G.O.D. Framework

Script: ai_dreamer.py - Generative and Imaginative AI Module

Introduction

The ai_dreamer.py module is part of the G.O.D Framework and serves as a generative AI module that focuses on creating imaginative outputs, such as text, images, or other creative content. The goal of this module is to simulate creativity and dreaming-like generative processes inspired by human and machine learning capability to perceive and imagine.

The module leverages state-of-the-art generative techniques like GANs (Generative Adversarial Networks) and transformer models to produce unique and context-aware artifacts.

Purpose

Key Features

Logic and Implementation

The ai_dreamer.py script revolves around pairing generative models with feedback mechanisms to ensure novel and contextually aligned creative outputs. For instance, dynamic vector generation feeds neural models to explore combinatorial possibilities.

An example implementation sketch is presented below:


            import torch
            from transformers import GPT2LMHeadModel, GPT2Tokenizer
            from torchvision.utils import save_image
            from torch import nn
            from torchvision.models import resnet18

            class Dreamer:
                """
                Generates creative outputs based on given prompts.
                Supports text and visual content creation.
                """

                def __init__(self, model_type='text', gpt_model='gpt2'):
                    """
                    Initialization and loading of required resources.
                    :param model_type: Type of generation - 'text' or 'image'.
                    :param gpt_model: The GPT model to generate text-based dreams.
                    """
                    if model_type == 'text':
                        self.tokenizer = GPT2Tokenizer.from_pretrained(gpt_model)
                        self.model = GPT2LMHeadModel.from_pretrained(gpt_model).to('cuda' if torch.cuda.is_available() else 'cpu')
                        self.model.eval()
                    elif model_type == 'image':
                        self.model = self._load_visual_model()

                def _load_visual_model(self):
                    """
                    Load a basic ResNet18 image synthesis framework.
                    """
                    model = resnet18(pretrained=True)
                    model.fc = nn.Linear(model.fc.in_features, 512)  # Replace for embedding representation
                    return model

                def generate_text(self, prompt, max_length=100):
                    """
                    Generate creative text based on GPT-2.
                    :param prompt: Text prompt for generation.
                    :param max_length: Maximum length of text generation.
                    """
                    inputs = self.tokenizer(prompt, return_tensors='pt').to(self.model.device)
                    outputs = self.model.generate(inputs.input_ids, max_length=max_length, num_return_sequences=1)
                    return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

                def generate_image(self, latent_vector, save_path='output.png'):
                    """
                    Generate an abstract visual representation.
                    :param latent_vector: Input noise/latent space vector.
                    :param save_path: Path where image will be saved as PNG.
                    """
                    if not isinstance(latent_vector, torch.Tensor):
                        latent_vector = torch.rand((1, 3, 64, 64))  # Random latent if not provided
                    save_image(latent_vector, save_path)
                    return save_path

            if __name__ == "__main__":
                dreamer = Dreamer(model_type='text')
                dream_text = dreamer.generate_text("Imagine a world where humans co-create with AI.")
                print("Generated Dream Text:", dream_text)

                # Image generation path
                visual_dreamer = Dreamer(model_type='image')
                output_image = visual_dreamer.generate_image(None, 'abstract_dream.png')
                print(f"Dream Image Saved at: {output_image}")

Dependencies

The module relies on the following dependencies:

Usage

The ai_dreamer.py module provides user-friendly interfaces for generating textual or visual creative outputs. Common usage is as follows:

  1. Initialize the Dreamer class, specifying the desired generation type (`text` or `image`).
  2. Pass prompts or latent vectors to the respective generation methods.
  3. Save or report the creative outputs generated by the models.

            # Example: Generate Text
            dreamer = Dreamer(model_type='text')
            dream_output = dreamer.generate_text("Write a story about an AI artist.")
            print(dream_output)

            # Example: Generate Visuals
            visual_dreamer = Dreamer(model_type='image')
            visual_dream_path = visual_dreamer.generate_image(None, save_path='ai_vision.png')
            print(f"Generated image saved at: {visual_dream_path}")

System Integration

Future Enhancements