Realistic Vision

realistic-vision-v6.0-b1

Realistic Vision generates lifelike images, ideal for creative and professional projects.

A40 48GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~10 sec

StatusActive

Version

0.0.1

Updatedabout 16 hours ago

Live Demo

Average runtime: ~10 seconds

Input

Configure model parameters

Negative Prompt

Negative_prompt involves specifying what should not be included in the generated image.

(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, 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

Prompt is the text input describing what you want the image to look like.

photo of old village, evening, clouds

Image

Image is the visual output generated by the model based on the prompt.

File upload is currently disabled

JPEGPNGJPGWEBP

Mask

Mask is used to specify which parts of the image to keep or alter during generation.

File upload is currently disabled

Width

Width determines the horizontal size of the generated image.

1024

Height

Height determines the vertical size of the generated image.

768

Num Inference Steps

Num_inference_steps defines how many steps the model should take to generate the image, affecting quality and speed.

Guidance Scale

Guidance_scale adjusts how much the model should follow the prompt versus being creative.

scheduler

Scheduler controls the method and order in which the image generation process is carried out.

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 $0.001 per second. With a $1 budget, you can run this model approximately 100 times, assuming an average execution time of 10 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-v6.0-b1",
            "version": "0.0.1",
            "input": {
  "mask": "your mask here",
  "seed": null,
  "image": "your_file.image/jpeg",
  "width": "512",
  "height": "728",
  "prompt": "photo of offroad car, forest, sunset, clouds",
  "strength": "1",
  "scheduler": "K_EULER_ANCESTRAL",
  "guidance_scale": "7.5",
  "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, 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",
  "use_karras_sigmas": false,
  "num_inference_steps": "20"
}
        }
    )
    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: ~10 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Realistic Vision V6.0 B1 represents a significant update in the Realistic Vision series, focusing on realism and photorealism. This beta version is part of a gradual release leading up to the full version. The model is optimized for various resolutions, enhancing its versatility in generating images ranging from face portraits to full-body compositions.

Technical Specifications

Model Version: 6.0 Beta 1

Base Model: Stable Diffusion 1.5

Supported Resolutions: Up to 896x896 for face portraits; higher resolutions for full-body images

Sampling Methods: DPM++ SDE Karras, DPM++ 2M SDE

Key Considerations

Content Generation: The model is capable of generating both SFW and NSFW content. Users should exercise discretion and comply with relevant guidelines and regulations when generating content.

Tips & Tricks

Prompt Crafting: Detailed and specific prompts yield better results. Including descriptive terms can guide the model to produce more accurate images.

Negative Prompts: Utilize negative prompts to exclude unwanted elements from the generated images, enhancing overall quality.

Experimentation: Adjusting parameters such as sampling steps, denoising strength, and upscaling factors can lead to varied and improved outputs. Experiment to find the optimal settings for your specific use case.

Capabilities

Photorealistic Image Generation:
Generates highly realistic images, suitable for professional and creative purposes.

Wide Range of Subjects:
Capable of producing images of human faces, full-body compositions, objects, and complex scenes.

High Customizability:
Allows users to fine-tune prompts, resolutions, sampling methods, and negative prompts for highly specific outputs.

High-Resolution Outputs:
Supports resolutions up to 1152x640 for full-body compositions and 896x896 for portraits, with options for high-resolution fixes.

Artistic Flexibility:
Compatible with artistic and professional projects, enabling users to create customized artwork or concept visuals.

Hyper-Realistic Visuals: Generates images with exceptional detail and realism.

Versatile Applications: Ideal for artistic, professional, and research purposes.

Scalable Performance: Efficiently handles diverse text-to-image tasks.

What can I use for?

Portrait Photography:
Create realistic portraits of individuals for artistic or professional use.

Advertising and Marketing:
Generate photorealistic visuals for campaigns without the need for costly photoshoots.

Game Development:
Design realistic characters or environments as assets for video games or virtual worlds.

Concept Art:
Visualize ideas quickly with detailed and lifelike renders.

Content Creation:
Enhance blogs, social media, and websites with visually appealing and unique imagery.

Prototyping and Design:
Use in product design or architecture to visualize concepts in realistic settings.

Creative Projects: Bring imaginative ideas to life with realistic visuals.

Professional Mockups: Generate compelling images for business and marketing needs.

Research and Experimentation: Explore advanced text-to-image capabilities for academic or experimental use.

Things to be aware of

Experiment with Different Styles:
Use prompts describing specific aesthetics like "cinematic lighting" or "modern minimalism."

Create a Unique Portrait:
Generate a detailed face portrait at 896x896 resolution with precise features and expressions.

Design a Scene:
Try a full-body or half-body composition set in a specific environment, such as "a bustling marketplace at sunset."

Use Negative Prompts:
Include terms like “cropped, low-quality, extra limbs” in the negative prompt field to remove artifacts and refine output.

Explore High-Resolution Fix:
Use the Hires.Fix option to upscale your favorite images and improve sharpness and detail.

Try Abstract Concepts:
Input a creative prompt such as "a surreal landscape with floating islands and glowing plants."

Adjust Sampling Parameters:
Experiment with different samplers like DPM++ SDE Karras and increase sampling steps for more refined images.

Specific Examples: Test with various text prompts to create diverse visuals.

Parameter Adjustments: Experiment with different settings to refine results.

Creative Applications: Use the model for unique and innovative visual projects.

Integration Scenarios: Combine with other tools for enhanced workflows.

Limitations

Image Quality:
The model performs best with high-resolution and well-structured input images. Low-quality or blurry inputs may result in suboptimal outputs or artifacts.

Prompt Specificity:
Realistic Vision heavily relies on detailed and well-crafted prompts. Ambiguous or overly general prompts may produce inconsistent results.

Output Format: PNG

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