# Stability AI models
This section describes the request parameters and response fields for Stability AI models. Use this information to make inference calls to Stability AI models with the [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) operation. This section also includes Python code examples that shows how to call Stability AI models. To use a model in an inference operation, you need the model ID for the model. To get the model ID, see [Supported foundation models in Amazon Bedrock](models-supported.md). Some models also work with the [Converse API](conversation-inference.md). To check if the Converse API supports a specific Stability AI model, see [models at a glance](model-cards.md). For more code examples, see [Code examples for Amazon Bedrock using AWS SDKs](service_code_examples.md).
Foundation models in Amazon Bedrock support input and output modalities, which vary from model to model. To check the modalities that Stability AI models support, see [Supported foundation models in Amazon Bedrock](models-supported.md). To check which Amazon Bedrock features the Stability AI models support, see [Supported foundation models in Amazon Bedrock](models-supported.md). To check which AWS Regions that Stability AI models are available in, see [Supported foundation models in Amazon Bedrock](models-supported.md).
When you make inference calls with Stability AI models, you include a prompt for the model. For general information about creating prompts for the models that Amazon Bedrock supports, see [Prompt engineering concepts](prompt-engineering-guidelines.md). For Stability AI specific prompt information, see the [Stability AI prompt engineering guide](https://platform.stability.ai/docs/getting-started).
## Supported models and image services
Amazon Bedrock supports the following Stability AI models and image services.
**Note**
Support for all other Stability AI models are in the process of being deprecated.
| Model | Use cases | Example |
| --- | --- | --- |
| [Stable Image Ultra](model-parameters-diffusion-stable-ultra-text-image-request-response.md) | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-stability-diffusion.html) | A luxury brand uses Stable Image Ultra to create stunning visuals of its latest collection for magazine spreads, ensuring a premium feel that matches its high standards. |
| [Stable Diffusion 3.5 Large](model-parameters-diffusion-3-5-large.md) | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-stability-diffusion.html) | A game development team uses SD3.5 Large to create detailed environmental textures and character concepts, accelerating their creative pipeline. |
| [Stable Image Core](model-parameters-diffusion-stable-image-core-text-image-request-response.md) | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-stability-diffusion.html) | An online retailer uses Stable Image Core to quickly generate product images for new arrivals, allowing it to list items faster and keep its catalog up-to-date. |
| [Stability AI Image Services](stable-image-services.md) | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-stability-diffusion.html) | A media company uses search and recolor, style transfer, and remove background to generate variations of images for an advertising campaign. |
**Topics**
+ [Supported models and image services](#supported-stability-models)
+ [Stable Image Ultra request and response](model-parameters-diffusion-stable-ultra-text-image-request-response.md)
+ [Stability.ai Stable Diffusion 3.5 Large](model-parameters-diffusion-3-5-large.md)
+ [Stable Image Core request and response](model-parameters-diffusion-stable-image-core-text-image-request-response.md)
+ [Stability AI Image Services](stable-image-services.md)
# Stable Image Ultra request and response
The request body is passed in the `body` field of a request to [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) operation.
**Model invocation request body field**
When you make an InvokeModel call using a Stable Image Ultra model, fill the body field with a JSON object that looks like the below.
+ **prompt** – (string) What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-stable-ultra-text-image-request-response.html)
**Model invocation responses body field**
When you make an `InvokeModel` call using a Stable Image Ultra model, the response looks like the below
```
{
'seeds': [2130420379],
"finish_reasons":[null],
"images":["..."]
}
```
A response with a finish reason that is not `null`, will look like the below:
```
{
"finish_reasons":["Filter reason: prompt"]
}
```
+ **seeds** – (string) List of seeds used to generate images for the model.
+ **finish\$1reasons** – Enum indicating whether the request was filtered or not. `null` will indicate that the request was successful. Current possible values: `"Filter reason: prompt", "Filter reason: output image", "Filter reason: input image", "Inference error", null`.
+ **images** – A list of generated images in base64 string format.
For more information, see [https://platform.us.stability.ai/docs/api-reference\$1tag/v1generation](https://platform.us.stability.ai/docs/api-reference#tag/v1generation).
------
#### [ Text to image ]
The Stability.ai Stable Image Ultra model has the following inference parameters for a text-to-image inference call.
