Stability.ai Difusi 1.0 gambar ke gambar

Model Stability.ai Diffusion 1.0 memiliki parameter inferensi berikut dan respons model untuk membuat panggilan inferensi gambar ke gambar.

Permintaan dan Tanggapan

Badan permintaan diteruskan di body bidang permintaan ke InvokeModelatau InvokeModelWithResponseStream.

Untuk informasi lebih lanjut, lihat https://platform.stability. ai/docs/api-reference#tag/v1generation/operation/imageToImage.

{
        "text_prompts": [
            {
                "text": string,
                "weight": float
            }
        ],
        "init_image" : string ,
        "init_image_mode" : string,
        "image_strength" : float,
        "cfg_scale": float,
        "clip_guidance_preset": string,
        "sampler": string,
        "samples" : int,
        "seed": int,
        "steps": int,
        "style_preset": string,
        "extras" : json object
}
                       

Contoh kode

Contoh berikut menunjukkan cara menjalankan inferensi dengan model Stability.ai Diffusion 1.0 dan throughput sesuai permintaan. Contoh mengirimkan prompt teks dan gambar referensi ke model, mengambil respons dari model, dan akhirnya menunjukkan gambar.

# 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()