Pixverse

pixverse

Pixverse is a model designed to generate dynamic video content from static images.

Partner Model

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~90 sec

StatusActive

Version

0.0.1

Updated2 days ago

Live Demo

Average runtime: ~90 seconds

Input

Configure model parameters

Prompt

lion runs towards camera

Quality

Model

Duration

Image

File upload is currently disabled

Output

View generated results

Result

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

Each execution costs $0.4 With $1 you can run this model about 2 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": "pixverse",
            "version": "0.0.1",
            "input": {
  "effect": "your effect here",
  "style": "your style here",
  "negative_prompt": "your negative prompt here",
  "duration": "5",
  "model": "v3.5",
  "motion_mode": "normal",
  "prompt": "lion runs towards camera",
  "quality": "540p",
  "image_url": "https://storage.googleapis.com/magicpoint/inputs/lion.png",
  "seed": null
}
        }
    )
    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: ~90 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Pixverse is a model designed to generate dynamic video content from static images. By leveraging advanced AI techniques, the model applies motion effects and stylistic transformations to create visually engaging animations. Users can fine-tune various parameters such as quality, motion mode, duration, and style to achieve their desired results.

Technical Specifications

Pixverse utilizes deep learning techniques to analyze image features and generate motion effects.
The model applies neural rendering methods to enhance video realism while preserving original image integrity.
Supports multiple motion modes for different animation speeds.
Provides customization options for video style, duration, and resolution.
Includes seed control for reproducibility in generation.

Key Considerations

Higher quality settings increase processing time and computational requirements.
The prompt should align with the image content for meaningful results.
Some styles may not work optimally with certain images; testing different styles is recommended.
The motion mode setting should match the intended use case (e.g., "fast" for quick previews, "normal" for refined outputs).
Negative prompts help exclude unwanted elements from the generated video.

Tips & Tricks

For sharper and clearer outputs, use 1080p resolution if possible.
If motion effects seem unnatural, try adjusting the motion mode setting.
Setting a specific seed value allows for consistency when generating multiple variations.
Use negative prompts to eliminate unwanted features and refine results.
Different styles work better with certain images—anime style suits illustrations, while cyberpunk fits futuristic visuals.

Capabilities

Converts static images into animated video sequences with Pixverse.
Supports multiple artistic styles, including anime, 3D animation, clay, comic, and cyberpunk.
Customizable motion mode to control animation smoothness.
Adjustable duration to define video length.
Allows prompt-based modifications to guide animation characteristics.

What can I use for?

Pixverse creates animated visual content from single images.
Enhancing illustrations with motion effects.
Generating short clips for storytelling or presentations.
Experimenting with different artistic styles to transform static art.
Producing stylized animations for social media and creative projects.

Things to be aware of

Compare different motion modes to see how they impact animation fluidity.
Use a negative prompt to refine the output by removing unwanted details.
Test various styles to find the best match for your input image.
Generate multiple variations with the same seed value to maintain consistency.

Limitations

Pixverse may struggle with complex scenes containing multiple objects.
Certain styles may not retain fine details from the original image.
Motion effects might appear exaggerated if the input image lacks depth or structure.
Processing times vary depending on resolution and motion complexity.

Output Format: MP4

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