+ **prompt** – (string) What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-stable-ultra-text-image-request-response.html)
**Optional fields**
+ **aspect\$1ratio** – (string) Controls the aspect ratio of the generated image. This parameter is only valid for text-to-image requests. Default 1:1. Enum: 16:9, 1:1, 21:9, 2:3, 3:2, 4:5, 5:4, 9:16, 9:21.
+ **mode** – Set to text-to-image. Default: text-to-image. Enum: `text-to-image`.
+ **output\$1format** – Specifies the format of the output image. Supported formats: JPEG, PNG. Supported dimensions: height 640 to 1,536 px, width 640 to 1,536 px.
+ **seed** – (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range: 0 to 4294967295.
+ **negative\$1prompt** – Keywords of what you do not wish to see in the output image. Max: 10.000 characters.
```
import boto3
import json
import base64
import io
from PIL import Image
bedrock = boto3.client('bedrock-runtime', region_name='us-west-2')
response = bedrock.invoke_model(
modelId='stability.sd3-ultra-v1:1',
body=json.dumps({
'prompt': 'A car made out of vegetables.'
})
)
output_body = json.loads(response["body"].read().decode("utf-8"))
base64_output_image = output_body["images"][0]
image_data = base64.b64decode(base64_output_image)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
```
------
#### [ Image to image ]
The Stability.ai Stable Image Ultra model has the following inference parameters for an image-to-image inference call.
+ **prompt** – (string) What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-stable-ultra-text-image-request-response.html)
**Optional fields**
+ **image** – (string) The Base64 image to use as the starting point for the generation. Supported formats: JPEG, PNG, WebP.
+ **strength** – (number) How much influence the image parameter has on the generated image. Images with lower strength values will look more like the original image. Range: 0.0 to 1.0. Default: 0.35.
+ **aspect\$1ratio** – (string) Controls the aspect ratio of the generated image. This parameter is only valid for text-to-image requests. Default 1:1. Enum: 16:9, 1:1, 21:9, 2:3, 3:2, 4:5, 5:4, 9:16, 9:21.
+ **output\$1format** – Specifies the format of the output image. Supported formats: JPEG, PNG. Supported dimensions: height 640 to 1,536 px, width 640 to 1,536 px.
+ **seed** – (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range: 0 to 4294967295.
+ **negative\$1prompt** – Keywords of what you do not wish to see in the output image. Max: 10.000 characters.
```
import boto3
import json
import base64
import io
from PIL import Image
bedrock = boto3.client('bedrock-runtime', region_name='us-west-2')
response = bedrock.invoke_model(
modelId='stability.sd3-ultra-v1:1',
body=json.dumps({
'prompt': 'A car made out of vegetables.'
})
)
output_body = json.loads(response["body"].read().decode("utf-8"))
base64_output_image = output_body["images"][0]
image_data = base64.b64decode(base64_output_image)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
```
------
# Stability.ai Stable Diffusion 3.5 Large
The Stable Diffusion 3.5 Large model uses 8 billion parameters and supports 1 megapixel resolution output for text-to-image and image-to-image generation.
The request body is passed in the `body` field of a request to [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html).
**Model invocation request body field**
When you make an InvokeModel call using a Stable Diffusion 3.5 Large model, fill the body field with a JSON object that looks like the below.
+ **prompt** – (string) Text description of the desired output image. Maximum 10,000 characters.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
**Model invocation responses body field**
When you make an `InvokeModel` call using a Stable Diffusion 3.5 Large model, the response looks like the below
```
{
'seeds': [2130420379],
"finish_reasons":[null],
"images":["..."]
}
```
A response with a finish reason that is not `null`, will look like the below:
```
{
"finish_reasons":["Filter reason: prompt"]
}
```
+ **seeds** – (string) List of seeds used to generate images for the model.
+ **finish\$1reasons** – Enum indicating whether the request was filtered or not. `null` will indicate that the request was successful. Current possible values: `"Filter reason: prompt", "Filter reason: output image", "Filter reason: input image", "Inference error", null`.
+ **images** – A list of generated images in base64 string format.
------
#### [ Text to image ]
The Stability.ai Stable Diffusion 3.5 Large model has the following inference parameters for a text-to-image inference call.
+ **prompt** (string) – Text description of the desired output image. Maximum 10,000 characters.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
**Optional parameters**
+ **aspect\$1ratio** (string) – Controls the aspect ratio of the generated image. Valid for text-to-image requests only. Enum: 16:9, 1:1, 21:9, 2:3, 3:2, 4:5, 5:4, 9:16, 9:21. Default 1:1.
