Discrete Diffusion Model

A Discrete Diffusion Model is a diffusion-based discrete generative model that applies diffusion processes directly to discrete tokens through masking or categorical transitions in discrete diffusion generation (without continuous embedding spaces).

• AKA: Token Diffusion Model, Categorical Diffusion Model, Masked Diffusion Model, Discrete-State Diffusion.
• Context:
  • It can typically perform Discrete Diffusion Token Masking through discrete diffusion mask schedules.
  • It can typically implement Discrete Diffusion Markov Transition via discrete diffusion transition matrixes.
  • It can typically maintain Discrete Diffusion Categorical Distribution for discrete diffusion state space.
  • It can typically enable Discrete Diffusion Text Generation through discrete diffusion language modeling.
  • It can typically preserve Discrete Diffusion Token Identity during discrete diffusion corruption process.
  • ...
  • It can often utilize Discrete Diffusion Absorbing State via discrete diffusion mask tokens.
  • It can often apply Discrete Diffusion Uniform Transition for discrete diffusion noise addition.
  • It can often support Discrete Diffusion Parallel Generation through discrete diffusion non-autoregressive decoding.
  • It can often facilitate Discrete Diffusion Controllable Generation via discrete diffusion conditional modeling.
  • ...
  • It can range from being a Simple Discrete Diffusion Model to being a Complex Discrete Diffusion Model, depending on its discrete diffusion architecture complexity.
  • It can range from being a Binary Discrete Diffusion Model to being a Multi-Category Discrete Diffusion Model, depending on its discrete diffusion state cardinality.
  • It can range from being an Absorbing Discrete Diffusion Model to being a Uniform Discrete Diffusion Model, depending on its discrete diffusion transition type.
  • It can range from being a Text-Only Discrete Diffusion Model to being a Multi-Modal Discrete Diffusion Model, depending on its discrete diffusion domain scope.
  • ...
  • It can integrate with Discrete Diffusion Score Network for discrete diffusion prediction.
  • It can coordinate with Discrete Diffusion Sampling Algorithm for discrete diffusion generation.
  • It can interface with Discrete Diffusion Loss Function for discrete diffusion training.
  • It can synchronize with Discrete Diffusion Schedule for discrete diffusion corruption control.
  • It can combine with Discrete Diffusion Acceleration for discrete diffusion efficiency.
  • ...
• Examples:
  • Text Discrete Diffusion Models, such as:
    • D3PM Models (Discrete Denoising Diffusion Probabilistic Models), such as:
      • Multinomial D3PM for vocabulary-based generation.
      • Binary D3PM for binary sequence modeling.
      • Absorbing D3PM for mask-based diffusion.
    • Argmax Flow Models, such as:
      • Text Argmax Flow for language generation.
      • Code Argmax Flow for program synthesis.
  • Hybrid Discrete Diffusion Models, such as:
    • Analog Bits Models, such as:
      • Logit Diffusion Model for soft token generation.
      • Probability Diffusion Model for distribution refinement.
    • Structured Discrete Diffusions, such as:
      • Graph Discrete Diffusion for molecular generation.
      • Tree Discrete Diffusion for hierarchical generation.
  • ...
• Counter-Examples:
  • Continuous Diffusion Model, which operates in continuous space rather than discrete tokens.
  • Autoregressive Model, which generates sequentially rather than through parallel diffusion.
  • VAE Model, which uses latent encoding rather than direct token diffusion.
• See: Diffusion Model, Discrete Generation, Masked Language Model, Token Generation, Categorical Distribution, Markov Chain, Non-Autoregressive Model, Text Generation, Parallel Decoding, Diffusion-based Large Language Model.

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