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/faceswap-v3"
# Request payload
data = {
"source_img": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/display_images/faceswapv2_target.jpg"), # Or use image_file_to_base64("IMAGE_PATH")
"target_img": image_url_to_base64("https://segmind-sd-models.s3.us-east-1.amazonaws.com/elon.jpg"), # Or use image_file_to_base64("IMAGE_PATH")
"input_faces_index": 0,
"source_faces_index": 0,
"face_restore": "codeformer-v0.1.0.pth",
"interpolation": "Bilinear",
"detection_face_order": "large-small",
"facedetection": "retinaface_resnet50",
"detect_gender_input": "no",
"detect_gender_source": "no",
"face_restore_weight": 0.75,
"image_format": "jpeg",
"image_quality": 95,
"base64": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Your face goes here. A closeup shot would be ideal.(image url)
The face you want to swap with (image url).
Index of the input faces: By default the model detects faces in images from 'large' to 'small'.Index of the first detected face is 0. Multiple faces can be selected using a comma (,)
min : 0,
max : 5
Index of the source faces: By default the model detects faces in images from 'large' to 'small'.Index of the first detected face is 0. Multiple faces can be selected using a comma (,)
min : 0,
max : 5
which face restore model to use
Allowed values:
Face restore interpolation method
Allowed values:
The order in which faces are detected.
Allowed values:
The model used to detect the face
Allowed values:
Detect the gender of the target face
Allowed values:
Detect the gender of the input face
Allowed values:
Face Restore Weight
min : 0,
max : 1
Output image format
Allowed values:
Image quality setting for output
min : 10,
max : 100
Base64 encoding of the output image.
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.
PixelFlow allows you to use all these features
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Segmented Creation Workflow
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customized Output
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Layering Different Models
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Workflow APIs
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Face Swap V3
FaceSwap V3 model is our latest additon offering seamless face-swapping capabilities, enriched with customizable features to achieve flawless image transformations. Key enhancements over Face Sawp V2 include resolved pixelation issues with face booster models, new face detection order customization, powerful face detection models, and optional gender detection for accurate representation. Whether you're working on a creative project, media production, or social media content, FaceSwap V3 is your ultimate tool for high-quality face swapping.
Key Features of Face Swap V3
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Input and Source Faces Index: Specify the index of faces you wish to swap. This feature supports multiple face selection, enabling complex edits and creative modifications.
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Advanced Face Restore Models: Select from cutting-edge model versions like codeformer-v0.1.0.pth to enhance image clarity and realism, ensuring a professional finish.
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Interpolation Techniques: Choose between Bilinear and Nearest interpolation methods for improved face blending, resulting in smoother transitions and natural-looking output.
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Face Detection Ordering: Customize the detection order from large to small faces, allowing you to prioritize key facial features and achieve more effective results.
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High-Precision Face Detection Models: Utilize powerful models, such as retinaface_resnet50, for accurate face localization, ensuring every detail is captured and swapped perfectly.
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Gender Detection: Toggle gender detection settings for both input and target faces. This feature helps tailor the swap, ensuring gender-specific attributes are accurately represented.
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Adjustable Face Restore Weight: Fine-tune the restoration intensity on a scale of 0 to 1, with a default setting of 0.75. This allows for control over how much restoration is applied to the final image.
Use cases
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Creative and Design Projects: Perfect for artists looking to innovate with digital imagery and photo manipulation.
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Entertainment and Media Production: Suitable for movies, video games, and other interactive media requiring realistic face transformations.
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Social Media and Content Creation: Empower influencers and creators to produce engaging content with unique and personalized face swaps.
Other Popular Models
sdxl1.0-txt2img
The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software
sd1.5-majicmix
The most versatile photorealistic model that blends various models to achieve the amazing realistic images.
codeformer
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.
sd2.1-faceswapper
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training