+ **mode** (string) (GenerationMode) - Default: text-to-image. Enum: image-to-image or text-to-image. Controls whether this is a text-to-image or image-to-image generation, which affects which parameters are required:
+ text-to-image requires only the prompt parameter.
+ image-to-image requires the prompt, image, and strength parameters.
+ **seed** (number) – Value to control randomness in generation. Range 0 to 4294967294. Default 0 (random seed).
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
+ **negative\$1prompt** (string) – Text describing elements to exclude from the output image. Maximum 10,000 characters.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
+ **output\$1format** (string) – Output image format. Enum: jpeg, png, webp. Default png.
```
import boto3
import json
bedrock = boto3.client('bedrock-runtime', region_name='us-west-2')
response = bedrock.invoke_model(
modelId='stability.sd3-5-large-v1:0',
body=json.dumps({
'prompt': 'A car made out of vegetables.'
})
)
```
------
#### [ Image to image ]
The Stability.ai Stable Diffusion 3.5 Large model has the following inference parameters for an image-to-image inference call.
+ **prompt** (string) – Text description of the desired output image. Maximum 10,000 characters.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
+ **image** (string) – Base64-encoded input image. Minimum 64 pixels per side. Supported formats: jpeg, png, webp.
+ **mode** (string) (GenerationMode) - Default: text-to-image. Enum: image-to-image or text-to-image. Controls whether this is a text-to-image or image-to-image generation, which affects which parameters are required:
+ text-to-image requires only the prompt parameter.
+ image-to-image requires the prompt, image, and strength parameters.
+ **strength** (number) – Controls influence of the input image on the output. Range 0 to 1. Value of 0 preserves the input image, value of 1 ignores the input image.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
+ **seed** (number) – Value to control randomness in generation. Range 0 to 4294967294. Default 0 (random seed).
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
+ **negative\$1prompt** (string) – Text describing elements to exclude from the output image. Maximum 10,000 characters.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-3-5-large.html)
+ **output\$1format** (string) – Output image format. Enum: jpeg, png, webp. Default png.
```
import boto3
import base64
import json
# Load and encode image
with open('input_image.jpg', 'rb') as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
bedrock = boto3.client('bedrock-runtime', region_name='us-west-2')
response = bedrock.invoke_model(
modelId='stability.sd3-5-large-v1:0',
body=json.dumps({
'prompt': 'A car made out of vegetables.',
'image': image_base64,
'strength': 0.7
})
)
```
------
# Stable Image Core request and response
The request body is passed in the `body` field of a request to [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html).
**Model invocation request body field**
When you make an InvokeModel call using a Stability AI Stable Diffusion Stable Image Core model, fill the body field with a JSON object that looks like the below.
```
{
'prompt': 'Create an image of a panda'
}
```
**Model invocation responses body field**
When you make an InvokeModel call using a Stability AI Stable Diffusion Stable Image Core model, the response looks like the below
```
{
'seeds': [2130420379],
'finish_reasons': [null],
'images': ['...']
}
```
+ **seeds** – (string) List of seeds used to generate images for the model.
+ **finish\$1reasons** – Enum indicating whether the request was filtered or not. `null` will indicate that the request was successful. Current possible values: `"Filter reason: prompt", "Filter reason: output image", "Filter reason: input image", "Inference error", null`.
+ **images** – A list of generated images in base64 string format.
For more information, see [https://platform.us.stability.ai/docs/api-reference\$1tag/v1generation](https://platform.us.stability.ai/docs/api-reference#tag/v1generation).
------
#### [ Text to image ]
The Stable Image Core model has the following inference parameters for a text to image inference call.
**text\$1prompts** (Required) – An array of text prompts to use for generation. Each element is a JSON object that contains a prompt and a weight for the prompt.
+ **prompt** – (string) What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-stable-image-core-text-image-request-response.html)
**Optional fields**
+ **aspect\$1ratio** – (string) Controls the aspect ratio of the generated image. This parameter is only valid for text-to-image requests. Default 1:1. Enum: 16:9, 1:1, 21:9, 2:3, 3:2, 4:5, 5:4, 9:16, 9:21.
