Generate Stunning Images with the Neymar 2.0 Cognitive Action

22 Apr 2025
Generate Stunning Images with the Neymar 2.0 Cognitive Action

In today's digital landscape, the ability to create unique and customized images based on textual descriptions is revolutionizing how developers approach content creation. The Neymar 2.0 Cognitive Actions offer a powerful image generation capability that allows developers to harness the potential of AI to produce detailed, high-quality images from textual prompts. In this article, we will explore how to integrate the Generate Image Using Custom Model action, detailing its features, inputs, outputs, and providing practical examples for seamless integration into your applications.

Prerequisites

Before diving into the action, ensure you have the following prerequisites:

  • An API key for the Cognitive Actions platform.
  • Basic understanding of JSON structures and HTTP requests.
  • Familiarity with Python for implementing the example code.

Authentication typically involves passing your API key in the headers of your requests to authorize access to the Cognitive Actions service.

Cognitive Actions Overview

Generate Image Using Custom Model

The Generate Image Using Custom Model action utilizes the 'Neymar 2.0' model to transform textual prompts into stunning images. It provides options for customizing image resolution, aspect ratio, and output quality, along with the ability to opt for faster processing through speed-optimized models.

Input

The input schema for this action requires a JSON object with various parameters. Here’s a breakdown of the most critical fields:

  • prompt (required): The textual description used to generate the image.
  • imageFormat: Specifies the output image format (default is webp, options include jpg and png).
  • outputCount: Number of images to generate (between 1 and 4).
  • loraIntensity: Strength of the main LoRA application (default is 1).
  • outputQuality: Quality level of the saved images (0 to 100).
  • enableFastMode: Option to enable faster predictions (default is false).
  • inferenceModel: Selects the model for inference (default is dev).
  • inferenceSteps: Number of denoising steps (default is 28).
  • promptStrength: Determines how much of the original image information is maintained (default is 0.8).
  • imageAspectRatio: Defines the aspect ratio (default is 1:1).

Here's an example input payload:

{
  "prompt": "Uma representação hiper-realista de um Neymar *extremamente obeso*, em pé em uma cozinha luxuosa. Ele está usando apenas uma toalha branca enrolada na cintura, *expondo um abdômen volumoso e proeminente, com dobras de pele visíveis*. Ele está comendo, com ambas as mãos, um hambúrguer grande e suculento, *mordendo-o avidamente com as bochechas infladas*. A cozinha é espaçosa e possui eletrodomésticos de última geração, bancadas de granito ou mármore, armários personalizados e uma grande ilha central. A iluminação é clara e uniforme, destacando os detalhes da cozinha e da comida, *assim como o brilho do suor na pele de Neymar*. O ângulo da câmera é um plano médio, mostrando Neymar da cintura para cima, com a cozinha luxuosa claramente visível ao fundo. *Restos de comida, como migalhas e molho, estão espalhados ao redor de sua boca, queixo e escorrendo por entre seus dedos*. *Algumas embalagens de fast-food amassadas e outros restos de comida estão espalhados pela bancada.*",
  "imageFormat": "png",
  "outputCount": 1,
  "loraIntensity": 1,
  "outputQuality": 100,
  "enableFastMode": false,
  "inferenceModel": "dev",
  "inferenceSteps": 28,
  "promptStrength": 0.8,
  "imageAspectRatio": "9:16",
  "diffusionGuidance": 3,
  "approximateMegapixels": "1",
  "additionalLoraIntensity": 1
}

Output

The action typically returns a URL to the generated image. Here is an example of what you might receive:

[
  "https://assets.cognitiveactions.com/invocations/186739a5-46db-4ec5-87ca-68655b7786d0/08147c18-ec48-4d9c-b5f3-929ea166f02d.png"
]

Conceptual Usage Example (Python)

Here’s how you might implement a call to the Generate Image Using Custom Model action using Python:

import requests
import json

# Replace with your Cognitive Actions API key and endpoint
COGNITIVE_ACTIONS_API_KEY = "YOUR_COGNITIVE_ACTIONS_API_KEY"
COGNITIVE_ACTIONS_EXECUTE_URL = "https://api.cognitiveactions.com/actions/execute" # Hypothetical endpoint

action_id = "a4b11ec0-a5c4-4bad-91c9-7ceda4866845" # Action ID for Generate Image Using Custom Model

# Construct the input payload based on the action's requirements
payload = {
    "prompt": "Uma representação hiper-realista de um Neymar *extremamente obeso*, em pé em uma cozinha luxuosa...",
    "imageFormat": "png",
    "outputCount": 1,
    "loraIntensity": 1,
    "outputQuality": 100,
    "enableFastMode": False,
    "inferenceModel": "dev",
    "inferenceSteps": 28,
    "promptStrength": 0.8,
    "imageAspectRatio": "9:16",
    "diffusionGuidance": 3,
    "approximateMegapixels": "1",
    "additionalLoraIntensity": 1
}

headers = {
    "Authorization": f"Bearer {COGNITIVE_ACTIONS_API_KEY}",
    "Content-Type": "application/json"
}

try:
    response = requests.post(
        COGNITIVE_ACTIONS_EXECUTE_URL,
        headers=headers,
        json={"action_id": action_id, "inputs": payload}
    )
    response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx)

    result = response.json()
    print("Action executed successfully:")
    print(json.dumps(result, indent=2))

except requests.exceptions.RequestException as e:
    print(f"Error executing action {action_id}: {e}")
    if e.response is not None:
        print(f"Response status: {e.response.status_code}")
        try:
            print(f"Response body: {e.response.json()}")
        except json.JSONDecodeError:
            print(f"Response body: {e.response.text}")

In this example, make sure to replace "YOUR_COGNITIVE_ACTIONS_API_KEY" with your actual API key. The payload is structured according to the action's requirements, ensuring all necessary parameters are included.

Conclusion

The Neymar 2.0 Cognitive Action for generating images offers developers a robust tool for creating visually stunning content from textual descriptions. By leveraging this action, you can enhance your applications with customized image generation capabilities, catering to a variety of creative needs. Consider exploring further use cases such as content marketing, social media, or even personalized gaming experiences. With the power of AI-driven image generation at your fingertips, the possibilities are endless!

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