API
If you're looking for an API, you can choose from your desired programming language.
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import requests
import base64
# Use this function to convert an image file from the filesystem to base64
def image_file_to_base64(image_path):
with open(image_path, 'rb') as f:
image_data = f.read()
return base64.b64encode(image_data).decode('utf-8')
# Use this function to fetch an image from a URL and convert it to base64
def image_url_to_base64(image_url):
response = requests.get(image_url)
image_data = response.content
return base64.b64encode(image_data).decode('utf-8')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/idm-vton"
# Request payload
data = {
"crop": False,
"seed": 42,
"steps": 30,
"category": "upper_body",
"force_dc": False,
"human_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/idm-ip.png",
"garm_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/idm-viton-dress.png",
"mask_only": False,
"garment_des": "Green colour semi Formal Blazer"
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Use cropping? (check this if your image is not 3:4)
min : 1,
max : 40
An enumeration.
Allowed values:
Use the DressCode version of IDM-VTON (this is default false, except if category=dresses)
Model, if this is not 3:4 check crop
Garment, should match the category, can be a product image or even a photo of someone
Mask image, optional (but faster)
Return only the mask
Description of garment e.g. Short Sleeve Round Neck T-shirt
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
IDM Viton
IDM Viton is cutting-edge solution for authentic virtual try-on experiences. It can handle a wide variety of garments and adapt to different body types, ensuring a seamless and inclusive virtual try-on session.
IDM Viton is currently considered the best-in-class virtual try-on model, utilizes an innovative two-stream conditional diffusion model. It uses two different modules to encode the semantics of garment image. Given the base UNet of the diffusion model, the high-level semantics extracted from a visual encoder are fused to the cross-attention layer, and then the low-level features extracted from parallel UNet are fused to the self-attention layer. This approach allows IDM-VTON to provide highly realistic and authentic virtual try-on experiences.
lDM VTON License
The IDM VTON model is licensed under the Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International Public License. This license allows for the free use, modification, and distribution of the software for non-commercial purposes only, provided that the original copyright notice and disclaimer are included in all copies or substantial portions of the software. However, it does not permit the sharing of adapted material. The license also does not permit the use of the name of the license holder or the names of its contributors to endorse or promote products derived from this software without specific prior written permission. Furthermore, the license is irrevocable, meaning once granted, it cannot be taken back.
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