Instant ID - Anime Generator

instant-id-ip-adapter

Turn a face into 3D, emoji, pixel art, video game, claymation or toy

L40S 45GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~12 sec

StatusActive

Version

0.0.1

Updated8 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": "instant-id-ip-adapter",
            "version": "0.0.1",
            "input": {
  "seed": null,
  "image": "your_file.image/jpeg",
  "style": "3D",
  "prompt": "a person",
  "lora_scale": "1",
  "custom_lora_url": "your custom lora url here",
  "negative_prompt": "your negative prompt here",
  "prompt_strength": "4.5",
  "denoising_strength": "0.65",
  "instant_id_strength": "1",
  "control_depth_strength": "0.8"
}
        }
    )
    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: ~12 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Instant ID - Anime Generator is an advanced AI model that transforms user inputs into personalized anime-style avatars with remarkable accuracy. It leverages powerful diffusion models, LoRA integrations, and customizable parameters to create high-quality visuals for various creative applications.

Technical Specifications

Output Variations: Generates multiple faces or transitions based on user inputs.
Input Image Requirements: Minimum 256x256 pixels; higher resolutions yield better results.

Key Considerations

Input Image Quality:

Blurry or low-quality images may result in suboptimal outputs.

Ethical Use:

Avoid using these tools for malicious purposes, such as deepfakes or unauthorized identity generation.

Cultural Sensitivity:

Ensure outputs are culturally appropriate and unbiased.

Background Uniformity:

For Instant ID, non-uniform backgrounds may interfere with output quality.

Tips & Tricks

Use the tool to create realistic images for testing in identity verification systems.
Leverage the model for creative purposes, such as character design or aging simulations
Parameter Tuning: Experiment with prompt_strength and lora_scale to fine-tune outputs.
Prompt Specificity: Provide clear, detailed prompts to control avatar features effectively.

Seed Parameter:

The seed parameter controls the randomness in the image generation process. Here’s how different values affect the output:
- Fixed Seed: Reproduces the same result for consistent outputs.
- Random Seed: Introduces variations, ideal for creative exploration.

Capabilities

Generates multiple facial variations, including different ages, styles, and expressions.

Supports applications in entertainment, security, and education.

Avatar Creation: Transform photos into anime-inspired avatars.

Creative Experimentation: Generate visually stunning assets for art, gaming, and storytelling.

What can I use for?

Design characters for creative projects with diverse facial traits.

Personal Profiles: Create customized avatars for social media or gaming.

Storytelling: Generate characters for visual narratives.

Art Inspiration: Explore new styles and combinations for artistic projects.

Game Design: Design unique characters for games or virtual environments.

Things to be aware of

Experiment with different styles 3D,Emoji,Video game,Pixels,Clay,Toy

Use negative prompts to eliminate unwanted features.

Explore external LoRA files via custom_lora_url for specialized avatars.

Adjust control_depth_strength to focus on intricate details.

Limitations

Generates realistic outputs but may struggle with extreme angles or poorly lit input images.

Output Variability: Results may vary slightly even with the same seed value due to randomness.

Output Format: PNG

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