+ **output\$1format** – Specifies the format of the output image. Supported formats: JPEG, PNG. Supported dimensions: height 640 to 1,536 px, width 640 to 1,536 px.
+ **seed** – (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range: 0 to 4294967295.
+ **negative\$1prompt** – Keywords of what you do not wish to see in the output image. Max: 10.000 characters.
```
import boto3
import json
import base64
import io
from PIL import Image
bedrock = boto3.client('bedrock-runtime', region_name='us-west-2')
response = bedrock.invoke_model(
modelId='stability.stable-image-core-v1:0',
body=json.dumps({
'prompt': 'A car made out of vegetables.'
})
)
output_body = json.loads(response["body"].read().decode("utf-8"))
base64_output_image = output_body["images"][0]
image_data = base64.b64decode(base64_output_image)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
```
------
# Stability AI Image Services
You can use Stability AI Image Services with Amazon Bedrock to access thirteen specialized image editing tools designed to accelerate professional creative workflows. With Stability AI Image Services you can generate images from a sketch, restructure and restyle an existing image, or remove and replace objects within an image.
This section describes how to make inference calls to Stability AI Image Services using the [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html). This section also provides code examples in Python and examples of images before and after using Stability AI Image Services.
Stability AI Image Services are available in the following categories:
+ **Edit** ‐ AI-based image editing services, including inpainting with masks (generative fill), or with words. Includes tools for product placement and advertising, as well as basic tools such as background removal.
+ **Control** ‐ May take prompts, maps and other guides. These services leverage ControlNets and similar technologies built on Stable Diffusion models.
**Note**
Subscribing to any edit or control Stability AI Image Service automatically enrolls you in all thirteen available Stability AI Image Services.
**Topics**
+ [Request and response](#model-parameters-stable-image-services-request-response)
+ [Upscale](#stable-image-services-upscale)
+ [Edit](#stable-image-services-edit)
+ [Control](#stable-image-services-control)
## Request and response
The request body is passed in the `body` field of a request to [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html).
**Model invocation request body field**
When you make an InvokeModel call using Stability AI Image Services, fill the body field with a JSON object that looks like the below.
```
{
'prompt': 'Create an image of a panda'
}
```
**Model invocation responses body field**
When you make an InvokeModel call using Stability AI Image Services, the response looks like the below
```
{
'seeds': [2130420379],
'finish_reasons': [null],
'images': ['...']
}
```
+ **seeds** – (string) List of seeds used to generate images for the model.
+ **finish\$1reasons** – Enum indicating whether the request was filtered or not. `null` will indicate that the request was successful. Current possible values: `"Filter reason: prompt", "Filter reason: output image", "Filter reason: input image", "Inference error", null`.
+ **images** – A list of generated images in base64 string format.
For more information, see [https://platform.us.stability.ai/docs/api-reference\$1tag/v1generation](https://platform.us.stability.ai/docs/api-reference#tag/v1generation).
## Upscale
The following section describes the upscale Stability AI Image Services.
### Creative Upscale
Creative Upscale takes images between 64x64 and 1 megapixel and upscales them to 4K resolution. This service can upscale images by 20 to 40 times while preserving and often enhancing quality. Creative Upscale works best on highly degraded images and is not for photos of 1 megapixel or above as it performs heavy reimagining.
Creative Upscale has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image to upscale. Every side of the image must be at least 64 pixels. Total pixel count must be between 4,096 and 1,048,576 pixels. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **creativity** ‐ (number) Indicates how creative the model should be when upscaling an image. Higher values will result in more details being added to the image during upscaling. Range between 0.1 and 0.5. Default 0.3
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **style\$1preset** ‐ Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-creative-upscale-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "This dreamlike digital art captures a vibrant, kaleidoscopic Big Ben in London",
"creativity": 0.30
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-creative-upscale-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "This dreamlike digital art captures a vibrant, kaleidoscopic Big Ben in London",
"creativity": 0.30
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Creative Upscale operation using the following prompt: *This dreamlike digital art captures a vibrant, kaleidoscopic bird in a lush rainforest*.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-creative-upscale.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-creative-upscale.jpg) |
### Conservative Upscale
Conservative Upscale takes images between 64x64 and 1 megapixel and upscale them to 4K resolution. This service can upscale images by 20 to 40 times while preserving all aspects. Conservative Upscale minimizes alterations to the image and should not be used to reimagine an image.
