Phase 1: Research and Development

Objective: Lay the groundwork for the text-to-image generation AI.

1. AI Architecture Design and Development:

• Define neural network architecture, exploring GANs, transformer models, or hybrid approaches for text-to-image synthesis.

• Experiment with model architectures such as CNNs, RNNs, or attention-based models to determine the most suitable structure.

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2. Data Collection and Annotation:

• Curate and compile diverse datasets of text-image pairs for training the AI model.

• Annotate the data, ensuring accurate alignments between textual descriptions and corresponding images.

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3. Model Training and Optimization:

• Implement training pipelines on powerful hardware, possibly utilizing GPU clusters or cloud-based infrastructure for accelerated model training.

• Optimize hyperparameters, loss functions, and regularization techniques to improve model convergence and the quality of image synthesis.

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4. Prototype Development:

• Build a basic prototype to demonstrate the initial capabilities of the text-to-image AI system.

• Verify and fine-tune the model’s performance with various text inputs to generate corresponding images.