Image Edit API (/images/edits)

Provider	Edit
OpenAI	✅
Azure OpenAI	✅
Bedrock	✅
Vertex	✅
Anthropic
Cohere
Gemini
Groq
Together-AI
xAI
DeepInfra

Provider	Model	Format	Size Limit	Image Count
OpenAI	gpt-image-1	PNG, WebP, JPG	< 50MB	Up to 16 images
OpenAI	dall-e-2	Square PNG	< 4MB	1 image only
Vertex AI	imagen-3.0-capability-001	PNG, JPG	< 20MB	1 image only
AWS Bedrock	amazon-nova-canvas	PNG, JPG	< 5MB	1 image only
Azure OpenAI	gpt-image-1	PNG, WebP, JPG	< 50MB	Up to 16 images

OpenAI (gpt-image-1, dall-e-2)

OpenAI supports both gpt-image-1 (up to 16 images, PNG/WebP/JPG, <50MB) and dall-e-2 (1 square PNG image, <4MB). The meaning of the parameters are present in the OpenAI API documentation.

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="openai-main/gpt-image-1",
    image=[
        open("image1.png", "rb"),  # First source image
        open("image2.png", "rb")   # Second source image (up to 16 images supported)
    ],
    prompt="Replace the background with a beach scene and add palm trees on both sides",
    mask=open("mask.png", "rb")    # Optional mask to specify edit areas
)

print(response.data[0].url)

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="openai-main/dall-e-2",
    image=open("image.png", "rb"),  # Single square PNG image (under 4MB)
    prompt="Add a sunset in the background",
    mask=open("mask.png", "rb")     # Optional mask
)

print(response.data[0].url)

gpt-image-1 supports up to 16 images in PNG, WebP, or JPG format with a 50MB size limit per image. dall-e-2 requires a single square PNG image under 4MB.

Vertex AI (imagen-3.0-capability-001)

Vertex AI’s Imagen 3.0 model supports editing a single image in PNG or JPG format (max 20MB). The meaning of the parameters are present in the Google Vertex documentation.

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="image-edit/imagen-3.0-capability-001",
    image=open("image.png", "rb"), # REQUIRED, source image
    prompt="Replace the background with a beach scene and add palm trees on both sides", # Optional
    mask=open("mask.png", "rb"), # REQUIRED
    n = 2,
    extra_body={
        "maskMode": "MASK_MODE_BACKGROUND",
        "maskClasses": [162, 170], # ONLY if maskMode="MASK_MODE_SEMANTIC"
        "dilation": 0.01,
        "baseSteps": 35,
        "editMode": "EDIT_MODE_INPAINT_REMOVAL"
    }

print(response.data[0].b64_json)

Vertex AI supports a single image in PNG or JPG format with a maximum size of 20MB.

AWS Bedrock (amazon-nova-canvas)

AWS Bedrock’s Nova Canvas model supports editing a single image in PNG or JPG format (max 5MB).

Amazon Nova Canvas

The meaning of the parameters are present in the AWS Bedrock documentation.

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="tfy-ai-bedrock/amazon-nova-canvas"
    image=open("image.png", "rb"), # REQUIRED, source image
    prompt="Replace the background with a beach scene and add palm trees on both sides",
    # Mask image OR mask prompt is required, not both
    mask=open("mask.png", "rb")    # Mask image to specify edit areas
    
    extra_body={
	    "taskType": "OUTPAINTING"          # Optional: defaults to INPAINTING
	    "maskPrompt": "Box in the center." # Required if mask image is not specified, should NOT be included if mask image is specified
	    "negativeText": "dogs, cats"       # Optional: A text prompt to define what not to include in the image.
	    
	    # If taskType=OUTPAINTING
	    "outPaintingMode": "PRECISE"       # Optional: DEFAULT | PRECISE
	    
	    # For VIRTUAL_TRY_ON, need to use curl request only
	  }
)

print(response.data[0].b64_json)

import requests
import json
import base64

# --- Configuration ---
# !! Replace with your actual token and file paths !!
API_URL = "{GATEWAY_BASE_URL}/images/edits"
BEARER_TOKEN = "YOUR_BEARER_TOKEN"  # Replace with your actual token
IMAGE_PATH = "image.jpg"            # Path to your main image
REFERENCE_IMAGE_PATH = "reference.jpg"  # Path to your reference image
MASK_IMAGE_PATH = "mask.jpg"        # Path for the mask (since we use maskType=IMAGE)

# ---------------------

# Define the non-file form fields
form_data = {
    "model": "tfy-ai-bedrock/amazon-nova-canvas",
    "maskType": "IMAGE",           # Example: Using IMAGE maskType
    
    # --- General Optional Fields ---
    "n": 1,
    "size": "1024x1024",
    "cfgScale": 9.0,
    "seed": 42,
    "quality": "hd",
    "preserveBodyPose": "ON",
    "preserveHands": "ON",
    "preserveFace": "ON",
    "mergeStyle": "BALANCED",
    "returnMask": "true"
    
    # --- Fields for maskType=GARMENT ---
    # "garmentClass": "UPPER_BODY",
    # "maskShape": "CONTOUR",
    
    # --- Fields for maskType=PROMPT ---
    # "maskPrompt": "box in the center",
    # "maskShape": "CONTOUR",
}

# Define the headers
headers = {
    "Authorization": f"Bearer {BEARER_TOKEN}"
    # 'Content-Type: multipart/form-data' is set automatically by requests
}

try:
    # Open the files in binary read mode
    with open(IMAGE_PATH, "rb") as image_file, \
         open(REFERENCE_IMAGE_PATH, "rb") as ref_image_file, \
         open(MASK_IMAGE_PATH, "rb") as mask_file:

        # Define the files to be uploaded
        # The format is ('filename', file_object, 'content_type')
        files_to_upload = {
            "image": (IMAGE_PATH, image_file, "image/jpeg"),
            "referenceImage": (REFERENCE_IMAGE_PATH, ref_image_file, "image/jpeg"),
            "mask": (MASK_IMAGE_PATH, mask_file, "image/jpeg") # Required for maskType=IMAGE
        }

        print(f"Sending request to {API_URL}...")
        
