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

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

/images/edits

LiteLLM provides image editing functionality that maps to OpenAI's /images/edits API endpoint. Now supports both single and multiple image editing.

Feature	Supported	Notes
Cost Tracking	✅	Works with all supported models
Logging	✅	Works across all integrations
End-user Tracking	✅
Fallbacks	✅	Works between supported models
Loadbalancing	✅	Works between supported models
Supported operations	Create image edits	Single and multiple images supported
Supported LiteLLM SDK Versions	1.63.8+	Gemini support requires 1.79.3+
Supported LiteLLM Proxy Versions	1.71.1+	Gemini support requires 1.79.3+
Supported LLM providers	OpenAI, Gemini (Google AI Studio), Vertex AI, Stability AI, AWS Bedrock (Stability)	Gemini supports the new gemini-2.5-flash-image family. Vertex AI supports both Gemini and Imagen models. Stability AI and Bedrock Stability support various image editing operations.

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

Usage

LiteLLM Python SDK

Basic Image Edit

import litellm

# Edit an image with a prompt
response = litellm.image_edit(
    model="gpt-image-1",
    image=open("original_image.png", "rb"),
    prompt="Add a red hat to the person in the image",
    n=1,
    size="1024x1024"
)

print(response)

Multiple Images Edit

import litellm

# Edit multiple images with a prompt
response = litellm.image_edit(
    model="gpt-image-1",
    image=[
        open("image1.png", "rb"),
        open("image2.png", "rb"),
        open("image3.png", "rb")
    ],
    prompt="Apply vintage filter to all images",
    n=1,
    size="1024x1024"
)

print(response)

Image Edit with Mask

import litellm

# Edit an image with a mask to specify the area to edit
response = litellm.image_edit(
    model="gpt-image-1",
    image=open("original_image.png", "rb"),
    mask=open("mask_image.png", "rb"),  # Transparent areas will be edited
    prompt="Replace the background with a beach scene",
    n=2,
    size="512x512",
    response_format="url"
)

print(response)

Async Image Edit

import litellm
import asyncio

async def edit_image():
    response = await litellm.aimage_edit(
        model="gpt-image-1",
        image=open("original_image.png", "rb"),
        prompt="Make the image look like a painting",
        n=1,
        size="1024x1024",
        response_format="b64_json"
    )
    return response

# Run the async function
response = asyncio.run(edit_image())
print(response)

Async Multiple Images Edit

import litellm
import asyncio

async def edit_multiple_images():
    response = await litellm.aimage_edit(
        model="gpt-image-1",
        image=[
            open("portrait1.png", "rb"),
            open("portrait2.png", "rb")
        ],
        prompt="Add professional lighting to the portraits",
        n=1,
        size="1024x1024",
        response_format="url"
    )
    return response

# Run the async function
response = asyncio.run(edit_multiple_images())
print(response)

Image Edit with Custom Parameters

import litellm

# Edit image with additional parameters
response = litellm.image_edit(
    model="gpt-image-1",
    image=open("portrait.png", "rb"),
    prompt="Add sunglasses and a smile",
    n=3,
    size="1024x1024",
    response_format="url",
    user="user-123",
    timeout=60,
    extra_headers={"Custom-Header": "value"}
)

print(f"Generated {len(response.data)} image variations")
for i, image_data in enumerate(response.data):
    print(f"Image {i+1}: {image_data.url}")

