GFPGAN
gfpgan
GFPGAN is a photo Enhancer AI model for improving overall photo quality and resolution.
Model Information
Input
Configure model parameters
Output
View generated results
Result
Preview, share or download your results with a single click.
Prerequisites
- Create an API Key from the Eachlabs Console
- Install the required dependencies for your chosen language (e.g., requests for Python)
API Integration Steps
1. 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.
import requestsimport timeAPI_KEY = "YOUR_API_KEY" # Replace with your API keyHEADERS = {"X-API-Key": API_KEY,"Content-Type": "application/json"}def create_prediction():response = requests.post("https://api.eachlabs.ai/v1/prediction/",headers=HEADERS,json={"model": "gfpgan","version": "0.0.1","input": {"img": "your_file.image/jpeg","scale": 2,"version": "v1.4"},"webhook_url": ""})prediction = response.json()if prediction["status"] != "success":raise Exception(f"Prediction failed: {prediction}")return prediction["predictionID"]
2. 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.
def get_prediction(prediction_id):while True:result = requests.get(f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",headers=HEADERS).json()if result["status"] == "success":return resultelif result["status"] == "error":raise Exception(f"Prediction failed: {result}")time.sleep(1) # Wait before polling again
3. Complete Example
Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.
try:# Create predictionprediction_id = create_prediction()print(f"Prediction created: {prediction_id}")# Get resultresult = get_prediction(prediction_id)print(f"Output URL: {result['output']}")print(f"Processing time: {result['metrics']['predict_time']}s")except Exception as e:print(f"Error: {e}")
Additional Information
- The API uses a two-step process: create prediction and poll for results
- Response time: ~5 seconds
- Rate limit: 60 requests/minute
- Concurrent requests: 10 maximum
- Use long-polling to check prediction status until completion
Overview
GFPGAN (Generative Facial Prior GAN) is a state-of-the-art AI model developed for high-quality face restoration in real-world scenarios. By leveraging generative adversarial networks (GANs) and facial priors, GFPGAN excels at restoring low-resolution, blurred, or damaged facial images while preserving high fidelity and naturalness. Its versatility makes it a popular choice for photo enhancement, historical image restoration, and creative applications.
Technical Specifications
Input Requirements
- For best results, use images with clear facial regions and minimal obstructions.
Model Architecture:
- Built on GAN architecture with pre-trained facial prior integration.
- Refined loss functions to balance restoration and fidelity.
Input Requirements:
- Formats: JPEG, PNG.
- Resolution: Recommended input is up to 512x512 for optimal performance.
Output Features:
- Restored images maintain original context and backgrounds.
- Faces are enhanced with reconstructed features.
Key Considerations
Over-Restoration:
- In some cases, the restored face might deviate slightly from the original.
Context Preservation:
- Non-facial regions are minimally processed. Ensure the background meets the desired quality before input.
Tips & Tricks
Fine-Tune Settings:
- Adjust restoration strength to balance detail enhancement and natural appearance.
Pre-Processing:
- Crop images to focus on faces for better results.
Scale
- 1.0: Minimal enhancement, retains most original features.
- 1.5: Balanced restoration for moderate improvements.
- 2.0: High-level enhancement, ideal for heavily degraded images.
- 2.5+: Aggressive restoration, may introduce artifacts on high-quality inputs.
Capabilities
Creative Uses:
- Enhances AI-generated faces or artistic projects with added realism.
Historical Photo Repair:
- Revives old photographs for personal or archival purposes.
Detail Enhancement:
- Recovers textures like skin, hair, and eyes with impressive clarity.
Face Restoration:
- Repairs blurred, damaged, or low-quality facial images.
What can I use for?
Media Projects:
- Restore archival images or enhance visuals for creative content.
Historical Preservation:
- Digitally repair vintage photos for museums or personal collections.
AI Art Improvement:
- Use as a finishing tool for AI-generated images to add detail and polish.
Personal Photo Enhancement:
- Improve selfies, family portraits, and treasured memories.
Things to be aware of
Restore Vintage Photos:
- Test the model on old or damaged images to witness its transformative abilities
Creative Enhancements:
- Apply the model to artistic or AI-generated portraits for added depth.
Limitations
Generalization:
- May struggle with extreme distortions or non-human faces.
Color Consistency:
- Slight color variations may require manual correction.
Background Restoration:
- Focuses primarily on faces, with less emphasis on backgrounds.
Output Format: PNG
