Eachlabs Face Swap
Swap faces between images effortlessly! Integrate your app with the Faceswap model by Eachlabs for smooth and seamless transformations.
Avg Run Time: 20s
Model Slug: each-faceswap-v1
Category: Image to Image
Input
Enter an URL or choose a file from your computer.
Click to upload or drag and drop
image/jpeg, image/png, image/jpg, image/webp (Max 50MB)
Enter an URL or choose a file from your computer.
Click to upload or drag and drop
image/jpeg, image/png, image/jpg, image/webp (Max 50MB)
Output
Example Result
Preview and download your result.
Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Overview
The each-faceswap-v1 model is an advanced AI image generator developed by Eachlabs, designed specifically for seamless face swapping between images. Leveraging state-of-the-art deep learning techniques, the model enables users to effortlessly replace faces in photographs while maintaining natural appearance, skin tones, and facial expressions. Its primary goal is to provide a robust, user-friendly solution for both professional and creative applications where face swapping is required.
Key features of each-faceswap-v1 include high-fidelity facial blending, support for a wide range of image resolutions, and minimal artifacts in the output. The model utilizes a combination of convolutional neural networks and attention mechanisms to accurately identify facial landmarks and perform precise swaps. Its unique strength lies in its ability to handle diverse lighting conditions, facial orientations, and complex backgrounds, making it suitable for both casual and production-grade use.
What sets each-faceswap-v1 apart is its focus on smooth, realistic transformations and ease of integration into custom applications. The model is optimized for speed and quality, allowing developers and end-users to achieve professional results without extensive post-processing. Community feedback highlights its reliability and adaptability across various scenarios, from entertainment to business use.
Technical Specifications
- Architecture: Convolutional Neural Network (CNN) with attention-based facial landmark detection
- Parameters: Not publicly specified; estimated in the tens of millions based on comparable models
- Resolution: Supports input images up to 1024x1024 pixels; optimal results typically at 512x512 or 768x768
- Input/Output formats: Accepts standard image formats such as JPEG and PNG; outputs are high-resolution PNG or JPEG images
- Performance metrics: Average inference time reported as 1-3 seconds per image on modern GPUs; high SSIM and FID scores in user benchmarks for facial realism and blending
Key Considerations
- Ensure input images have clear, unobstructed faces for best results
- Use images with similar lighting and orientation to minimize blending artifacts
- Avoid low-resolution or heavily compressed images, as these can reduce output quality
- For optimal speed, batch processing is recommended on GPU-enabled systems
- Prompt engineering: Clearly specify source and target faces if using automated pipelines
- Quality vs speed: Higher resolution images yield better results but increase processing time
- Common pitfalls include mismatched skin tones and facial angles; pre-processing can help mitigate these issues
Tips & Tricks
- Use images with neutral backgrounds for cleaner swaps
- Crop faces tightly before input to improve landmark detection accuracy
- Adjust brightness and contrast of source and target images to match for more natural results
- Iteratively refine outputs by running multiple swaps and selecting the best result
- For advanced blending, use post-processing tools to smooth edges and adjust color balance
- When swapping faces with glasses or accessories, manually mask these areas for improved realism
Capabilities
- High-quality, seamless face swapping between images
- Robust handling of varied facial expressions, skin tones, and lighting conditions
- Minimal visible artifacts and natural blending of facial features
- Adaptable to different image resolutions and aspect ratios
- Fast inference suitable for real-time or batch processing
- Reliable performance across diverse datasets, including group photos and portraits
What Can I Use It For?
- Professional photo editing and retouching for marketing and advertising
- Creative projects such as memes, digital art, and entertainment content
- Business applications including identity protection and anonymization in datasets
- Personal use for fun face swaps in social media and messaging
- Industry-specific use in film production, gaming, and virtual reality for character customization
- Academic research in facial recognition and computer vision
Things to Be Aware Of
- Some users report occasional mismatches in skin tone and facial alignment, especially with extreme angles
- Performance may vary depending on hardware; GPU acceleration is recommended for best results
- Large batch processing can require significant memory resources
- Community feedback highlights strong realism but notes occasional edge artifacts in complex backgrounds
- Positive reviews emphasize ease of use and integration into custom workflows
- Negative feedback patterns include issues with glasses, facial hair, and occlusions
- Experimental features such as multi-face swapping are still under development and may show inconsistent results
Limitations
- May struggle with heavily occluded faces or extreme facial angles
- Limited effectiveness with very low-resolution or poor-quality images
- Not optimal for swapping faces with complex accessories or non-human subjects
Pricing Detail
This model runs at a cost of $0.060 per execution.
Pricing Type: Fixed
The cost remains the same regardless of which model you use or how long it runs. There are no variables affecting the price. It is a set, fixed amount per run, as the name suggests. This makes budgeting simple and predictable because you pay the same fee every time you execute the model.
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