# Sana v1.5 1.6B

> Sana v1.5 1.6B is a lightweight text-to-image model that delivers 4K image generation with impressive efficiency.


## Overview

- **Endpoint**: `https://fal.run/fal-ai/sana/v1.5/1.6b`
- **Model ID**: `fal-ai/sana/v1.5/1.6b`
- **Category**: text-to-image
- **Kind**: inference
**Tags**: text to image, 4k, lightweight



## Pricing

- **Price**: $0.0075 per megapixels

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_):
  The prompt to generate an image from.
  - Examples: "Underwater coral reef ecosystem during peak bioluminescent activity, multiple layers of marine life - from microscopic plankton to massive coral structures, light refracting through crystal-clear tropical waters, creating prismatic color gradients, hyper-detailed texture of marine organisms"

- **`negative_prompt`** (`string`, _optional_):
  The negative prompt to use. Use it to address details that you don't want
  in the image. This could be colors, objects, scenery and even the small details
  (e.g. moustache, blurry, low resolution). Default value: `""`
  - Default: `""`
  - Examples: ""

- **`image_size`** (`ImageSize | Enum`, _optional_):
  The size of the generated image.
  - Default: `{"height":2160,"width":3840}`
  - One of: ImageSize | Enum

- **`num_inference_steps`** (`integer`, _optional_):
  The number of inference steps to perform. Default value: `18`
  - Default: `18`
  - Range: `1` to `50`

- **`seed`** (`integer`, _optional_):
  The same seed and the same prompt given to the same version of the model
  will output the same image every time.

- **`guidance_scale`** (`float`, _optional_):
  The CFG (Classifier Free Guidance) scale is a measure of how close you want
  the model to stick to your prompt when looking for a related image to show you. Default value: `5`
  - Default: `5`
  - Range: `0` to `20`

- **`sync_mode`** (`boolean`, _optional_):
  If `True`, the media will be returned as a data URI and the output data won't be available in the request history.
  - Default: `false`

- **`num_images`** (`integer`, _optional_):
  The number of images to generate. Default value: `1`
  - Default: `1`
  - Range: `1` to `4`

- **`enable_safety_checker`** (`boolean`, _optional_):
  If set to true, the safety checker will be enabled. Default value: `true`
  - Default: `true`

- **`output_format`** (`OutputFormatEnum`, _optional_):
  The format of the generated image. Default value: `"jpeg"`
  - Default: `"jpeg"`
  - Options: `"jpeg"`, `"png"`

- **`style_name`** (`StyleNameEnum`, _optional_):
  The style to generate the image in. Default value: `"(No style)"`
  - Default: `"(No style)"`
  - Options: `"(No style)"`, `"Cinematic"`, `"Photographic"`, `"Anime"`, `"Manga"`, `"Digital Art"`, `"Pixel art"`, `"Fantasy art"`, `"Neonpunk"`, `"3D Model"`



**Required Parameters Example**:

```json
{
  "prompt": "Underwater coral reef ecosystem during peak bioluminescent activity, multiple layers of marine life - from microscopic plankton to massive coral structures, light refracting through crystal-clear tropical waters, creating prismatic color gradients, hyper-detailed texture of marine organisms"
}
```

**Full Example**:

```json
{
  "prompt": "Underwater coral reef ecosystem during peak bioluminescent activity, multiple layers of marine life - from microscopic plankton to massive coral structures, light refracting through crystal-clear tropical waters, creating prismatic color gradients, hyper-detailed texture of marine organisms",
  "negative_prompt": "",
  "image_size": {
    "height": 2160,
    "width": 3840
  },
  "num_inference_steps": 18,
  "guidance_scale": 5,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "jpeg",
  "style_name": "(No style)"
}
```


### Output Schema

The API returns the following output format:

- **`images`** (`list<Image>`, _required_):
  The generated image files info.
  - Array of Image

- **`timings`** (`Timings`, _required_)

- **`seed`** (`integer`, _required_):
  Seed of the generated Image. It will be the same value of the one passed in the
  input or the randomly generated that was used in case none was passed.

- **`has_nsfw_concepts`** (`list<boolean>`, _required_):
  Whether the generated images contain NSFW concepts.
  - Array of boolean

- **`prompt`** (`string`, _required_):
  The prompt used for generating the image.



**Example Response**:

```json
{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/sana/v1.5/1.6b \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "prompt": "Underwater coral reef ecosystem during peak bioluminescent activity, multiple layers of marine life - from microscopic plankton to massive coral structures, light refracting through crystal-clear tropical waters, creating prismatic color gradients, hyper-detailed texture of marine organisms"
   }'
```

### 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(
    "fal-ai/sana/v1.5/1.6b",
    arguments={
        "prompt": "Underwater coral reef ecosystem during peak bioluminescent activity, multiple layers of marine life - from microscopic plankton to massive coral structures, light refracting through crystal-clear tropical waters, creating prismatic color gradients, hyper-detailed texture of marine organisms"
    },
    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("fal-ai/sana/v1.5/1.6b", {
  input: {
    prompt: "Underwater coral reef ecosystem during peak bioluminescent activity, multiple layers of marine life - from microscopic plankton to massive coral structures, light refracting through crystal-clear tropical waters, creating prismatic color gradients, hyper-detailed texture of marine organisms"
  },
  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/fal-ai/sana/v1.5/1.6b)
- [API Documentation](https://fal.ai/models/fal-ai/sana/v1.5/1.6b/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/sana/v1.5/1.6b)

### fal.ai Platform

- [Platform Documentation](https://docs.fal.ai)
- [Python Client](https://docs.fal.ai/clients/python)
- [JavaScript Client](https://docs.fal.ai/clients/javascript)