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Have you ever ever wished you might rapidly visualize how a brand new outfit may look on you earlier than making a purchase order? Or how a chunk of furnishings would look in your front room? Right this moment, we’re excited to introduce a brand new digital try-on functionality in Amazon Nova Canvas that makes this attainable. As well as, we’re including eight new type choices for improved type consistency for text-to-image based mostly type prompting. These options increase Nova Canvas AI-powered picture technology capabilities making it simpler than ever to create lifelike product visualizations and stylized pictures that may improve the expertise of your clients.
Let’s take a fast take a look at how one can begin utilizing these right now.
Getting began
The very first thing is to just remember to have entry to the Nova Canvas mannequin by the same old means. Head to the Amazon Bedrock console, select Mannequin entry and allow Amazon Nova Canvas to your account ensuring that you choose the suitable areas to your workloads. If you have already got entry and have been utilizing Nova Canvas, you can begin utilizing the brand new options instantly as they’re mechanically out there to you.
Digital try-on
The primary thrilling new characteristic is digital try-on. With this, you possibly can add two footage and ask Amazon Nova Canvas to place them along with lifelike outcomes. These could possibly be footage of attire, equipment, dwelling furnishings, and every other merchandise together with clothes. For instance, you possibly can present the image of a human because the supply picture and the image of a garment because the reference picture, and Amazon Nova Canvas will create a brand new picture with that very same individual sporting the garment. Let’s do that out!
My start line is to pick out two pictures. I picked one among myself in a pose that I feel would work nicely for a garments swap and an image of an AWS-branded hoodie.
Notice that Nova Canvas accepts pictures containing a most of 4.1M pixels – the equal of two,048 x 2,048 – so make sure to scale your pictures to suit these constraints if crucial. Additionally, for those who’d prefer to run the Python code featured on this article, guarantee you will have Python 3.9 or later put in in addition to the Python packages boto3 and pillow.
To use the hoodie to my photograph, I exploit the Amazon Bedrock Runtime invoke API. You’ll find full particulars on the request and response constructions for this API within the Amazon Nova Consumer Information. The code is easy, requiring only some inference parameters. I exploit the brand new taskType of "VIRTUAL_TRY_ON". I then specify the specified settings, together with each the supply picture and reference picture, utilizing the virtualTryOnParams object to set a number of required parameters. Notice that each pictures should be transformed to Base64 strings.
import base64
def load_image_as_base64(image_path):
"""Helper operate for getting ready picture knowledge."""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.learn()).decode("utf-8")
inference_params = {
"taskType": "VIRTUAL_TRY_ON",
"virtualTryOnParams": {
"sourceImage": load_image_as_base64("individual.png"),
"referenceImage": load_image_as_base64("aws-hoodie.jpg"),
"maskType": "GARMENT",
"garmentBasedMask": {"garmentClass": "UPPER_BODY"}
}
}Nova Canvas makes use of masking to govern pictures. This is a way that permits AI picture technology to give attention to particular areas or areas of a picture whereas preserving others, much like utilizing painter’s tape to guard areas you don’t need to paint.
You should utilize three totally different masking modes, which you’ll select by setting maskType to the right worth. On this case, I’m utilizing "GARMENT", which requires me to specify which a part of the physique I need to be masked. I’m utilizing "UPPER_BODY" , however you need to use others equivalent to "LOWER_BODY", "FULL_BODY", or "FOOTWEAR" if you wish to particularly goal the ft. Discuss with the documentation for a full listing of choices.
I then name the invoke API, passing in these inference arguments and saving the generated picture to disk.
# Notice: The inference_params variable from above is referenced beneath.
import base64
import io
import json
import boto3
from PIL import Picture
# Create the Bedrock Runtime shopper.
bedrock = boto3.shopper(service_name="bedrock-runtime", region_name="us-east-1")
# Put together the invocation payload.
body_json = json.dumps(inference_params, indent=2)
# Invoke Nova Canvas.
response = bedrock.invoke_model(
physique=body_json,
modelId="amazon.nova-canvas-v1:0",
settle for="software/json",
contentType="software/json"
)
# Extract the pictures from the response.
response_body_json = json.masses(response.get("physique").learn())
pictures = response_body_json.get("pictures", [])
# Verify for errors.
if response_body_json.get("error"):
print(response_body_json.get("error"))
# Decode every picture from Base64 and save as a PNG file.
for index, image_base64 in enumerate(pictures):
image_bytes = base64.b64decode(image_base64)
image_buffer = io.BytesIO(image_bytes)
picture = Picture.open(image_buffer)
picture.save(f"image_{index}.png")
I get a really thrilling end result!
