
Face Enhancer Fast
Face Enhancer Fast is a lightweight version of the Face Enhancer model for quick and accurate facial detail improvement.
Avg Run Time: 62.000s
Model Slug: real-esrgan-a100
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)
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
Real-ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) uses advanced deep learning techniques to upscale low-resolution images. It not only improves the resolution but also restores details, textures, and sharpness, making it ideal for professional photographers, graphic designers, and everyday users.
Technical Specifications
Model Architecture:
- Based on ESRGAN (Enhanced Super-Resolution GAN).
- Trained on large datasets of diverse images for generalization.
Input Requirements:
- Formats: JPEG, PNG, TIFF.
- Recommended Resolution: Up to 720p for optimal performance on the 4x upscale model.
- Max File Size: 20 MB.
Output:
- Formats: JPEG, PNG, TIFF.
- Maximum Resolution: Supports 8K output with no visible loss of quality.
Key Considerations
Artifacts in Low-Quality Images:
- Overly compressed or noisy images may introduce artifacts during upscaling.
Output Consistency:
- Results may vary for complex or heavily edited images.
Tips & Tricks
Maximize Detail Restoration:
- Use the scale 4 model for images requiring the most detail recovery.
Image Cropping:
- Crop images into smaller sections for faster processing of high-res files.
Enable the "Face Enhance" feature for better facial details in portraits.
Capabilities
Super-Resolution Upscaling:
- Enhance images by up to 4x their original resolution without losing quality.
Detail Restoration:
- Rebuild lost textures and sharpen edges for a natural look.
What Can I Use It For?
Photography Enhancement:
- Perfect for improving resolution and details in professional or personal photos.
Graphic Design:
- Enhance assets like icons, logos, or textures for high-res displays.
Video Frame Upscaling:
- Use upscaled images as keyframes in video editing or restoration projects.
Art Preservation:
- Restore and upscale digital or scanned artworks.
E-Commerce and Marketing:
- Improve product images for websites or advertisements.
Restoring old or low-resolution photos.
Enhancing image quality for professional use.
Upscaling images for printing or large-format displays.
Improving facial details in portrait photography.
Things to Be Aware Of
Upscale Old Photos:
- Revive family photos by enhancing resolution and restoring lost details.
Improve Social Media Images:
- Transform compressed images into sharp, professional-looking visuals.
Enhance Game Textures:
- Use for modding or improving in-game textures in older titles.
Create Print-Ready Images:
- Prepare low-res digital images for high-quality printing.
Test on Unique Styles:
- Try upscaling cartoon, anime, or stylized images to explore model versatility.
Restoration: Upload a vintage photo and upscale it using a scale of 2x.
Face Enhancement: Enable the face_enhance option for high-quality portraits.
Creative Editing: Experiment with different scale values to achieve desired effects.
Limitations
Texture Consistency:
- Fine textures like grass or water may occasionally look unnatural after enhancement.
Output Format: PNG
Pricing Detail
This model runs at a cost of $0.001265 per second.
The average execution time is 62 seconds, but this may vary depending on your input data.
The average cost per run is $0.078430
Pricing Type : Execution Time
Cost Per Second means the total cost is calculated based on how long the model runs. Instead of paying a fixed fee per run, you are charged for every second the model is actively processing. This pricing method provides flexibility, especially for models with variable execution times, because you only pay for the actual time used.
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