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Imagen 3 Fast

imagen-3-fast

Imagen 3 Fast model is the quickest and most affordable choice when cost or speed matters more than image quality.

Fast Inference
REST API
Try in ConsoleAPI DocsExamples

Model Information

Response Time~10 sec
StatusActive
Version
0.0.1
Updated12 days ago
API Reference
View Full Documentation

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 requests
import time


API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "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": "imagen-3-fast",
            "version": "0.0.1",
            "input": {
  "prompt": "your prompt here",
  "aspect_ratio": "1:1",
  "negative_prompt": "your negative prompt here",
  "safety_filter_level": "block_medium_and_above"
}
        }
    )
    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 result
        elif 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 prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = 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: ~10 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

Imagen 3 Fast is a text-to-image generative model optimized by Google Deepmind for speed while maintaining high-quality image generation. It interprets textual descriptions to create visually compelling images with improved coherence, lighting, and texture. The Imagen 3 Fast supports multiple aspect ratios, fine-grained prompt control, and filtering options to customize generated outputs.

Technical Specifications

  • Architecture: Imagen 3 Fast is based on a diffusion model architecture optimized for rapid inference, balancing speed and quality.
  • Resolution: Generates images at a base resolution of 1024x1024 pixels, with support for upscaling.
  • Language Processing: Supports multiple languages for text input, allowing diverse linguistic accessibility.
  • Text Rendering: Can generate images containing text, though accuracy may vary in complex layouts.

Key Considerations

  • Prompt Clarity: More specific prompts lead to better image coherence. Vague prompts may yield unpredictable results.
  • Safety Filter Impact: Stricter filtering may prevent certain creative outputs. Adjust appropriately based on content needs.
  • Negative Prompts: Effective use of negative prompts can refine image composition by removing undesired elements.
  • Aspect Ratio Effects: Different aspect ratios impact how elements are arranged in an image. Choosing the right one improves visual balance.


Legal Information for Imagen 3 Fast

By using this Imagen 3 Fast, you agree to:

Tips & Tricks

  • Optimizing Prompts for Imagen 3:
    • Use descriptive details such as colors, textures, and lighting conditions.
    • Reference specific artistic styles (e.g., "watercolor painting," "cyberpunk style").
  • Negative Prompting for Refinement:
    • If faces appear distorted, use negative_prompt like "blurred faces, distorted features."
    • To remove specific backgrounds, include "cluttered background" in negative_prompt.
  • Aspect Ratio Selection:
    • 1:1 – Best for balanced compositions or social media posts.
    • 9:16 – Ideal for portrait-oriented images.
    • 16:9 – Suited for landscape scenes or widescreen content.
    • 3:4 and 4:3 – Useful for natural framing of objects and characters.
  • Fine-Tuning Safety Levels:
    • If content is being blocked unexpectedly, lower the safety_filter_level cautiously.
    • For stricter moderation, increase the filtering level.

Capabilities

  • Rapid Image Generation with Imagen 3: Produces high-quality images quickly based on text prompts.
  • Diverse Visual Styles: Supports multiple artistic styles and aesthetics.
  • Customizable Composition: Users can adjust aspect ratios, prompts, and negative prompts for tailored results.
  • Content Filtering: Allows control over content sensitivity with adjustable safety filters.

What can I use for?

  • Creative Content: Generate concept art, illustrations, and digital artwork.
  • Marketing Visuals: Create promotional materials, banners, and social media graphics.
  • Storytelling & Visualization: Develop visual assets for books, comics, and presentations.
  • Educational & Research Purposes: Generate instructional visuals or scientific illustrations.

Things to be aware of

  • Experiment with different aspect ratios to see how they affect composition.
  • Use detailed prompts with lighting descriptions for more realistic results.
  • Adjust negative_prompt to refine unwanted details in images.
  • Fine-tune the safety_filter_level to align with content needs.

Limitations

  • Complex Object Interactions: The Imagen 3 Fast may struggle with overlapping or highly detailed object relationships.
  • Text Generation Limitations: While improved, rendered text may not always be perfectly legible.
  • Potential Bias: Like other AI models, Imagen 3 Fast may reflect biases from its training data.

Output Format: PNG

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