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<title>Qwen-Image — text-to-image foundation model</title>
<meta name="description" content="Reference for Qwen-Image, Alibaba's open text-to-image foundation model, with emphasis on in-image text rendering. Includes diffusers and hosted API usage." />
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<span>WaveSpeed AI</span>
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<a class="jump opt" href="#overview">Overview</a>
<a class="jump opt" href="#strengths">What it does well</a>
<a class="jump" href="#run">Run it</a>
<a class="jump opt" href="#local">Local</a>
<a class="jump" href="#resources">Resources</a>
<a href="https://wavespeed.ai/models/wavespeed-ai/qwen-image/text-to-image?utm_source=huggingface&utm_medium=space&utm_campaign=qwen_image" target="_blank" rel="noopener">wavespeed.ai ↗</a>
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<p class="eyebrow">Alibaba Cloud · Qwen Team</p>
<h1>Qwen-Image</h1>
<p class="lede">An open image generation foundation model in the Qwen series. Its distinguishing strength is rendering legible text inside the image — including Chinese — rather than the smeared glyphs most diffusion models produce.</p>
<ul class="meta">
<li><b>Developer</b> Alibaba Cloud Qwen Team</li>
<li><b>Task</b> text-to-image</li>
<li><b>License</b> Apache-2.0</li>
<li><b>Weights</b> open</li>
</ul>
</div>
<section id="overview">
<h2>Overview</h2>
<p class="section-note">Qwen-Image is a general-purpose text-to-image model that also handles editing and several image-understanding tasks. The authors' benchmark summary is reproduced below.</p>
<div class="figure-single">
<figure>
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/merge3.jpg" alt="Grid of Qwen-Image sample outputs" loading="lazy" />
<figcaption>Sample outputs from the official release.</figcaption>
</figure>
</div>
</section>
<section id="bench">
<h2>Reported benchmarks</h2>
<p class="section-note">Published by the Qwen team with the model release. These are the authors' own figures and have not been independently reproduced here.</p>
<div class="figure-single">
<figure>
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/bench.png" alt="Qwen-Image benchmark chart" loading="lazy" />
<figcaption>Benchmark summary from the Qwen-Image model card.</figcaption>
</figure>
</div>
</section>
<section id="strengths">
<h2>What it does well</h2>
<div class="grid">
<div class="card">
<h3>Text inside the image</h3>
<p>Typography is generated as part of the scene, holding layout and letterforms together for both alphabetic scripts and Chinese — the capability the model is built around.</p>
</div>
<div class="card">
<h3>Style range</h3>
<p>Photographic, painterly, anime and flat-design outputs all come from the same checkpoint, steered by prompt rather than by LoRA.</p>
</div>
<div class="card">
<h3>Editing operations</h3>
<p>Style transfer, object insertion and removal, detail enhancement and text replacement inside an existing image.</p>
</div>
<div class="card">
<h3>Understanding tasks</h3>
<p>Detection, segmentation, depth and edge estimation, novel view synthesis and super-resolution, framed as conditional generation.</p>
</div>
</div>
</section>
<section id="showcase">
<h2>Showcase</h2>
<div class="figure-grid">
<figure>
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s1.jpg" alt="Text rendering examples" loading="lazy" />
<figcaption><b>Text rendering.</b> Signage and dense copy integrated into the scene.</figcaption>
</figure>
<figure>
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s2.jpg" alt="Artistic style examples" loading="lazy" />
<figcaption><b>Styles.</b> Photoreal through to illustrative, one checkpoint.</figcaption>
</figure>
<figure>
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s3.jpg" alt="Image editing examples" loading="lazy" />
<figcaption><b>Editing.</b> Insertion, removal and in-image text edits.</figcaption>
</figure>
<figure>
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s4.jpg" alt="Image understanding examples" loading="lazy" />
<figcaption><b>Understanding.</b> Depth, edges, segmentation and view synthesis.</figcaption>
</figure>
</div>
</section>
<section id="run">
<h2>Run it</h2>
<p class="section-note">The hosted endpoint runs the same weights without a local GPU.</p>
<div class="code">
<div class="code-tabs" role="tablist">
<button type="button" role="tab" aria-selected="true" data-panel="run-0">cURL</button>
<button type="button" role="tab" aria-selected="false" data-panel="run-1">Python</button>
<button type="button" role="tab" aria-selected="false" data-panel="run-2">JavaScript</button>
</div>
<pre id="run-0" role="tabpanel"><code># 1. submit the job
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image/text-to-image" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A chalkboard outside a coffee shop reading \"Qwen Coffee \u2014 $2 a cup\", warm morning light, shallow depth of field",
"size": "1328*1328",
"enable_sync_mode": false
}'
