# Ideogram Vector

> Generates editable SVG vector graphics from text prompts, with optional reference images, three quality levels, and aspect ratio controls.


## Overview

- **Endpoint**: `https://fal.run/ideogram/vector/text-to-vector`
- **Model ID**: `ideogram/vector/text-to-vector`
- **Category**: text-to-image
- **Kind**: inference
**Description**: Ideogram Vector generates SVG vector graphics from text prompts, with optional reference images to guide the output. It supports low, medium, and high quality, configurable aspect ratios, seeds, and up to eight SVG outputs per request. Up to five reference images can be provided.

**Tags**: svg, vector, illustration, logo, design, text-to-image



## Pricing

Billing is per generated SVG: **$0.13** for **low**, **$0.16** for **medium**, and **$0.20** for **high** quality. With reference images, **high** quality costs **$0.32** per SVG; low and medium prices are unchanged. For example, 2 medium-quality SVGs cost **$0.32**.

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`prompt`** (`string`, _required_):
  A text description of the SVG to generate.
  - Examples: "A playful cat logo with bold outlines and flat colors"

- **`quality`** (`QualityEnum`, _optional_):
  Generation quality. `low` is fastest, `high` is highest quality. Default value: `"medium"`
  - Default: `"medium"`
  - Options: `"low"`, `"medium"`, `"high"`

- **`num_images`** (`integer`, _optional_):
  Number of SVGs to generate. Default value: `1`
  - Default: `1`
  - Range: `1` to `8`

- **`aspect_ratio`** (`AspectRatioEnum`, _optional_):
  The aspect ratio of the generated SVG. Default value: `"auto"`
  - Default: `"auto"`
  - Options: `"auto"`, `"1:1"`, `"1:4"`, `"1:3"`, `"1:2"`, `"9:16"`, `"10:16"`, `"2:3"`, `"3:4"`, `"4:5"`, `"5:4"`, `"4:3"`, `"3:2"`, `"16:10"`, `"16:9"`, `"2:1"`, `"3:1"`, `"4:1"`

- **`seed`** (`integer`, _optional_):
  Seed for reproducible generation.
  - Range: `0` to `2147483647`

- **`image_urls`** (`list<string>`, _optional_):
  Optional reference images (up to 5, max 10MB each). The first image is the primary source; the others are additional references.
  - Array of string



**Required Parameters Example**:

```json
{
  "prompt": "A playful cat logo with bold outlines and flat colors"
}
```

**Full Example**:

```json
{
  "prompt": "A playful cat logo with bold outlines and flat colors",
  "quality": "medium",
  "num_images": 1,
  "aspect_ratio": "auto"
}
```


### Output Schema

The API returns the following output format:

- **`images`** (`list<File>`, _required_):
  The generated SVG files.
  - Array of File

- **`seed`** (`integer`, _optional_):
  Seed used for generation, if provided.



**Example Response**:

```json
{
  "images": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019
    }
  ]
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/ideogram/vector/text-to-vector \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "prompt": "A playful cat logo with bold outlines and flat colors"
   }'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```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(
    "ideogram/vector/text-to-vector",
    arguments={
        "prompt": "A playful cat logo with bold outlines and flat colors"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

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

Then use the API client to make requests:

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

const result = await fal.subscribe("ideogram/vector/text-to-vector", {
  input: {
    prompt: "A playful cat logo with bold outlines and flat colors"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/ideogram/vector/text-to-vector)
- [API Documentation](https://fal.ai/models/ideogram/vector/text-to-vector/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=ideogram/vector/text-to-vector)

### fal.ai Platform

- [Platform Documentation](https://fal.ai/docs/documentation)
- [Python Client](https://fal.ai/docs/api-reference/client-libraries/python)
- [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript)

### Other agent-readable surfaces

This file covers one model. To find anything else:

- [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs
- [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page
- [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined
- Any other model: `https://fal.ai/models/<endpoint-id>/llms.txt`