Stability.ai Diffusion 1.0 de imagem para imagem

O modelo Stability.ai Diffusion 1.0 tem os parâmetros de inferência e a resposta de modelo a seguir para fazer chamadas de inferência de imagem para imagem.

Solicitação e reposta

O corpo da solicitação é passado no body campo de uma solicitação para InvokeModelou InvokeModelWithResponseStream.

Para obter mais informações, consulte https://platform.stability.ai/docs/api-reference#tag/v1generation/operation/ imageToImage.

Exemplo de código

O exemplo a seguir mostra como executar inferência com o modelo Stability.ai Diffusion 1.0 e o throughput sob demanda. O exemplo envia um prompt de texto e uma imagem de referência para um modelo, recupera a resposta do modelo e, por fim, mostra a imagem.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate an image from a reference image with SDXL 1.0 (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 SDXL"
    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 SDXL 1.0 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 SDXL 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())
    print(response_body['result'])

    base64_image = response_body.get("artifacts")[0].get("base64")
    base64_bytes = base64_image.encode('ascii')
    image_bytes = base64.b64decode(base64_bytes)

    finish_reason = response_body.get("artifacts")[0].get("finishReason")

    if finish_reason == 'ERROR' or finish_reason == 'CONTENT_FILTERED':
        raise ImageError(f"Image generation error. Error code is {finish_reason}")


    logger.info("Successfully generated image withvthe SDXL 1.0 model %s", model_id)

    return image_bytes



def main():
    """
    Entrypoint for SDXL example.
    """

    logging.basicConfig(level = logging.INFO,
                        format = "%(levelname)s: %(message)s")

    model_id='stability.stable-diffusion-xl-v1'

    prompt="""A space ship."""

    # Read reference image from file and encode as base64 strings.
    with open("/path/to/image", "rb") as image_file:
        init_image = base64.b64encode(image_file.read()).decode('utf8')

    # Create request body.
    body=json.dumps({
        "text_prompts": [
        {
        "text": prompt
        }
    ],
    "init_image": init_image,
    "style_preset" : "isometric"
    })

    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 text with SDXL model {model_id}.")


if __name__ == "__main__":
    main()