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AI Face Swap Actor

Deep learning-powered face swapping tool for professional image transformation. Swap faces between two images using state-of-the-art InsightFace models.

Overview

The AI Face Swap Actor enables you to automatically replace faces in images using advanced deep learning technology. Provide a source image (containing the face you want to use) and a target image (where you want the face applied), and receive a high-quality face-swapped result.

This tool is optimized for the Apify platform with efficient memory management, fast processing times, and reliable cloud execution.

Features

• Deep Learning Face Swap - Advanced AI models for natural, realistic face swapping
• Cloud Storage Support - Works with Google Drive, Dropbox, OneDrive, iCloud, and Box links
• High Quality Output - PNG lossless or JPEG optimized output formats
• Memory Optimized - Efficient resource management for stable performance
• Fast Processing - Pre-downloaded models for quick execution
• Graceful Shutdown - Automatic state preservation for interrupted runs

Use Cases

• Portrait Photography - Swap faces for creative photography projects
• Image Editing - Professional face replacement for compositing
• Content Creation - Generate unique images for marketing and social media
• Film & Video Production - Face replacement for visual effects workflows
• Gaming & Virtual Avatars - Create custom character faces

Pricing

This Actor uses pay-per-event pricing model:

Users set a maximum spending limit per run in Apify Console.

Input

Supported URL Formats

• Direct image URLs (HTTP/HTTPS)
• Google Drive sharing links
• Dropbox sharing links
• OneDrive sharing links
• iCloud sharing links
• Box sharing links
• MediaFire sharing links

Output

The Actor returns a dataset item containing:

{
"success":true,
"sourceUrl":"https://example.com/source.jpg",
"targetUrl":"https://example.com/target.jpg",
"processingTime":12.5,
"fileSize":1048576,
"outputFormat":"PNG",
"resultUrl":"https://api.apify.com/v2/key-value-stores/...",
"timestamp":1700000000
}

How It Works

1. Download Images - Both source and target images are fetched from provided URLs
2. Face Detection - AI analyzes both images to locate and extract faces
3. Face Swapping - The source face is mapped onto the target face using deep learning
4. Result Delivery - Output image is stored and URL is returned in dataset

The Actor uses the InsightFace Buffalo-L model for face analysis and the Inswapper 128 model for face swapping, ensuring high-quality results with natural-looking transformations.

Performance Notes

• Processing time typically ranges from 10-30 seconds depending on image size
• Memory usage is optimized for runs up to 4GB RAM
• Images are automatically resized if larger than 2048px to optimize processing

Error Handling

The Actor handles various error conditions:

• Invalid URL formats
• Image download failures
• Private IP address detection (blocked for security)
• No faces detected in images
• Processing failures

Failed operations return error dataset items with descriptive messages.

FAQ

Q: What image formats are supported? A: Input supports JPG, JPEG, PNG, and WebP. Output can be PNG (lossless) or JPEG (optimized).

Q: Can I use faces from cloud storage? A: Yes, the Actor automatically converts Google Drive, Dropbox, OneDrive, iCloud, Box, and MediaFire sharing links to direct download URLs.

Q: What happens if no face is detected? A: The Actor returns an error item with "No faces detected" message. No charge is applied for this case.

Q: Is there a size limit for images? A: Images over 15MB or larger than 2048px dimension are rejected with appropriate error messages.

Q: How accurate is the face swap? A: The AI produces natural-looking results with proper lighting and blending. Complex lighting or unusual angles may affect quality.

Related Tools

• AI Image Processing - Other image transformation Actors
• Face Detection - Extract faces from images
• Background Removal - Remove backgrounds from images

Technical Details

Support

For issues, feature requests, or custom implementations, open a GitHub issue or contact the developer.

License

MIT License - See repository for details.