Bibliography (15):

  1. https://x.com/dilipkay/status/1610091360203476993

  2. Paella: Fast Text-Conditional Discrete Denoising on Vector-Quantized Latent Spaces

  3. MaskGIT: Masked Generative Image Transformer

  4. https://www.youtube.com/watch?v=2AsoWS2t484

  5. Attention Is All You Need

  6. Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

  7. https://openai.com/dall-e-2

  8. https://parti.research.google/

  9. High-Resolution Image Synthesis with Latent Diffusion Models

  10. Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning

  11. Microsoft COCO: Common Objects in Context

  12. CLIP: Connecting Text and Images: We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the ‘zero-shot’ capabilities of GPT-2 and GPT-3

  13. https://muse-model.github.io/

  14. Wikipedia Bibliography:

    1. Autoregressive model

    2. Fréchet inception distance