Alibaba's Qwen image editing model for instruction-based image modifications and transformations
Example outputs coming soon
Details
qwen-image-edit-2509
Starting from
Prices shown are in USD · Some prices estimated from per-megapixel or per-token pricing
Full pricing detailsProviders & Pricing (1)
Qwen Image Edit 2509 is available exclusively through fal.ai, starting at $0.03/image.
fal/qwen-image-edit-2509
qwen-image-edit-plus-2509 API OpenAI-compatible
Integrate Qwen Image Edit 2509 into workflows via Lumenfall's OpenAI-compatible API to perform text-to-image generation and complex image transformations using natural language instructions.
https://api.lumenfall.ai/openai/v1
qwen-image-edit-2509
Code Examples
Image Edit
/v1/images/editscurl -X POST \ https://api.lumenfall.ai/openai/v1/images/edits \ -H "Authorization: Bearer $LUMENFALL_API_KEY" \ -F "model=qwen-image-edit-2509" \ -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-edit-2509', 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 OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.lumenfall.ai/openai/v1")response = client.images.edit( model="qwen-image-edit-2509", 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
Core Parameters
| Parameter | Type | Description | Modes |
|---|---|---|---|
prompt
|
string | Required. Edit instruction for the image |
Edit
|
negative_prompt
|
string | Negative prompt to guide generation away from undesired content |
Edit
|
seed
|
integer | Random seed for reproducibility |
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.
|
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.
|
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.
|
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.
|
Edit
|
Output & Format
| Parameter | Type | Description | Modes |
|---|---|---|---|
response_format
|
string |
How to return the image
url
b64_json
Default:
"url" |
Edit
|
output_format
|
string |
Output image format
png
jpeg
gif
webp
avif
Gateway converts to requested format if provider doesn't support it natively.
|
Edit
|
output_compression
|
integer | Compression level for lossy formats (JPEG, WebP, AVIF) |
Edit
|
n
|
integer |
Number of images to generate
Default:
1Gateway generates multiple images in parallel even if provider only supports 1.
|
Edit
|
Additional Parameters
| Parameter | Type | Description | Modes |
|---|---|---|---|
cfg_scale
|
number | Classifier-free guidance scale — higher values stick more closely to the prompt |
Edit
|
acceleration
fal
|
string |
Acceleration level for image generation. Options: 'none', 'regular'. Higher acceleration increases speed. 'regular' balances speed and quality.
none
regular
|
Edit
|
enable_safety_checker
fal
|
boolean | If set to true, the safety checker will be enabled. |
Edit
|
num_inference_steps
fal
|
integer | The number of inference steps to perform. |
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. |
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 1 imagesQwen Image Edit 2509 FAQ
How much does Qwen Image Edit 2509 cost?
Qwen Image Edit 2509 starts at $0.03 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.
How do I use Qwen Image Edit 2509 via API?
You can use Qwen Image Edit 2509 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "qwen-image-edit-2509". Code examples are available in Python, JavaScript, and cURL.
Which providers offer Qwen Image Edit 2509?
Qwen Image Edit 2509 is available through fal.ai on Lumenfall. Lumenfall automatically routes requests to the best available provider.
Overview
Qwen Image Edit 2509 is a specialized vision-language model developed by Alibaba designed for instruction-based image manipulation. Unlike standard text-to-image generators, this model accepts both an initial image and a natural language prompt to perform targeted modifications and transformations. It is distinctive for its ability to interpret complex editing instructions while maintaining the structural integrity of the original source image.
Strengths
- Instruction Adherence: The model accurately maps natural language verbs and nouns to visual changes, such as “change the color of the shirt” or “add a sunset to the background.”
- Contextual Consistency: It excels at preserving the identity and spatial layout of primary subjects while altering specific attributes or environmental elements.
- Zero-shot Composition: The model handles various editing tasks—including stylization, object insertion, and attribute modification—without requiring mask-based inputs or fine-tuning for specific styles.
- Complex Transformation: Beyond simple filters, it can handle structural transformations such as changing a character’s pose or modifying the lighting conditions of a scene based on text descriptions.
Limitations
- High-Detail Text Rendering: Like many diffusion-based or vision-language architectures, it may struggle with rendering precise, small-scale legible text within an edited image.
- Large-Scale Compositional Overhauls: While it handles local edits and style transfers well, it may produce artifacts if the prompt asks for a complete reimagining that contradicts the fundamental geometry of the source image.
- Anatomical Accuracy: In complex edits involving human figures, there is a risk of generating anatomical inconsistencies, particularly in hands or overlapping limbs.
Technical Background
Developed as part of the Qwen model family, Qwen Image Edit 2509 utilizes a vision-encoder paired with a generative backbone trained on large-scale paired datasets of images and their corresponding edit instructions. The architecture focuses on cross-modal alignment, ensuring that the text embeddings effectively guide the latent representation of the source image during the denoising or reconstruction process. This approach prioritizes semantic understanding of the “before” and “after” relationship described in the prompt.
Best For
Qwen Image Edit 2509 is best suited for workflows requiring rapid prototyping of visual concepts, such as changing product backgrounds, adjusting fashion photography attributes, or iterative character design. It is an excellent choice for developers building creative tools that require “natural language” control over existing visual assets rather than generating images from scratch.
This model is available through Lumenfall’s unified API and playground, allowing for easy integration into multi-model pipelines alongside text and vision-analysis models.
Try Qwen Image Edit 2509 in Playground
Generate images with custom prompts — no API key needed.