Text-to-image generation has become one of the most exciting areas in AI research, enabling computers to create visual content from textual descriptions. In this blog post, I’ll break down a fascinating implementation that combines Vector Quantized Variational Autoencoders (VQ-VAE) with Transformer models to generate images from text descriptions.
The Architecture: A Two-Stage Approach
The implementation uses a two-stage architecture:
- VQ-VAE: First, a Vector Quantized Variational Autoencoder learns to compress images into discrete codes and reconstruct them.
- Transformer: Then, a transformer model learns to predict these discrete image codes based on text descriptions.
This approach separates the challenging task of image generation into two more manageable problems: learning a compressed representation of images, and then mapping text to these representations.
The VQ-VAE Component
The VQ-VAE consists of three main parts:
- Encoder: Compresses input images into a latent representation
- Vector Quantizer: Maps continuous encodings to discrete codes from a codebook
- Decoder: Reconstructs images from the quantized representations
The Vector Quantizer is particularly interesting as it creates a discrete “visual vocabulary” – similar to how we represent language with discrete words. This discretization makes it easier for the transformer to generate images token by token, similar to how language models generate text.
The Transformer Component
After the VQ-VAE training, the transformer learns to predict the sequence of image tokens given a text description. This is conceptually similar to language translation, but instead of translating between languages, we’re translating from text to image tokens.
The transformer includes:
- Text token embeddings
- Positional encodings for sequential information
- Image token embeddings for the discrete visual tokens
- Standard transformer architecture with self-attention mechanisms
Training Process
The training happens in two distinct phases:
- VQ-VAE Training: The model learns to encode and decode images while building the visual codebook.
- Transformer Training: Using the frozen VQ-VAE, the transformer learns to predict image codes from text.
The implementation uses several techniques to improve training stability:
- Residual blocks in both encoder and decoder
- Batch normalization
- Mixed precision training with gradient scaling
- Regular checkpointing to prevent progress loss
Dataset: Flickr8k
The code uses the Flickr8k dataset, which contains 8,000 images, each paired with five different captions. This provides diverse text descriptions for each image, helping the model learn robust text-to-image mappings.
Results and Applications
The model generates images based on textual prompts like “a photo of a cat” or “a landscape with mountains.” While the results from this implementation won’t match the quality of larger models like DALL-E or Stable Diffusion (which use billions of parameters and train on vastly larger datasets), it demonstrates the core techniques that make text-to-image generation possible.
Technical Highlights
Some interesting technical aspects of this implementation:
- Codebook Learning: The VQ-VAE learns a codebook of 1024 visual tokens, creating a visual vocabulary.
- Commitment Loss: A special loss term keeps the encoder committed to the codebook vectors.
- Autoregressive Generation: The transformer predicts image tokens one-by-one in an autoregressive fashion.
- Teacher Forcing: During training, the transformer sees the ground truth image tokens.
Limitations and Future Improvements
This implementation is educational but has limitations:
- Limited dataset size affects the diversity of generations
- Relatively small model size limits image quality and complexity
- Current resolution (256×256) is modest by modern standards
Potential improvements could include:
- Training on larger datasets like LAION or COYO
- Hierarchical VQ-VAE for higher resolution images
- Conditioning augmentation techniques like CLIP guidance
Conclusion
This implementation shows how modern text-to-image generation systems work at their core. By breaking the problem into discrete representation learning and autoregressive generation, it becomes possible to create images from text descriptions. While state-of-the-art models use additional techniques and vastly more compute, this approach demonstrates the foundational concepts that make text-to-image generation possible.
If you’re interested in implementing your own text-to-image model, this code provides an excellent starting point to understand the core mechanisms before diving into more complex architectures.
To check out the project: Click here
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