Conservative Upscale has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image to upscale. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **creativity** ‐ (number) Indicates how creative the model should be when upscaling an image. Higher values will result in more details being added to the image during upscaling. Range between 0.1 and 0.5. Default 0.35
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-conservative-upscale-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "This dreamlike digital art captures a vibrant, kaleidoscopic Big Ben in London",
"creativity": 0.30
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-conservative-upscale-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "This dreamlike digital art captures a vibrant, kaleidoscopic Big Ben in London",
"creativity": 0.30
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Conservative Upscale operation using the following prompt: *photo of a giant chicken in a forest*.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-conservative-upscale.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-conservative-upscale.jpg) |
### Fast Upscale
Fast Upscale enhances image resolution by 4 times using predictive and generative AI. This lightweight and fast service is ideal for enhancing the quality of compressed images, making it suitable for social media posts and other applications.
Fast upscale has the following required parameters:
+ **image** ‐ (string) The Base64 image to upscale. Width must be between 32 and 1,536 pixels. Height must be between 32 and 1,536 pixels. Total pixel count must be between 1,024 and 1,048,576 pixels. Supported formats: jpeg, png, webp.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-fast-upscale-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-fast-upscale-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Fast Upscale operation.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-fast-upscale.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-fast-upscale.jpg) |
## Edit
The following section describes the edit Stability AI Image Services.
### Inpaint
Inpaint intelligently modifies images by filling in or replacing specified areas with new content based on the content of a mask image.
Inpaint has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image to inpaint. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **style\$1preset** ‐ (string) Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **mask** ‐ (string) Controls the strength of the inpainting process on a per-pixel basis, either via a second image (passed into this parameter) or via the alpha channel of the image parameter.
+ **Passing in a Mask** ‐ The image passed to this parameter should be a black and white image that represents, at any pixel, the strength of inpainting based on how dark or light the given pixel is. Completely black pixels represent no inpainting strength while completely white pixels represent maximum strength. In the event the mask is a different size than the image parameter, it will be automatically resized.
+ **Alpha Channel Support** ‐ If you don't provide an explicit mask, one will be derived from the alpha channel of the image parameter. Transparent pixels will be inpainted while opaque pixels will be preserved. In the event an image with an alpha channel is provided along with a mask, the mask will take precedence.
+ **grow\$1mask** ‐ Grows the edges of the mask outward in all directions by the specified number of pixels. The expanded area around the mask will be blurred, which can help smooth the transition between inpainted content and the original image. Range between 0 and 20. Default 5. Try this parameter if you notice seams or rough edges around the inpainted content. Note that excessive growth may obscure fine details in the mask and/or merge nearby masked regions.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.png"
mask = "./content/mask.png"
region = "us-east-1"
model_id = "us.stability.stable-image-inpaint-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
with open(mask, "rb") as mask_file:
mask_base64 = base64.b64encode(mask_file.read()).decode('utf-8')
params = {
"image": image_base64,
"mask": mask_base64,
"prompt": "artificer of time and space"
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.png"
mask = "./content/mask.png"
region = "us-east-1"
model_id = "us.stability.stable-image-inpaint-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
with open(mask, "rb") as mask_file:
mask_base64 = base64.b64encode(mask_file.read()).decode('utf-8')
params = {
"image": image_base64,
"mask": mask_base64,
"prompt": "artificer of time and space"
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of an Inpaint operation.
| Input | Mask | Output |
| --- | --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-image-inpaint.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/mask-image-inpaint.png) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-image-inpaint.jpg) |
### Outpaint
Outpaint inserts additional content in an image to fill in the space in any direction. Compared to other automated or manual attempts to expand the content in an image, the Outpaint service minimizes indications that the original image has been edited.
Outpaint has the following required parameters:
+ **image** ‐ (string) The Base64 image to outpaint. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
**Note**
At least one outpaint direction: (left, right, up, or down) must be supplied with a non-zero value. For best quality results, consider the composition and content of your original image when choosing outpainting directions.
The following parameters are optional:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **style\$1preset** ‐ (string) Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **creativity** ‐ (number) Indicates how creative the model should be when outpainting an image. Higher values will result in more creative content being added to the image during outpainting. Range between 0.1 and 1.0. Default 0.5.
+ **left** ‐ (integer) The number of pixels to outpaint on the left side of the image. At least one outpainting direction must be supplied with a non-zero value. Range 0 to 2000. Detault 0.
