Realistic Vision Image Generation

realistic-vision-v5.1

Generates highly detailed and lifelike images with Realistic Vision v5.1 with VAE

L40S 45GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~6 sec

StatusActive

Version

0.0.1

Updated15 days ago

Live Demo

Average runtime: ~6 seconds

Input

Configure model parameters

Negative Prompt

A negative prompt is an input that specifies what the AI model should avoid generating in the output.

(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck

Prompt

A prompt is a starting text or input that guides the AI model on what to generate.

RAW photo, a portrait photo of a latina woman in casual clothes, natural skin, 8k uhd, high quality, film grain, Fujifilm XT3

Guidance

Guidance refers to instructions provided to an AI model to direct its output towards a desired result.

scheduler

A scheduler is a component that manages the timing of tasks or processes to optimize performance in AI models.

Seed

A seed is an initial value used to ensure reproducibility and consistency in the model's output.

1339

Output

View generated results

Result

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

Cost is calculated based on execution time.The model is charged at $0.0011 per second. With a $1 budget, you can run this model approximately 151 times, assuming an average execution time of 6 seconds per run.

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": "realistic-vision-v5.1",
            "version": "0.0.1",
            "input": {
  "seed": "0",
  "steps": "20",
  "width": "512",
  "height": "728",
  "prompt": "RAW photo, a portrait photo of a latina woman in casual clothes, natural skin, 8k uhd, high quality, film grain, Fujifilm XT3",
  "guidance": "5",
  "scheduler": "EulerA",
  "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"
}
        }
    )
    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: ~6 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Realistic Vision Image Generation model is designed to generate high-quality, realistic visual outputs based on user-provided prompts. It leverages advanced techniques in image synthesis, providing users with flexibility to control various aspects of the output, such as style, composition, and resolution. Whether for artistic creation or professional use, the model delivers consistent and customizable results.

Technical Specifications

Model Architecture: Realistic Vision Image Generation is built using advanced diffusion algorithms optimized for high-resolution image generation.
Core Functionality: It synthesizes images by iteratively refining noisy data into coherent visuals, guided by user inputs.
Supported Configurations:
Steps: Adjustable between 0 and 100, determining the number of refinement iterations.
Scheduler: Choose between EulerA and MultistepDPM-Solver for controlling the refinement process.
Resolution: Width and height range from 0 to 1920 pixels, supporting a variety of output sizes.
Seed: Specify a seed value to recreate specific outputs, ensuring reproducibility.

Key Considerations

Input Clarity: Ensure that the prompt is free from ambiguity to guide the model effectively.

Negative Prompting: Always define undesirable elements explicitly to reduce unwanted details in the output.

Scheduler Selection:

EulerA: Suitable for artistic and abstract styles.
MultistepDPM-Solver: Recommended for realistic and detailed outputs.

Tips & Tricks

Prompt for Realistic Vision Image Generation:

Use vivid and detailed descriptions. Example: “A serene mountain landscape during sunset, with golden hues and soft clouds”.
Avoid vague prompts like “beautiful scene”.

Negative Prompt:

Specify unwanted elements clearly. Example: “No text, no watermarks, no distortion”.

Steps:

For quick previews, use 20-40 steps.
For high-quality outputs, 50-70 steps are ideal. Avoid going beyond 80 unless necessary, as diminishing returns may occur.

Guidance:

Set between 5-8 for balanced outputs. Higher values (e.g., 9-10) enforce stricter adherence to the prompt but may limit creativity.

Scheduler:

Use EulerA for faster iterations and experimental outputs.
Use MultistepDPM-Solver for precision and realism.

Resolution:

Maintain standard aspect ratios like 16:9 or 1:1 for balanced compositions.
For detailed scenes, use higher resolutions like 1920x1080 but ensure your prompt provides sufficient detail to avoid empty spaces.

Seed:

Keep the seed consistent for reproducibility. Randomize for varied results.

Capabilities

High-Resolution Outputs: Generate detailed images up to 1920x1920 pixels.

Customizable Styles: Flexible settings allow for artistic and realistic outputs.

Reproducibility: Use seed values to recreate consistent results.

What can I use for?

Creative Projects: Ideal for digital art, concept design, and illustrations.

Visual Content Creation: Generate unique and engaging visuals for presentations, blogs, or social media.

Exploration: Experiment with different styles and compositions for inspiration.

Things to be aware of

Experiment with Prompts: Combine descriptive keywords with specific styles, such as “A futuristic cityscape in cyberpunk style”.

Play with Guidance: Observe how adjusting the guidance scale alters the balance between creativity and prompt adherence.

Test Aspect Ratios: Explore different aspect ratios like 4:5 for portraits or 16:9 for landscapes.

Recreate Outputs: Use the same seed and parameters to compare results with slight prompt variations.

Limitations

Artifact Risk: High guidance or excessively complex prompts may introduce unwanted artifacts.

Output Variability: Slight variations can occur due to the stochastic nature of the model.

Resolution Constraints: Outputs at extreme resolutions may suffer from blurriness or incomplete details.

Output Format: PNG

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