Amazon Titan Image Generator G1

Eine Textaufforderung zum Generieren des Bildes muss <= 512 Zeichen lang sein. Auflösungen <= 1.408 auf der längeren Seite. I NegativeText (optional) — Eine Textaufforderung, um zu definieren, was nicht in das Bild aufgenommen werden soll -- <= 512 Zeichen. In der folgenden Tabelle finden Sie eine vollständige Liste der Auflösungen.

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

Die textToImageParams-Felder werden im Folgenden beschrieben.

text (optional): Eine Textaufforderung, um festzulegen, was innerhalb der Maske geändert werden soll. Wenn Sie dieses Feld nicht angeben, versucht das Modell, den gesamten Maskenbereich durch den Hintergrund zu ersetzen. Muss <= 512 Zeichen lang sein. negativeText (optional) — Eine Textaufforderung, um zu definieren, was nicht in das Bild aufgenommen werden soll. Muss <= 512 Zeichen lang sein. Die Größenbeschränkungen für das Eingabebild und die Eingabemaske liegen auf der längeren Seite des Bildes bei <= 1.408. Die Ausgabegröße entspricht der Eingabegröße.

{
    "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
    }
}

Die inPaintingParams-Felder werden im Folgenden beschrieben. Die Maske definiert den Teil des Bildes, den Sie bearbeiten möchten.

text (erforderlich): Eine Textaufforderung, um festzulegen, was innerhalb der Maske geändert werden soll. Muss <= 512 Zeichen lang sein. negativeText (optional) — Eine Textaufforderung, um zu definieren, was nicht in das Bild aufgenommen werden soll. Muss <= 512 Zeichen lang sein. Die Größenbeschränkungen für das Eingabebild und die Eingabemaske liegen auf der längeren Seite des Bildes bei <= 1.408. Die Ausgabegröße entspricht der Eingabegröße.

{
    "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
    }
}

Die outPaintingParams-Felder werden im Folgenden definiert. Die Maske definiert den Bereich des Bildes, den Sie nicht bearbeiten möchten. Die Generierung erweitert den von Ihnen definierten Bereich nahtlos.

Mithilfe von Bildvariationen können Sie Variationen Ihres Originalbilds auf der Grundlage der Parameterwerte erstellen. Die Größenbeschränkung für das Eingabebild liegt auf der längeren Seite des Bildes bei <= 1.408. In der Tabelle unten finden Sie eine vollständige Liste der Auflösungen.

{
     "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
     }
}

Die imageVariationParams-Felder werden im Folgenden definiert.

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

Der Antworttext ist ein Streaming-Objekt, das eines der folgenden Felder enthält.

# 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()

# 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()

# 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()

# 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()