Alibaba

llms.txt

Qwen Image

Name: Qwen Image
Brand: Alibaba
Price: 0.02 USD
Availability: InStock

AI Image Generation Model

Image

Alibaba's Qwen image model

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Supported Modes

Text to Image Image Edit

Active

Details

Model ID

qwen-image

Creator

Alibaba

Family

qwen

Released

August 2025

Ready to integrate?

Access qwen-image via our unified API.

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Available at 3 providers

Starting from

$0.020 /image via fal.ai · +2 more

Prices shown are in USD · Some prices estimated from per-megapixel or per-token pricing

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Providers & Pricing (3)

Qwen Image is available from 3 providers, with per-image pricing starting at $0.02 through fal.ai.

fal.ai

Text to Image

fal/qwen-image

Provider Model ID: fal-ai/qwen-image

$0.020 /megapixel

View pricing details

Replicate

Text to Image Image Edit

replicate/qwen-image

Provider Model ID: qwen/qwen-image

$0.025 /image

View pricing details

Alibaba Cloud

Text to Image

alibaba/qwen-image

Provider Model ID: qwen-image-plus

$0.030 /image

View pricing details

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.

Base URL

https://api.lumenfall.ai/openai/v1

Model

qwen-image

Code Examples

Text to Image

/v1/images/generations

curl -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/edits

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/edits \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -F "model=qwen-image" \

  -F "[email protected]" \

  -F "prompt=Add a starry night sky to this image" \

  -F "size=1024x1024"

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

import fs from 'fs';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.edit({

  model: 'qwen-image',

  image: fs.createReadStream('source.png'),

  prompt: 'Add a starry night sky to this image',

  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.edit(

    model="qwen-image",

    image=open("source.png", "rb"),

    prompt="Add a starry night sky to this image",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Parameter Reference

Required Supported Not available

Core Parameters

Parameter	Type	Description	Modes
`prompt`	string	Required. Text prompt for image generation	T2I Edit
`negative_prompt`	string	Negative prompt to guide generation away from undesired content	T2I Edit
`seed`	integer	Random seed for reproducibility	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") `1365x768` `768x1365` `1254x836` `836x1254` `887x1182` `1024x1024` `1183x887` 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") `9:16` `2:3` `3:4` `1:1` `4:3` `3:2` `16:9` 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") `1K` 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

Output	`size`		`aspect_ratio` + `resolution`
Flexible
Custom 1–14142px per side	`"WxH"`	—	Any pixel dimensions within model constraints

1K 7 sizes

Output	`size`		`aspect_ratio` + `resolution`
1183 × 887	`"1183x887"`	or	`"4:3"` + `"1K"`
1024 × 1024	`"1024x1024"`	or	`"1:1"` + `"1K"`
887 × 1182	`"887x1182"`	or	`"3:4"` + `"1K"`
836 × 1254	`"836x1254"`	or	`"2:3"` + `"1K"`
1254 × 836	`"1254x836"`	or	`"3:2"` + `"1K"`
768 × 1365	`"768x1365"`	or	`"9:16"` + `"1K"`
1365 × 768	`"1365x768"`	or	`"16:9"` + `"1K"`

How these parameters work

size

Exact pixel dimensions

"1920x1080"

aspect_ratio

Shape only, default scale

"16:9"

resolution

Scale tier, preserves shape

"1K"

Priority when combined

size › aspect_ratio + resolution › aspect_ratio › resolution

size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.

How matching works

Shape matching – we pick the closest supported ratio. Ask for 7:1 on a model with 4:1 and 8:1, you get 8:1.

Scale matching – providers use different tier formats: K tiers (0.5K 1K 2K 4K) or megapixel tiers (0.25 1). If the exact tier isn't available, you get the nearest one.

Dimension clamping – if a model has pixel limits, we clamp dimensions to fit and keep the aspect ratio intact.

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: `1` Gateway generates multiple images in parallel even if provider only supports 1.	T2I Edit

Additional Parameters

Parameter	Type	Description	Modes
`cfg_scale`	number	Classifier-free guidance scale — higher values stick more closely to the prompt	T2I Edit
`strength`	number	How much to transform the input image: 0 keeps it unchanged, 1 fully regenerates from the prompt	T2I Edit
`acceleration` fal	string	Acceleration level for image generation. Options: 'none', 'regular', 'high'. Higher acceleration increases speed. 'regular' balances speed and quality. 'high' is recommended for images without text. `high` `none` `regular`	T2I Edit
`disable_safety_checker` replicate	boolean	Disable safety checker for generated images.	T2I Edit
`enable_safety_checker` fal	boolean	If set to true, the safety checker will be enabled.	T2I Edit
`enhance_prompt` replicate	boolean	Enhance the prompt with positive magic.	T2I Edit
`extra_lora_scale` replicate	array	Scales for additional LoRAs as an array of numbers (e.g., 0.5, 0.7). Must match the number of weights in extra_lora_weights.	T2I Edit
`extra_lora_weights` replicate	array	Additional LoRA weights as an array of URLs. Same formats supported as lora_weights (e.g., ['https://huggingface.co/flymy-ai/qwen-image-lora/resolve/main/pytorch_lora_weights.safetensors', 'https://huggingface.co/flymy-ai/qwen-image-realism-lora/resolve/main/flymy_realism.safetensors'])	T2I Edit
`go_fast` replicate	boolean	Run faster predictions with additional optimizations.	T2I Edit
`image_size` replicate	string	Image size for the generated image `optimize_for_quality` `optimize_for_speed`	T2I Edit
`lora_scale` replicate	number	Determines how strongly the main LoRA should be applied.	T2I Edit
`lora_weights` replicate	string	Load LoRA weights. Only works with text to image pipeline. Supports arbitrary .safetensors URLs, tar files, and zip files from the Internet (for example, 'https://huggingface.co/flymy-ai/qwen-image-lora/resolve/main/pytorch_lora_weights.safetensors', 'https://example.com/lora_weights.tar.gz', or 'https://example.com/lora_weights.zip')	T2I Edit
`loras` fal	array	The LoRAs to use for the image generation. You can use up to 3 LoRAs and they will be merged together to generate the final image.	T2I Edit
`num_inference_steps`	integer	The number of inference steps to perform.	T2I Edit
`output_quality` replicate	integer	Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs	T2I Edit
`replicate_weights` replicate	string	Load LoRA weights from Replicate training. Only works with text to image pipeline. Supports arbitrary .safetensors URLs, tar files, and zip files from the Internet.	T2I Edit
`sync_mode` fal	boolean	If `True`, the media will be returned as a data URI and the output data won't be available in the request history.	T2I Edit
`use_turbo` fal	boolean	Enable turbo mode for faster generation with high quality. When enabled, uses optimized settings (10 steps, CFG=1.2).	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.

Full API Reference Authentication, endpoints, and more

Gallery

View all 4 images

Qwen 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.

Open Playground