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/images/edits

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

FeatureSupportedNotes
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 operationsCreate image editsSingle and multiple images supported
Supported LiteLLM SDK Versions1.63.8+Gemini support requires 1.79.3+
Supported LiteLLM Proxy Versions1.71.1+Gemini support requires 1.79.3+
Supported LLM providersOpenAI, Gemini (Google AI Studio), Vertex AI, OpenRouter, Stability AI, AWS Bedrock (Stability), Black Forest LabsGemini supports the new gemini-2.5-flash-image family. Vertex AI supports both Gemini and Imagen models. OpenRouter routes image edits through chat completions. Stability AI and Bedrock Stability support various image editing operations. Black Forest Labs supports FLUX Kontext models.

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

Usage​

LiteLLM Python SDK​

Basic Image Edit​

OpenAI 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​

OpenAI 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​

OpenAI 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​

Async OpenAI 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​

Async OpenAI 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​

OpenAI 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​

Gemini 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))

LiteLLM Proxy with OpenAI SDK​

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

OpenAI Proxy Configuration
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:

Start LiteLLM Proxy Server
litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

Basic Image Edit via Proxy​

OpenAI Proxy Image Edit
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 Image Edit Request
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 Multiple Images Edit Request
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
  1. Start the LiteLLM proxy server:
Start LiteLLM Proxy Server
litellm --config /path/to/config.yaml
  1. Make an image edit request (Gemini responses are base64-only):
Gemini Proxy Image Edit
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"

Supported Image Edit Parameters​

ParameterTypeDescriptionRequired
imageFileTypesThe image to edit. Must be a valid PNG file, less than 4MB, and square.✅
promptstrA text description of the desired image edit.✅
modelstrThe model to use for image editingOptional (defaults to dall-e-2)
maskstrAn 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
nintThe number of images to generate. Must be between 1 and 10.Optional (defaults to 1)
sizestrThe size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.Optional (defaults to 1024x1024)
response_formatstrThe format in which the generated images are returned. Must be one of url or b64_json.Optional (defaults to url)
userstrA unique identifier representing your end-user.Optional

Response Format​

The response follows the OpenAI Images API format:

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

For b64_json format:

Base64 Response Structure
{
    "created": 1677649800,
    "data": [
        {
            "b64_json": "iVBORw0KGgoAAAANSUhEUgAA..."
        }
    ]
}