Amazon Titan Image Generator G1 - Amazon Bedrock

Amazon Titan Image Generator G1

The Amazon Titan Image Generator G1 model supports the following inference parameters and model responses when carrying out model inference.

Request and response format

When you make an InvokeModel call using the Amazon Titan Image Generator G1, replace the body field of the request with the format that matches your use-case. All tasks share an imageGenerationConfig object, but each task has a parameters object specific to that task. The following use-cases are supported.

taskType Task parameters field Type of task Definition
TEXT_IMAGE textToImageParams Generation

Generate an image using a text prompt.

INPAINTING inPaintingParams Editing

Modify an image by changing the inside of a mask to match the surrounding background.

OUTPAINTING outPaintingParams Editing Modify an image by seamlessly extending the region defined by the mask.
IMAGE_VARIATION imageVariationParams Editing Modify an image by producing variations of the original image.

Editing tasks require an image field in the input. This field consists of a string that defines the pixels in the image. Each pixel is defined by 3 RGB channels, each of which ranges from 0 to 255 (for example, (255 255 0) would represent the color yellow). These channels are encoded in base64.

The image you use must be in JPEG or PNG format.

If you carry out inpainting or outpainting, you also define a mask, a region or regions that define parts of the image to be modified. You can define the mask in one of two ways.

  • maskPrompt – Write a text prompt to describe the part of the image to be masked.

  • maskImage – Input a base64-encoded string that defines the masked regions by marking each pixel in the input image as (0 0 0) or (255 255 255).

    • A pixel defined as (0 0 0) is a pixel inside the mask.

    • A pixel defined as (255 255 255) is a pixel outside the mask.

    You can use a photo editing tool to draw masks. You can then convert the output JPEG or PNG image to base64-encoding to input into this field. Otherwise, use the maskPrompt field instead to allow the model to infer the mask.

Select a tab to view API request bodies for different image generation use-cases and explanations of the fields.

Text-to-image generation (Request)

A text prompt to generate the image must be less than or equal to 512 characters. negativeText (Optional) – A text prompt to define what not to include in the image-- less than or equal to 512 characters.

{ "taskType": "TEXT_IMAGE", "textToImageParams": { "text": "string", "negativeText": "string" }, "imageGenerationConfig": { "numberOfImages": int, "height": int, "width": int, "cfgScale": float, "seed": int } }

The textToImageParams fields are described below.

  • text (Required) – A text prompt to generate the image.

  • negativeText (Optional) – A text prompt to define what not to include in the image.

    Note

    Don't use negative words in the negativeText prompt. For example, if you don't want to include mirrors in an image, enter mirrors in the negativeText prompt. Don't enter no mirrors.

Inpainting (Request)

text (Optional) – A text prompt to define what to change inside the mask. If you don't include this field, the model tries to replace the entire mask area with the background. Must be less than or equal to 512 characters. negativeText (Optional) – A text prompt to define what not to include in the image. Must be less than or equal to 512 characters.

{ "taskType": "INPAINTING", "inPaintingParams": { "image": "base64-encoded string", "text": "string", "negativeText": "string", "maskPrompt": "string", "maskImage": "base64-encoded string", }, "imageGenerationConfig": { "numberOfImages": int, "height": int, "width": int, "cfgScale": float } }

The inPaintingParams fields are described below. The mask defines the part of the image that you want to modify.

  • image (Required) – The JPEG or PNG image to modify, formatted as a string that specifies a sequence of pixels, each defined in RGB values and encoded in base64. For examples of how to encode an image into base64 and decode a base64-encoded string and transform it into an image, see the code examples.

  • You must define one of the following fields (but not both) in order to define.

    • maskPrompt – A text prompt that defines the mask.

    • maskImage – A string that defines the mask by specifying a sequence of pixels that is the same size as the image. Each pixel is turned into an RGB value of (0 0 0) (a pixel inside the mask) or (255 255 255) (a pixel outside the mask). For examples of how to encode an image into base64 and decode a base64-encoded string and transform it into an image, see the code examples.

