Diffusion Forward Process

A Diffusion Forward Process is a corruption diffusion model process that progressively adds noise or masks to clean data to create increasingly corrupted samples for diffusion forward process training (in diffusion forward process models).

• AKA: Forward Diffusion Process, Noising Process, Corruption Process, Forward Markov Process.
• Context:
  • It can typically apply Diffusion Forward Process Noise Addition through diffusion forward process noise schedules.
  • It can typically generate Diffusion Forward Process Training Target via diffusion forward process corruption steps.
  • It can typically create Diffusion Forward Process Latent Representation for diffusion forward process intermediate states.
  • It can typically establish Diffusion Forward Process Markov Chain through diffusion forward process transition probabilitys.
  • It can typically enable Diffusion Forward Process Gradient Computation for diffusion forward process model training.
  • ...
  • It can often utilize Diffusion Forward Process Variance Schedule via diffusion forward process beta parameters.
  • It can often implement Diffusion Forward Process Masking Strategy for diffusion forward process discrete data.
  • It can often maintain Diffusion Forward Process Deterministic Mapping through diffusion forward process closed form.
  • It can often support Diffusion Forward Process Continuous Time via diffusion forward process SDE formulation.
  • ...
  • It can range from being a Simple Diffusion Forward Process to being a Complex Diffusion Forward Process, depending on its diffusion forward process sophistication.
  • It can range from being a Linear Diffusion Forward Process to being a Nonlinear Diffusion Forward Process, depending on its diffusion forward process trajectory.
  • It can range from being a Discrete Diffusion Forward Process to being a Continuous Diffusion Forward Process, depending on its diffusion forward process time formulation.
  • It can range from being a Fixed Diffusion Forward Process to being a Learned Diffusion Forward Process, depending on its diffusion forward process adaptability.
  • ...
  • It can pair with Diffusion Reverse Process for diffusion generation pipeline.
  • It can coordinate with Diffusion Noise Schedule for diffusion corruption control.
  • It can integrate with Diffusion Loss Function for diffusion training objective.
  • It can synchronize with Diffusion Sampling Algorithm for diffusion inference preparation.
  • It can interface with Diffusion Score Network for diffusion denoising training.
  • ...
• Examples:
  • Diffusion Forward Process Types, such as:
    • Gaussian Diffusion Forward Processes, such as:
      • DDPM Forward Process for DDPM image generation.
      • Score-Based Forward Process for score-based generative modeling.
      • Variance Preserving Forward Process for stable diffusion training.
    • Discrete Diffusion Forward Processes, such as:
      • Mask Diffusion Forward Process for text diffusion modeling.
      • Categorical Diffusion Forward Process for discrete token corruption.
  • Diffusion Forward Process Schedule Variants, such as:
    • Linear Schedule Forward Processes, such as:
      • Linear Beta Schedule for uniform noise addition.
      • Quadratic Schedule for accelerated corruption.
    • Cosine Schedule Forward Processes, such as:
      • Improved Cosine Schedule for better sample quality.
      • Sigmoid Schedule for smooth transitions.
  • ...
• Counter-Examples:
  • Diffusion Reverse Process, which removes noise rather than adding it.
  • Direct Generation Process, which creates samples without corruption phase.
  • Deterministic Encoding, which uses fixed transformations rather than stochastic corruption.
• See: Diffusion Model, Diffusion Reverse Process, Noise Schedule, Markov Chain, Stochastic Process, Denoising Diffusion, Score-Based Model, Generative Model, Training Process, Variance Schedule.

---