kwaivgi/kolors-tryon

Name: Kling Kolors Virtual TryOn v1.5
Author: KwaiVGI

Kolors Tryon by KwaiVGI - AI-powered image editing, style transfer, and transformation. Edit photos with natural language instructions, remove backgrounds, change styles, and enhance images effortlessly.

Input

Image URL or upload(string)

Upload

PNG, JPEG, WebP, or GIF · 20 MiB maximum

Url to the garment image.

Image URL or upload(string)

Upload

PNG, JPEG, WebP, or GIF · 20 MiB maximum

Url for the human image.

Idle

Example output — click Run to generate your own

Production endpoint

Send your first request

OpenAI-compatible endpoint with unified authentication and usage tracking.

POSThttps://api.sandbase.ai/v1/run

Model IDkwaivgi/kolors-tryon

Input Schema

2 parameters · 0 required · 2 optional

Parameter	Type	Required	Description
`human_image_url`	string	Optional	Url for the human image.
`garment_image_url`	string	Optional	Url to the garment image.

Output Schema

Field	Type	Description
`id`	string	Unique identifier for the generation task
`status`	string	Task status: pending, running, completed, failed, timeout
`model`	string	Model used for the generation
`outputs`	array	Array of output items
`outputs[].url`	string	URL of the generated artifact
`outputs[].content_type`	string	MIME type (e.g. image/png, video/mp4)
`error`	object \| null	Error details if failed, null on success
`error.type`	string	Machine-readable error type code
`error.message`	string	Human-readable error description

Async Workflow

This model uses asynchronous execution. Submit a request and poll for the result.

Submit — POST to /v1/run, receive an id
Poll — GET /v1/run/{id} until status is completed, failed, or timeout
Retrieve — Read outputs from the completed response

Code Examples

Ready-to-run snippets

# Step 1: Submit
curl -X POST https://api.sandbase.ai/v1/run \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
  "model": "kwaivgi/kolors-tryon",
  "human_image_url": "https://static.sandbase.ai/examples/kwaivgi/kolors-tryon/input_human_image_url_0.jpg",
  "garment_image_url": "https://static.sandbase.ai/examples/kwaivgi/kolors-tryon/input_garment_image_url_1.jpg",
  "prompt": "a beautiful sunset over mountains"
}'

# Step 2: Poll result (replace <id>)
curl https://api.sandbase.ai/v1/run/<id> \
  -H "Authorization: Bearer YOUR_API_KEY"

← Back to Kling Kolors Virtual TryOn v1.5

API README

Kling Kolors Virtual TryOn v1.5

Kling Kolors Virtual TryOn v1.5 is a virtual garment try-on endpoint in the Kolors family. It is built for creators who need to turn a concrete creative brief into a controlled visual sequence: the request establishes the source material, the intended subject behavior, the camera language, and the atmosphere of the finished shot. The standard route keeps that workflow explicit instead of hiding its input assumptions behind a generic video-generation label.

In practice, this route accepts a written prompt as creative context and exposes focused generation controls for delivery planning. That makes it suitable for shot-based pipelines where teams must preserve a source, direct a transformation, or control the final format without losing sight of the model's central task. Write prompts as a compact shot plan—subject, action, setting, camera, light, and timing—then use the structured fields for constraints that should remain deterministic across iterations.

Highlights

Garment transfer. Combines a person photograph and a garment photograph to visualize the clothing on the subject.
Identity-preserving input structure. Separate human and garment inputs keep the wearer and apparel roles explicit.
Commerce-ready previews. Useful for quickly producing wardrobe previews without staging a new photo shoot.
Minimal request surface. The focused two-image workflow avoids unrelated generation controls.

Pricing

Billing unit	Price
Base request	`$0.070000`

When to Use

Scenario	Recommendation
Choose this route	Use it when the deliverable specifically calls for virtual garment try-on, rather than a neighboring generation mode.
Prepare the source	Provide a precise written brief in the format described by the request schema.
Direct the shot	Describe the subject, action, environment, camera movement, lighting, and temporal progression in that order.
Control continuity	Use endpoint frames, reference media, strength, or audio controls when those fields are available instead of burying hard constraints in prose.
Plan delivery	Set duration, frame count, resolution, and aspect ratio explicitly when the schema exposes them, then compare iterations with a stable seed where supported.

Prompt Guide

For virtual garment try-on, describe one coherent shot rather than a list of visual keywords. Put the main subject and action first, follow with location and staging, then add camera movement, lens or framing, lighting, mood, and any timed change. Keep URLs and hard delivery choices in their dedicated fields.

{
  "human_image_url": "https://static.sandbase.ai/examples/kwaivgi/kolors-tryon/input_human_image_url_0.jpg",
  "garment_image_url": "https://static.sandbase.ai/examples/kwaivgi/kolors-tryon/input_garment_image_url_1.jpg"
}

Technical Specs

Specification	Value
Model ID	`kwaivgi/kolors-tryon`
Required inputs	None declared
Execution	Asynchronous generation job
Request controls	2 documented fields
Output	`url`, `content_type`

Request fields

Field	Type and constraints
`human_image_url`	string; Optional
`garment_image_url`	string; Optional