  • text (Optional) – A text prompt to define what to change inside the mask. If you don't include this field, the model tries to replace the entire mask area with the background.

  • negativeText (Optional) – A text prompt to define what not to include in the image.

    Note

    Don't use negative words in the negativeText prompt. For example, if you don't want to include mirrors in an image, enter mirrors in the negativeText prompt. Don't enter no mirrors.

Outpainting (Request)

text (Required) – A text prompt to define what to change outside the mask. Must be less than or equal to 512 characters. negativeText (Optional) – A text prompt to define what not to include in the image. Must be less than or equal to 512 characters.

{ "taskType": "OUTPAINTING", "outPaintingParams": { "text": "string", "negativeText": "string", "image": "base64-encoded string", "maskPrompt": "string", "maskImage": "base64-encoded string", "outPaintingMode": "DEFAULT | PRECISE" }, "imageGenerationConfig": { "numberOfImages": int, "height": int, "width": int, "cfgScale": float } }

The outPaintingParams fields are defined below. The mask defines the region in the image whose that you don't want to modify. The generation seamlessly extends the region you define.

  • image (Required) – The JPEG or PNG image to modify, formatted as a string that specifies a sequence of pixels, each defined in RGB values and encoded in base64. For examples of how to encode an image into base64 and decode a base64-encoded string and transform it into an image, see the code examples.

  • You must define one of the following fields (but not both) in order to define.

    • maskPrompt – A text prompt that defines the mask.

    • maskImage – A string that defines the mask by specifying a sequence of pixels that is the same size as the image. Each pixel is turned into an RGB value of (0 0 0) (a pixel inside the mask) or (255 255 255) (a pixel outside the mask). For examples of how to encode an image into base64 and decode a base64-encoded string and transform it into an image, see the code examples.

  • text (Required) – A text prompt to define what to change outside the mask.

  • negativeText (Optional) – A text prompt to define what not to include in the image.

    Note

    Don't use negative words in the negativeText prompt. For example, if you don't want to include mirrors in an image, enter mirrors in the negativeText prompt. Don't enter no mirrors.

  • outPaintingMode – Specifies whether to allow modification of the pixels inside the mask or not. The following values are possible.

    • DEFAULT – Use this option to allow modification of the image inside the mask in order to keep it consistent with the reconstructed background.

    • PRECISE – Use this option to prevent modification of the image inside the mask.

Image variation (Request)
  • text (Optional) – A text prompt that can define what to preserve and what to change in the image. Must be less than or equal to 512 characters.

  • negativeText (Optional) – A text prompt to define what not to include in the image. Must be less than or equal to 512 characters.

  • text (Optional) – A text prompt that can define what to preserve and what to change in the image. Must be less than or equal to 512 characters.

  • similarityStrength (Optional) – Specifies how similar the generated image should be to the input image(s) Use a lower value to introduce more randomness in the generation. Accepted range is between 0.2 and 1.0 (both inclusive), while a default of 0.7 is used if this parameter is missing in the request.

{ "taskType": "IMAGE_VARIATION", "imageVariationParams": { "text": "string", "negativeText": "string", "images": ["base64-encoded string"], "similarityStrength": 0.7, # Range: 0.2 to 1.0 }, "imageGenerationConfig": { "numberOfImages": int, "height": int, "width": int, "cfgScale": float } }

The imageVariationParams fields are defined below.

  • images (Required) – A list of images for which to generate variations. You can include 1 to 5 images. An image is defined as a base64-encoded image string. For examples of how to encode an image into base64 and decode a base64-encoded string and transform it into an image, see the code examples.

  • text (Optional) – A text prompt that can define what to preserve and what to change in the image.

  • similarityStrength (Optional) – Specifies how similar the generated image should be to the input images(s). Range in 0.2 to 1.0 with lower values used to introduce more randomness.

  • negativeText (Optional) – A text prompt to define what not to include in the image.

    Note

    Don't use negative words in the negativeText prompt. For example, if you don't want to include mirrors in an image, enter mirrors in the negativeText prompt. Don't enter no mirrors.