        # Make the POST request
        response = requests.post(
            API_URL,
            headers=headers,
            data=form_data,    # Non-file form fields
            files=files_to_upload  # File form fields
        )

        # Check for HTTP errors
        response.raise_for_status()

        print("Request successful.")
        response_json = response.json()

        # Process the response (similar to your original script)
        if "data" in response_json and len(response_json["data"]) > 0:
            first_result = response_json["data"][0]

            if "url" in first_result:
                print(f"Image URL: {first_result['url']}")

            if "b64_json" in first_result:
                print("Found base64 image. Saving to edited_image.png...")
                image_bytes = base64.b64decode(first_result["b64_json"])
                
                with open("edited_image.png", "wb") as f:
                    f.write(image_bytes)
                print("Image saved as edited_image.png")
            
            # You might also want to check for the returned mask if returnMask=true
            if "mask" in first_result and "b64_json" in first_result["mask"]:
                 print("Found base64 mask. Saving to returned_mask.png...")
                 mask_bytes = base64.b64decode(first_result["mask"]["b64_json"])
                 with open("returned_mask.png", "wb") as f:
                     f.write(mask_bytes)
                 print("Mask saved as returned_mask.png")

        else:
            print("Response received, but no data found or data is empty.")
            print("Response JSON:", response_json)

except requests.exceptions.HTTPError as e:
    print(f"HTTP error: {e}")
    print("Response body:", e.response.text)
except requests.exceptions.RequestException as e:
    print(f"Request error: {e}")
except FileNotFoundError as e:
    print(f"File error: {e}. Make sure files exist at the specified paths.")
except (KeyError, json.JSONDecodeError) as e:
    print(f"Parsing error: {e}")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

Stability AI

The meaning of the parameters are present in the AWS Bedrock documentation.

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="tfy-ai-bedrock/stable-image-inpaint1",
    image=open("image.png", "rb"), # REQUIRED, source image
    prompt="Replace the background with a beach scene and add palm trees on both sides", # REQUIRED
		output_format="jpeg", # Optional
    mask=open(filePath, "rb"), # Optional
    extra_body={
       "style_preset": "anime", # Optional
       "negative_prompt": "dogs", # Optional
       "seed": 42, # Optional
       "grow_mask": 7 # Optional
   }

print(response.data[0].b64_json)

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="tfy-ai-bedrock/stable-image-search-recolor-v1",
    image=open("image.png", "rb"), # REQUIRED, source image
    prompt="Replace the background with a beach scene and add palm trees on both sides", # REQUIRED
		output_format="jpeg", # Optional
    mask=open(filePath, "rb"), # Optional
    extra_body={
       "select_prompt": "Select the jacket.", # REQUIRED
       "style_preset": "anime", # Optional
       "negative_prompt": "dogs", # Optional
       "seed": 42, # Optional
       "grow_mask": 7 # Optional
   }

print(response.data[0].b64_json)

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="tfy-ai-bedrock/stable-image-search-replace-v1",
    image=open("image.png", "rb"), # REQUIRED, source image
    prompt="Replace the background with a beach scene and add palm trees on both sides", # REQUIRED
		output_format="jpeg", # Optional
    mask=open(filePath, "rb"), # Optional
    extra_body={
       "search_prompt": "Select the jacket.", # REQUIRED
       "style_preset": "anime", # Optional
       "negative_prompt": "dogs", # Optional
       "seed": 42, # Optional
       "grow_mask": 7 # Optional
   }

print(response.data[0].b64_json)

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="tfy-ai-bedrock/stable-image-erase-object-v1",
    image=open("image.png", "rb"), # REQUIRED, source image
    prompt="Replace the background with a beach scene and add palm trees on both sides", # REQUIRED
		output_format="jpeg", # Optional
    mask=open(filePath, "rb"), # Optional
    extra_body={
       "seed": 42, # Optional
       "grow_mask": 7 # Optional
   }

print(response.data[0].b64_json)

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="tfy-ai-bedrock/stable-image-remove-background-v1",
    image=open("image.png", "rb"), # REQUIRED, source image
	output_format="jpeg", # Optional
		
print(response.data[0].b64_json)

AWS Bedrock supports a single image in PNG or JPG format with a maximum size of 5MB.

Azure OpenAI (gpt-image-1)

Azure OpenAI’s gpt-image-1 model supports up to 16 images in PNG, WebP, or JPG format (max 50MB each). The meaning of the parameters are present in the Azure OpenAI documentation.

from openai import OpenAI

BASE_URL = "{GATEWAY_BASE_URL}"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.images.edit(
    model="azure-main/gpt-image-1",
    image=[
        open("image1.png", "rb"),  # First source image
        open("image2.png", "rb")   # Second source image (up to 16 images supported)
    ],
    prompt="Replace the background with a beach scene and add palm trees on both sides",
    mask=open("mask.png", "rb")    # Optional mask to specify edit areas
)

print(response.data[0].url)

Azure OpenAI supports up to 16 images in PNG, WebP, or JPG format with a 50MB size limit per image.

Image Edit API (/images/edits)

Provider capabilities

Supported Providers

Requirements

Example Usage

Response Format

​Provider capabilities

​Supported Providers

​Requirements

​Example Usage

​Response Format

Provider capabilities

Supported Providers

Requirements

Example Usage

Response Format