Alibaba's Qwen image model
Details
qwen-image
Starting from
Prices shown are in USD · Some prices estimated from per-megapixel or per-token pricing
See all providersProviders & Pricing (3)
Qwen Image is available from 3 providers, with per-image pricing starting at $0.02 through fal.ai.
All modes
fal/qwen-image
replicate/qwen-image
alibaba/qwen-image
Qwen Image API OpenAI-compatible
Integrate Qwen Image into your workflow via Lumenfall’s OpenAI-compatible API to generate high-quality images from text prompts with a single unified integration.
https://api.lumenfall.ai/openai/v1
qwen-image
Code Examples
Text to Image
/v1/images/generationscurl -X POST \
https://api.lumenfall.ai/openai/v1/images/generations \
-H "Authorization: Bearer $LUMENFALL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-image",
"prompt": "",
"size": "1024x1024"
}'
# Response:
# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: 'YOUR_API_KEY',
baseURL: 'https://api.lumenfall.ai/openai/v1'
});
const response = await client.images.generate({
model: 'qwen-image',
prompt: '',
size: '1024x1024'
});
// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }
console.log(response.data[0].url);
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.lumenfall.ai/openai/v1"
)
response = client.images.generate(
model="qwen-image",
prompt="",
size="1024x1024"
)
# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }
print(response.data[0].url)
Image Edit
/v1/images/editsParameter Reference
Core Parameters
| Parameter | Type | Description | Modes |
|---|---|---|---|
prompt
|
string | Required. Text prompt for image generation |
T2I
Edit
|
Size & Layout
| Parameter | Type | Description | Modes |
|---|---|---|---|
size
|
string |
Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9")
WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.
|
T2I
Edit
|
aspect_ratio
|
string |
Aspect ratio of the output image (e.g. "16:9", "1:1")
Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.
|
T2I
Edit
|
resolution
|
string |
Output resolution tier (e.g. "1K", "4K")
Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.
|
T2I
Edit
|
size
Exact pixel dimensions
"1920x1080"
aspect_ratio
Shape only, default scale
"16:9"
resolution
Scale tier, preserves shape
"1K"
Priority when combined
size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.
How matching works
7:1 on a model with
4:1 and 8:1,
you get 8:1.
0.5K 1K 2K 4K)
or megapixel tiers (0.25 1).
If the exact tier isn't available, you get the nearest one.
Media Inputs
| Parameter | Type | Description | Modes |
|---|---|---|---|
image
|
file |
Required.
Input image(s) to edit
Supports PNG, JPEG, WebP.
|
T2I
Edit
|
Output & Format
| Parameter | Type | Description | Modes |
|---|---|---|---|
response_format
|
string |
How to return the image
url
b64_json
Default:
"url" |
T2I
Edit
|
output_format
|
string |
Output image format
png
jpeg
gif
webp
avif
Gateway converts to requested format if provider doesn't support it natively.
|
T2I
Edit
|
output_compression
|
integer | Compression level for lossy formats (JPEG, WebP, AVIF) |
T2I
Edit
|
n
|
integer |
Number of images to generate
Default:
1Gateway generates multiple images in parallel even if provider only supports 1.
|
T2I
Edit
|
Parameter Normalization
How we handle parameters across different providers
Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:
| Behavior | What happens | Example |
|---|---|---|
passthrough |
Sent as-is to the provider | style, quality |
renamed |
Same value, mapped to the field name the provider expects | prompt |
converted |
Transformed to the provider's native format | size |
emulated |
Works even if the provider has no concept of it | n, response_format |
Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.
Gallery
View all 4 imagesQwen Image FAQ
How much does Qwen Image cost?
Qwen Image starts at $0.02 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.
How do I use Qwen Image via API?
You can use Qwen Image through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "qwen-image". Code examples are available in Python, JavaScript, and cURL.
Which providers offer Qwen Image?
Qwen Image is available through fal.ai, Replicate, and Alibaba Cloud on Lumenfall. Lumenfall automatically routes requests to the best available provider.
Overview
Qwen Image is a text-to-image generation model developed by Alibaba Cloud’s Qwen team. It serves as the visual synthesis component of the broader Qwen ecosystem, designed to transform natural language prompts into high-fidelity imagery. The model is distinguished by its strong alignment with complex linguistic instructions and its ability to handle both English and Chinese prompts with high semantic accuracy.
Strengths
- Multilingual Prompt Comprehension: The model demonstrates superior performance in processing Chinese-language prompts, accurately capturing cultural nuances and idioms that Western-centric models often misinterpret.
- Compositional Accuracy: It excels at spatial reasoning and multi-object placement, ensuring that elements described in a prompt maintain the correct relationship to one another.
- Text Rendering: Qwen Image shows higher-than-average stability when generating legible text within images, such as signage, labels, or posters, reducing the common “gibberish” artifacts found in earlier diffusion models.
- Fine-Grained Detail: The model is optimized for high-resolution output with a focus on realistic textures, particularly in skin tones, fabric weaves, and architectural materials.
Limitations
- Anatomical Consistency: Like many diffusion-based models, it can occasionally struggle with complex human anatomy, such as the specific number of digits on hands or complex overlapping limbs in action shots.
- Stylistic Range: While versatile, the model tends toward a “digital photography” or “clean 3D render” aesthetic by default; achieving hyper-abstract or specific traditional art styles may require more intensive prompt engineering compared to models like Midjourney.
Technical Background
Qwen Image belongs to the Qwen family of models, leveraging a large-scale diffusion transformer architecture tailored for high-dimensional visual synthesis. The training process involves a multi-stage pipeline that utilizes high-quality captioned image datasets, with a specific focus on cross-modal alignment between the Qwen LLM’s text embeddings and the visual latent space. This allows the model to inherit the deep semantic understanding found in Alibaba’s flagship language models.
Best For
Qwen Image is particularly effective for marketing localization projects involving Chinese text, technical illustrations requiring precise object placement, and general-purpose asset generation for web and mobile interfaces. Its price point of $0.02 makes it a cost-effective choice for developers building high-volume image generation workflows.
Qwen Image is available for immediate deployment and testing through Lumenfall’s unified API and playground, allowing you to integrate its generative capabilities into your applications with minimal setup.
Try Qwen Image in Playground
Generate images with custom prompts — no API key needed.