+ **right** ‐ (integer) The number of pixels to outpaint on the right side of the image. At least one outpainting direction must be supplied with a non-zero value. Range 0 to 2000. Detault 0.
+ **up** ‐ (integer) The number of pixels to outpaint on the top of the image. At least one outpainting direction must be supplied with a non-zero value. Range 0 to 2000. Detault 0.
+ **down** ‐ (integer) The number of pixels to outpaint on the bottom of the image. At least one outpainting direction must be supplied with a non-zero value. Range 0 to 2000. Detault 0.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-outpaint-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"left": 512,
"right": 512,
"up": 200,
"down": 100
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.png"
region = "us-east-1"
model_id = "us.stability.stable-outpaint-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"left": 512,
"right": 512,
"up": 200,
"down": 100
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of an Outpaint operation.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-image-outpaint.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-image-outpaint.jpg) |
### Search and Recolor
Search and Recolor allows you to change the color of a specific object in an image using a prompt. This service is a specific version of inpainting that does not require a mask. It will automatically segment the object and recolor it using the colors requested in the prompt.
Search and Recolor has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image to recolor. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
+ **select\$1prompt** ‐ (string) Short description of what to search for in the image. Maximum 10000 characters.
The following parameters are optional:
+ **style\$1preset** ‐ (string) Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **grow\$1mask** ‐ Grows the edges of the mask outward in all directions by the specified number of pixels. The expanded area around the mask will be blurred, which can help smooth the transition between inpainted content and the original image. Range between 0 and 20. Default 5. Try this parameter if you notice seams or rough edges around the inpainted content. Note that excessive growth may obscure fine details in the mask and/or merge nearby masked regions.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.png"
region = "us-east-1"
model_id = "us.stability.stable-image-search-recolor-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "pink jacket",
"select_prompt": "jacket"
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.png"
region = "us-east-1"
model_id = "us.stability.stable-image-search-recolor-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "pink jacket",
"select_prompt": "jacket"
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Search and Recolor operation using the following prompt: *pink jacket*.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-search-recolor.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-search-recolor.jpg) |
### Search and Replace
Search and Replace allows you to use a search prompt to identify an object in simple language to be replaced. The service will automatically segment the object and replace it with the object requested in the prompt without requiring a mask.
Search and Replace has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image to recolor. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
+ **search\$1prompt** ‐ (string) Short description of what to inpaint in the image. Maximum 10000 characters.
The following parameters are optional:
+ **style\$1preset** ‐ (string) Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **grow\$1mask** ‐ Grows the edges of the mask outward in all directions by the specified number of pixels. The expanded area around the mask will be blurred, which can help smooth the transition between inpainted content and the original image. Range between 0 and 20. Default 5. Try this parameter if you notice seams or rough edges around the inpainted content. Note that excessive growth may obscure fine details in the mask and/or merge nearby masked regions.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.png"
region = "us-east-1"
model_id = "us.stability.stable-image-search-replace-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "jacket",
"search_prompt": "sweater",
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.png"
region = "us-east-1"
model_id = "us.stability.stable-image-search-replace-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "jacket",
"search_prompt": "sweater",
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Search and Replace operation using the following prompt: *jacket*.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-search-replace.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-search-replace.jpg) |
### Erase
Erase allows you to remove unwanted elements using image masks, while intelligently maintaining background consistency.
Erase has the following required parameters:
+ **image** ‐ (string) The Base64 image to erase from. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **mask** ‐ (string) Controls the strength of the inpainting process on a per-pixel basis, either via a second image (passed into this parameter) or via the alpha channel of the image parameter.
+ **Passing in a Mask** ‐ The image passed to this parameter should be a black and white image that represents, at any pixel, the strength of inpainting based on how dark or light the given pixel is. Completely black pixels represent no inpainting strength while completely white pixels represent maximum strength. In the event the mask is a different size than the image parameter, it will be automatically resized.
+ **Alpha Channel Support** ‐ If you don't provide an explicit mask, one will be derived from the alpha channel of the image parameter. Transparent pixels will be inpainted while opaque pixels will be preserved. In the event an image with an alpha channel is provided along with a mask, the mask will take precedence.
+ **grow\$1mask** ‐ Grows the edges of the mask outward in all directions by the specified number of pixels. The expanded area around the mask will be blurred, which can help smooth the transition between inpainted content and the original image. Range between 0 and 20. Default 5. Try this parameter if you notice seams or rough edges around the inpainted content. Note that excessive growth may obscure fine details in the mask and/or merge nearby masked regions.
