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Have you ever ever wished you would rapidly visualize how a brand new outfit would possibly look on you earlier than making a purchase order? Or how a bit of furnishings would look in your lounge? Right now, 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 model choices for improved model consistency for text-to-image based mostly model prompting. These options broaden Nova Canvas AI-powered picture technology capabilities making it simpler than ever to create real looking 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 this moment.
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 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 robotically out there to you.
Digital try-on
The primary thrilling new characteristic is digital try-on. With this, you’ll be able to add two photos and ask Amazon Nova Canvas to place them along with real looking outcomes. These could possibly be photos of attire, equipment, house 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 that out!
My start line is to pick two pictures. I picked considered one of myself in a pose that I feel would work nicely for a garments swap and an image of an AWS-branded hoodie.
Observe that Nova Canvas accepts pictures containing a most of 4.1M pixels – the equal of two,048 x 2,048 – so be sure you scale your pictures to suit these constraints if crucial. Additionally, in the event you’d prefer to run the Python code featured on this article, guarantee you may 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 take advantage of the Amazon Bedrock Runtime invoke API. Yow will discover full particulars on the request and response constructions for this API within the Amazon Nova Consumer Information. The code is simple, requiring only some inference parameters. I take advantage of 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. Observe that each pictures have to 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 enables AI picture technology 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 proper 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 should utilize others equivalent to "LOWER_BODY", "FULL_BODY", or "FOOTWEAR" if you wish to particularly goal the toes. Seek advice from the documentation for a full checklist of choices.
I then name the invoke API, passing in these inference arguments and saving the generated picture to disk.
# Observe: The inference_params variable from above is referenced under.
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="utility/json",
contentType="utility/json"
)
# Extract the pictures from the response.
response_body_json = json.masses(response.get("physique").learn())
pictures = response_body_json.get("pictures", [])
# 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(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 sort, it’s also possible to use the "PROMPT" or "IMAGE" masks. With "PROMPT", you additionally present the supply and reference pictures, 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 individual 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 clients make higher buying choices by seeing how merchandise look earlier than shopping for.
Utilizing model choices
I’ve all the time questioned what I’d appear to be 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 abilities to get it proper. Now, Nova Canvas comes with pre-trained kinds that you would be able to apply to your pictures to get high-quality outcomes that comply with the creative model 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 attempt an instance.
I wish to generate a picture of an AWS superhero utilizing the 3D animated household movie model. To do that, I specify a taskType of "TEXT_IMAGE" and a textToImageParams object containing two parameters: textual content and model. 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 model parameter specifies one of many predefined model values. I’m utilizing "3D_ANIMATED_FAMILY_FILM" right here, however yow will discover the total checklist 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.",
"model": "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 decide for your self, however I’ve to say I’m fairly happy with the AWS superhero carrying my favourite coloration following the 3D animated household movie model 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 model attribute to generate a picture in a totally totally different model. Let’s do that out. I set model to PHOTOREALISM.
inference_params = {
"taskType": "TEXT_IMAGE",
"textToImageParams": {
"textual content": "a superhero in a yellow outfit with a giant AWS brand and a cape.",
"model": "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.
