Purpose & architecture
The shared implementation constructs a linear or cosine noise schedule, adds noise, predicts noise with the local network and performs a reverse-step calculation. Generate begins from random noise. Train compares the predicted and reconstructed noise with mean squared error and returns that loss.
Constructor names and parameter flags are local settings. Source inspection does not establish a text encoder, image autoencoder, original model architecture, compatible checkpoint or implementation of every named model feature. Train itself does not update weights.
This source generation
This record follows the claude/improve-math-logic-ehIW1 branch of Janady13/complete-agi-core at commit 33aa84f2aa61cedbe010240ada7f4efbdfe795a7. The snapshot identifies the source inspected in this audit. It is not a statement about which revision is currently running on a device.
| Field | Source record |
|---|---|
| Revision | 33aa84f2aa61cedbe010240ada7f4efbdfe795a7 |
| Branch | claude/improve-math-logic-ehIW1 |
| Recorded generation | Generator Core · diffusion |
Recovered implementation structure
These paths locate the components and entry points represented by this build. The source register contains their inspected revisions.
- generator_core/pkg/generators/diffusion/diffusion.go
Behavior & verification limits
Constructor names and parameter flags are local settings. Source inspection does not establish a text encoder, image autoencoder, original model architecture, compatible checkpoint or implementation of every named model feature. Train itself does not update weights.
Original project documentation
The following documentation is preserved as a dated source account. Claims in it retain the source’s wording and must be read alongside the implementation notes above.
Package diffusion implements diffusion generators (11-25) q(x_t|x_{t-1}) = N(√(1-β_t)x_{t-1}, β_t I) ═══════════════════════════════════════════════════════════════════════════════ DIFFUSION GENERATOR BASE ═══════════════════════════════════════════════════════════════════════════════ DiffusionGenerator implements denoising diffusion probabilistic models NewDiffusionGenerator creates a new diffusion generator Initialize initializes the diffusion generator
Equations in this build
1 linked mathematical records. Each opens its notation, interpretation and source.