**Note**
For optimal erase results, ensure your mask accurately defines the areas to be removed. If no explicit mask is provided, the service will use the alpha channel of the input image. The mask will take precedence if both are provided.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.png"
mask = "./content/mask.png"
region = "us-east-1"
model_id = "us.stability.stable-image-erase-object-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8'),
with open(mask, "rb") as mask_file:
mask_base64 = base64.b64encode(mask_file.read()).decode('utf-8')
params = {
"image": image_base64,
"mask": mask_base64
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.png"
mask = "./content/mask.png"
region = "us-east-1"
model_id = "us.stability.stable-image-erase-object-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8'),
with open(mask, "rb") as mask_file:
mask_base64 = base64.b64encode(mask_file.read()).decode('utf-8')
params = {
"image": image_base64,
"mask": mask_base64
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of an Erase operation.
| Input | Mask | Output |
| --- | --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-erase-object.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/mask-erase-object.png) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-erase-object.jpg) |
### Remove Background
Remove Background allows you to isolate subjects from the background with precision.
Remove Background has the following required parameters:
+ **image** ‐ (string) The Base64 image to remove the background from. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.png"
region = "us-east-1"
model_id = "us.stability.stable-image-remove-background-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.png"
region = "us-east-1"
model_id = "us.stability.stable-image-remove-background-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Remove Background operation.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-remove-background.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-remove-background.jpg) |
## Control
The following section describes the control Stability AI Image Services.
### Control Sketch
Upgrade rough hand-drawn sketches to refined outputs with precise control. For non-sketch images, Control Sketch allows detailed manipulation of the final appearance by leveraging the contour lines and edges within the image.
Control Sketch has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image of the sketch. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **control\$1strength** ‐ (number) How much influence, or control, the image has on the generation. Represented as a float between 0 and 1, where 0 is the least influence and 1 is the maximum. Default 0.7.
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **style\$1preset** ‐ Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-image-control-sketch-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "a house with background of mountains and river flowing nearby"
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-image-control-sketch-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "a house with background of mountains and river flowing nearby"
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Control Sketch call using the following prompt: *a house with background of mountains and river flowing nearby*.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-control-sketch.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-control-sketch.jpg) |
| | |
### Control Structure
Control Structure allows you to generate images while maintaining the structure of an input image. This is especially valuable for advanced content creation scenarios such as recreating scenes or rendering characters from models.
Control Structure has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image of the sketch. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **control\$1strength** ‐ (number) How much influence, or control, the image has on the generation. Represented as a float between 0 and 1, where 0 is the least influence and 1 is the maximum. Default 0.7.
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **style\$1preset** ‐ Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-image-control-structure-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "surreal structure with motion generated sparks lighting the scene"
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-image-control-structure-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "surreal structure with motion generated sparks lighting the scene"
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Control Structure operation using the following prompt: *surreal structure with motion generated sparks lighting the scene*.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-control-structure.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-control-structure.jpg) |
### Style Guide
Style Guide allows you to extract stylistic elements from an input image and use it to guide the creation of an output image based on the prompt. The result is a new image in the same style as the input image.
Style Guide has the following required parameters:
+ **prompt** ‐ What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue. Minimum 0 and Maximum 10000 characters.
+ **image** ‐ (string) The Base64 image of the sketch. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **aspect\$1ratio** ‐ (string) Controls the aspect ratio of the generated image. This parameter is only valid for text-to-image requests. Default 1:1. Enum: 16:9, 1:1, 21:9, 2:3, 3:2, 4:5, 5:4, 9:16, 9:21. Default 1:1.
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **fidelity** ‐ (number) How closely the output image's style resembles the input image's style. Range 0 to 1. Default 0.5.
+ **style\$1preset** ‐ Guides the image model towards a particular style. Enum: 3d-model, analog-film, anime, cinematic, comic-book, digital-art, enhance, fantasy-art, isometric, line-art, low-poly, modeling-compound, neon-punk, origami, photographic, pixel-art, tile-texture.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-image-style-guide-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "wide shot of modern metropolis"
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/input.jpg"
region = "us-east-1"
model_id = "us.stability.stable-image-style-guide-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
params = {
"image": image_base64,
"prompt": "wide shot of modern metropolis"
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Style Guide call using the following prompt: *wide shot of modern metropolis*.
| Input | Output |
| --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-style-guide.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-style-guide.jpg) |
### Style Transfer
Style Transfer allows you to apply visual characteristics from reference style images to target images. While the Style Guide service extracts stylistic elements from an input image and uses them to guide the creation of an output image based on the prompt, Style Transfer specifically transforms existing content while preserving the original composition. This tool helps create consistent content across multiple assets.