</TabItem>

<TabItem value="gemini" label="Gemini">

#### Basic Image Edit
```python showLineNumbers title="Gemini Image Edit"
import base64
import os
from litellm import image_edit

os.environ["GEMINI_API_KEY"] = "your-api-key"

response = image_edit(
    model="gemini/gemini-2.5-flash-image",
    image=open("original_image.png", "rb"),
    prompt="Add aurora borealis to the night sky",
    size="1792x1024",  # mapped to aspectRatio=16:9 for Gemini
)

edited_image_bytes = base64.b64decode(response.data[0].b64_json)
with open("edited_image.png", "wb") as f:
    f.write(edited_image_bytes)

Multiple Images Edit

import base64
import os
from litellm import image_edit

os.environ["GEMINI_API_KEY"] = "your-api-key"

response = image_edit(
    model="gemini/gemini-2.5-flash-image",
    image=[
        open("scene.png", "rb"),
        open("style_reference.png", "rb"),
    ],
    prompt="Blend the reference style into the scene while keeping the subject sharp.",
)

for idx, image_obj in enumerate(response.data):
    with open(f"gemini_edit_{idx}.png", "wb") as f:
        f.write(base64.b64decode(image_obj.b64_json))

Basic Image Edit (Gemini)

import os
import litellm

# Set Vertex AI credentials
os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/service-account.json"

response = litellm.image_edit(
    model="vertex_ai/gemini-2.5-flash",
    image=open("original_image.png", "rb"),
    prompt="Add neon lights in the background",
    size="1024x1024",
)

print(response)

Image Edit with Imagen (Supports Masks)

import os
import litellm

# Set Vertex AI credentials
os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/service-account.json"

# Imagen supports mask for inpainting
response = litellm.image_edit(
    model="vertex_ai/imagen-3.0-capability-001",
    image=open("original_image.png", "rb"),
    mask=open("mask_image.png", "rb"),  # Optional: for inpainting
    prompt="Turn this into watercolor style scenery",
    n=2,  # Number of variations
    size="1024x1024",
)

print(response)

LiteLLM Proxy with OpenAI SDK

First, add this to your litellm proxy config.yaml:

model_list:
  - model_name: gpt-image-1
    litellm_params:
      model: gpt-image-1
      api_key: os.environ/OPENAI_API_KEY

Start the LiteLLM proxy server:

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

# RUNNING on http://0.0.0.0:4000

Basic Image Edit via Proxy

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-api-key"             # Your proxy API key
)

# Edit an image
response = client.images.edit(
    model="gpt-image-1",
    image=open("original_image.png", "rb"),
    prompt="Add a red hat to the person in the image",
    n=1,
    size="1024x1024"
)

print(response)

cURL Example

curl -X POST "http://localhost:4000/v1/images/edits" \
  -H "Authorization: Bearer your-api-key" \
  -F "model=gpt-image-1" \
  -F "image=@original_image.png" \
  -F "mask=@mask_image.png" \
  -F "prompt=Add a beautiful sunset in the background" \
  -F "n=1" \
  -F "size=1024x1024" \
  -F "response_format=url"

cURL Multiple Images Example

curl -X POST "http://localhost:4000/v1/images/edits" \
  -H "Authorization: Bearer your-api-key" \
  -F "model=gpt-image-1" \
  -F "image=@image1.png" \
  -F "image=@image2.png" \
  -F "image=@image3.png" \
  -F "prompt=Apply artistic filter to all images" \
  -F "n=1" \
  -F "size=1024x1024" \
  -F "response_format=url"

</TabItem>

<TabItem value="gemini" label="Gemini">

1. Add the Gemini image edit model to your `config.yaml`:
```yaml showLineNumbers title="Gemini Proxy Configuration"
model_list:
  - model_name: gemini-image-edit
    litellm_params:
      model: gemini/gemini-2.5-flash-image
      api_key: os.environ/GEMINI_API_KEY

2. Start the LiteLLM proxy server:

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

3. Make an image edit request (Gemini responses are base64-only):

curl -X POST "http://0.0.0.0:4000/v1/images/edits" \
  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
  -F "model=gemini-image-edit" \
  -F "image=@original_image.png" \
  -F "prompt=Add a warm golden-hour glow to the scene" \
  -F "size=1024x1024"

1. Add Vertex AI image edit models to your config.yaml:

model_list:
  - model_name: vertex-gemini-image-edit
    litellm_params:
      model: vertex_ai/gemini-2.5-flash
      vertex_project: os.environ/VERTEXAI_PROJECT
      vertex_location: os.environ/VERTEXAI_LOCATION
      vertex_credentials: os.environ/GOOGLE_APPLICATION_CREDENTIALS

  - model_name: vertex-imagen-image-edit
    litellm_params:
      model: vertex_ai/imagen-3.0-capability-001
      vertex_project: os.environ/VERTEXAI_PROJECT
      vertex_location: os.environ/VERTEXAI_LOCATION
      vertex_credentials: os.environ/GOOGLE_APPLICATION_CREDENTIALS

2. Start the LiteLLM proxy server:

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

3. Make an image edit request:

curl -X POST "http://0.0.0.0:4000/v1/images/edits" \
  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
  -F "model=vertex-gemini-image-edit" \
  -F "image=@original_image.png" \
  -F "prompt=Add neon lights in the background" \
  -F "size=1024x1024"

4. Imagen image edit with mask:

curl -X POST "http://0.0.0.0:4000/v1/images/edits" \
  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
  -F "model=vertex-imagen-image-edit" \
  -F "image=@original_image.png" \
  -F "mask=@mask_image.png" \
  -F "prompt=Turn this into watercolor style scenery" \
  -F "n=2" \
  -F "size=1024x1024"

Supported Image Edit Parameters

Parameter	Type	Description	Required
image	FileTypes	The image to edit. Must be a valid PNG file, less than 4MB, and square.	✅
prompt	str	A text description of the desired image edit.	✅
model	str	The model to use for image editing	Optional (defaults to dall-e-2)
mask	str	An additional image whose fully transparent areas indicate where the original image should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as image.	Optional
n	int	The number of images to generate. Must be between 1 and 10.	Optional (defaults to 1)
size	str	The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.	Optional (defaults to 1024x1024)
response_format	str	The format in which the generated images are returned. Must be one of url or b64_json.	Optional (defaults to url)
user	str	A unique identifier representing your end-user.	Optional

Response Format

The response follows the OpenAI Images API format:

{
    "created": 1677649800,
    "data": [
        {
            "url": "https://example.com/edited_image_1.png"
        },
        {
            "url": "https://example.com/edited_image_2.png"
        }
    ]
}

For b64_json format:

{
    "created": 1677649800,
    "data": [
        {
            "b64_json": "iVBORw0KGgoAAAANSUhEUgAA..."
        }
    ]
}