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Have you ever ever wished you possibly can rapidly visualize how a brand new outfit may look on you earlier than making a purchase order? Or how a bit of furnishings would look in your lounge? Immediately, we’re excited to introduce a brand new digital try-on functionality in Amazon Nova Canvas that makes this doable. As well as, we’re including eight new type choices for improved type consistency for text-to-image based mostly type prompting. These options broaden Nova Canvas AI-powered picture era capabilities making it simpler than ever to create lifelike product visualizations and stylized photos that may improve the expertise of your prospects.
Let’s take a fast take a look at how one can begin utilizing these right this moment.
Getting began
The very first thing is to just be sure you have entry to the Nova Canvas mannequin by way of the standard means. Head to the Amazon Bedrock console, select Mannequin entry and allow Amazon Nova Canvas on your account ensuring that you choose the suitable areas on 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 function is digital try-on. With this, you’ll be able to add two photos and ask Amazon Nova Canvas to place them along with lifelike outcomes. These could possibly be photos of attire, equipment, dwelling furnishings, and another merchandise together with clothes. For instance, you’ll be able to 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 carrying the garment. Let’s do this out!
My place to begin is to pick out two photos. I picked certainly one of myself in a pose that I feel would work properly for a garments swap and an image of an AWS-branded hoodie.
Word that Nova Canvas accepts photos containing a most of 4.1M pixels – the equal of two,048 x 2,048 – so be sure you scale your photos to suit these constraints if mandatory. Additionally, should you’d prefer to run the Python code featured on this article, guarantee you could 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 will discover full particulars on the request and response buildings for this API within the Amazon Nova Consumer Information. The code is simple, 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 just a few required parameters. Word that each photos have to be transformed to Base64 strings.
import base64
def load_image_as_base64(image_path):
"""Helper operate for making 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 photos. This is a method that enables AI picture era to concentrate on particular areas or areas of a picture whereas preserving others, much like utilizing painter’s tape to guard areas you don’t wish to paint.
You need to use three totally different masking modes, which you’ll be able to 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 wish to be masked. I’m utilizing "UPPER_BODY" , however you need to use others reminiscent of "LOWER_BODY", "FULL_BODY", or "FOOTWEAR" if you wish to particularly goal the ft. Consult 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.
# Word: 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 consumer.
bedrock = boto3.consumer(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.hundreds(response.get("physique").learn())
photos = response_body_json.get("photos", [])
# Examine 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(photos):
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 consequence!
And similar to that, I’m the proud wearer of an AWS-branded hoodie!
Along with the "GARMENT" masks sort, you may as well use the "PROMPT" or "IMAGE" masks. With "PROMPT", you additionally present the supply and reference photos, nevertheless, 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 personal picture masks, then you definitely select the "IMAGE" masks sort and supply a black-and-white picture for use as masks, the place black signifies the pixels that you simply wish to get replaced on the supply picture, and white those you wish to protect.
This functionality is particularly helpful for retailers. They will use it to assist their prospects make higher buying choices by seeing how merchandise look earlier than shopping for.
Utilizing type choices
I’ve all the time puzzled what I’d appear to be as an anime superhero. Beforehand, I might 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 types you could apply to your photos to get high-quality outcomes that comply with the inventive type of your selection. There are eight out there types together with 3D animated household movie, design sketch, flat vector illustration, graphic novel, maximalism, midcentury retro, photorealism, and delicate digital portray.
Making use of them is as simple as passing in an additional parameter to the Nova Canvas API. Let’s attempt an instance.
I wish 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 wish to create which on this case is “a superhero in a yellow outfit with a giant 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 will discover the total listing within the Nova Canvas Consumer Information.
inference_params = {
"taskType": "TEXT_IMAGE",
"textToImageParams": {
"textual content": "a superhero in a yellow outfit with a giant 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 consequence? Nicely, I’ll allow you to decide for your self, however I’ve to say I’m fairly happy with the AWS superhero carrying my favourite colour following the 3D animated household movie type precisely as I envisioned.
What’s actually cool is that I can maintain 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 this out. I set type to PHOTOREALISM.
inference_params = {
"taskType": "TEXT_IMAGE",
"textToImageParams": {
"textual content": "a superhero in a yellow outfit with a giant 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’ll be able to go to nova.amazon.com the place you’ll be able to add a picture of an individual and a garment to visualise totally different clothes mixtures.
In case you are able to get began, please take a look at the Nova Canvas Consumer Information or go to the AWS Console.