Response body
{ "images": [ "base64-encoded string", ... ], "error": "string" }

The response body is a streaming object that contains one of the following fields.

  • images – If the request is successful, it returns this field, a list of base64-encoded strings, each defining a generated image. Each image is formatted as a string that specifies a sequence of pixels, each defined in RGB values and encoded in base64. For examples of how to encode an image into base64 and decode a base64-encoded string and transform it into an image, see the code examples.

  • error – If the request violates the content moderation policy in one of the following situations, a message is returned in this field.

    • If the input text, image, or mask image is flagged by the content moderation policy.

    • If at least one output image is flagged by the content moderation policy

The shared and optional imageGenerationConfig contains the following fields. If you don't include this object, the default configurations are used.

  • numberOfImages (Optional) – The number of images to generate.

    Minimum Maximum Default
    1 5 1
  • cfgScale (Optional) – Specifies how strongly the generated image should adhere to the prompt. Use a lower value to introduce more randomness in the generation.

    Minimum Maximum Default
    1.1 10.0 8.0
  • The following parameters define the size that you want the output image to be. For more details about pricing by image size, see Amazon Bedrock pricing.

    • height (Optional) – The height of the image in pixels. The default value is 1408.

    • width (Optional) – The width of the image in pixels. The default value is 1408.

    The following sizes are permissible.

    Width Height Aspect ratio Price equivalent to
    1024 1024 1:1 1024 x 1024
    768 768 1:1 512 x 512
    512 512 1:1 512 x 512
    768 1152 2:3 1024 x 1024
    384 576 2:3 512 x 512
    1152 768 3:2 1024 x 1024
    576 384 3:2 512 x 512
    768 1280 3:5 1024 x 1024
    384 640 3:5 512 x 512
    1280 768 5:3 1024 x 1024
    640 384 5:3 512 x 512
    896 1152 7:9 1024 x 1024
    448 576 7:9 512 x 512
    1152 896 9:7 1024 x 1024
    576 448 9:7 512 x 512
    768 1408 6:11 1024 x 1024
    384 704 6:11 512 x 512
    1408 768 11:6 1024 x 1024
    704 384 11:6 512 x 512
    640 1408 5:11 1024 x 1024
    320 704 5:11 512 x 512
    1408 640 11:5 1024 x 1024
    704 320 11:5 512 x 512
    1152 640 9:5 1024 x 1024
    1173 640 16:9 1024 x 1024
  • seed (Optional) – Use to control and reproduce results. Determines the initial noise setting. Use the same seed and the same settings as a previous run to allow inference to create a similar image.

    Note

    You can only set a seed for a TEXT_IMAGE generation task.

    Minimum Maximum Default
    0 2,147,483,646 0

Code examples

The following examples show how to invoke the Amazon Titan Image Generator G1 model with on-demand throughput in the Python SDK. Select a tab to view an example for each use-case. Each example displays the image at the end.

