docs/my-website/docs/image_generation.md

import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# Image Generations

## Quick Start

### LiteLLM Python SDK

```python showLineNumbers
from litellm import image_generation
import os 

# set api keys 
os.environ["OPENAI_API_KEY"] = ""

response = image_generation(prompt="A cute baby sea otter", model="dall-e-3")

print(f"response: {response}")
```

### LiteLLM Proxy

### Setup config.yaml 

```yaml showLineNumbers
model_list:
  - model_name: gpt-image-1 ### RECEIVED MODEL NAME ###
    litellm_params: # all params accepted by litellm.image_generation()
      model: azure/gpt-image-1 ### MODEL NAME sent to `litellm.image_generation()` ###
      api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
      api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")

```

### Start proxy 

```bash showLineNumbers
litellm --config /path/to/config.yaml 

# RUNNING on http://0.0.0.0:4000
```

### Test 

<Tabs>
<TabItem value="curl" label="Curl">

```bash
curl -X POST 'http://0.0.0.0:4000/v1/images/generations' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
    "model": "gpt-image-1",
    "prompt": "A cute baby sea otter",
    "n": 1,
    "size": "1024x1024"
}'
```

</TabItem>
<TabItem value="openai" label="OpenAI">

```python showLineNumbers
from openai import OpenAI
client = openai.OpenAI(
    api_key="sk-1234",
    base_url="http://0.0.0.0:4000"
)


image = client.images.generate(
    prompt="A cute baby sea otter",
    model="dall-e-3",
)

print(image)
```
</TabItem>
</Tabs>

## Input Params for `litellm.image_generation()`

:::info

Any non-openai params, will be treated as provider-specific params, and sent in the request body as kwargs to the provider.

[**See Reserved Params**](https://github.com/BerriAI/litellm/blob/2f5f85cb52f36448d1f8bbfbd3b8af8167d0c4c8/litellm/main.py#L4082)
:::

### Required Fields

- `prompt`: *string* - A text description of the desired image(s).  

### Optional LiteLLM Fields

    model: Optional[str] = None,
    n: Optional[int] = None,
    quality: Optional[str] = None,
    response_format: Optional[str] = None,
    size: Optional[str] = None,
    style: Optional[str] = None,
    user: Optional[str] = None,
    timeout=600,  # default to 10 minutes
    api_key: Optional[str] = None,
    api_base: Optional[str] = None,
    api_version: Optional[str] = None,
    litellm_logging_obj=None,
    custom_llm_provider=None,

- `model`: *string (optional)* The model to use for image generation. Defaults to openai/gpt-image-1

- `n`: *int (optional)* The number of images to generate. Must be between 1 and 10. For dall-e-3, only n=1 is supported.

- `quality`: *string (optional)* The quality of the image that will be generated.
  *   `auto` (default value) will automatically select the best quality for the given model.
  *   `high`, `medium` and `low` are supported for `gpt-image-1`.
  *   `hd` and `standard` are supported for `dall-e-3`.
  *   `standard` is the only option for `dall-e-2`.
  
- `response_format`: *string (optional)* The format in which the generated images are returned. Must be one of url or b64_json.

- `size`: *string (optional)* The size of the generated images. Must be one of `1024x1024`, `1536x1024` (landscape), `1024x1536` (portrait), or `auto` (default value) for `gpt-image-1`, one of `256x256`, `512x512`, or `1024x1024` for `dall-e-2`, and one of `1024x1024`, `1792x1024`, or `1024x1792` for `dall-e-3`.

- `timeout`: *integer* - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes).

- `user`: *string (optional)* A unique identifier representing your end-user, 

- `api_base`: *string (optional)* - The api endpoint you want to call the model with

- `api_version`: *string (optional)* - (Azure-specific) the api version for the call; required for dall-e-3 on Azure

- `api_key`: *string (optional)* - The API key to authenticate and authorize requests. If not provided, the default API key is used.

- `api_type`: *string (optional)* - The type of API to use.

### Output from `litellm.image_generation()`

```json

{
    "created": 1703658209,
    "data": [{
        'b64_json': None, 
        'revised_prompt': 'Adorable baby sea otter with a coat of thick brown fur, playfully swimming in blue ocean waters. Its curious, bright eyes gleam as it is surfaced above water, tiny paws held close to its chest, as it playfully spins in the gentle waves under the soft rays of a setting sun.', 
        'url': 'https://oaidalleapiprodscus.blob.core.windows.net/private/org-ikDc4ex8NB5ZzfTf8m5WYVB7/user-JpwZsbIXubBZvan3Y3GchiiB/img-dpa3g5LmkTrotY6M93dMYrdE.png?st=2023-12-27T05%3A23%3A29Z&se=2023-12-27T07%3A23%3A29Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-26T13%3A22%3A56Z&ske=2023-12-27T13%3A22%3A56Z&sks=b&skv=2021-08-06&sig=hUuQjYLS%2BvtsDdffEAp2gwewjC8b3ilggvkd9hgY6Uw%3D'
    }],
    "usage": {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
}
```

## OpenAI Image Generation Models

### Usage
```python
from litellm import image_generation
import os
os.environ['OPENAI_API_KEY'] = ""
response = image_generation(model='gpt-image-1', prompt="cute baby otter")
```

| Model Name           | Function Call                               | Required OS Variables                |
|----------------------|---------------------------------------------|--------------------------------------|
| gpt-image-1 | `image_generation(model='gpt-image-1', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |
| dall-e-3 | `image_generation(model='dall-e-3', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |
| dall-e-2 | `image_generation(model='dall-e-2', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |

## Azure OpenAI Image Generation Models

### API keys
This can be set as env variables or passed as **params to litellm.image_generation()**
```python
import os
os.environ['AZURE_API_KEY'] = 
os.environ['AZURE_API_BASE'] = 
os.environ['AZURE_API_VERSION'] = 
```

### Usage
```python
from litellm import embedding
response = embedding(
    model="azure/<your deployment name>",
    prompt="cute baby otter",
    api_key=api_key,
    api_base=api_base,
    api_version=api_version,
)
print(response)
```

| Model Name           | Function Call                               |
|----------------------|---------------------------------------------|
| gpt-image-1 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
| dall-e-3 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
| dall-e-2 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |


## OpenAI Compatible Image Generation Models
Use this for calling `/image_generation` endpoints on OpenAI Compatible Servers, example https://github.com/xorbitsai/inference

**Note add `openai/` prefix to model so litellm knows to route to OpenAI**

### Usage
```python
from litellm import image_generation
response = image_generation(
  model = "openai/<your-llm-name>",     # add `openai/` prefix to model so litellm knows to route to OpenAI
  api_base="http://0.0.0.0:8000/"       # set API Base of your Custom OpenAI Endpoint
  prompt="cute baby otter"
)
```

## Bedrock - Stable Diffusion
Use this for stable diffusion on bedrock


### Usage
```python
import os
from litellm import image_generation

os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""

response = image_generation(
            prompt="A cute baby sea otter",
            model="bedrock/stability.stable-diffusion-xl-v0",
        )
print(f"response: {response}")
```

## VertexAI - Image Generation Models

### Usage 

Use this for image generation models on VertexAI

```python
response = litellm.image_generation(
    prompt="An olympic size swimming pool",
    model="vertex_ai/imagegeneration@006",
    vertex_ai_project="adroit-crow-413218",
    vertex_ai_location="us-central1",
)
print(f"response: {response}")
```