# -> {"code": 200, "data": {"id": "<request-id>", "status": "created", ...}}
# 2. poll until status is "completed"
curl "https://api.wavespeed.ai/api/v3/predictions/<request-id>/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# -> {"code": 200, "data": {"status": "completed", "outputs": ["https://..."]}}</code></pre>
<pre id="run-1" role="tabpanel" hidden><code>import os, time, requests
API = "https://api.wavespeed.ai/api/v3"
KEY = os.environ["WAVESPEED_API_KEY"]
HEADERS = {"Authorization": f"Bearer {KEY}"}
# submit
res = requests.post(
f"{API}/wavespeed-ai/qwen-image/text-to-image",
headers={**HEADERS, "Content-Type": "application/json"},
json={
"prompt": "A chalkboard outside a coffee shop reading \"Qwen Coffee \u2014 $2 a cup\", warm morning light, shallow depth of field",
"size": "1328*1328",
"enable_sync_mode": false
},
timeout=30,
)
res.raise_for_status()
request_id = res.json()["data"]["id"]
# poll
while True:
data = requests.get(
f"{API}/predictions/{request_id}/result",
headers=HEADERS,
timeout=30,
).json()["data"]
if data["status"] == "completed":
print(data["outputs"][0])
break
if data["status"] == "failed":
raise RuntimeError(data.get("error", "generation failed"))
time.sleep(1.5)</code></pre>
<pre id="run-2" role="tabpanel" hidden><code>const API = "https://api.wavespeed.ai/api/v3";
const KEY = process.env.WAVESPEED_API_KEY;
const headers = { Authorization: `Bearer ${KEY}` };
// submit
const submit = await fetch(`${API}/wavespeed-ai/qwen-image/text-to-image`, {
method: "POST",
headers: { ...headers, "Content-Type": "application/json" },
body: JSON.stringify({
"prompt": "A chalkboard outside a coffee shop reading \"Qwen Coffee \u2014 $2 a cup\", warm morning light, shallow depth of field",
"size": "1328*1328",
"enable_sync_mode": false
}),
});
const { data: { id } } = await submit.json();
// poll
for (;;) {
const res = await fetch(`${API}/predictions/${id}/result`, { headers });
const { data } = await res.json();
if (data.status === "completed") {
console.log(data.outputs[0]);
break;
}
if (data.status === "failed") throw new Error(data.error ?? "generation failed");
await new Promise((r) => setTimeout(r, 1500));
}</code></pre>
</div>
<div class="callout"><p>Requests are asynchronous: <code>POST</code> returns a request id, then you poll <code>/predictions/<id>/result</code> until <code>status</code> is <code>completed</code>. Set <code>enable_sync_mode: true</code> to have the call block and return outputs directly.</p><p>API keys are created in the <a href="https://wavespeed.ai/dashboard?utm_source=huggingface&utm_medium=space&utm_campaign=qwen_image" target="_blank" rel="noopener">WaveSpeed dashboard</a>.</p></div>
<div class="btn-row">
<a class="btn" href="https://wavespeed.ai/models/wavespeed-ai/qwen-image/text-to-image?utm_source=huggingface&utm_medium=space&utm_campaign=qwen_image" target="_blank" rel="noopener">Open on WaveSpeed</a>
<a class="btn secondary" href="https://wavespeed.ai/docs?utm_source=huggingface&utm_medium=space&utm_campaign=qwen_image" target="_blank" rel="noopener">API reference</a>
</div>
</section>
<section id="local">
<h2>Running locally</h2>
<p class="section-note">Weights are Apache-2.0 and load through <code>diffusers</code>.</p>
<div class="code">
<div class="code-tabs" role="tablist">
<button type="button" role="tab" aria-selected="true" data-panel="local-0">Python</button>
</div>
<pre id="local-0" role="tabpanel"><code>import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"Qwen/Qwen-Image",
torch_dtype=torch.bfloat16,
).to("cuda")
# The Qwen team recommends appending a quality suffix to the prompt.
magic = {"en": "Ultra HD, 4K, cinematic composition.", "zh": "超清，4K，电影级构图"}
image = pipe(
prompt='A chalkboard reading "Qwen Coffee — $2 a cup". ' + magic["en"],
negative_prompt=" ",
width=1664,
height=928, # 1:1 1328x1328 · 16:9 1664x928 · 4:3 1472x1140
num_inference_steps=50,
true_cfg_scale=4.0,
generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]
image.save("out.png")</code></pre>
</div>
</section>
<section id="resources">
<h2>Resources</h2>
<ul class="links">
<li><a href="https://huggingface.co/Qwen/Qwen-Image" target="_blank" rel="noopener"><span>Qwen/Qwen-Image weights</span><span class="host">huggingface.co</span></a></li>
<li><a href="https://github.com/QwenLM/Qwen-Image" target="_blank" rel="noopener"><span>Qwen-Image on GitHub</span><span class="host">github.com</span></a></li>
<li><a href="https://wavespeed.ai/models/wavespeed-ai/qwen-image/text-to-image?utm_source=huggingface&utm_medium=space&utm_campaign=qwen_image" target="_blank" rel="noopener"><span>Hosted endpoint</span><span class="host">wavespeed.ai</span></a></li>
<li><a href="https://huggingface.co/spaces/wavespeed/qwen-edit-image" target="_blank" rel="noopener"><span>Editing variant</span><span class="host">huggingface.co</span></a></li>
</ul>
</section>
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<p>This page is a model reference maintained by WaveSpeed AI. The model itself is developed and released by its respective authors; trademarks belong to them. WaveSpeed AI provides hosted inference for it.</p>
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