Imagen 4 Fast

Fast Inference

REST API

Model Information

Response Time:~13 sec

Status:Active

Version:

0.0.1

Updated:5 days ago

imagen-4-fast

Live Demo

Average runtime: ~13 seconds

Input

Configure model parameters

Prompt

Text prompt for image generation

Create a cinematic photorealistic medium shot capturing the intense atmosphere of a high speed coastal highway at golden hour. Slightly off center stands a young man with wind blown dark hair sun kissed skin and a piercing focused gaze locked directly into the camera. His expression is calm yet determined with a subtle tension in his jaw. He wears a fitted storm gray and burnt orange racing jacket unzipped to clearly reveal a matte black t shirt that prominently displays the text Imagen 4 Fast in bold stylized motion lettering across the chest with on Eachlabs written just beneath it in clean modern typography both lines fully visible and unobstructed. A pair of aviator sunglasses rests naturally on his head glinting softly in the low angle sunlight. The background features a windswept coastal cliff road with a blurred motion trail of a speeding car curving into the distance. The ocean sparkles far below while warm golden highlights from the setting sun cast long dramatic shadows. The composition balances natural beauty and tension with high contrast lighting drawing crisp attention to the man face and torso while the background remains soft and slightly blurred. Every element from the movement in his hair to the clarity of the printed text reinforces a grounded high impact cinematic realism.

Output

View generated results

Result

Preview, share or download your results with a single click.

Each execution costs $0.02 With $1 you can run this model about 50 times.

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-4-fast",
            "version": "0.0.1",
            "input": {
  "prompt": "your prompt here",
  "aspect_ratio": "1:1",
  "output_format": "jpg",
  "safety_filter_level": "block_only_high"
},
            "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 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: ~13 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

A text-to-image diffusion model developed by Google DeepMind. It generates high-quality, photorealistic images from text prompts. Bring your ideas to life—faster, sharper, and truer to your vision.

Technical Specifications

Aspect ratios supported:

1:1: 1024×1024
3:4: 896×1280
4:3: 1280×896
9:16: 768×1408
16:9: 1408×768

Prompt languages: English, Chinese (Simplified & Traditional), Hindi, Japanese, Korean,Portuguese, Spanish.

Prompt length: On average, 1 token equals about 4 characters, so prompts can be up to approximately 1,900 characters long.

Key Considerations

Prompt quality matters: Clear, detailed prompts lead to better results.

Not fact-grounded: Expect some inconsistencies in details and realism.

Style & text limits: Handles diverse styles and text better—but not perfectly.

Tips & Tricks

Be specific: Use clear, detailed prompts. Define the subject, key features, and any actions it’s performing.

Set the scene: Describe the environment and mood—include background elements, lighting, weather, or time of day.

Specify a style: Mention the desired artistic style, such as photorealism, vector art, or a specific art movement.

Guide composition: Include parameters for camera angle and compositional elements. Structured, descriptive language helps generate more targeted, intentional visuals.

Capabilities

Photorealism: Create lifelike images of people, animals, landscapes, and more—down to the finest detail.

Ultra-sharp details: Rich textures, vibrant colors, and stunning close-ups with natural depth and gradients.

Smarter text rendering: Better spelling, longer passages, and more sophisticated typography—ideal for comics, collectibles, and design work.

More styles, more control: From hyper-realistic to abstract, it handles a wide range of visual styles with improved accuracy.

Fast mode: Explore ideas at lightning speed—up to 10× faster than Google’s earlier models.

High-resolution output: Generate crisp, creative visuals at up to 2K resolution.

What can I use for?

Comics and storybooks: Generate characters, scenes, and panels with readable text and clear visuals.

Concept art: Quickly explore visual ideas for games, films, and animation.

Commercial and marketing visuals: Produce eye-catching imagery for product mockups, posters, and digital content.

Collectibles and packaging: Design greeting cards, covers, and layouts with improved text rendering.

Things to be aware of

No customization tools: It doesn’t support style transfer, subject tuning, or few-shot personalization.

No image editing: Features like inpainting, outpainting, masking, or image upscaling are not available.

No negative prompting: You can’t exclude elements (e.g., “no text,” “no watermark”) via prompts.

Limitations

Lack of factual grounding: Imagen isn’t built for real-world accuracy. It can introduce artifacts in complex scenes, especially

with small faces, text, or thin structures.

Centering issues: Struggles with perfect alignment, such as placing a circle exactly in the center.

Unclear prompts: Nonsensical input (like random characters or emojis) can lead to unpredictable results.

Output Format: JPG,PNG

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