fal-ai/kling/v1-5/kolors-virtual-try-on

Kolors Virtual Try On - Free Change Clothes with AI Online | Kling v1.5

Virtual try-on model for fashion e-commerce. Composites a garment onto a person in a photo using Kling Kolors v1.5.

Endpoint

fal-ai/kling/v1-5/kolors-virtual-try-on

Cost: $0.07 / generation
Best for: Garment-on-model compositing, fashion e-commerce

About the Model

Kling Kolors Virtual Try-On v1.5 is a commercial-grade virtual try-on model developed by Kuaishou as part of the Kling image generation suite. It uses diffusion-based inpainting to realistically composite a garment onto a person: preserving the model's pose, skin tone, and body shape while accurately rendering fabric texture, color, and fit.

The model is image-in, image-out. It takes a photo of a person and a photo of a garment, and returns a single composited result. It is optimized for fashion e-commerce use cases and is cleared for commercial use.

Kolors refers to the underlying image generation backbone, which is Kuaishou's proprietary text-to-image model trained on a large Chinese and multilingual dataset. In the context of virtual try-on, the Kolors backbone handles perceptual quality and photorealism, while the Kling v1.5 wrapper handles garment alignment and pose conditioning.

Quick Start

JavaScript / Node.js

bash
npm install --save @fal-ai/client

bash
export FAL_KEY="YOUR_API_KEY"

javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/kling/v1-5/kolors-virtual-try-on", {
  input: {
    human_image_url: "https://example.com/person.jpg",
    garment_image_url: "https://example.com/shirt.jpg"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

console.log(result.data.image.url); // output image URL

Python

bash
pip install fal-client

bash
export FAL_KEY="YOUR_API_KEY"

python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
            print(log["message"])

result = fal_client.subscribe(
    "fal-ai/kling/v1-5/kolors-virtual-try-on",
    arguments={
        "human_image_url": "https://example.com/person.jpg",
        "garment_image_url": "https://example.com/shirt.jpg"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)

print(result["image"]["url"])  # output image URL

Schema

Input

json
{
  "human_image_url": "https://example.com/person.jpg",
  "garment_image_url": "https://example.com/shirt.jpg"
}

Output

json
{
  "image": {
    "url": "https://v3.fal.media/files/panda/Hoy3zhimzVKi3F2uoGBnh_result.png",
    "width": 768,
    "height": 1024,
    "content_type": "image/png",
    "file_name": "result.png",
    "file_size": 595094
  }
}

Note: The output is a single image object, not an array. Access the URL at `result["image"]["url"]` (Python) or `result.data.image.url` (JS), not `result[0]`.

Strengths and Limitations

Works well with:

• Upper body garments (shirts, jackets, tops)
• Simple silhouettes
• Front-facing models

Known limitations:

• Struggles with garment type switching (e.g. swapping a skirt for pants)
• Complex layering may not render accurately

Input Image Tips

Person photo:

• Front-facing pose
• Full or upper-body visibility
• Minimal occlusion (arms not crossed, etc.)

Garment photo:

• Flat lay or model shot with clean edges
• White or plain background preferred
• Avoid heavy shadows or folds

File Handling

You can pass images as public URLs or upload them via the client:

javascript
// Upload a local file and use the returned URL
const file = new File([fileData], "garment.jpg", { type: "image/jpeg" });
const url = await fal.storage.upload(file);

python
url = fal_client.upload_file("path/to/garment.jpg")

Base64 data URIs are also accepted, though they may slow down large requests.

Queue (Long-running Requests)

For async usage, submit to the queue and poll for results:

javascript
const { request_id } = await fal.queue.submit("fal-ai/kling/v1-5/kolors-virtual-try-on", {
  input: { human_image_url: "...", garment_image_url: "..." },
  webhookUrl: "https://your-webhook.url/results", // optional
});

const status = await fal.queue.status("fal-ai/kling/v1-5/kolors-virtual-try-on", {
  requestId: request_id,
  logs: true,
});

const result = await fal.queue.result("fal-ai/kling/v1-5/kolors-virtual-try-on", {
  requestId: request_id,
});

Related Endpoints

If this model doesn't fit your use case, consider:

• `fal-ai/leffa/virtual-tryon`
• `fal-ai/image-apps-v2/virtual-try-on`