Text-to-image generation
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to generate an image from a text prompt with the Amazon Titan Image Generator G1 model (on demand). """ import base64 import io import json import logging import boto3 from PIL import Image from botocore.exceptions import ClientError class ImageError(Exception): "Custom exception for errors returned by Amazon Titan Image Generator G1" def __init__(self, message): self.message = message logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_image(model_id, body): """ Generate an image using Amazon Titan Image Generator G1 model on demand. Args: model_id (str): The model ID to use. body (str) : The request body to use. Returns: image_bytes (bytes): The image generated by the model. """ logger.info( "Generating image with Amazon Titan Image Generator G1 model %s", model_id) bedrock = boto3.client(service_name='bedrock-runtime') accept = "application/json" content_type = "application/json" response = bedrock.invoke_model( body=body, modelId=model_id, accept=accept, contentType=content_type ) response_body = json.loads(response.get("body").read()) base64_image = response_body.get("images")[0] base64_bytes = base64_image.encode('ascii') image_bytes = base64.b64decode(base64_bytes) finish_reason = response_body.get("error") if finish_reason is not None: raise ImageError(f"Image generation error. Error is {finish_reason}") logger.info( "Successfully generated image with Amazon Titan Image Generator G1 model %s", model_id) return image_bytes def main(): """ Entrypoint for Amazon Titan Image Generator G1 example. """ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = 'amazon.titan-image-generator-v1' prompt = """A photograph of a cup of coffee from the side.""" body = json.dumps({ "taskType": "TEXT_IMAGE", "textToImageParams": { "text": prompt }, "imageGenerationConfig": { "numberOfImages": 1, "height": 1024, "width": 1024, "cfgScale": 8.0, "seed": 0 } }) try: image_bytes = generate_image(model_id=model_id, body=body) image = Image.open(io.BytesIO(image_bytes)) image.show() except ClientError as err: message = err.response["Error"]["Message"] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) except ImageError as err: logger.error(err.message) print(err.message) else: print( f"Finished generating image with Amazon Titan Image Generator G1 model {model_id}.") if __name__ == "__main__": main()
Inpainting
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to use inpainting to generate an image from a source image with the Amazon Titan Image Generator G1 model (on demand). The example uses a mask prompt to specify the area to inpaint. """ import base64 import io import json import logging import boto3 from PIL import Image from botocore.exceptions import ClientError class ImageError(Exception): "Custom exception for errors returned by Amazon Titan Image Generator G1" def __init__(self, message): self.message = message logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_image(model_id, body): """ Generate an image using Amazon Titan Image Generator G1 model on demand. Args: model_id (str): The model ID to use. body (str) : The request body to use. Returns: image_bytes (bytes): The image generated by the model. """ logger.info( "Generating image with Amazon Titan Image Generator G1 model %s", model_id) bedrock = boto3.client(service_name='bedrock-runtime') accept = "application/json" content_type = "application/json" response = bedrock.invoke_model( body=body, modelId=model_id, accept=accept, contentType=content_type ) response_body = json.loads(response.get("body").read()) base64_image = response_body.get("images")[0] base64_bytes = base64_image.encode('ascii') image_bytes = base64.b64decode(base64_bytes) finish_reason = response_body.get("error") if finish_reason is not None: raise ImageError(f"Image generation error. Error is {finish_reason}") logger.info( "Successfully generated image with Amazon Titan Image Generator G1 model %s", model_id) return image_bytes def main(): """ Entrypoint for Amazon Titan Image Generator G1 example. """ try: logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = 'amazon.titan-image-generator-v1' # Read image from file and encode it as base64 string. with open("/path/to/image", "rb") as image_file: input_image = base64.b64encode(image_file.read()).decode('utf8') body = json.dumps({ "taskType": "INPAINTING", "inPaintingParams": { "text": "Modernize the windows of the house", "negativeText": "bad quality, low res", "image": input_image, "maskPrompt": "windows" }, "imageGenerationConfig": { "numberOfImages": 1, "height": 512, "width": 512, "cfgScale": 8.0 } }) image_bytes = generate_image(model_id=model_id, body=body) image = Image.open(io.BytesIO(image_bytes)) image.show() except ClientError as err: message = err.response["Error"]["Message"] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) except ImageError as err: logger.error(err.message) print(err.message) else: print( f"Finished generating image with Amazon Titan Image Generator G1 model {model_id}.") if __name__ == "__main__": main()
Outpainting
