Hailuo I2V Director

hailuo-i2v-0.1

Hailuo I2V-01-Director by Minimax is an AI video model that generates videos from image-to-video inputs.

Partner Model

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~200 sec

StatusActive

Version

0.0.1

Updated18 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": "hailuo-i2v-0-1",
            "version": "0.0.1",
            "input": {
  "prompt_optimizer": false,
  "prompt": "your prompt here",
  "first_frame_image": "your_file.png"
}
        }
    )
    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: ~200 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Hailuo I2V Director is an advanced video model designed for generating high-quality videos from text prompts and initial images. By leveraging deep learning techniques, it enables users to create dynamic and visually compelling sequences from static inputs.

Technical Specifications

Model Architecture: Hailuo I2V Director utilizes a multi-stage deep learning approach to generate video sequences from static images and textual prompts.
Input Modalities:
- Text Prompts: Guides the overall scene composition, motion, and thematic elements.
- First Frame Image: Provides initial visual reference for consistency across frames.
Output Format: Generated videos are output in common formats suitable for direct use in creative workflows.

Key Considerations

Prompt Specificity: Highly detailed prompts yield better video coherence and motion realism.
First Frame Selection: A high-quality first frame ensures smoother transitions and maintains visual fidelity.
Resource Requirements: Longer or more complex videos may require substantial computational power.
Variability in Outputs: Due to the model's generative nature, results may slightly vary even with identical inputs.
Aspect Ratio and Resolution: Matching the input resolution to the desired output format improves final video quality.

Tips & Tricks

To achieve optimal results with Hailuo I2V Director, consider the following input options:

Prompt:
- Use clear, structured descriptions to define motion, scene transitions, and overall style.
- Example: "A futuristic cityscape at night with neon lights, smooth camera pan from left to right, cinematic style."
Prompt Optimizer:
- Enable this option if the input prompt is not yielding desired results.
- Helps refine wording for improved video structure and coherence.
First Frame Image:
- Use a high-resolution, well-lit image to maintain visual consistency.
- Example: If generating a cityscape animation, ensure the first frame has clear details to guide the model.

Capabilities

Image-to-Video Generation: Converts static images into dynamic video sequences.
Text-Driven Animation: Uses detailed text descriptions to define movement, transitions, and scene composition.
Visual Continuity: Maintains coherence between frames, ensuring a smooth viewing experience.
Creative Adaptability: Supports various artistic and cinematic styles, from photorealistic scenes to abstract animations.

What can I use for?

Cinematic Storytelling: Generate video content for storytelling, marketing, and entertainment purposes.
Concept Visualizations: Bring ideas to life through motion-enhanced visualizations.
Artistic Exploration: Experiment with unique animation styles and motion effects.
Video Enhancement: Improve static imagery by adding movement and depth.

Things to be aware of

Frame Rate Consistency: Generated videos may require post-processing adjustments for specific frame rate requirements.
Prompt Clarity: Vague or overly abstract prompts may produce unpredictable results.
First Frame Influence: The provided image heavily dictates visual consistency; ensure it's well-suited to the desired outcome.
Post-Processing Needs: Some outputs might need refinement, such as color grading or motion smoothing, for professional use.

Limitations

Complex Motion Handling: While the model generates smooth transitions, highly intricate motions may sometimes appear unnatural.
Style Adaptation: Results may vary when attempting to match specific artistic styles not well-represented in the model's training data.
Processing Speed: High-quality outputs require longer computation times, especially for extended video sequences.
Content Constraints: The model may struggle with highly abstract or ambiguous prompts, leading to inconsistent outputs.

Output Format: MP4

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