AWS Bedrock - Image Generation

Use Bedrock for image generation with Stable Diffusion, Amazon Titan Image Generator, and Amazon Nova Canvas models.

Supported Models

Usage

Basic Usage

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}")

Set Optional Parameters

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",
    ### OPENAI-COMPATIBLE ###
    size="128x512", # width=128, height=512
    ### PROVIDER-SPECIFIC ### see `AmazonStabilityConfig` in bedrock.py for all params
    seed=30
)
print(f"response: {response}")

1. Setup config.yaml

model_list:
  - model_name: amazon.nova-canvas-v1:0
    litellm_params:
      model: bedrock/amazon.nova-canvas-v1:0
      aws_region_name: "us-east-1"
      aws_secret_access_key: my-key # OPTIONAL - all boto3 auth params supported
      aws_secret_access_id: my-id # OPTIONAL - all boto3 auth params supported

2. Start proxy

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

3. Test it!

Text to Image:

curl -L -X POST 'http://0.0.0.0:4000/v1/images/generations' \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer $LITELLM_VIRTUAL_KEY' \\
-d '{
    "model": "amazon.nova-canvas-v1:0",
    "prompt": "A cute baby sea otter"
}'

Color Guided Generation:

curl -L -X POST 'http://0.0.0.0:4000/v1/images/generations' \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer $LITELLM_VIRTUAL_KEY' \\
-d '{
    "model": "amazon.nova-canvas-v1:0",
    "prompt": "A cute baby sea otter",
    "taskType": "COLOR_GUIDED_GENERATION",
    "colorGuidedGenerationParams":{"colors":["#FFFFFF"]}
}'

Amazon Nova Canvas - Image Edit

Use OpenAI-compatible image_edit() with Bedrock Nova Canvas (amazon.nova-canvas-v1:0). Requests use the same InvokeModel API as generation; LiteLLM maps inputs to Nova Canvas task types:

from litellm import image_edit

response = image_edit(
    image=open("photo.png", "rb"),
    prompt="Add soft sunset lighting",
    model="bedrock/amazon.nova-canvas-v1:0",
)

For BACKGROUND_REMOVAL, the AWS request must not include imageGenerationConfig; LiteLLM omits it for that task even if you pass size, n, seed, etc. Additional Nova Canvas inference IDs for image edit should set supports_nova_canvas_image_edit: true in model_prices_and_context_window.json (see amazon.nova-canvas-v1:0).

Using Inference Profiles with Image Generation

For AWS Bedrock Application Inference Profiles with image generation, use the model_id parameter to specify the inference profile ARN:

from litellm import image_generation

response = image_generation(
    model="bedrock/amazon.nova-canvas-v1:0",
    model_id="arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0",
    prompt="A cute baby sea otter"
)
print(f"response: {response}")

model_list:
  - model_name: nova-canvas-inference-profile
    litellm_params:
      model: bedrock/amazon.nova-canvas-v1:0
      model_id: arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0
      aws_region_name: "eu-west-1"

Authentication

All standard Bedrock authentication methods are supported for image generation. See Bedrock Authentication for details.