xu and Yi Wang and Yichang Zhang and Yongqiang Zhu and Yujia Wu and Yuxuan Cai and Zenan Liu},
year={2025},
eprint={2508.02324},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.02324},
}
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