# Kling Image V1.5

> Run Kling Image V1.5 by klingai online or via API. $0.014/generation, $0.028/generation with image reference, no setup.

**URL:** https://inference.sh/apps/klingai/image-v1-5
**Last updated:** 2026-08-07

---

Kling Image V1.5 (Kolors V1.5) - text-to-image with subject and face reference for character consistency. Generate images preserving a person's appearance.

- app: `klingai/image-v1-5`
- maker: [klingai](https://inference.sh/apps/klingai.md)
- category: [image](https://inference.sh/apps/category/image.md)
- input → output: text → image
- tasks: [text to image](https://inference.sh/apps/tag/text-to-image.md)
- price: $0.014/generation, $0.028/generation with image reference
- runs via: api, javascript and python sdk, belt cli, mcp

## image-v1-5 API Guide

### About
kling image v1.5 (kolors v1.5) - text-to-image with subject and face reference for character consistency. generate images preserving a person's appearance.

### Get an API Key
Create one in [settings → api keys](https://app.inference.sh/settings/keys). The examples read it from `INFERENCE_API_KEY`.

```bash
export INFERENCE_API_KEY="inf_your_key"
```

### 1. Calling the API

#### Install the Client

```bash
pip install inferencesh
```

#### Run and Get Result
Submit a request and wait for the final result.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @5qh1j6ee pinned
result = client.run({
        "app": "klingai/image-v1-5@5qh1j6ee",
        "input": {
            "prompt": "prompt"
        }
    })

print(result["output"])
```

#### Stream Live Updates
Get real-time progress updates as the task runs.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @5qh1j6ee pinned
## stream=True yields updates as they arrive
for update in client.run({
        "app": "klingai/image-v1-5@5qh1j6ee",
        "input": {
            "prompt": "prompt"
        }
    }, stream=True):
    if update.get("progress"):
        print(f"progress: {update['progress']}%")
    if update.get("output"):
        print(f"output: {update['output']}")
```

#### Run in the Background
Start the task and get its id back right away, then check on it with `GET /tasks/{id}`. It is done when `status` is 10 (completed), 11 (failed) or 12 (cancelled).

```python
import os
from inferencesh import inference, TaskStatus

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## returns as soon as the task is created
task = client.run({
        "app": "klingai/image-v1-5@5qh1j6ee",
        "input": {
            "prompt": "prompt"
        }
    }, wait=False)

## later: read its current state
state = client.tasks.get(task["id"]).data
if state["status"] == TaskStatus.COMPLETED:
    print(state["output"])
```

### 2. Files

#### Automatic Upload

```python
## local file paths are automatically uploaded
result = client.run({
    "app": "klingai/image-v1-5@5qh1j6ee",
    "input": {
        "image": "/path/to/local/image.png",  # detected & uploaded
        "audio": "https://example.com/audio.mp3",  # url passed through
    }
})
```

#### Manual Upload

```python
## upload and get a hosted URL
file = client.files.upload("/path/to/file.png")
print(file.uri)  # https://cloud.inference.sh/...
```

### 3. Schema

#### Input
- **prompt** (string) *required*: Text description. Max 2500 chars.
- **image** (string): Reference image for subject/face consistency.
- **image_reference** (string): Reference type: 'subject' for character features, 'face' for facial appearance.
- **image_fidelity** (number): Reference strength [0-1].
- **human_fidelity** (number): Facial similarity [0-1]. Only for 'subject' type.
- **negative_prompt** (string): What to avoid. Not supported with image reference.
- **aspect_ratio** (string): Output aspect ratio.
- **n** (integer): Number of images (1-9).

#### Output
- **image** (string): Generated image.

## other image apps

- [Seedream 5.5](https://inference.sh/apps/bytedance/seedream-5-5.md): $0.020 per 1K/1.5K image, $0.035 per 2K image, plus $0.008 per reference image
- [MiniMax H3 Image](https://inference.sh/apps/minimax/h3-image.md): $2.30/M text in, $1.50/M image in, $3.80/M image out (token-based)
- [GPT Image 2.6 Flare](https://inference.sh/apps/openai/gpt-image-2-6-flare.md): $30.00/M output, $5.00/M text input, $8.00/M image input, $0.10/M SVG input, $0.50/M SVG output
- [GPT Image 2.6 Sunburst](https://inference.sh/apps/openai/gpt-image-2-6-sunburst.md): $30.00/M out, $5.00/M text in, $8.00/M img in; SVG $0.10/$0.50/M in/out
- [Gemini Nano Banana 2.1](https://inference.sh/apps/google/gemini-nano-banana-2-1.md): $1.50/M input, $0.0336–$0.1134/image (1K–4K), +$0.014/query Google Search

## faq

### how much does Kling Image V1.5 cost?

$0.014/generation, $0.028/generation with image reference. you pay per run from your inference shell credits. no subscription is required.

### how do I run Kling Image V1.5 via api?

call klingai/image-v1-5 with the javascript or python sdk, the rest api, or `belt app run klingai/image-v1-5` from a terminal. the api reference on this page lists every input.

### who makes Kling Image V1.5?

Kling Image V1.5 is made by klingai and runs on inference shell. every klingai app is listed at https://inference.sh/apps/klingai.

### can my agent use Kling Image V1.5?

yes. agents call it as a tool through the inference shell mcp server (https://api.inference.sh/mcp), the belt cli or the sdk, and pay per run like you do.

### what are the alternatives to Kling Image V1.5?

other image apps on inference shell include Seedream 5.5, MiniMax H3 Image, GPT Image 2.6 Flare, GPT Image 2.6 Sunburst. they run through the same api, so switching is a one-line change.