Style Transfer has the following required parameters:
+ **init\$1image** ‐ (string) A Base64 image containing the subject you wish to restyle. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
+ **style\$1image** ‐ (string) A Base64 image containing the subject you wish to restyle. Every side of the image must be at least 64 pixels. The total pixel count cannot exceed 9,437,184 pixels. Image aspect ratio must be between 1:2.5 and 2.5:1. Supported formats: jpeg, png, webp.
The following parameters are optional:
+ **prompt** ‐ (string) What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results. To control the weight of a given word use the format (word:weight), where word is the word you'd like to control the weight of and weight is a value. A value 0 and 1.0 de-emphasized the word and a value between 1.1 and 2 emphasized the word . For example: The sky was a crisp (blue:0.3) and (green:1.8) would convey a sky that was blue and green, but more green than blue.
+ **negative\$1prompt** ‐ (string) A blurb of text describing what you do not wish to see in the output image. This is an advanced feature. Maximum 10000 characters.
+ **seed** ‐ (number) A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass 0 to use a random seed.) Range 0 to 4294967294. Default 0.
+ **output\$1format** ‐ (string) Dictates the content-type of the generated image. Enum: jpeg, png, webp. Default png.
+ **composition\$1fidelity** ‐ (number) How closely the output image's style resembles the input image's style. Range between 0 and 1. Default 0.9.
+ **style\$1strength** ‐ (number) Sometimes referred to as denoising, this parameter controls how much influence the style\$1image parameter has on the generated image. A value of 0 would yield an image that is identical to the input. A value of 1 would be as if you passed in no image at all. Range between 0 and 1. Default 1.
+ **change\$1strength** ‐ (number) How much the original image should change. Range between 0.1 and 1. Default 0.9.
------
#### [ API ]
```
import base64
import json
import requests
import io
import os
from PIL import Image
image = "./content/input.jpg"
style_image = "./content/style.jpg"
region = "us-east-1"
model_id = "us.stability.stable-style-transfer-v1:0"
url = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"
api_key = os.getenv("AWS_BEARER_TOKEN_BEDROCK") # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-keys.html
headers = {
"Content-Type":"application/json",
"Authorization":f"Bearer {api_key}"
}
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
with open(style_image, "rb") as style_image_file:
style_image_base64 = base64.b64encode(style_image_file.read()).decode('utf-8')
params = {
"init_image": image_base64,
"style_image": style_image_base64,
"prompt": "statue"
}
response = requests.request("POST", url, json=params, headers=headers)
response.raise_for_status()
model_response = json.loads(response.text)
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
#### [ Python ]
```
import boto3
import base64
import io
import json
from PIL import Image
image = "./content/cat_statue_512x512.jpg"
style_image = "./content/glowbot_style.jpg"
region = "us-east-1"
model_id = "us.stability.stable-style-transfer-v1:0"
bedrock = boto3.client("bedrock-runtime", region_name=region)
try:
with open(image, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
with open(style_image, "rb") as style_image_file:
style_image_base64 = base64.b64encode(style_image_file.read()).decode('utf-8')
params = {
"init_image": image_base64,
"style_image": style_image_base64,
"prompt": "statue"
}
request = json.dumps(params)
response = bedrock.invoke_model(modelId=model_id, body=request)
model_response = json.loads(response["body"].read())
base64_image_data = model_response["images"][0]
if not base64_image_data:
raise ValueError("No image data found in model response.")
image_data = base64.b64decode(base64_image_data)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")
print("Successfully saved image.")
except Exception as e:
print(e)
```
------
The following table shows the input and output images of a Style Transfer call.
| Input | Style | Output |
| --- | --- | --- |
| ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/input-style-transfer.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/style-style-transfer.jpg) | ![\[alt text not found\]](http://docs.aws.amazon.com/bedrock/latest/userguide/images/stable-image-services/output-style-transfer.jpg) |