And identical to that, I’m the proud wearer of an AWS-branded hoodie!
Along with the "GARMENT" masks kind, you can too use the "PROMPT" or "IMAGE" masks. With "PROMPT", you additionally present the supply and reference pictures, nonetheless, you present a pure language immediate to specify which a part of the supply picture you’d like to get replaced. That is much like how the "INPAINTING" and "OUTPAINTING" duties work in Nova Canvas. If you wish to use your individual picture masks, then you definately select the "IMAGE" masks kind and supply a black-and-white picture for use as masks, the place black signifies the pixels that you just need to get replaced on the supply picture, and white those you need to protect.
This functionality is particularly helpful for retailers. They will use it to assist their clients make higher buying choices by seeing how merchandise look earlier than shopping for.
Utilizing type choices
I’ve all the time questioned what I’d seem like as an anime superhero. Beforehand, I may use Nova Canvas to govern a picture of myself, however I must depend on my good immediate engineering expertise to get it proper. Now, Nova Canvas comes with pre-trained kinds that you may apply to your pictures to get high-quality outcomes that comply with the inventive type of your selection. There are eight out there kinds together with 3D animated household movie, design sketch, flat vector illustration, graphic novel, maximalism, midcentury retro, photorealism, and smooth digital portray.
Making use of them is as easy as passing in an additional parameter to the Nova Canvas API. Let’s strive an instance.
I need to generate a picture of an AWS superhero utilizing the 3D animated household movie type. To do that, I specify a taskType of "TEXT_IMAGE" and a textToImageParams object containing two parameters: textual content and type. The textual content parameter comprises the immediate describing the picture I need to create which on this case is “a superhero in a yellow outfit with an enormous AWS brand and a cape.” The type parameter specifies one of many predefined type values. I’m utilizing "3D_ANIMATED_FAMILY_FILM" right here, however you’ll find the complete listing within the Nova Canvas Consumer Information.
inference_params = {
"taskType": "TEXT_IMAGE",
"textToImageParams": {
"textual content": "a superhero in a yellow outfit with an enormous AWS brand and a cape.",
"type": "3D_ANIMATED_FAMILY_FILM",
},
"imageGenerationConfig": {
"width": 1280,
"peak": 720,
"seed": 321
}
}Then, I name the invoke API simply as I did within the earlier instance. (The code has been omitted right here for brevity.) And the end result? Effectively, I’ll allow you to choose for your self, however I’ve to say I’m fairly happy with the AWS superhero sporting my favourite shade following the 3D animated household movie type precisely as I envisioned.
What’s actually cool is that I can hold my code and immediate precisely the identical and solely change the worth of the type attribute to generate a picture in a very totally different type. Let’s do that out. I set type to PHOTOREALISM.
inference_params = {
"taskType": "TEXT_IMAGE",
"textToImageParams": {
"textual content": "a superhero in a yellow outfit with an enormous AWS brand and a cape.",
"type": "PHOTOREALISM",
},
"imageGenerationConfig": {
"width": 1280,
"peak": 720,
"seed": 7
}
}And the result’s spectacular! A photorealistic superhero precisely as I described, which is a far departure from the earlier generated cartoon and all it took was altering one line of code.
Issues to know
Availability – Digital try-on and magnificence choices can be found in Amazon Nova Canvas within the US East (N. Virginia), Asia Pacific (Tokyo), and Europe (Eire). Present customers of Amazon Nova Canvas can instantly use these capabilities with out migrating to a brand new mannequin.
Pricing – See the Amazon Bedrock pricing web page for particulars on prices.
For a preview of digital try-on of clothes, you possibly can go to nova.amazon.com the place you possibly can add a picture of an individual and a garment to visualise totally different clothes mixtures.
In case you are able to get began, please try the Nova Canvas Consumer Information or go to the AWS Console.
