> ## Documentation Index
> Fetch the complete documentation index at: https://pixelcut.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Image Generation

> Generate images from text prompts, reference images, or natural-language editing instructions.

Generate product photos, marketing visuals, and design concepts by describing what you want. Attach reference images for more control. All results are saved to your Pixelcut workspace.

## Text-to-image

Use the `generate_media` tool with a prompt and model ID. Call `models` (action: `list`) first to find available model IDs:

```
Generate a studio product photo of a water bottle on a marble countertop.
```

The agent calls `models` to find a model, then `generate_media` with the chosen model ID and prompt.

## Reference-based generation

Pass image URLs or asset IDs in the `attachments` parameter of `generate_media`. The model uses the attachment as a visual reference while composing the new scene:

```
Generate a lifestyle scene of this product on a kitchen counter with morning light.
```

## Generation parameters

The `generate_media` tool accepts:

| Parameter          | Description                               | Example                                 |
| ------------------ | ----------------------------------------- | --------------------------------------- |
| `prompt`           | The text description (required)           | `"product photo of sneakers on marble"` |
| `model`            | Model ID for generation (required)        | `nano-banana-2`                         |
| `aspect_ratio`     | Output aspect ratio                       | `1:1`, `16:9`, `4:3`, `9:16`            |
| `media_type`       | `image` or `video`                        | `image`                                 |
| `output_format`    | File format                               | `png`, `jpg`, `webp`                    |
| `num_variations`   | Number of variations to produce           | `1`-`4`                                 |
| `attachments`      | Reference image URLs or asset IDs         | —                                       |
| `brand_library_id` | Brand library for brand-consistent output | `lib_abc123`                            |

## Browsing models

The `models` tool supports `list`, `search`, `get`, and `recommend` actions. Use `recommend` with a natural language description of your use case to get ranked suggestions:

```
Find a good model for generating product lifestyle photos.
```

## Batch generation

Generate multiple variations in a single call by setting `num_variations`:

```
Generate 4 lifestyle photos of this watch on a wooden desk.
```

To produce the same concept across different aspect ratios, ask for separate runs:

```
Generate this product shot in 1:1 for Instagram, 16:9 for the website banner, and 9:16 for stories.
```

## Prompt engineering tips

<Tip>
  Write prompts as if briefing a photographer: subject first, then environment, lighting, and mood.
</Tip>

| Technique                     | Example                                               |
| ----------------------------- | ----------------------------------------------------- |
| Be specific about the subject | "white ceramic mug" instead of "a mug"                |
| Describe the environment      | "on a rustic wooden table, blurred garden background" |
| Specify lighting              | "soft natural light from the left, gentle shadows"    |
| Set the mood or style         | "minimal, editorial, high-end product photography"    |
| Mention camera details        | "shot at eye level, shallow depth of field"           |

<Info>
  When passing a `brand_library_id`, the model pulls brand colors, fonts, and style references automatically. Combine this with a descriptive prompt for brand-consistent output.
</Info>

## Related

* [Image Generation (User Guide)](/user-guide/key-features/image-generation) -- in-app generation workflow
* [MCP Server](/agents/mcp) -- full MCP tool reference