# Image generation ## Background/generate parameters Let's see all the parameters of background generate section that can be used in image generation API. They will be described in detail down below.

Field name	Description
model_type	This parameter chooses model which will be used for image generation, realistic is the default one: gemini-3-pro-image-preview see-dream-4.5 qwen flux2-klein9b z-image-turbo realistic fantasy premium google-gemini-image-flash
description	Generation prompt. By default "high quality, highly detailed, 8K" phrase is added to it.
sample_num	Works like generator random seed.
adapter_type	This parameter controls how generator will use the input image. generate_background - (default value) background is generated around the main object found in the input image face - generator will use first found face of the input image to create an avatar control - (also known as "image to image") generator will generate image based on image and image edges, it's useful for adding details to existing photos, f.e. adding furniture to empty room control2 - generator will generate image based only on image edges, it's useful for generating images from drawings upscale - used for specifying prompts for generative upscale inpainting - described in separated chapter
face_id	true/false - Works only when adapter_type is face. Selects different face generation algorithm when just face details are used (skips for example hair style from original photo).
controlnet_conditioning_scale	Float value from 0 to 1 describes how much edges will be preserved. Default value is 0.5

Let's check some examples. ## Based on text only Image can be generated based on text prompt in given resolution. ```json { "width": 2048, "height": 1024, "background": { "generate": { "description": "woman in a futuristic suit holding a gun in her hand, looking at the camera, cyberpunk art, neo-figurative, anime" } } } ``` And the result:

Now, the same prompt but with **fantasy** model: ```json { "width": 2048, "height": 1024, "background": { "generate": { "description": "woman in a futuristic suit holding a gun in her hand, looking at the camera, cyberpunk art, neo-figurative, anime", "model_type": "fantasy" } } } ```

Now, the same prompt but with **premium** model: ```json { "width": 2048, "height": 1024, "background": { "generate": { "description": "woman in a futuristic suit holding a gun in her hand, looking at the camera, cyberpunk art, neo-figurative, anime", "model_type": "premium" } } } ```

## Based on image Images can be generated based on face details or image canny edges. Let's check some examples. ```json { "url": "https://deep-image.ai/api-example.png", "width": 1024, "height": 1024, "background": { "generate": { "description": "item on the beach", "adapter_type": "generate_background" } } } ``` And the result

```json { "url": "https://deep-image.ai/api-example3.jpg", "width": 1024, "height": 1024, "background": { "generate": { "description": "model on the beach", "adapter_type": "face" } } } ``` And the result

```json { "url": "https://deep-image.ai/api-example3.jpg", "background": { "generate": { "description": "model in the room", "adapter_type": "control" } } } ``` And the result

## Generative upscaling Let's try to use generative upscaling. This algorithm can do some real magic :slight\_smile:. It also modifies image slightly because it's based on diffusion algorithm so do not use it when you really want to preserve exact image colors and original image details. ```json { "url": "https://deep-image.ai/api-example.png", "width": 3000, "generative_upscale": true } ``` {% tabs %} {% tab title="Curl" %} ``` curl --request POST \ --url https://deep-image.ai/rest_api/process_result \ --header 'content-type: application/json' \ --header 'x-api-key: API_KEY' \ --data '{ "url": "https://deep-image.ai/api-example.png", "width": 3000, "generative_upscale": true }' ``` {% endtab %} {% tab title="Python client" %} ```python import os import deep_image_ai_client configuration = deep_image_ai_client.Configuration( host="https://deep-image.ai" ) configuration.api_key['ApiKeyAuth'] = os.environ["API_KEY"] with deep_image_ai_client.ApiClient(configuration) as api_client: api_instance = deep_image_ai_client.DefaultApi(api_client) process_payload = {"url": "https://deep-image.ai/api-example.jpg", "width": 3000, generative_upscale: True} api_response = api_instance.rest_api_process_result_post(process_payload) ``` {% endtab %} {% endtabs %} And the result:

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