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to use outpainting to generate an image from a source image with the Amazon Titan Image Generator G1 model (on demand). The example uses a mask image to outpaint the original image. """ import base64 import io import json import logging import boto3 from PIL import Image from botocore.exceptions import ClientError class ImageError(Exception): "Custom exception for errors returned by Amazon Titan Image Generator G1" def __init__(self, message): self.message = message logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_image(model_id, body): """ Generate an image using Amazon Titan Image Generator G1 model on demand. Args: model_id (str): The model ID to use. body (str) : The request body to use. Returns: image_bytes (bytes): The image generated by the model. """ logger.info( "Generating image with Amazon Titan Image Generator G1 model %s", model_id) bedrock = boto3.client(service_name='bedrock-runtime') accept = "application/json" content_type = "application/json" response = bedrock.invoke_model( body=body, modelId=model_id, accept=accept, contentType=content_type ) response_body = json.loads(response.get("body").read()) base64_image = response_body.get("images")[0] base64_bytes = base64_image.encode('ascii') image_bytes = base64.b64decode(base64_bytes) finish_reason = response_body.get("error") if finish_reason is not None: raise ImageError(f"Image generation error. Error is {finish_reason}") logger.info( "Successfully generated image with Amazon Titan Image Generator G1 model %s", model_id) return image_bytes def main(): """ Entrypoint for Amazon Titan Image Generator G1 example. """ try: logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = 'amazon.titan-image-generator-v1' # Read image and mask image from file and encode as base64 strings. with open("/path/to/image", "rb") as image_file: input_image = base64.b64encode(image_file.read()).decode('utf8') with open("/path/to/mask_image", "rb") as mask_image_file: input_mask_image = base64.b64encode( mask_image_file.read()).decode('utf8') body = json.dumps({ "taskType": "OUTPAINTING", "outPaintingParams": { "text": "Draw a chocolate chip cookie", "negativeText": "bad quality, low res", "image": input_image, "maskImage": input_mask_image, "outPaintingMode": "DEFAULT" }, "imageGenerationConfig": { "numberOfImages": 1, "height": 512, "width": 512, "cfgScale": 8.0 } } ) image_bytes = generate_image(model_id=model_id, body=body) image = Image.open(io.BytesIO(image_bytes)) image.show() except ClientError as err: message = err.response["Error"]["Message"] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) except ImageError as err: logger.error(err.message) print(err.message) else: print( f"Finished generating image with Amazon Titan Image Generator G1 model {model_id}.") if __name__ == "__main__": main()
Image variation
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to generate an image variation from a source image with the Amazon Titan Image Generator G1 model (on demand). """ import base64 import io import json import logging import boto3 from PIL import Image from botocore.exceptions import ClientError class ImageError(Exception): "Custom exception for errors returned by Amazon Titan Image Generator G1" def __init__(self, message): self.message = message logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_image(model_id, body): """ Generate an image using Amazon Titan Image Generator G1 model on demand. Args: model_id (str): The model ID to use. body (str) : The request body to use. Returns: image_bytes (bytes): The image generated by the model. """ logger.info( "Generating image with Amazon Titan Image Generator G1 model %s", model_id) bedrock = boto3.client(service_name='bedrock-runtime') accept = "application/json" content_type = "application/json" response = bedrock.invoke_model( body=body, modelId=model_id, accept=accept, contentType=content_type ) response_body = json.loads(response.get("body").read()) base64_image = response_body.get("images")[0] base64_bytes = base64_image.encode('ascii') image_bytes = base64.b64decode(base64_bytes) finish_reason = response_body.get("error") if finish_reason is not None: raise ImageError(f"Image generation error. Error is {finish_reason}") logger.info( "Successfully generated image with Amazon Titan Image Generator G1 model %s", model_id) return image_bytes def main(): """ Entrypoint for Amazon Titan Image Generator G1 example. """ try: logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = 'amazon.titan-image-generator-v1' # Read image from file and encode it as base64 string. with open("/path/to/image", "rb") as image_file: input_image = base64.b64encode(image_file.read()).decode('utf8') body = json.dumps({ "taskType": "IMAGE_VARIATION", "imageVariationParams": { "text": "Modernize the house, photo-realistic, 8k, hdr", "negativeText": "bad quality, low resolution, cartoon", "images": [input_image], "similarityStrength": 0.7, # Range: 0.2 to 1.0 }, "imageGenerationConfig": { "numberOfImages": 1, "height": 512, "width": 512, "cfgScale": 8.0 } }) image_bytes = generate_image(model_id=model_id, body=body) image = Image.open(io.BytesIO(image_bytes)) image.show() except ClientError as err: message = err.response["Error"]["Message"] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) except ImageError as err: logger.error(err.message) print(err.message) else: print( f"Finished generating image with Amazon Titan Image Generator G1 model {model_id}.") if __name__ == "__main__": main()