> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/googleapis/python-genai/llms.txt
> Use this file to discover all available pages before exploring further.

# Imagen

> Generate, upscale, and edit images using Imagen models

Imagen models enable you to generate, upscale, and edit images using the Google Gen AI Python SDK.

## Generate images

Generate images from text prompts using the `generate_images` method:

```python theme={null}
from google.genai import types

response = client.models.generate_images(
    model='imagen-4.0-generate-001',
    prompt='An umbrella in the foreground, and a rainy night sky in the background',
    config=types.GenerateImagesConfig(
        number_of_images=1,
        include_rai_reason=True,
        output_mime_type='image/jpeg',
    ),
)
response.generated_images[0].image.show()
```

### Configuration options

The `GenerateImagesConfig` supports the following parameters:

* **number\_of\_images** - Number of images to generate (default: 1)
* **include\_rai\_reason** - Include Responsible AI filtering reasons
* **output\_mime\_type** - Output format: `image/jpeg` or `image/png`

## Upscale images

<Note>
  Image upscaling is only supported in Vertex AI.
</Note>

Upscale existing images to higher resolutions:

```python theme={null}
from google.genai import types

response = client.models.upscale_image(
    model='imagen-4.0-upscale-preview',
    image=response.generated_images[0].image,
    upscale_factor='x2',
    config=types.UpscaleImageConfig(
        include_rai_reason=True,
        output_mime_type='image/jpeg',
    ),
)
response.generated_images[0].image.show()
```

The `upscale_factor` parameter accepts `'x2'` or `'x4'` for 2x or 4x upscaling.

## Edit images

<Note>
  Image editing is only supported in Vertex AI and uses a separate model (`imagen-3.0-capability-001`).
</Note>

Edit images using inpainting and masking techniques:

```python theme={null}
from google.genai import types
from google.genai.types import RawReferenceImage, MaskReferenceImage

# Define the reference image
raw_ref_image = RawReferenceImage(
    reference_id=1,
    reference_image=response.generated_images[0].image,
)

# Define the mask (model computes a mask of the background)
mask_ref_image = MaskReferenceImage(
    reference_id=2,
    config=types.MaskReferenceConfig(
        mask_mode='MASK_MODE_BACKGROUND',
        mask_dilation=0,
    ),
)

response = client.models.edit_image(
    model='imagen-3.0-capability-001',
    prompt='Sunlight and clear sky',
    reference_images=[raw_ref_image, mask_ref_image],
    config=types.EditImageConfig(
        edit_mode='EDIT_MODE_INPAINT_INSERTION',
        number_of_images=1,
        include_rai_reason=True,
        output_mime_type='image/jpeg',
    ),
)
response.generated_images[0].image.show()
```

### Edit modes

The `edit_mode` parameter supports:

* **EDIT\_MODE\_INPAINT\_INSERTION** - Insert new content into masked regions
* **EDIT\_MODE\_INPAINT\_REMOVAL** - Remove content from masked regions
* **EDIT\_MODE\_OUTPAINT** - Extend the image beyond its borders

### Mask modes

The `mask_mode` parameter supports:

* **MASK\_MODE\_BACKGROUND** - Mask the background
* **MASK\_MODE\_FOREGROUND** - Mask the foreground
* **MASK\_MODE\_SEMANTIC** - Mask based on semantic segmentation