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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 ConsoleAPI DocsExamples

Model Information

Response Time~0 sec
StatusActive
Version
0.0.1
Updated3 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": "true",
  "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: -
  • 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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