litellm/docs/my-website/docs/image_generation.md

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Image Generations

Quick Start

LiteLLM Python SDK

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

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

litellm --config /path/to/config.yaml 

# RUNNING on http://0.0.0.0:4000

Test

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"
}'

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)

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 :::

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()

{
    "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

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()

import os
os.environ['AZURE_API_KEY'] = 
os.environ['AZURE_API_BASE'] = 
os.environ['AZURE_API_VERSION'] = 

Usage

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")

Xinference Image Generation Models

Use this for Stable Diffusion models hosted on Xinference

Usage

See Xinference usage with LiteLLM here

Recraft Image Generation Models

Use this for AI-powered design and image generation with Recraft

Usage

from litellm import image_generation
import os

os.environ['RECRAFT_API_KEY'] = "your-api-key"

response = image_generation(
    model="recraft/recraftv3",
    prompt="A beautiful sunset over a calm ocean",
)
print(response)

See Recraft usage with LiteLLM here

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

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

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

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}")

Supported Providers

⚡️See all supported models and providers at models.litellm.ai

Provider	Documentation Link
OpenAI	OpenAI Image Generation →
Azure OpenAI	Azure OpenAI Image Generation →
Google AI Studio	Google AI Studio Image Generation →
Vertex AI	Vertex AI Image Generation →
AWS Bedrock	Bedrock Image Generation →
Recraft	Recraft Image Generation →
Xinference	Xinference Image Generation →
Nscale	Nscale Image Generation →